# threads

id: 019b9568-5e53-7271-9261-389b13cc7665
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
approval_mode: on-request
git_sha: 6cfd8aa741a0b345c51521a6836716b72e99b841
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.77.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/06/rollout-2026-01-06T22-23-23-019b9568-5e53-7271-9261-389b13cc7665.jsonl

title:
  context from previous agent. 

  ✅ TEST SUMMARY

  Docker Build

  Docker not available in this environment
  All prerequisites validated (Python, dependencies, GPU access)
  Pipeline Test Results

  Test	Status	Details
  Python imports	✅ Pass	torch, supabase, r2, analytics, export all work
  GPU detection	✅ Pass	8× GPUs detected (torch 2.8.0+cu128)
  Supabase connection	✅ Pass	171,945 PENDING videos in queue
  R2 connection	✅ Pass	Source and Production buckets accessible
  Environment validation	✅ Pass	All required env vars present
  Single video processing	✅ Pass	OBMrIJI_dn0 processed in 34.8s
  Multi-GPU workers	✅ Pass	8 workers running in parallel
  Analytics	✅ Pass	Workers reporting to Supabase
  TAR creation	✅ Pass	Segments + metadata only (no _original.flac)
  Fix Applied

  Fixed _original.flac removal in synchronous export path (pipeline.py:1021-1026)

  TAR Contents (After Fix)

  {video_id}/
  ├── metadata.json # Full processing metadata
  └── segments/ # Individual speaker clips (FLAC)
  ├── SPEAKER_00_0000_xxx.flac
  ├── SPEAKER_00_0001_xxx.flac
  └── ...

  Ready for Deployment

  ✅ Codebase tested and working
  ✅ Supabase queue functional (171K+ videos pending)
  ✅ R2 upload ready
  ✅ Deployment scripts created
  ✅ Monitoring tools ready
  When you return with SSH keys, run:

  1. Edit cluster config
  vi scripts/deploy/cluster_config.json

  2. Deploy to all clusters
  python scripts/deploy/deploy_all.py --deploy

  3. Start workers
  python scripts/deploy/deploy_all.py --start

  4. Monitor
  python scripts/monitor/dashboard.py --watch

  And can you check the documenation of vast ai either using SDK or cli from vast_ai.md and vast_ai_sdk.md and give me a highlevel overview of how we're going to deploy the application into multiple clusters at vast ai. If you need me to add SSH key into vast ai's console, give instructions for that and then by default it shall be added to any machines we create. and then create plan for creating instances of 24-48GB with high reliability, and is multiple of 8s as a cluster and is cheaper than 4$/hr as a cluster in total. And we can choose anything like 3090s, 4090s, or anything similar from 24GB to 48GB and is the entire cluster is less than 4$/hr in total.

  and do you think it's better if we cerate a docker image and deploy the workflow or should we directly depend on teh scripts at deploy_all.py scripts and is it reliable. i'm worried hte new clusters might have different versions and environments. so it's better if we create a docker image and bake all the models that we're gonna use into it. and we just deploy the docker image and it works seamlessly.


  Vast.ai Multi-Cluster Deployment (High Level)

  Vast.ai runs each “machine” as a Docker container; we deploy by launching multiple identical instances and running massive_process.py workers on each (they coordinate via Supabase queue + R2, so no inter-node networking needed).
  Provision via CLI (vastai search offers → vastai create instance) or Python SDK (VastAI.search_offers() / launch_instance() in vast_ai_sdk.md (line 105)), then label/group instances and monitor with logs/analytics.
  SSH Key (So New Instances Get It Automatically)

  Generate a key: ssh-keygen -t ed25519 -C "you@example.com" -f ~/.ssh/maya3_vast (vast_ai.md (line 286)).
  Add it to your Vast account: maya3_vast.pub)" and verify with vastai show ssh-keys (vast_ai.md (line 294)).
  Important: keys only auto-apply to instances created after you add them (vast_ai.md (line 331)); for existing instances use vastai attach ssh <instance_id> (vast_ai.md (line 367)).
  Plan: 24–48GB GPUs, “clusters” in 8s, < $4/hr total

  Pick cluster shape: either 1×8-GPU instance (simpler ops) or 8×1-GPU instances (often easier to source); budget is sum(dph_total) across the instances.
  Search offers (examples; tune thresholds):
  8-GPU node: vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 reliability>0.99 verified=true rentable=true dph_total<4 gpu_name=RTX_4090' -o dph_total (vast_ai.md (line 45))
  1-GPU nodes: same but num_gpus=1 dph_total<0.5, then create 8 of them.
  Standardize with a template so every instance uses the same image/env/onstart (vast_ai.md (line 578)), then create N instances and label them maya3-cluster-01, maya3-cluster-02, etc.
  Expectation: 48GB-per-GPU options are much less likely to fit <$4/hr for 8 GPUs; 24GB (3090/4090-class) is the realistic target.
  Docker Image vs deploy_all.py

  For reliability, prefer a pinned Docker image + template: the repo already has a production-ish container (Dockerfile (line 13)) that installs deps into .venv and runs massive_process.py as the container entrypoint (Dockerfile (line 72)), which avoids “different machine, different env” drift.
  Current SSH deploy scripts (deploy_all.py (line 36), deploy_cluster.sh (line 21)) assume ssh user@host with no port; Vast.ai SSH is typically host+port (vastai ssh-url <id>), so you’ll either need a small patch to support ports or lean into container-native ops (start/stop/recycle + vastai logs <id>).
  Do you want a “cluster” to be one 8-GPU instance or eight 1-GPU instances, and are bid/interruptible instances acceptable or on-demand only?

  Can you build docker image and serve it locally and test first in this machine, before serving & scaling it to other systems.


  Docker can’t be built/tested in this environment: docker (and podman/buildah/nerdctl) isn’t installed, so there’s no local container runtime to run docker build/docker run.

  What I did to make local Docker testing reliable/safe on your GPU machine:

  Added .dockerignore to prevent baking secrets like .env into the image (and to shrink build context): .dockerignore (line 1)
  Added a one-command local build + smoke-test script: smoke_test.sh (line 1)
  Documented usage in README.md (line 47)
  On a machine with Docker + NVIDIA Container Toolkit:

  Build + basic smoke tests (GPU + imports + Supabase + R2): smoke_test.sh
  End-to-end single-video test (claims 1 Supabase PENDING video on GPU 0, verifies TAR has no _original.flac): smoke_test.sh
  “Serve” locally (run workers continuously): docker compose up -d (logs: docker compose logs -f)




  Can you continue with docker testing from this machine, so we can safely proceed with next steps of vhoosing machines. For now, build docker, run the docker application, monitor analytics, then reset everything and be ready so that we can surely deploy and monitor in vast ai

sandbox_policy:
  {"type":"workspace-write","writable_roots":["/home/ubuntu/maya3"],"network_access":false,"exclude_tmpdir_env_var":false,"exclude_slash_tmp":false}

first_user_message:
  context from previous agent. 

  ✅ TEST SUMMARY

  Docker Build

  Docker not available in this environment
  All prerequisites validated (Python, dependencies, GPU access)
  Pipeline Test Results

  Test	Status	Details
  Python imports	✅ Pass	torch, supabase, r2, analytics, export all work
  GPU detection	✅ Pass	8× GPUs detected (torch 2.8.0+cu128)
  Supabase connection	✅ Pass	171,945 PENDING videos in queue
  R2 connection	✅ Pass	Source and Production buckets accessible
  Environment validation	✅ Pass	All required env vars present
  Single video processing	✅ Pass	OBMrIJI_dn0 processed in 34.8s
  Multi-GPU workers	✅ Pass	8 workers running in parallel
  Analytics	✅ Pass	Workers reporting to Supabase
  TAR creation	✅ Pass	Segments + metadata only (no _original.flac)
  Fix Applied

  Fixed _original.flac removal in synchronous export path (pipeline.py:1021-1026)

  TAR Contents (After Fix)

  {video_id}/
  ├── metadata.json # Full processing metadata
  └── segments/ # Individual speaker clips (FLAC)
  ├── SPEAKER_00_0000_xxx.flac
  ├── SPEAKER_00_0001_xxx.flac
  └── ...

  Ready for Deployment

  ✅ Codebase tested and working
  ✅ Supabase queue functional (171K+ videos pending)
  ✅ R2 upload ready
  ✅ Deployment scripts created
  ✅ Monitoring tools ready
  When you return with SSH keys, run:

  1. Edit cluster config
  vi scripts/deploy/cluster_config.json

  2. Deploy to all clusters
  python scripts/deploy/deploy_all.py --deploy

  3. Start workers
  python scripts/deploy/deploy_all.py --start

  4. Monitor
  python scripts/monitor/dashboard.py --watch

  And can you check the documenation of vast ai either using SDK or cli from vast_ai.md and vast_ai_sdk.md and give me a highlevel overview of how we're going to deploy the application into multiple clusters at vast ai. If you need me to add SSH key into vast ai's console, give instructions for that and then by default it shall be added to any machines we create. and then create plan for creating instances of 24-48GB with high reliability, and is multiple of 8s as a cluster and is cheaper than 4$/hr as a cluster in total. And we can choose anything like 3090s, 4090s, or anything similar from 24GB to 48GB and is the entire cluster is less than 4$/hr in total.

  and do you think it's better if we cerate a docker image and deploy the workflow or should we directly depend on teh scripts at deploy_all.py scripts and is it reliable. i'm worried hte new clusters might have different versions and environments. so it's better if we create a docker image and bake all the models that we're gonna use into it. and we just deploy the docker image and it works seamlessly.


  Vast.ai Multi-Cluster Deployment (High Level)

  Vast.ai runs each “machine” as a Docker container; we deploy by launching multiple identical instances and running massive_process.py workers on each (they coordinate via Supabase queue + R2, so no inter-node networking needed).
  Provision via CLI (vastai search offers → vastai create instance) or Python SDK (VastAI.search_offers() / launch_instance() in vast_ai_sdk.md (line 105)), then label/group instances and monitor with logs/analytics.
  SSH Key (So New Instances Get It Automatically)

  Generate a key: ssh-keygen -t ed25519 -C "you@example.com" -f ~/.ssh/maya3_vast (vast_ai.md (line 286)).
  Add it to your Vast account: maya3_vast.pub)" and verify with vastai show ssh-keys (vast_ai.md (line 294)).
  Important: keys only auto-apply to instances created after you add them (vast_ai.md (line 331)); for existing instances use vastai attach ssh <instance_id> (vast_ai.md (line 367)).
  Plan: 24–48GB GPUs, “clusters” in 8s, < $4/hr total

  Pick cluster shape: either 1×8-GPU instance (simpler ops) or 8×1-GPU instances (often easier to source); budget is sum(dph_total) across the instances.
  Search offers (examples; tune thresholds):
  8-GPU node: vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 reliability>0.99 verified=true rentable=true dph_total<4 gpu_name=RTX_4090' -o dph_total (vast_ai.md (line 45))
  1-GPU nodes: same but num_gpus=1 dph_total<0.5, then create 8 of them.
  Standardize with a template so every instance uses the same image/env/onstart (vast_ai.md (line 578)), then create N instances and label them maya3-cluster-01, maya3-cluster-02, etc.
  Expectation: 48GB-per-GPU options are much less likely to fit <$4/hr for 8 GPUs; 24GB (3090/4090-class) is the realistic target.
  Docker Image vs deploy_all.py

  For reliability, prefer a pinned Docker image + template: the repo already has a production-ish container (Dockerfile (line 13)) that installs deps into .venv and runs massive_process.py as the container entrypoint (Dockerfile (line 72)), which avoids “different machine, different env” drift.
  Current SSH deploy scripts (deploy_all.py (line 36), deploy_cluster.sh (line 21)) assume ssh user@host with no port; Vast.ai SSH is typically host+port (vastai ssh-url <id>), so you’ll either need a small patch to support ports or lean into container-native ops (start/stop/recycle + vastai logs <id>).
  Do you want a “cluster” to be one 8-GPU instance or eight 1-GPU instances, and are bid/interruptible instances acceptable or on-demand only?

  Can you build docker image and serve it locally and test first in this machine, before serving & scaling it to other systems.


  Docker can’t be built/tested in this environment: docker (and podman/buildah/nerdctl) isn’t installed, so there’s no local container runtime to run docker build/docker run.

  What I did to make local Docker testing reliable/safe on your GPU machine:

  Added .dockerignore to prevent baking secrets like .env into the image (and to shrink build context): .dockerignore (line 1)
  Added a one-command local build + smoke-test script: smoke_test.sh (line 1)
  Documented usage in README.md (line 47)
  On a machine with Docker + NVIDIA Container Toolkit:

  Build + basic smoke tests (GPU + imports + Supabase + R2): smoke_test.sh
  End-to-end single-video test (claims 1 Supabase PENDING video on GPU 0, verifies TAR has no _original.flac): smoke_test.sh
  “Serve” locally (run workers continuously): docker compose up -d (logs: docker compose logs -f)




  Can you continue with docker testing from this machine, so we can safely proceed with next steps of vhoosing machines. For now, build docker, run the docker application, monitor analytics, then reset everything and be ready so that we can surely deploy and monitor in vast ai


id: 019b9569-b35d-7f03-b0a3-21e08f98da15
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: 6cfd8aa741a0b345c51521a6836716b72e99b841
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.77.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/06/rollout-2026-01-06T22-24-51-019b9569-b35d-7f03-b0a3-21e08f98da15.jsonl

title:
  context from previous agent. 

  ✅ TEST SUMMARY

  Docker Build

  Docker not available in this environment
  All prerequisites validated (Python, dependencies, GPU access)
  Pipeline Test Results

  Test	Status	Details
  Python imports	✅ Pass	torch, supabase, r2, analytics, export all work
  GPU detection	✅ Pass	8× GPUs detected (torch 2.8.0+cu128)
  Supabase connection	✅ Pass	171,945 PENDING videos in queue
  R2 connection	✅ Pass	Source and Production buckets accessible
  Environment validation	✅ Pass	All required env vars present
  Single video processing	✅ Pass	OBMrIJI_dn0 processed in 34.8s
  Multi-GPU workers	✅ Pass	8 workers running in parallel
  Analytics	✅ Pass	Workers reporting to Supabase
  TAR creation	✅ Pass	Segments + metadata only (no _original.flac)
  Fix Applied

  Fixed _original.flac removal in synchronous export path (pipeline.py:1021-1026)

  TAR Contents (After Fix)

  {video_id}/
  ├── metadata.json # Full processing metadata
  └── segments/ # Individual speaker clips (FLAC)
  ├── SPEAKER_00_0000_xxx.flac
  ├── SPEAKER_00_0001_xxx.flac
  └── ...

  Ready for Deployment

  ✅ Codebase tested and working
  ✅ Supabase queue functional (171K+ videos pending)
  ✅ R2 upload ready
  ✅ Deployment scripts created
  ✅ Monitoring tools ready
  When you return with SSH keys, run:

  1. Edit cluster config
  vi scripts/deploy/cluster_config.json

  2. Deploy to all clusters
  python scripts/deploy/deploy_all.py --deploy

  3. Start workers
  python scripts/deploy/deploy_all.py --start

  4. Monitor
  python scripts/monitor/dashboard.py --watch

  And can you check the documenation of vast ai either using SDK or cli from vast_ai.md and vast_ai_sdk.md and give me a highlevel overview of how we're going to deploy the application into multiple clusters at vast ai. If you need me to add SSH key into vast ai's console, give instructions for that and then by default it shall be added to any machines we create. and then create plan for creating instances of 24-48GB with high reliability, and is multiple of 8s as a cluster and is cheaper than 4$/hr as a cluster in total. And we can choose anything like 3090s, 4090s, or anything similar from 24GB to 48GB and is the entire cluster is less than 4$/hr in total.

  and do you think it's better if we cerate a docker image and deploy the workflow or should we directly depend on teh scripts at deploy_all.py scripts and is it reliable. i'm worried hte new clusters might have different versions and environments. so it's better if we create a docker image and bake all the models that we're gonna use into it. and we just deploy the docker image and it works seamlessly.


  Vast.ai Multi-Cluster Deployment (High Level)

  Vast.ai runs each “machine” as a Docker container; we deploy by launching multiple identical instances and running massive_process.py workers on each (they coordinate via Supabase queue + R2, so no inter-node networking needed).
  Provision via CLI (vastai search offers → vastai create instance) or Python SDK (VastAI.search_offers() / launch_instance() in vast_ai_sdk.md (line 105)), then label/group instances and monitor with logs/analytics.
  SSH Key (So New Instances Get It Automatically)

  Generate a key: ssh-keygen -t ed25519 -C "you@example.com" -f ~/.ssh/maya3_vast (vast_ai.md (line 286)).
  Add it to your Vast account: maya3_vast.pub)" and verify with vastai show ssh-keys (vast_ai.md (line 294)).
  Important: keys only auto-apply to instances created after you add them (vast_ai.md (line 331)); for existing instances use vastai attach ssh <instance_id> (vast_ai.md (line 367)).
  Plan: 24–48GB GPUs, “clusters” in 8s, < $4/hr total

  Pick cluster shape: either 1×8-GPU instance (simpler ops) or 8×1-GPU instances (often easier to source); budget is sum(dph_total) across the instances.
  Search offers (examples; tune thresholds):
  8-GPU node: vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 reliability>0.99 verified=true rentable=true dph_total<4 gpu_name=RTX_4090' -o dph_total (vast_ai.md (line 45))
  1-GPU nodes: same but num_gpus=1 dph_total<0.5, then create 8 of them.
  Standardize with a template so every instance uses the same image/env/onstart (vast_ai.md (line 578)), then create N instances and label them maya3-cluster-01, maya3-cluster-02, etc.
  Expectation: 48GB-per-GPU options are much less likely to fit <$4/hr for 8 GPUs; 24GB (3090/4090-class) is the realistic target.
  Docker Image vs deploy_all.py

  For reliability, prefer a pinned Docker image + template: the repo already has a production-ish container (Dockerfile (line 13)) that installs deps into .venv and runs massive_process.py as the container entrypoint (Dockerfile (line 72)), which avoids “different machine, different env” drift.
  Current SSH deploy scripts (deploy_all.py (line 36), deploy_cluster.sh (line 21)) assume ssh user@host with no port; Vast.ai SSH is typically host+port (vastai ssh-url <id>), so you’ll either need a small patch to support ports or lean into container-native ops (start/stop/recycle + vastai logs <id>).
  Do you want a “cluster” to be one 8-GPU instance or eight 1-GPU instances, and are bid/interruptible instances acceptable or on-demand only?

  Can you build docker image and serve it locally and test first in this machine, before serving & scaling it to other systems.


  Docker can’t be built/tested in this environment: docker (and podman/buildah/nerdctl) isn’t installed, so there’s no local container runtime to run docker build/docker run.

  What I did to make local Docker testing reliable/safe on your GPU machine:

  Added .dockerignore to prevent baking secrets like .env into the image (and to shrink build context): .dockerignore (line 1)
  Added a one-command local build + smoke-test script: smoke_test.sh (line 1)
  Documented usage in README.md (line 47)
  On a machine with Docker + NVIDIA Container Toolkit:

  Build + basic smoke tests (GPU + imports + Supabase + R2): smoke_test.sh
  End-to-end single-video test (claims 1 Supabase PENDING video on GPU 0, verifies TAR has no _original.flac): smoke_test.sh
  “Serve” locally (run workers continuously): docker compose up -d (logs: docker compose logs -f)




  Can you continue with docker testing from this machine, so we can safely proceed with next steps of vhoosing machines. For now, build docker, run the docker application, monitor analytics, then reset everything and be ready so that we can surely deploy and monitor in vast ai

first_user_message:
[cell repeats an earlier one in this file: 6352 characters]


id: 019b9b78-9c79-7ed0-89f5-0d487a175607
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: 6cfd8aa741a0b345c51521a6836716b72e99b841
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.77.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T02-38-51-019b9b78-9c79-7ed0-89f5-0d487a175607.jsonl

title:
  i've rebuilt the docker with new changes, and pushed it. reset the entire DB with all videos pending and all old ones cleared. we are now ready to deploy and refresh the 10 instances. can you recycle instances and restart all of them with new docker image. documentation is in [vast_ai.md](vast_ai.md) and [vast_ai_sdk.md](vast_ai_sdk.md)  check current instances, reset them or whatever to repull the image, and start with newer information again.

first_user_message:
  i've rebuilt the docker with new changes, and pushed it. reset the entire DB with all videos pending and all old ones cleared. we are now ready to deploy and refresh the 10 instances. can you recycle instances and restart all of them with new docker image. documentation is in [vast_ai.md](vast_ai.md) and [vast_ai_sdk.md](vast_ai_sdk.md)  check current instances, reset them or whatever to repull the image, and start with newer information again.


id: 019b9be4-fd74-7532-a437-817493e0a95b
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: c0ef00bdd126d1a43f7b72bed5feaea577db3345
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.77.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T04-37-14-019b9be4-fd74-7532-a437-817493e0a95b.jsonl

title:
  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

first_user_message:
  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`


id: 019b9bed-3159-7332-aa60-cfdd3e3620c5
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: c0ef00bdd126d1a43f7b72bed5feaea577db3345
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.77.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T04-46-11-019b9bed-3159-7332-aa60-cfdd3e3620c5.jsonl

title:
  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

  [GPU5] 5/9  Embeddings     3.5s  | 303 chunks
  Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
  04:39:16 | Worker-3     | INFO     | Processing: a7E_Gjk54qo - Sohni Mahiwal Dey Ishq Da Kharrak | Qissa Goi Podc
  [GPU1] PICK   a7E_Gjk54qo
  [GPU5] 6/9  Clustering     0.5s
  [GPU3] 1/9  Download      33.4s
  [GPU5] 6/9  MicroContami   0.0s
  [GPU0] 1/9  Download      12.7s
  04:39:18 | FastPipelineV6.Models | ERROR    | ❌ Failed to load PANNs: Error(s) in loading state_dict for Cnn14:
  	size mismatch for fc_audioset.weight: copying a param with shape torch.Size([527, 2048]) from checkpoint, the shape in current model is torch.Size([235, 2048]).
  	size mismatch for fc_audioset.bias: copying a param with shape torch.Size([527]) from checkpoint, the shape in current model is torch.Size([235]).
  [GPU5] 1/9  Download      20.1s
  [GPU2] 5/9  Embeddings     2.8s  | 640 chunks
  [GPU2] 6/9  Clustering     0.5s
  [GPU7] 1/9  Download      41.2


  can you also fix this issue with music model. and also, i've just checked the files are not being uploaded to R2. debug why that is happening as well. 

  can you check R2 for where the files are being uploaded. are they being uploaded at all ? i cant seem to find files in R2. check and debug vast's logs as well.



  Steps.
  1. Fix all these issues. 
  2. delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  3. i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones. 
  4. quick suggestions for new instances, clusters of 8*3090s, 8*4090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability. 
  5. deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running. 
  6. if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.

first_user_message:
  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

  [GPU5] 5/9  Embeddings     3.5s  | 303 chunks
  Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
  04:39:16 | Worker-3     | INFO     | Processing: a7E_Gjk54qo - Sohni Mahiwal Dey Ishq Da Kharrak | Qissa Goi Podc
  [GPU1] PICK   a7E_Gjk54qo
  [GPU5] 6/9  Clustering     0.5s
  [GPU3] 1/9  Download      33.4s
  [GPU5] 6/9  MicroContami   0.0s
  [GPU0] 1/9  Download      12.7s
  04:39:18 | FastPipelineV6.Models | ERROR    | ❌ Failed to load PANNs: Error(s) in loading state_dict for Cnn14:
  	size mismatch for fc_audioset.weight: copying a param with shape torch.Size([527, 2048]) from checkpoint, the shape in current model is torch.Size([235, 2048]).
  	size mismatch for fc_audioset.bias: copying a param with shape torch.Size([527]) from checkpoint, the shape in current model is torch.Size([235]).
  [GPU5] 1/9  Download      20.1s
  [GPU2] 5/9  Embeddings     2.8s  | 640 chunks
  [GPU2] 6/9  Clustering     0.5s
  [GPU7] 1/9  Download      41.2


  can you also fix this issue with music model. and also, i've just checked the files are not being uploaded to R2. debug why that is happening as well. 

  can you check R2 for where the files are being uploaded. are they being uploaded at all ? i cant seem to find files in R2. check and debug vast's logs as well.



  Steps.
  1. Fix all these issues. 
  2. delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  3. i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones. 
  4. quick suggestions for new instances, clusters of 8*3090s, 8*4090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability. 
  5. deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running. 
  6. if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.


id: 019b9bf8-f4a6-7ad0-841c-2b766686f68a
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: c0ef00bdd126d1a43f7b72bed5feaea577db3345
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.77.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T04-59-02-019b9bf8-f4a6-7ad0-841c-2b766686f68a.jsonl

title:
  the dashboard code in current repository that has UI, has some flaws i've been trying to fix. starting with the Audio Processed vs Usable (24h) and Instance Audio (Cumulative 24h), both showing hourly basis like 7-8pm, 8-9pm, 9-10pm and not 7-10pm, like timeseries is supposed to move along with the time right. i need it to accumulate for 7days straight. can it adjust accordingly ? why not show every one hour steps for however long it goes right. we can just reduce the axis verbosity as timeflows. dont limit it to only current live one hour.  once fixed, push to vercel

first_user_message:
  the dashboard code in current repository that has UI, has some flaws i've been trying to fix. starting with the Audio Processed vs Usable (24h) and Instance Audio (Cumulative 24h), both showing hourly basis like 7-8pm, 8-9pm, 9-10pm and not 7-10pm, like timeseries is supposed to move along with the time right. i need it to accumulate for 7days straight. can it adjust accordingly ? why not show every one hour steps for however long it goes right. we can just reduce the axis verbosity as timeflows. dont limit it to only current live one hour.  once fixed, push to vercel


id: 019b9bfd-375d-7e33-9642-c08ad524644c
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: c0ef00bdd126d1a43f7b72bed5feaea577db3345
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.77.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T05-03-41-019b9bfd-375d-7e33-9642-c08ad524644c.jsonl

title:
  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

  [GPU5] 5/9  Embeddings     3.5s  | 303 chunks
  Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
  04:39:16 | Worker-3     | INFO     | Processing: a7E_Gjk54qo - Sohni Mahiwal Dey Ishq Da Kharrak | Qissa Goi Podc
  [GPU1] PICK   a7E_Gjk54qo
  [GPU5] 6/9  Clustering     0.5s
  [GPU3] 1/9  Download      33.4s
  [GPU5] 6/9  MicroContami   0.0s
  [GPU0] 1/9  Download      12.7s
  04:39:18 | FastPipelineV6.Models | ERROR    | ❌ Failed to load PANNs: Error(s) in loading state_dict for Cnn14:
  	size mismatch for fc_audioset.weight: copying a param with shape torch.Size([527, 2048]) from checkpoint, the shape in current model is torch.Size([235, 2048]).
  	size mismatch for fc_audioset.bias: copying a param with shape torch.Size([527]) from checkpoint, the shape in current model is torch.Size([235]).
  [GPU5] 1/9  Download      20.1s
  [GPU2] 5/9  Embeddings     2.8s  | 640 chunks
  [GPU2] 6/9  Clustering     0.5s
  [GPU7] 1/9  Download      41.2


  can you also fix this issue with music model. and also, i've just checked the files are not being uploaded to R2. debug why that is happening as well. 

  can you check R2 for where the files are being uploaded. are they being uploaded at all ? i cant seem to find files in R2. check and debug vast's logs as well.


  and one more issue i found in logs is, although not sure if the worker is retrying after this, will leave it up to you. 
  [GPU2] 6/9  Clustering     0.0s
  04:58:49 | Worker-9     | INFO     | Processing: ZOxuMyEtbWo - What customer crave | PART - 03 | Magic 20 தமிழ்
  [GPU4] PICK   ZOxuMyEtbWo
  [GPU6] 1/9  Download       2.8s
  [GPU2] 6/9  MicroContami   0.0s
  04:58:49 | Worker-0     | ERROR    | Worker failed: The read operation timed out
  Traceback (most recent call last):
    File "/workspace/maya3_data/massive_process.py", line 536, in gpu_worker_supabase
      full_video = supabase.get_video(video_id)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 380, in get_video
      result = self._request('GET', 'videos', params=params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 105, in _request
      with urlopen(req, timeout=30) as resp:
           ^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 216, in urlopen
      return opener.open(url, data, timeout)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 519, in open
      response = self._open(req, data)
                 ^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 536, in _open
      result = self._call_chain(self.handle_open, protocol, protocol +
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 496, in _call_chain
      result = func(*args)
               ^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1391, in https_open
      return self.do_open(http.client.HTTPSConnection, req,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1352, in do_open
      r = h.getresponse()
          ^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 1374, in getresponse
      response.begin()
    File "/usr/lib/python3.11/http/client.py", line 318, in begin
      version, status, reason = self._read_status()
                                ^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 279, in _read_status
      line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/socket.py", line 705, in readinto
      return self._sock.recv_into(b)
             ^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1278, in recv_into
      return self.read(nbytes, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1134, in read
      return self._sslobj.read(len, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  TimeoutError: The read operation timed out
  [GPU2] 7/9  MusicDetecti   1.0s
  [GPU2] 8/9  Finalize       0.0s




  Steps.
  1. Fix all these issues. 
  2. delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  3. i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones. 
  4. quick suggestions for new instances, clusters of 8*3090s, 8*4090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability. 
  5. deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running. 
  6. if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.

first_user_message:
  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

  [GPU5] 5/9  Embeddings     3.5s  | 303 chunks
  Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
  04:39:16 | Worker-3     | INFO     | Processing: a7E_Gjk54qo - Sohni Mahiwal Dey Ishq Da Kharrak | Qissa Goi Podc
  [GPU1] PICK   a7E_Gjk54qo
  [GPU5] 6/9  Clustering     0.5s
  [GPU3] 1/9  Download      33.4s
  [GPU5] 6/9  MicroContami   0.0s
  [GPU0] 1/9  Download      12.7s
  04:39:18 | FastPipelineV6.Models | ERROR    | ❌ Failed to load PANNs: Error(s) in loading state_dict for Cnn14:
  	size mismatch for fc_audioset.weight: copying a param with shape torch.Size([527, 2048]) from checkpoint, the shape in current model is torch.Size([235, 2048]).
  	size mismatch for fc_audioset.bias: copying a param with shape torch.Size([527]) from checkpoint, the shape in current model is torch.Size([235]).
  [GPU5] 1/9  Download      20.1s
  [GPU2] 5/9  Embeddings     2.8s  | 640 chunks
  [GPU2] 6/9  Clustering     0.5s
  [GPU7] 1/9  Download      41.2


  can you also fix this issue with music model. and also, i've just checked the files are not being uploaded to R2. debug why that is happening as well. 

  can you check R2 for where the files are being uploaded. are they being uploaded at all ? i cant seem to find files in R2. check and debug vast's logs as well.


  and one more issue i found in logs is, although not sure if the worker is retrying after this, will leave it up to you. 
  [GPU2] 6/9  Clustering     0.0s
  04:58:49 | Worker-9     | INFO     | Processing: ZOxuMyEtbWo - What customer crave | PART - 03 | Magic 20 தமிழ்
  [GPU4] PICK   ZOxuMyEtbWo
  [GPU6] 1/9  Download       2.8s
  [GPU2] 6/9  MicroContami   0.0s
  04:58:49 | Worker-0     | ERROR    | Worker failed: The read operation timed out
  Traceback (most recent call last):
    File "/workspace/maya3_data/massive_process.py", line 536, in gpu_worker_supabase
      full_video = supabase.get_video(video_id)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 380, in get_video
      result = self._request('GET', 'videos', params=params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 105, in _request
      with urlopen(req, timeout=30) as resp:
           ^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 216, in urlopen
      return opener.open(url, data, timeout)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 519, in open
      response = self._open(req, data)
                 ^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 536, in _open
      result = self._call_chain(self.handle_open, protocol, protocol +
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 496, in _call_chain
      result = func(*args)
               ^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1391, in https_open
      return self.do_open(http.client.HTTPSConnection, req,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1352, in do_open
      r = h.getresponse()
          ^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 1374, in getresponse
      response.begin()
    File "/usr/lib/python3.11/http/client.py", line 318, in begin
      version, status, reason = self._read_status()
                                ^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 279, in _read_status
      line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/socket.py", line 705, in readinto
      return self._sock.recv_into(b)
             ^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1278, in recv_into
      return self.read(nbytes, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1134, in read
      return self._sslobj.read(len, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  TimeoutError: The read operation timed out
  [GPU2] 7/9  MusicDetecti   1.0s
  [GPU2] 8/9  Finalize       0.0s




  Steps.
  1. Fix all these issues. 
  2. delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  3. i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones. 
  4. quick suggestions for new instances, clusters of 8*3090s, 8*4090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability. 
  5. deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running. 
  6. if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.


id: 019b9bfe-db40-7441-9fe4-52aaee7f2064
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: c0ef00bdd126d1a43f7b72bed5feaea577db3345
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.77.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T05-05-29-019b9bfe-db40-7441-9fe4-52aaee7f2064.jsonl

title:
  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

  [GPU5] 5/9  Embeddings     3.5s  | 303 chunks
  Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
  04:39:16 | Worker-3     | INFO     | Processing: a7E_Gjk54qo - Sohni Mahiwal Dey Ishq Da Kharrak | Qissa Goi Podc
  [GPU1] PICK   a7E_Gjk54qo
  [GPU5] 6/9  Clustering     0.5s
  [GPU3] 1/9  Download      33.4s
  [GPU5] 6/9  MicroContami   0.0s
  [GPU0] 1/9  Download      12.7s
  04:39:18 | FastPipelineV6.Models | ERROR    | ❌ Failed to load PANNs: Error(s) in loading state_dict for Cnn14:
  	size mismatch for fc_audioset.weight: copying a param with shape torch.Size([527, 2048]) from checkpoint, the shape in current model is torch.Size([235, 2048]).
  	size mismatch for fc_audioset.bias: copying a param with shape torch.Size([527]) from checkpoint, the shape in current model is torch.Size([235]).
  [GPU5] 1/9  Download      20.1s
  [GPU2] 5/9  Embeddings     2.8s  | 640 chunks
  [GPU2] 6/9  Clustering     0.5s
  [GPU7] 1/9  Download      41.2


  can you also fix this issue with music model. and also, i've just checked the files are not being uploaded to R2. debug why that is happening as well. 

  can you check R2 for where the files are being uploaded. are they being uploaded at all ? i cant seem to find files in R2. check and debug vast's logs as well.


  and one more issue i found in logs is, although not sure if the worker is retrying after this, will leave it up to you. 
  [GPU2] 6/9  Clustering     0.0s
  04:58:49 | Worker-9     | INFO     | Processing: ZOxuMyEtbWo - What customer crave | PART - 03 | Magic 20 தமிழ்
  [GPU4] PICK   ZOxuMyEtbWo
  [GPU6] 1/9  Download       2.8s
  [GPU2] 6/9  MicroContami   0.0s
  04:58:49 | Worker-0     | ERROR    | Worker failed: The read operation timed out
  Traceback (most recent call last):
    File "/workspace/maya3_data/massive_process.py", line 536, in gpu_worker_supabase
      full_video = supabase.get_video(video_id)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 380, in get_video
      result = self._request('GET', 'videos', params=params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 105, in _request
      with urlopen(req, timeout=30) as resp:
           ^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 216, in urlopen
      return opener.open(url, data, timeout)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 519, in open
      response = self._open(req, data)
                 ^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 536, in _open
      result = self._call_chain(self.handle_open, protocol, protocol +
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 496, in _call_chain
      result = func(*args)
               ^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1391, in https_open
      return self.do_open(http.client.HTTPSConnection, req,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1352, in do_open
      r = h.getresponse()
          ^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 1374, in getresponse
      response.begin()
    File "/usr/lib/python3.11/http/client.py", line 318, in begin
      version, status, reason = self._read_status()
                                ^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 279, in _read_status
      line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/socket.py", line 705, in readinto
      return self._sock.recv_into(b)
             ^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1278, in recv_into
      return self.read(nbytes, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1134, in read
      return self._sslobj.read(len, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  TimeoutError: The read operation timed out
  [GPU2] 7/9  MusicDetecti   1.0s
  [GPU2] 8/9  Finalize       0.0s


  most importantly, please please please push a fix or update to identify the machine from supabase's analytics to current vast-ai's machine as for us to understand which machine is what. currently we are pushing docker IDs and there is no way for us to backtrack or propograte to map the Vast-ai's instances with the analytics's instances IDs, or whatever. check what instnace ID is being pushed and update it to push current unique ID which we shall have for identification or, push vast's machine ID or isntance ID or whatever to have unified map. 


  Steps.
  1. Fix all these issues. 
  2. delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  3. i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones. 
  4. quick suggestions for new instances, clusters of 8*3090s, 8*4090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability. 
  5. deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running. 
  6. if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.

first_user_message:
  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

  [GPU5] 5/9  Embeddings     3.5s  | 303 chunks
  Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
  04:39:16 | Worker-3     | INFO     | Processing: a7E_Gjk54qo - Sohni Mahiwal Dey Ishq Da Kharrak | Qissa Goi Podc
  [GPU1] PICK   a7E_Gjk54qo
  [GPU5] 6/9  Clustering     0.5s
  [GPU3] 1/9  Download      33.4s
  [GPU5] 6/9  MicroContami   0.0s
  [GPU0] 1/9  Download      12.7s
  04:39:18 | FastPipelineV6.Models | ERROR    | ❌ Failed to load PANNs: Error(s) in loading state_dict for Cnn14:
  	size mismatch for fc_audioset.weight: copying a param with shape torch.Size([527, 2048]) from checkpoint, the shape in current model is torch.Size([235, 2048]).
  	size mismatch for fc_audioset.bias: copying a param with shape torch.Size([527]) from checkpoint, the shape in current model is torch.Size([235]).
  [GPU5] 1/9  Download      20.1s
  [GPU2] 5/9  Embeddings     2.8s  | 640 chunks
  [GPU2] 6/9  Clustering     0.5s
  [GPU7] 1/9  Download      41.2


  can you also fix this issue with music model. and also, i've just checked the files are not being uploaded to R2. debug why that is happening as well. 

  can you check R2 for where the files are being uploaded. are they being uploaded at all ? i cant seem to find files in R2. check and debug vast's logs as well.


  and one more issue i found in logs is, although not sure if the worker is retrying after this, will leave it up to you. 
  [GPU2] 6/9  Clustering     0.0s
  04:58:49 | Worker-9     | INFO     | Processing: ZOxuMyEtbWo - What customer crave | PART - 03 | Magic 20 தமிழ்
  [GPU4] PICK   ZOxuMyEtbWo
  [GPU6] 1/9  Download       2.8s
  [GPU2] 6/9  MicroContami   0.0s
  04:58:49 | Worker-0     | ERROR    | Worker failed: The read operation timed out
  Traceback (most recent call last):
    File "/workspace/maya3_data/massive_process.py", line 536, in gpu_worker_supabase
      full_video = supabase.get_video(video_id)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 380, in get_video
      result = self._request('GET', 'videos', params=params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 105, in _request
      with urlopen(req, timeout=30) as resp:
           ^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 216, in urlopen
      return opener.open(url, data, timeout)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 519, in open
      response = self._open(req, data)
                 ^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 536, in _open
      result = self._call_chain(self.handle_open, protocol, protocol +
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 496, in _call_chain
      result = func(*args)
               ^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1391, in https_open
      return self.do_open(http.client.HTTPSConnection, req,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1352, in do_open
      r = h.getresponse()
          ^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 1374, in getresponse
      response.begin()
    File "/usr/lib/python3.11/http/client.py", line 318, in begin
      version, status, reason = self._read_status()
                                ^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 279, in _read_status
      line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/socket.py", line 705, in readinto
      return self._sock.recv_into(b)
             ^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1278, in recv_into
      return self.read(nbytes, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1134, in read
      return self._sslobj.read(len, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  TimeoutError: The read operation timed out
  [GPU2] 7/9  MusicDetecti   1.0s
  [GPU2] 8/9  Finalize       0.0s


  most importantly, please please please push a fix or update to identify the machine from supabase's analytics to current vast-ai's machine as for us to understand which machine is what. currently we are pushing docker IDs and there is no way for us to backtrack or propograte to map the Vast-ai's instances with the analytics's instances IDs, or whatever. check what instnace ID is being pushed and update it to push current unique ID which we shall have for identification or, push vast's machine ID or isntance ID or whatever to have unified map. 


  Steps.
  1. Fix all these issues. 
  2. delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  3. i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones. 
  4. quick suggestions for new instances, clusters of 8*3090s, 8*4090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability. 
  5. deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running. 
  6. if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.


id: 019b9c2a-ce77-7a82-850b-1ff7ce76af94
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: b555d1a83041da0a71a45f14f1b83e3ad1d6301b
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.77.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T05-53-29-019b9c2a-ce77-7a82-850b-1ff7ce76af94.jsonl

title:
  here is hwat i sent to previous agent, 

  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

  [GPU5] 5/9  Embeddings     3.5s  | 303 chunks
  Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
  04:39:16 | Worker-3     | INFO     | Processing: a7E_Gjk54qo - Sohni Mahiwal Dey Ishq Da Kharrak | Qissa Goi Podc
  [GPU1] PICK   a7E_Gjk54qo
  [GPU5] 6/9  Clustering     0.5s
  [GPU3] 1/9  Download      33.4s
  [GPU5] 6/9  MicroContami   0.0s
  [GPU0] 1/9  Download      12.7s
  04:39:18 | FastPipelineV6.Models | ERROR    | ❌ Failed to load PANNs: Error(s) in loading state_dict for Cnn14:
  	size mismatch for fc_audioset.weight: copying a param with shape torch.Size([527, 2048]) from checkpoint, the shape in current model is torch.Size([235, 2048]).
  	size mismatch for fc_audioset.bias: copying a param with shape torch.Size([527]) from checkpoint, the shape in current model is torch.Size([235]).
  [GPU5] 1/9  Download      20.1s
  [GPU2] 5/9  Embeddings     2.8s  | 640 chunks
  [GPU2] 6/9  Clustering     0.5s
  [GPU7] 1/9  Download      41.2


  can you also fix this issue with music model. and also, i've just checked the files are not being uploaded to R2. debug why that is happening as well. 

  can you check R2 for where the files are being uploaded. are they being uploaded at all ? i cant seem to find files in R2. check and debug vast's logs as well.


  and one more issue i found in logs is, although not sure if the worker is retrying after this, will leave it up to you. 
  [GPU2] 6/9  Clustering     0.0s
  04:58:49 | Worker-9     | INFO     | Processing: ZOxuMyEtbWo - What customer crave | PART - 03 | Magic 20 தமிழ்
  [GPU4] PICK   ZOxuMyEtbWo
  [GPU6] 1/9  Download       2.8s
  [GPU2] 6/9  MicroContami   0.0s
  04:58:49 | Worker-0     | ERROR    | Worker failed: The read operation timed out
  Traceback (most recent call last):
    File "/workspace/maya3_data/massive_process.py", line 536, in gpu_worker_supabase
      full_video = supabase.get_video(video_id)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 380, in get_video
      result = self._request('GET', 'videos', params=params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 105, in _request
      with urlopen(req, timeout=30) as resp:
           ^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 216, in urlopen
      return opener.open(url, data, timeout)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 519, in open
      response = self._open(req, data)
                 ^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 536, in _open
      result = self._call_chain(self.handle_open, protocol, protocol +
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 496, in _call_chain
      result = func(*args)
               ^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1391, in https_open
      return self.do_open(http.client.HTTPSConnection, req,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1352, in do_open
      r = h.getresponse()
          ^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 1374, in getresponse
      response.begin()
    File "/usr/lib/python3.11/http/client.py", line 318, in begin
      version, status, reason = self._read_status()
                                ^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 279, in _read_status
      line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/socket.py", line 705, in readinto
      return self._sock.recv_into(b)
             ^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1278, in recv_into
      return self.read(nbytes, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1134, in read
      return self._sslobj.read(len, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  TimeoutError: The read operation timed out
  [GPU2] 7/9  MusicDetecti   1.0s
  [GPU2] 8/9  Finalize       0.0s


  most importantly, please please please push a fix or update to identify the machine from supabase's analytics to current vast-ai's machine as for us to understand which machine is what. currently we are pushing docker IDs and there is no way for us to backtrack or propograte to map the Vast-ai's instances with the analytics's instances IDs, or whatever. check what instnace ID is being pushed and update it to push current unique ID which we shall have for identification or, push vast's machine ID or isntance ID or whatever to have unified map. 


  Steps.
  1. Fix all these issues. 
  2. delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  3. i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones. 
  4. quick suggestions for new instances, clusters of 8*3090s, 8*4090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability. 
  5. deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running. 
  6. if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.




  i want you to truely finish all these steps. Fix all these issues.
  delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones.
  quick suggestions for new instances, clusters of 83090s, 84090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability.
  deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running.
  if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.



  build and new changes and everything is done and is in place. i think it is done until where the docker image is built. Can you now, do the remaining steps. deploy with one instance and test, then if working, i will let you know, we can deploy to other machines. else, let's fix, reset db and everything redo. docs are in [vast_ai.md](vast_ai.md) and [vast_ai_sdk.md](vast_ai_sdk.md) 
  and all env variables are in .env in home

first_user_message:
  here is hwat i sent to previous agent, 

  Logs for instance 29758334
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:19 UTC 2026
  Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:24 UTC 2026
  Warning: Permanently added 'ssh7.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker


  And 


  Logs for instance 29758333
  Search Filter
  ex. Rate Limit Error
  Line Count
  1000
  Thu Jan  8 04:34:24 UTC 2026
  Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease
  Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease
  Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease
  Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease
  Hit:5 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease
  Reading package lists...
  Reading package lists...
  Building dependency tree...
  Reading state information...
  curl is already the newest version (7.81.0-1ubuntu1.21).
  less is already the newest version (590-1ubuntu0.22.04.3).
  locales is already the newest version (2.35-0ubuntu3.11).
  openssh-server is already the newest version (1:8.9p1-3ubuntu0.13).
  software-properties-common is already the newest version (0.99.22.9).
  sudo is already the newest version (1.9.9-1ubuntu2.5).
  tmux is already the newest version (3.2a-4ubuntu0.2).
  wget is already the newest version (1.21.2-2ubuntu1.1).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  Reading package lists...
  Building dependency tree...
  Reading state information...
  git is already the newest version (1:2.34.1-1ubuntu1.15).
  rsync is already the newest version (3.2.7-0ubuntu0.22.04.4).
  0 upgraded, 0 newly installed, 0 to remove and 65 not upgraded.
  rm: cannot remove '/etc/forward_port2': No such file or directory
  Code not found, cloning...
  Cloning into '/tmp/code'...
  Server listening on 0.0.0.0 port 22.
  Server listening on :: port 22.
  Thu Jan  8 04:34:29 UTC 2026
  Warning: Permanently added 'ssh8.vast.ai' (ED25519) to the list of known hosts.
  /usr/bin/python: No module named src.worker



  delete the current last 2 instances and Instance ID: 29758333 and Instance ID: 29758334, and create 2 new instances with proper code details as well to avoid the startup error as `/usr/bin/python: No module named src.worker`

  [GPU5] 5/9  Embeddings     3.5s  | 303 chunks
  Checkpoint path: /root/panns_data/Cnn14_mAP=0.431.pth
  04:39:16 | Worker-3     | INFO     | Processing: a7E_Gjk54qo - Sohni Mahiwal Dey Ishq Da Kharrak | Qissa Goi Podc
  [GPU1] PICK   a7E_Gjk54qo
  [GPU5] 6/9  Clustering     0.5s
  [GPU3] 1/9  Download      33.4s
  [GPU5] 6/9  MicroContami   0.0s
  [GPU0] 1/9  Download      12.7s
  04:39:18 | FastPipelineV6.Models | ERROR    | ❌ Failed to load PANNs: Error(s) in loading state_dict for Cnn14:
  	size mismatch for fc_audioset.weight: copying a param with shape torch.Size([527, 2048]) from checkpoint, the shape in current model is torch.Size([235, 2048]).
  	size mismatch for fc_audioset.bias: copying a param with shape torch.Size([527]) from checkpoint, the shape in current model is torch.Size([235]).
  [GPU5] 1/9  Download      20.1s
  [GPU2] 5/9  Embeddings     2.8s  | 640 chunks
  [GPU2] 6/9  Clustering     0.5s
  [GPU7] 1/9  Download      41.2


  can you also fix this issue with music model. and also, i've just checked the files are not being uploaded to R2. debug why that is happening as well. 

  can you check R2 for where the files are being uploaded. are they being uploaded at all ? i cant seem to find files in R2. check and debug vast's logs as well.


  and one more issue i found in logs is, although not sure if the worker is retrying after this, will leave it up to you. 
  [GPU2] 6/9  Clustering     0.0s
  04:58:49 | Worker-9     | INFO     | Processing: ZOxuMyEtbWo - What customer crave | PART - 03 | Magic 20 தமிழ்
  [GPU4] PICK   ZOxuMyEtbWo
  [GPU6] 1/9  Download       2.8s
  [GPU2] 6/9  MicroContami   0.0s
  04:58:49 | Worker-0     | ERROR    | Worker failed: The read operation timed out
  Traceback (most recent call last):
    File "/workspace/maya3_data/massive_process.py", line 536, in gpu_worker_supabase
      full_video = supabase.get_video(video_id)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 380, in get_video
      result = self._request('GET', 'videos', params=params)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/workspace/maya3_data/src/supabase_client.py", line 105, in _request
      with urlopen(req, timeout=30) as resp:
           ^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 216, in urlopen
      return opener.open(url, data, timeout)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 519, in open
      response = self._open(req, data)
                 ^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 536, in _open
      result = self._call_chain(self.handle_open, protocol, protocol +
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 496, in _call_chain
      result = func(*args)
               ^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1391, in https_open
      return self.do_open(http.client.HTTPSConnection, req,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/urllib/request.py", line 1352, in do_open
      r = h.getresponse()
          ^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 1374, in getresponse
      response.begin()
    File "/usr/lib/python3.11/http/client.py", line 318, in begin
      version, status, reason = self._read_status()
                                ^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/http/client.py", line 279, in _read_status
      line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/socket.py", line 705, in readinto
      return self._sock.recv_into(b)
             ^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1278, in recv_into
      return self.read(nbytes, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/lib/python3.11/ssl.py", line 1134, in read
      return self._sslobj.read(len, buffer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  TimeoutError: The read operation timed out
  [GPU2] 7/9  MusicDetecti   1.0s
  [GPU2] 8/9  Finalize       0.0s


  most importantly, please please please push a fix or update to identify the machine from supabase's analytics to current vast-ai's machine as for us to understand which machine is what. currently we are pushing docker IDs and there is no way for us to backtrack or propograte to map the Vast-ai's instances with the analytics's instances IDs, or whatever. check what instnace ID is being pushed and update it to push current unique ID which we shall have for identification or, push vast's machine ID or isntance ID or whatever to have unified map. 


  Steps.
  1. Fix all these issues. 
  2. delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  3. i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones. 
  4. quick suggestions for new instances, clusters of 8*3090s, 8*4090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability. 
  5. deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running. 
  6. if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.




  i want you to truely finish all these steps. Fix all these issues.
  delete all the data from supabase from analytics table and reset entire videosIDs to pending so everything starts from beginning.
  i didnt get any success with recycle command before. i hope there is an option to tell the instance to repull the image and restart, with newer code changes. if not i'd prefer delete the instances and create new ones.
  quick suggestions for new instances, clusters of 83090s, 84090s and avoid chinese as their internet is slower, use any region to deploy these as long as they have proper disk, internet, and reliability.
  deploy into one instance, wait for deployment logs, check running status, and validate if R2 uploads are being done peroperly and everything is alright, then deploy into 9 more totalling 10 instances and 80GPUs running.
  if any of these fail, i will monitor with UI and will delete them so we can chose a diff instance.



  build and new changes and everything is done and is in place. i think it is done until where the docker image is built. Can you now, do the remaining steps. deploy with one instance and test, then if working, i will let you know, we can deploy to other machines. else, let's fix, reset db and everything redo. docs are in [vast_ai.md](vast_ai.md) and [vast_ai_sdk.md](vast_ai_sdk.md) 
  and all env variables are in .env in home


id: 019b9e34-0c74-7d50-b9ad-94c0baafbbe1
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: 52d72177e5a6ae0eea9b60777957a2aa1674769f
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T15-22-49-019b9e34-0c74-7d50-b9ad-94c0baafbbe1.jsonl

title:
  can you debug the logs as to see why the workers inside vast-29774952 are failing. come up with your rootcause analysis first and then let's discuss the fix before you mkae any. all information you need regarding accessing the console and it's logs are in current directory. and current code is deployed as docker image. check .env for keys and [vast_ai_sdk.md](vast_ai_sdk.md) and [vast_ai.md](vast_ai.md) for documentation.

first_user_message:
  can you debug the logs as to see why the workers inside vast-29774952 are failing. come up with your rootcause analysis first and then let's discuss the fix before you mkae any. all information you need regarding accessing the console and it's logs are in current directory. and current code is deployed as docker image. check .env for keys and [vast_ai_sdk.md](vast_ai_sdk.md) and [vast_ai.md](vast_ai.md) for documentation.


id: 019b9e37-808b-7d52-b713-5fa7e82f709b
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: 52d72177e5a6ae0eea9b60777957a2aa1674769f
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T15-26-36-019b9e37-808b-7d52-b713-5fa7e82f709b.jsonl

title:
  we are currently at 40,676 videos done. Can you go through the supabase and confirm if each of the completed video has a corresponding R2 url and that exists actually in R2 ?

first_user_message:
  we are currently at 40,676 videos done. Can you go through the supabase and confirm if each of the completed video has a corresponding R2 url and that exists actually in R2 ?


id: 019b9e64-ea6c-75d2-81ef-c53f47b4f34d
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"read-only"}
approval_mode: on-request
git_sha: 52d72177e5a6ae0eea9b60777957a2aa1674769f
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T16-16-12-019b9e64-ea6c-75d2-81ef-c53f47b4f34d.jsonl


id: 019b9f2c-86ee-7200-8031-6ef8821ad6f9
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: 52d72177e5a6ae0eea9b60777957a2aa1674769f
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/08/rollout-2026-01-08T19-54-14-019b9f2c-86ee-7200-8031-6ef8821ad6f9.jsonl

title:
  let's debug into some details here.  can you pull up this file from R2's cleaned bucket and debug the 4th cleaned chunk from the audio. --jp1imJD9w , the 4th chunk specifically has multi-speaker which is a poison to our data. can you see the reason to why it has 2 speakers in it ? the chunk named `SPEAKER_00_0021_199.00-204.34.flac` and why our system has failed to segment this into 2 diff speakers.

first_user_message:
  let's debug into some details here.  can you pull up this file from R2's cleaned bucket and debug the 4th cleaned chunk from the audio. --jp1imJD9w , the 4th chunk specifically has multi-speaker which is a poison to our data. can you see the reason to why it has 2 speakers in it ? the chunk named `SPEAKER_00_0021_199.00-204.34.flac` and why our system has failed to segment this into 2 diff speakers.


id: 019ba185-027d-7072-9338-a09a2a3772c7
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"read-only"}
approval_mode: on-request
git_sha: 52d72177e5a6ae0eea9b60777957a2aa1674769f
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/09/rollout-2026-01-09T06-50-07-019ba185-027d-7072-9338-a09a2a3772c7.jsonl


id: 019ba18a-f0ae-7d83-9841-8f628e3e0cbc
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"read-only"}
approval_mode: on-request
git_sha: 52d72177e5a6ae0eea9b60777957a2aa1674769f
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/09/rollout-2026-01-09T06-56-36-019ba18a-f0ae-7d83-9841-8f628e3e0cbc.jsonl


id: 019bb36e-efc1-7682-92a5-6493dfb781e8
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya_data
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: 475d62330c3bb8b6dd597e3a69b159335c51218b
git_branch: master
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/scraping_yt_videoIDs.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/12/rollout-2026-01-12T18-19-10-019bb36e-efc1-7682-92a5-6493dfb781e8.jsonl

title:
  these fucking dumbass mf models are unable to git push this repo to remote. all i wanted was to track the db files with git lfs and push to remote. can you help with this.

first_user_message:
  these fucking dumbass mf models are unable to git push this repo to remote. all i wanted was to track the db files with git lfs and push to remote. can you help with this.


id: 019bb3e4-2e82-7491-9000-693a82e602fc
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: 52d72177e5a6ae0eea9b60777957a2aa1674769f
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/12/rollout-2026-01-12T20-27-14-019bb3e4-2e82-7491-9000-693a82e602fc.jsonl

title:
  /home/ubuntu/maya3/discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv

  Now, check with my db in supabase and add the new telugu videoIDs with their available metadata into them and then set all of these videoIDs to pending so we can trigger vast ai clusters so workers can start pulling videos and start working towards it. first let's start with syncing this csv to supabase. give analytics with now existing videoIds in supabase vs new videos added to pending. you can keep the failed ones as is, as they are actual failures genuinely. here is current analytics, give post addition to supabase analytics and once i verify we can go ahead and trigger vast ai so those workers start finishing these items. 

  MAYA3 PIPELINE - COMPLETE ANALYTICS REPORT

    Overall Summary
    ┌───────────────────────┬─────────────┐
    │        Metric         │    Value    │
    ├───────────────────────┼─────────────┤
    │ Total videos in DB    │ 487,830     │
    ├───────────────────────┼─────────────┤
    │ Completed             │ 471,431     │
    ├───────────────────────┼─────────────┤
    │ Source audio hours    │ 222,468 hrs │
    ├───────────────────────┼─────────────┤
    │ Usable audio hours    │ 138,353 hrs │
    ├───────────────────────┼─────────────┤
    │ Total R2 storage      │ 18.55 TB    │
    ├───────────────────────┼─────────────┤
    │ Avg usable percentage │ 61.2%       │
    ├───────────────────────┼─────────────┤
    │ Avg quality score     │ 0.94        │
    └───────────────────────┴─────────────┘
    Sample Rate / Quality
    ┌────────────────────┬─────────┬───────┬────────────┐
    │    Sample Rate     │  Count  │   %   │ Usable Hrs │
    ├────────────────────┼─────────┼───────┼────────────┤
    │ 48,000 Hz (Native) │ 470,358 │ 99.8% │ 138,085    │
    ├────────────────────┼─────────┼───────┼────────────┤
    │ 44,100 Hz          │ 1,073   │ 0.2%  │ 268        │
    └────────────────────┴─────────┴───────┴────────────┘
    Per-Language Breakdown (Top 15)
    ┌────────────────┬────────┬────────────┬──────────┬───────────┐
    │    Language    │ Videos │ Usable Hrs │ Usable % │ Size (GB) │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ unknown        │ 64,664 │ 19,707     │ 65.2%    │ 2,674     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Malayalam (ml) │ 48,587 │ 13,849     │ 61.1%    │ 1,912     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Gujarati (gu)  │ 36,140 │ 12,738     │ 58.2%    │ 1,754     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Punjabi (pa)   │ 39,697 │ 12,228     │ 64.0%    │ 1,664     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Kannada (kn)   │ 32,238 │ 10,137     │ 59.4%    │ 1,392     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Hindi          │ 30,235 │ 9,214      │ 60.3%    │ 1,230     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Tamil          │ 37,907 │ 7,895      │ 62.9%    │ 1,055     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ hi             │ 26,233 │ 7,547      │ 51.8%    │ 1,057     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Punjabi        │ 16,051 │ 5,833      │ 64.8%    │ 803       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Telugu         │ 22,468 │ 5,716      │ 64.2%    │ 773       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Odia (or)      │ 16,602 │ 4,391      │ 56.5%    │ 593       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Malayalam      │ 16,686 │ 4,334      │ 62.6%    │ 606       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Bengali (bn)   │ 13,196 │ 3,750      │ 53.7%    │ 538       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Kannada        │ 13,451 │ 3,441      │ 66.5%    │ 505       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Tamil (ta)     │ 12,292 │ 3,363      │ 64.3%    │ 493       │
    └────────────────┴────────┴────────────┴──────────┴───────────┘
    Speaker Distribution
    ┌──────────────┬─────────┬────────────┐
    │   Speakers   │  Count  │ Usable Hrs │
    ├──────────────┼─────────┼────────────┤
    │ 1 speaker    │ 112,384 │ 18,321     │
    ├──────────────┼─────────┼────────────┤
    │ 2 speakers   │ 107,597 │ 21,899     │
    ├──────────────┼─────────┼────────────┤
    │ 3-5 speakers │ 157,965 │ 52,435     │
    ├──────────────┼─────────┼────────────┤
    │ 6+ speakers  │ 93,292  │ 45,698     │
    └──────────────┴─────────┴────────────┘
    Segment Statistics
    ┌──────────────────────┬─────────────┐
    │        Metric        │    Value    │
    ├──────────────────────┼─────────────┤
    │ Total segments       │ 196,161,852 │
    ├──────────────────────┼─────────────┤
    │ Usable segments      │ 80,685,832  │
    ├──────────────────────┼─────────────┤
    │ Needed Demucs        │ 9,238,005   │
    ├──────────────────────┼─────────────┤
    │ Heavy music segments │ 10,551,357  │
    ├──────────────────────┼─────────────┤
    │ Avg usable segment % │ 41.6%       │
    └──────────────────────┴─────────────┘
    Video Duration Distribution
    ┌───────────┬─────────┬────────────┐
    │ Duration  │  Count  │ Usable Hrs │
    ├───────────┼─────────┼────────────┤
    │ 0-5 min   │ 32,705  │ 1,068      │
    ├───────────┼─────────┼────────────┤
    │ 5-15 min  │ 155,885 │ 15,161     │
    ├───────────┼─────────┼────────────┤
    │ 15-30 min │ 139,218 │ 31,349     │
    ├───────────┼─────────┼────────────┤
    │ 30-60 min │ 93,097  │ 43,104     │
    ├───────────┼─────────┼────────────┤
    │ 1-2 hours │ 43,923  │ 36,832     │
    ├───────────┼─────────┼────────────┤
    │ 2+ hours  │ 6,603   │ 10,840     │
    └───────────┴─────────┴────────────┘
    Audio Format

    - All audio is Mono (471,431 videos)
    - 99.8% native 48kHz quality

first_user_message:
  /home/ubuntu/maya3/discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv

  Now, check with my db in supabase and add the new telugu videoIDs with their available metadata into them and then set all of these videoIDs to pending so we can trigger vast ai clusters so workers can start pulling videos and start working towards it. first let's start with syncing this csv to supabase. give analytics with now existing videoIds in supabase vs new videos added to pending. you can keep the failed ones as is, as they are actual failures genuinely. here is current analytics, give post addition to supabase analytics and once i verify we can go ahead and trigger vast ai so those workers start finishing these items. 

  MAYA3 PIPELINE - COMPLETE ANALYTICS REPORT

    Overall Summary
    ┌───────────────────────┬─────────────┐
    │        Metric         │    Value    │
    ├───────────────────────┼─────────────┤
    │ Total videos in DB    │ 487,830     │
    ├───────────────────────┼─────────────┤
    │ Completed             │ 471,431     │
    ├───────────────────────┼─────────────┤
    │ Source audio hours    │ 222,468 hrs │
    ├───────────────────────┼─────────────┤
    │ Usable audio hours    │ 138,353 hrs │
    ├───────────────────────┼─────────────┤
    │ Total R2 storage      │ 18.55 TB    │
    ├───────────────────────┼─────────────┤
    │ Avg usable percentage │ 61.2%       │
    ├───────────────────────┼─────────────┤
    │ Avg quality score     │ 0.94        │
    └───────────────────────┴─────────────┘
    Sample Rate / Quality
    ┌────────────────────┬─────────┬───────┬────────────┐
    │    Sample Rate     │  Count  │   %   │ Usable Hrs │
    ├────────────────────┼─────────┼───────┼────────────┤
    │ 48,000 Hz (Native) │ 470,358 │ 99.8% │ 138,085    │
    ├────────────────────┼─────────┼───────┼────────────┤
    │ 44,100 Hz          │ 1,073   │ 0.2%  │ 268        │
    └────────────────────┴─────────┴───────┴────────────┘
    Per-Language Breakdown (Top 15)
    ┌────────────────┬────────┬────────────┬──────────┬───────────┐
    │    Language    │ Videos │ Usable Hrs │ Usable % │ Size (GB) │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ unknown        │ 64,664 │ 19,707     │ 65.2%    │ 2,674     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Malayalam (ml) │ 48,587 │ 13,849     │ 61.1%    │ 1,912     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Gujarati (gu)  │ 36,140 │ 12,738     │ 58.2%    │ 1,754     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Punjabi (pa)   │ 39,697 │ 12,228     │ 64.0%    │ 1,664     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Kannada (kn)   │ 32,238 │ 10,137     │ 59.4%    │ 1,392     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Hindi          │ 30,235 │ 9,214      │ 60.3%    │ 1,230     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Tamil          │ 37,907 │ 7,895      │ 62.9%    │ 1,055     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ hi             │ 26,233 │ 7,547      │ 51.8%    │ 1,057     │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Punjabi        │ 16,051 │ 5,833      │ 64.8%    │ 803       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Telugu         │ 22,468 │ 5,716      │ 64.2%    │ 773       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Odia (or)      │ 16,602 │ 4,391      │ 56.5%    │ 593       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Malayalam      │ 16,686 │ 4,334      │ 62.6%    │ 606       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Bengali (bn)   │ 13,196 │ 3,750      │ 53.7%    │ 538       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Kannada        │ 13,451 │ 3,441      │ 66.5%    │ 505       │
    ├────────────────┼────────┼────────────┼──────────┼───────────┤
    │ Tamil (ta)     │ 12,292 │ 3,363      │ 64.3%    │ 493       │
    └────────────────┴────────┴────────────┴──────────┴───────────┘
    Speaker Distribution
    ┌──────────────┬─────────┬────────────┐
    │   Speakers   │  Count  │ Usable Hrs │
    ├──────────────┼─────────┼────────────┤
    │ 1 speaker    │ 112,384 │ 18,321     │
    ├──────────────┼─────────┼────────────┤
    │ 2 speakers   │ 107,597 │ 21,899     │
    ├──────────────┼─────────┼────────────┤
    │ 3-5 speakers │ 157,965 │ 52,435     │
    ├──────────────┼─────────┼────────────┤
    │ 6+ speakers  │ 93,292  │ 45,698     │
    └──────────────┴─────────┴────────────┘
    Segment Statistics
    ┌──────────────────────┬─────────────┐
    │        Metric        │    Value    │
    ├──────────────────────┼─────────────┤
    │ Total segments       │ 196,161,852 │
    ├──────────────────────┼─────────────┤
    │ Usable segments      │ 80,685,832  │
    ├──────────────────────┼─────────────┤
    │ Needed Demucs        │ 9,238,005   │
    ├──────────────────────┼─────────────┤
    │ Heavy music segments │ 10,551,357  │
    ├──────────────────────┼─────────────┤
    │ Avg usable segment % │ 41.6%       │
    └──────────────────────┴─────────────┘
    Video Duration Distribution
    ┌───────────┬─────────┬────────────┐
    │ Duration  │  Count  │ Usable Hrs │
    ├───────────┼─────────┼────────────┤
    │ 0-5 min   │ 32,705  │ 1,068      │
    ├───────────┼─────────┼────────────┤
    │ 5-15 min  │ 155,885 │ 15,161     │
    ├───────────┼─────────┼────────────┤
    │ 15-30 min │ 139,218 │ 31,349     │
    ├───────────┼─────────┼────────────┤
    │ 30-60 min │ 93,097  │ 43,104     │
    ├───────────┼─────────┼────────────┤
    │ 1-2 hours │ 43,923  │ 36,832     │
    ├───────────┼─────────┼────────────┤
    │ 2+ hours  │ 6,603   │ 10,840     │
    └───────────┴─────────┴────────────┘
    Audio Format

    - All audio is Mono (471,431 videos)
    - 99.8% native 48kHz quality


id: 019bb546-04c9-7700-b6e2-88c883de5a19
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"read-only"}
approval_mode: on-request
git_sha: bf68610269423b353384a793a6003e7a1d5de8d0
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/13/rollout-2026-01-13T02-53-43-019bb546-04c9-7700-b6e2-88c883de5a19.jsonl


id: 019bb548-d49e-7bc3-9e99-1a62f7e3ee06
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: bf68610269423b353384a793a6003e7a1d5de8d0
git_branch: massed_compute
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/13/rollout-2026-01-13T02-56-47-019bb548-d49e-7bc3-9e99-1a62f7e3ee06.jsonl

title:
  okay so here's the situation. Now, we have 2 buckets in R2. 

  1. test bucket - where our source truth lies and where our original files are existing in.
  2. 1-cleaned-data - where our final cleaned data for each of the data samples we picked from supabase. 



  now, since we had a different lists of items in different csv files, we generally pushed all raw audio files into the test bucket and synced the csv files with supabase. and had our workers request videoIDs from suapabse and keep track of statusses. and keep working on claimed videos and finish working and push to 1-cleaned-data bucket. Now, my question is there is a descrepency between these 2 buckets with a total of 20k videos. as per the total metadata provided by R2. the test bucket has total of 536.16k items or objects and the 1-cleaned-data bucket has total number of files somewhere at - 502.24k and a few 1000 files yet to come(will come in the next few minutes or seconds)


  the point and question is - whatever might be the reason for this descrepency, i'm more concerned about if there is a quickest and fasted way of identifying the missing or descrepency of the difference of 20k-30k videos from both buckets and is there a way for us to identify those IDs without having to search the metadata which takes a hella lot of time and as many classA searches. well i can afford to move ahead as i already have enough 500k and these 20k dont much bother me. but however if there is a quickest way to find that list, i dont wanna lose up on that. so, tell me if there a fastest and cheapest way to find that descrepency or do we have to do it the hard way.

first_user_message:
  okay so here's the situation. Now, we have 2 buckets in R2. 

  1. test bucket - where our source truth lies and where our original files are existing in.
  2. 1-cleaned-data - where our final cleaned data for each of the data samples we picked from supabase. 



  now, since we had a different lists of items in different csv files, we generally pushed all raw audio files into the test bucket and synced the csv files with supabase. and had our workers request videoIDs from suapabse and keep track of statusses. and keep working on claimed videos and finish working and push to 1-cleaned-data bucket. Now, my question is there is a descrepency between these 2 buckets with a total of 20k videos. as per the total metadata provided by R2. the test bucket has total of 536.16k items or objects and the 1-cleaned-data bucket has total number of files somewhere at - 502.24k and a few 1000 files yet to come(will come in the next few minutes or seconds)


  the point and question is - whatever might be the reason for this descrepency, i'm more concerned about if there is a quickest and fasted way of identifying the missing or descrepency of the difference of 20k-30k videos from both buckets and is there a way for us to identify those IDs without having to search the metadata which takes a hella lot of time and as many classA searches. well i can afford to move ahead as i already have enough 500k and these 20k dont much bother me. but however if there is a quickest way to find that list, i dont wanna lose up on that. so, tell me if there a fastest and cheapest way to find that descrepency or do we have to do it the hard way.


id: 019bb556-d07b-7f13-8e06-8fd60c9a5465
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya-transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/13/rollout-2026-01-13T03-12-04-019bb556-d07b-7f13-8e06-8fd60c9a5465.jsonl

title:
  Task - [task.md](task.md) 
  Reference docs by other AI agents - /home/ubuntu/maya-transcribe/docs
  Agents - [Agents.md](Agents.md) 
  secrets - /home/ubuntu/maya-transcribe/.env

first_user_message:
  Task - [task.md](task.md) 
  Reference docs by other AI agents - /home/ubuntu/maya-transcribe/docs
  Agents - [Agents.md](Agents.md) 
  secrets - /home/ubuntu/maya-transcribe/.env


id: 019bb587-6014-77e0-8321-a15ba34dc6e0
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya-transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/13/rollout-2026-01-13T04-05-06-019bb587-6014-77e0-8321-a15ba34dc6e0.jsonl

title:
  Start implementing this plan. 

  Task - [task.md](task.md) 
  Reference docs by other AI agents - /home/ubuntu/maya-transcribe/docs
  secrets - /home/ubuntu/maya-transcribe/.env
  Master plan for you to follow [master.md](master.md) 
  ignore the agents.md as it was intended for another agent and finished it's task and has created the master plan. 

  First create a robust version with full performance mode and we can always perform optimisations and see where we can compromise and where we cant. if you want to test your pipleine, few options we have are all secrets are in .env in root. and all metadata for each videoID is in supabase and R2 has cleaned, segmented audio files in the bucket named `1-cleaned-data` sometimes it might have some prefixes as well, so if you couldnt find it, search recursively or any other simpler approach or whatever. when you unzip the tar, you will find a folder named segments which contains all audio files. Architect > implement > test > report WERs & end to end latencies and estimations.

first_user_message:
  Start implementing this plan. 

  Task - [task.md](task.md) 
  Reference docs by other AI agents - /home/ubuntu/maya-transcribe/docs
  secrets - /home/ubuntu/maya-transcribe/.env
  Master plan for you to follow [master.md](master.md) 
  ignore the agents.md as it was intended for another agent and finished it's task and has created the master plan. 

  First create a robust version with full performance mode and we can always perform optimisations and see where we can compromise and where we cant. if you want to test your pipleine, few options we have are all secrets are in .env in root. and all metadata for each videoID is in supabase and R2 has cleaned, segmented audio files in the bucket named `1-cleaned-data` sometimes it might have some prefixes as well, so if you couldnt find it, search recursively or any other simpler approach or whatever. when you unzip the tar, you will find a folder named segments which contains all audio files. Architect > implement > test > report WERs & end to end latencies and estimations.


id: 019bb608-6b43-7a32-9eab-ef242f211eb7
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya-transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/13/rollout-2026-01-13T06-26-03-019bb608-6b43-7a32-9eab-ef242f211eb7.jsonl

title:
  1) Code readiness for open benchmarking

  Overall yes: you now have reproducible IDs (ids.py (line 16)), audit-friendly outputs (pipeline.py (line 262)), and eval that reports WER+CER+tails+oracle gap (eval.py (line 54)).
  One critical check before benchmarking with MMS CTC: facebook/mms-1b-all (and similar) needs processor.tokenizer.set_target_lang(<iso3>) before tokenizing text; otherwise Indic scripts become mostly <unk> and CTC scoring will silently fail via too_many_unk_tokens. I don’t see any set_target_lang call in ctc_fit.py (line 101) yet. If you’re using MMS as the fallback CTC model, patch this (or ensure your ctc_model_map always points to monolingual CTC models that don’t require it).
  Benchmarking will work best only if you actually run ≥2 diverse ASR hypotheses; otherwise ROVER/CTC selection is meaningless (watch ensemble.warnings in results.jsonl).
  2) Benchmarks/datasets to use (internet findings)

  AI4Bharat Vistaar (benchmarks + downloads + baseline WER tables): https://github.com/AI4Bharat/vistaar
  Includes benchmark zips for Kathbath, Kathbath-Hard, FLEURS, CommonVoice, IndicTTS, MUCS, GramVaani and reports IndicWhisper WER (e.g., overall avg WER 24.6%; Hindi subset avg 13.6%) in the repo README.
  Direct benchmark downloads are linked in their README (example): https://indicwhisper.objectstore.e2enetworks.net/vistaar_benchmarks/kathbath.zip (same pattern for others).
  For Assamese + English (not covered in Vistaar’s 12-language table): use google/fleurs and Common Voice (HuggingFace datasets) and evaluate the same way.
  Optional additional benchmark: collabora/indic-superb (HF dataset; has audio + transcription, but is large).
  3) Handover: how to run benchmarks + analyze language patterns (no new files)

  Download + unpack one benchmark (Vistaar example):
  kathbath.zip
  unzip kathbath_bench.zip -d bench/vistaar/kathbath
  Create refs.jsonl for one language folder (Vistaar structure is *.wav + transcript.txt per their README). Run this from repo root:
  python3 - <<'PY'
  from pathlib import Path
  import json
  from maya_transcribe.ids import segment_identity

  lang_dir = Path("bench/vistaar/kathbath/hindi")  # adjust
  audio_dir = lang_dir / "audio"
  txt_path = lang_dir / "transcript.txt"
  out_path = lang_dir / "refs.jsonl"

  def parse_line(line: str):
      line=line.strip()
      for sep in ("\t","|"," ",","):
          if sep in line:
              a,b=line.split(sep,1)
              return a.strip(), b.strip()
      return None

  rows=[]
  for line in txt_path.read_text(encoding="utf-8").splitlines():
      if not line.strip():
          continue
      parsed=parse_line(line)
      if not parsed:
          continue
      fname, text = parsed
      src = audio_dir / fname
      ident = segment_identity(src, root=audio_dir)
      rows.append({"segment_id": ident.segment_id, "text": text})

  out_path.write_text("\n".join(json.dumps(r, ensure_ascii=False) for r in rows) + "\n", encoding="utf-8")
  print("wrote", out_path, "rows", len(rows))
  PY
  Run pipeline + eval (start with no CTC to validate end-to-end; then enable CTC):
  maya-transcribe run --segments-dir bench/vistaar/kathbath/hindi/audio --out bench_runs/kathbath_hi --whisper-model large-v3 \
    --transformers-whisper-model <indic_whisper_hf_id>

  maya-transcribe eval --results bench_runs/kathbath_hi/results.jsonl --refs bench/vistaar/kathbath/hindi/refs.jsonl --out bench_runs/kathbath_hi/eval.json
  Pattern analysis to tune (estimate human workload + where WER comes from):
  python3 - <<'PY'
  import json, collections
  p="bench_runs/kathbath_hi/results.jsonl"
  dec=collections.Counter(); reasons=collections.Counter()
  for line in open(p,encoding="utf-8"):
      r=json.loads(line)
      v=r.get("verification",{})
      dec[v.get("decision")] += 1
      for reason in (v.get("reasons") or []):
          reasons[reason]+=1
  print("decisions:",dec)
  print("top reasons:",reasons.most_common(20))
  PY
  Repeat per language/dataset; use eval.json + oracle stats to decide:
  High oracle_best_wer ⇒ you need a better base ASR for that language/domain.
  High selection_gap_wer_mean ⇒ selection/gating (ROVER/CTC thresholds) needs tuning or the CTC model is mismatched.
  If you tell me which exact datasets you want first (Vistaar-only vs Vistaar+FLEURS for Assamese/English), I can give a minimal “benchmark matrix” (languages × datasets × model configs) to run as ablations.


  can you run the benchmarks with few 100s of samples for each language. maybe if it's nto available in a single dataset, please pull from various sources but in overall, like get some 1000-2000 samples per each language and do a benchmark with our pipeline. goal is minimal WER as possible, if particular languages show higher, analyse why ? and fix or tweak them but make it adjustable tweakable and make it robust. this is the place for us to run the automated benchmarking tests on our pipeline here. Constantly keep evaluating, evolving and only conclude when you feel like this is the lowest WER possible and i refuse to believe until we're like at 2.0 wer or achieve even lesser. just validated that i now have access to shrutilipi, svarah, kathbath and etc. 


  And to make it even easier, create a new benchmark dataset under our huggingface ID using the key in .env, ensure it's private. 



  more info on datasets to choose samples from huggingface. 

  For the benchmark datasets we discussed, these are the Hugging Face dataset repos to check access for:

  AI4Bharat (these are gated=auto → you must be logged in and click “Agree/Request access” on HF):

  ai4bharat/Kathbath
  ai4bharat/IndicVoices
  ai4bharat/indicvoices_r
  ai4bharat/Lahaja
  ai4bharat/Svarah
  (optional, not ASR) ai4bharat/IndicVoices-ST
  Open / ungated (should work without requesting access):

  google/fleurs
  mozilla-foundation/common_voice_17_0 (or whichever CV version you use)
  collabora/indic-superb
  Not needed on HF if you use Vistaar’s benchmark zips: the Vistaar benchmark suite is downloadable directly (no HF dataset access required).

  Quick “do I actually have access?” check (doesn’t download everything):

  python3 -c "from datasets import get_dataset_config_names; print(get_dataset_config_names('ai4bharat/Kathbath'))"
  then try streaming one split: python3 -c "from datasets import load_dataset; ds=load_dataset('ai4bharat/Kathbath','hindi',split='validation',streaming=True); print(next(iter(ds)))"

first_user_message:
  1) Code readiness for open benchmarking

  Overall yes: you now have reproducible IDs (ids.py (line 16)), audit-friendly outputs (pipeline.py (line 262)), and eval that reports WER+CER+tails+oracle gap (eval.py (line 54)).
  One critical check before benchmarking with MMS CTC: facebook/mms-1b-all (and similar) needs processor.tokenizer.set_target_lang(<iso3>) before tokenizing text; otherwise Indic scripts become mostly <unk> and CTC scoring will silently fail via too_many_unk_tokens. I don’t see any set_target_lang call in ctc_fit.py (line 101) yet. If you’re using MMS as the fallback CTC model, patch this (or ensure your ctc_model_map always points to monolingual CTC models that don’t require it).
  Benchmarking will work best only if you actually run ≥2 diverse ASR hypotheses; otherwise ROVER/CTC selection is meaningless (watch ensemble.warnings in results.jsonl).
  2) Benchmarks/datasets to use (internet findings)

  AI4Bharat Vistaar (benchmarks + downloads + baseline WER tables): https://github.com/AI4Bharat/vistaar
  Includes benchmark zips for Kathbath, Kathbath-Hard, FLEURS, CommonVoice, IndicTTS, MUCS, GramVaani and reports IndicWhisper WER (e.g., overall avg WER 24.6%; Hindi subset avg 13.6%) in the repo README.
  Direct benchmark downloads are linked in their README (example): https://indicwhisper.objectstore.e2enetworks.net/vistaar_benchmarks/kathbath.zip (same pattern for others).
  For Assamese + English (not covered in Vistaar’s 12-language table): use google/fleurs and Common Voice (HuggingFace datasets) and evaluate the same way.
  Optional additional benchmark: collabora/indic-superb (HF dataset; has audio + transcription, but is large).
  3) Handover: how to run benchmarks + analyze language patterns (no new files)

  Download + unpack one benchmark (Vistaar example):
  kathbath.zip
  unzip kathbath_bench.zip -d bench/vistaar/kathbath
  Create refs.jsonl for one language folder (Vistaar structure is *.wav + transcript.txt per their README). Run this from repo root:
  python3 - <<'PY'
  from pathlib import Path
  import json
  from maya_transcribe.ids import segment_identity

  lang_dir = Path("bench/vistaar/kathbath/hindi")  # adjust
  audio_dir = lang_dir / "audio"
  txt_path = lang_dir / "transcript.txt"
  out_path = lang_dir / "refs.jsonl"

  def parse_line(line: str):
      line=line.strip()
      for sep in ("\t","|"," ",","):
          if sep in line:
              a,b=line.split(sep,1)
              return a.strip(), b.strip()
      return None

  rows=[]
  for line in txt_path.read_text(encoding="utf-8").splitlines():
      if not line.strip():
          continue
      parsed=parse_line(line)
      if not parsed:
          continue
      fname, text = parsed
      src = audio_dir / fname
      ident = segment_identity(src, root=audio_dir)
      rows.append({"segment_id": ident.segment_id, "text": text})

  out_path.write_text("\n".join(json.dumps(r, ensure_ascii=False) for r in rows) + "\n", encoding="utf-8")
  print("wrote", out_path, "rows", len(rows))
  PY
  Run pipeline + eval (start with no CTC to validate end-to-end; then enable CTC):
  maya-transcribe run --segments-dir bench/vistaar/kathbath/hindi/audio --out bench_runs/kathbath_hi --whisper-model large-v3 \
    --transformers-whisper-model <indic_whisper_hf_id>

  maya-transcribe eval --results bench_runs/kathbath_hi/results.jsonl --refs bench/vistaar/kathbath/hindi/refs.jsonl --out bench_runs/kathbath_hi/eval.json
  Pattern analysis to tune (estimate human workload + where WER comes from):
  python3 - <<'PY'
  import json, collections
  p="bench_runs/kathbath_hi/results.jsonl"
  dec=collections.Counter(); reasons=collections.Counter()
  for line in open(p,encoding="utf-8"):
      r=json.loads(line)
      v=r.get("verification",{})
      dec[v.get("decision")] += 1
      for reason in (v.get("reasons") or []):
          reasons[reason]+=1
  print("decisions:",dec)
  print("top reasons:",reasons.most_common(20))
  PY
  Repeat per language/dataset; use eval.json + oracle stats to decide:
  High oracle_best_wer ⇒ you need a better base ASR for that language/domain.
  High selection_gap_wer_mean ⇒ selection/gating (ROVER/CTC thresholds) needs tuning or the CTC model is mismatched.
  If you tell me which exact datasets you want first (Vistaar-only vs Vistaar+FLEURS for Assamese/English), I can give a minimal “benchmark matrix” (languages × datasets × model configs) to run as ablations.


  can you run the benchmarks with few 100s of samples for each language. maybe if it's nto available in a single dataset, please pull from various sources but in overall, like get some 1000-2000 samples per each language and do a benchmark with our pipeline. goal is minimal WER as possible, if particular languages show higher, analyse why ? and fix or tweak them but make it adjustable tweakable and make it robust. this is the place for us to run the automated benchmarking tests on our pipeline here. Constantly keep evaluating, evolving and only conclude when you feel like this is the lowest WER possible and i refuse to believe until we're like at 2.0 wer or achieve even lesser. just validated that i now have access to shrutilipi, svarah, kathbath and etc. 


  And to make it even easier, create a new benchmark dataset under our huggingface ID using the key in .env, ensure it's private. 



  more info on datasets to choose samples from huggingface. 

  For the benchmark datasets we discussed, these are the Hugging Face dataset repos to check access for:

  AI4Bharat (these are gated=auto → you must be logged in and click “Agree/Request access” on HF):

  ai4bharat/Kathbath
  ai4bharat/IndicVoices
  ai4bharat/indicvoices_r
  ai4bharat/Lahaja
  ai4bharat/Svarah
  (optional, not ASR) ai4bharat/IndicVoices-ST
  Open / ungated (should work without requesting access):

  google/fleurs
  mozilla-foundation/common_voice_17_0 (or whichever CV version you use)
  collabora/indic-superb
  Not needed on HF if you use Vistaar’s benchmark zips: the Vistaar benchmark suite is downloadable directly (no HF dataset access required).

  Quick “do I actually have access?” check (doesn’t download everything):

  python3 -c "from datasets import get_dataset_config_names; print(get_dataset_config_names('ai4bharat/Kathbath'))"
  then try streaming one split: python3 -c "from datasets import load_dataset; ds=load_dataset('ai4bharat/Kathbath','hindi',split='validation',streaming=True); print(next(iter(ds)))"


id: 019bbe12-f542-7101-8837-dab601d3c673
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya-transcribe
sandbox_policy: {"type":"read-only"}
approval_mode: on-request
cli_version: 0.78.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/14/rollout-2026-01-14T19-54-32-019bbe12-f542-7101-8837-dab601d3c673.jsonl


id: 019bbeaa-212c-7042-9d20-cf016926b80b
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya-transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/14/rollout-2026-01-14T22-39-39-019bbeaa-212c-7042-9d20-cf016926b80b.jsonl

title:
  plan was [master.md](master.md) 

  Full Benchmark Analysis Summary

    Run Comparison
    ┌─────────────┬────────────────────┬──────────────────┬───────────────────┐
    │   Metric    │ Baseline (FW only) │ + Vaani (strict) │ + Vaani (relaxed) │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ WER (final) │ 30.6%              │ 23.7%            │ 23.7%             │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Accept rate │ 0/200              │ 11/200           │ 81/200            │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Human rate  │ 200/200            │ 189/200          │ 119/200           │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Oracle best │ 29.7%              │ 9.8%             │ 9.8%              │
    └─────────────┴────────────────────┴──────────────────┴───────────────────┘
    Decision Quality with Relaxed Threshold
    ┌──────────┬───────┬──────────┬───────────────┐
    │ Decision │ Count │ WER Mean │ Disagree Rate │
    ├──────────┼───────┼──────────┼───────────────┤
    │ Accept   │ 81    │ 18.6%    │ 8.5%          │
    ├──────────┼───────┼──────────┼───────────────┤
    │ Human    │ 119   │ 27.1%    │ 13.2%         │
    └──────────┴───────┴──────────┴───────────────┘
    WER by Disagreement Rate Bucket

    - 0-5%: n=35, WER=6.0% (excellent)
    - 5-10%: n=47, WER=19.6%
    - 10-15%: n=68, WER=24.6%
    - 15-25%: n=44, WER=36.9%
    - 25-100%: n=6, WER=50.9%

    Remaining Human Decision Reasons

    - lid_uncertain: 63 (LID confused between Hindi/Urdu/related languages)
    - asr_disagreement: 50 (still above 15% threshold)
    - ctc_ambiguous: 49 (CTC margin too low)

    LID Analysis

    - 82% correctly identified as Hindi
    - 31.5% flagged uncertain due to low margin (Hindi vs Urdu/Gujarati/Bhojpuri)
    - This is expected: MMS-LID struggles with closely related Indic languages

    Final Selection Distribution

    - ROVER: 64.5% (129/200)
    - Vaani Hindi: 34.0% (68/200)
    - FW large-v3: 1.5% (3/200)

    Key Insights

    1. Oracle potential is real: 9.8% WER is achievable with perfect selection
    2. Current selection gap: 13.9pp left on the table due to suboptimal hypothesis selection
    3. Gating works directionally: Accept segments (18.6% WER) are better than Human segments (27.1% WER)
    4. Disagreement correlates with quality: Strong correlation between disagreement rate and WER
    5. LID is the new bottleneck: 31.5% flagged uncertain due to Hindi/Urdu confusion

    Recommendations

    1. Consider LID-Urdu equivalence for Hindi: Treat Hindi and Urdu as equivalent for routing since they share script (when using Devanagari transcription)
    2. Better CTC selection: Current CTC selection isn't capturing the oracle improvement - the selection_gap of 13.9pp suggests the CTC model isn't reliably picking the best hypothesis
    3. Two-stage approach: Accept high-confidence segments (low disagreement), use LLM-based selection for ambiguous cases

  ✻ Sautéed for 32m 59s

  ● Background command "Run benchmark with relaxed disagreement threshold" completed (exit code 0).

  ● I've already analyzed those results above. The benchmark with relaxed threshold (15% disagreement rate) completed successfully:

    Key result: Accept rate improved from 11 → 81 segments (40.5%) while maintaining the same 23.7% WER.

    The analysis shows the pipeline is working - accepted segments have lower WER (18.6%) than segments routed to human review (27.1%). The main remaining bottlenecks are:
    - LID uncertainty (Hindi/Urdu confusion) - 63 segments
    - Disagreement still above threshold - 50 segments
    - CTC ambiguity - 49 segments

    The oracle potential (9.8% WER) is significantly better than what we're achieving (23.7%), indicating room for improved hypothesis selection.



  But these WER scores are not what i'm looking for. i need <5% WER scores for all languages consistently. For that to happen, i can run any LLMs like Gemma3n, voxtral. 

  Can we try these LLMs for audio transcription. can you test and give the performance scores or WER scores when compared to our recent ones above.

first_user_message:
  plan was [master.md](master.md) 

  Full Benchmark Analysis Summary

    Run Comparison
    ┌─────────────┬────────────────────┬──────────────────┬───────────────────┐
    │   Metric    │ Baseline (FW only) │ + Vaani (strict) │ + Vaani (relaxed) │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ WER (final) │ 30.6%              │ 23.7%            │ 23.7%             │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Accept rate │ 0/200              │ 11/200           │ 81/200            │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Human rate  │ 200/200            │ 189/200          │ 119/200           │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Oracle best │ 29.7%              │ 9.8%             │ 9.8%              │
    └─────────────┴────────────────────┴──────────────────┴───────────────────┘
    Decision Quality with Relaxed Threshold
    ┌──────────┬───────┬──────────┬───────────────┐
    │ Decision │ Count │ WER Mean │ Disagree Rate │
    ├──────────┼───────┼──────────┼───────────────┤
    │ Accept   │ 81    │ 18.6%    │ 8.5%          │
    ├──────────┼───────┼──────────┼───────────────┤
    │ Human    │ 119   │ 27.1%    │ 13.2%         │
    └──────────┴───────┴──────────┴───────────────┘
    WER by Disagreement Rate Bucket

    - 0-5%: n=35, WER=6.0% (excellent)
    - 5-10%: n=47, WER=19.6%
    - 10-15%: n=68, WER=24.6%
    - 15-25%: n=44, WER=36.9%
    - 25-100%: n=6, WER=50.9%

    Remaining Human Decision Reasons

    - lid_uncertain: 63 (LID confused between Hindi/Urdu/related languages)
    - asr_disagreement: 50 (still above 15% threshold)
    - ctc_ambiguous: 49 (CTC margin too low)

    LID Analysis

    - 82% correctly identified as Hindi
    - 31.5% flagged uncertain due to low margin (Hindi vs Urdu/Gujarati/Bhojpuri)
    - This is expected: MMS-LID struggles with closely related Indic languages

    Final Selection Distribution

    - ROVER: 64.5% (129/200)
    - Vaani Hindi: 34.0% (68/200)
    - FW large-v3: 1.5% (3/200)

    Key Insights

    1. Oracle potential is real: 9.8% WER is achievable with perfect selection
    2. Current selection gap: 13.9pp left on the table due to suboptimal hypothesis selection
    3. Gating works directionally: Accept segments (18.6% WER) are better than Human segments (27.1% WER)
    4. Disagreement correlates with quality: Strong correlation between disagreement rate and WER
    5. LID is the new bottleneck: 31.5% flagged uncertain due to Hindi/Urdu confusion

    Recommendations

    1. Consider LID-Urdu equivalence for Hindi: Treat Hindi and Urdu as equivalent for routing since they share script (when using Devanagari transcription)
    2. Better CTC selection: Current CTC selection isn't capturing the oracle improvement - the selection_gap of 13.9pp suggests the CTC model isn't reliably picking the best hypothesis
    3. Two-stage approach: Accept high-confidence segments (low disagreement), use LLM-based selection for ambiguous cases

  ✻ Sautéed for 32m 59s

  ● Background command "Run benchmark with relaxed disagreement threshold" completed (exit code 0).

  ● I've already analyzed those results above. The benchmark with relaxed threshold (15% disagreement rate) completed successfully:

    Key result: Accept rate improved from 11 → 81 segments (40.5%) while maintaining the same 23.7% WER.

    The analysis shows the pipeline is working - accepted segments have lower WER (18.6%) than segments routed to human review (27.1%). The main remaining bottlenecks are:
    - LID uncertainty (Hindi/Urdu confusion) - 63 segments
    - Disagreement still above threshold - 50 segments
    - CTC ambiguity - 49 segments

    The oracle potential (9.8% WER) is significantly better than what we're achieving (23.7%), indicating room for improved hypothesis selection.



  But these WER scores are not what i'm looking for. i need <5% WER scores for all languages consistently. For that to happen, i can run any LLMs like Gemma3n, voxtral. 

  Can we try these LLMs for audio transcription. can you test and give the performance scores or WER scores when compared to our recent ones above.


id: 019bbeb8-95fd-7de2-89a4-bc24cec4559b
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya-transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/14/rollout-2026-01-14T22-55-26-019bbeb8-95fd-7de2-89a4-bc24cec4559b.jsonl

title:
  plan was [master.md](master.md) 

  Full Benchmark Analysis Summary

    Run Comparison
    ┌─────────────┬────────────────────┬──────────────────┬───────────────────┐
    │   Metric    │ Baseline (FW only) │ + Vaani (strict) │ + Vaani (relaxed) │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ WER (final) │ 30.6%              │ 23.7%            │ 23.7%             │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Accept rate │ 0/200              │ 11/200           │ 81/200            │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Human rate  │ 200/200            │ 189/200          │ 119/200           │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Oracle best │ 29.7%              │ 9.8%             │ 9.8%              │
    └─────────────┴────────────────────┴──────────────────┴───────────────────┘
    Decision Quality with Relaxed Threshold
    ┌──────────┬───────┬──────────┬───────────────┐
    │ Decision │ Count │ WER Mean │ Disagree Rate │
    ├──────────┼───────┼──────────┼───────────────┤
    │ Accept   │ 81    │ 18.6%    │ 8.5%          │
    ├──────────┼───────┼──────────┼───────────────┤
    │ Human    │ 119   │ 27.1%    │ 13.2%         │
    └──────────┴───────┴──────────┴───────────────┘
    WER by Disagreement Rate Bucket

    - 0-5%: n=35, WER=6.0% (excellent)
    - 5-10%: n=47, WER=19.6%
    - 10-15%: n=68, WER=24.6%
    - 15-25%: n=44, WER=36.9%
    - 25-100%: n=6, WER=50.9%

    Remaining Human Decision Reasons

    - lid_uncertain: 63 (LID confused between Hindi/Urdu/related languages)
    - asr_disagreement: 50 (still above 15% threshold)
    - ctc_ambiguous: 49 (CTC margin too low)

    LID Analysis

    - 82% correctly identified as Hindi
    - 31.5% flagged uncertain due to low margin (Hindi vs Urdu/Gujarati/Bhojpuri)
    - This is expected: MMS-LID struggles with closely related Indic languages

    Final Selection Distribution

    - ROVER: 64.5% (129/200)
    - Vaani Hindi: 34.0% (68/200)
    - FW large-v3: 1.5% (3/200)

    Key Insights

    1. Oracle potential is real: 9.8% WER is achievable with perfect selection
    2. Current selection gap: 13.9pp left on the table due to suboptimal hypothesis selection
    3. Gating works directionally: Accept segments (18.6% WER) are better than Human segments (27.1% WER)
    4. Disagreement correlates with quality: Strong correlation between disagreement rate and WER
    5. LID is the new bottleneck: 31.5% flagged uncertain due to Hindi/Urdu confusion

    Recommendations

    1. Consider LID-Urdu equivalence for Hindi: Treat Hindi and Urdu as equivalent for routing since they share script (when using Devanagari transcription)
    2. Better CTC selection: Current CTC selection isn't capturing the oracle improvement - the selection_gap of 13.9pp suggests the CTC model isn't reliably picking the best hypothesis
    3. Two-stage approach: Accept high-confidence segments (low disagreement), use LLM-based selection for ambiguous cases

  ✻ Sautéed for 32m 59s

  ● Background command "Run benchmark with relaxed disagreement threshold" completed (exit code 0).

  ● I've already analyzed those results above. The benchmark with relaxed threshold (15% disagreement rate) completed successfully:

    Key result: Accept rate improved from 11 → 81 segments (40.5%) while maintaining the same 23.7% WER.

    The analysis shows the pipeline is working - accepted segments have lower WER (18.6%) than segments routed to human review (27.1%). The main remaining bottlenecks are:
    - LID uncertainty (Hindi/Urdu confusion) - 63 segments
    - Disagreement still above threshold - 50 segments
    - CTC ambiguity - 49 segments

    The oracle potential (9.8% WER) is significantly better than what we're achieving (23.7%), indicating room for improved hypothesis selection.



  But these WER scores are not what i'm looking for. i need <5% WER scores for all languages consistently. For that to happen, i can run any LLMs like Gemma3n, voxtral. 

  Can we try these LLMs for audio transcription. can you test and give the performance scores or WER scores when compared to our recent ones above. download these models from huggingface and integrate them and do inference on transcription tasks and get full scores. both models wont fit at once, onload one, then another. you should basically not run transformers based infernece. use vLLM or any other inference engine for this to run the gemma3n models or voxtral models.

first_user_message:
  plan was [master.md](master.md) 

  Full Benchmark Analysis Summary

    Run Comparison
    ┌─────────────┬────────────────────┬──────────────────┬───────────────────┐
    │   Metric    │ Baseline (FW only) │ + Vaani (strict) │ + Vaani (relaxed) │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ WER (final) │ 30.6%              │ 23.7%            │ 23.7%             │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Accept rate │ 0/200              │ 11/200           │ 81/200            │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Human rate  │ 200/200            │ 189/200          │ 119/200           │
    ├─────────────┼────────────────────┼──────────────────┼───────────────────┤
    │ Oracle best │ 29.7%              │ 9.8%             │ 9.8%              │
    └─────────────┴────────────────────┴──────────────────┴───────────────────┘
    Decision Quality with Relaxed Threshold
    ┌──────────┬───────┬──────────┬───────────────┐
    │ Decision │ Count │ WER Mean │ Disagree Rate │
    ├──────────┼───────┼──────────┼───────────────┤
    │ Accept   │ 81    │ 18.6%    │ 8.5%          │
    ├──────────┼───────┼──────────┼───────────────┤
    │ Human    │ 119   │ 27.1%    │ 13.2%         │
    └──────────┴───────┴──────────┴───────────────┘
    WER by Disagreement Rate Bucket

    - 0-5%: n=35, WER=6.0% (excellent)
    - 5-10%: n=47, WER=19.6%
    - 10-15%: n=68, WER=24.6%
    - 15-25%: n=44, WER=36.9%
    - 25-100%: n=6, WER=50.9%

    Remaining Human Decision Reasons

    - lid_uncertain: 63 (LID confused between Hindi/Urdu/related languages)
    - asr_disagreement: 50 (still above 15% threshold)
    - ctc_ambiguous: 49 (CTC margin too low)

    LID Analysis

    - 82% correctly identified as Hindi
    - 31.5% flagged uncertain due to low margin (Hindi vs Urdu/Gujarati/Bhojpuri)
    - This is expected: MMS-LID struggles with closely related Indic languages

    Final Selection Distribution

    - ROVER: 64.5% (129/200)
    - Vaani Hindi: 34.0% (68/200)
    - FW large-v3: 1.5% (3/200)

    Key Insights

    1. Oracle potential is real: 9.8% WER is achievable with perfect selection
    2. Current selection gap: 13.9pp left on the table due to suboptimal hypothesis selection
    3. Gating works directionally: Accept segments (18.6% WER) are better than Human segments (27.1% WER)
    4. Disagreement correlates with quality: Strong correlation between disagreement rate and WER
    5. LID is the new bottleneck: 31.5% flagged uncertain due to Hindi/Urdu confusion

    Recommendations

    1. Consider LID-Urdu equivalence for Hindi: Treat Hindi and Urdu as equivalent for routing since they share script (when using Devanagari transcription)
    2. Better CTC selection: Current CTC selection isn't capturing the oracle improvement - the selection_gap of 13.9pp suggests the CTC model isn't reliably picking the best hypothesis
    3. Two-stage approach: Accept high-confidence segments (low disagreement), use LLM-based selection for ambiguous cases

  ✻ Sautéed for 32m 59s

  ● Background command "Run benchmark with relaxed disagreement threshold" completed (exit code 0).

  ● I've already analyzed those results above. The benchmark with relaxed threshold (15% disagreement rate) completed successfully:

    Key result: Accept rate improved from 11 → 81 segments (40.5%) while maintaining the same 23.7% WER.

    The analysis shows the pipeline is working - accepted segments have lower WER (18.6%) than segments routed to human review (27.1%). The main remaining bottlenecks are:
    - LID uncertainty (Hindi/Urdu confusion) - 63 segments
    - Disagreement still above threshold - 50 segments
    - CTC ambiguity - 49 segments

    The oracle potential (9.8% WER) is significantly better than what we're achieving (23.7%), indicating room for improved hypothesis selection.



  But these WER scores are not what i'm looking for. i need <5% WER scores for all languages consistently. For that to happen, i can run any LLMs like Gemma3n, voxtral. 

  Can we try these LLMs for audio transcription. can you test and give the performance scores or WER scores when compared to our recent ones above. download these models from huggingface and integrate them and do inference on transcription tasks and get full scores. both models wont fit at once, onload one, then another. you should basically not run transformers based infernece. use vLLM or any other inference engine for this to run the gemma3n models or voxtral models.


id: 019bc37d-54ed-7c22-93a2-022972a366b6
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya-transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/15/rollout-2026-01-15T21-08-49-019bc37d-54ed-7c22-93a2-022972a366b6.jsonl

title:
  ## Multilingual 100-sample Runs (Transcription Dumps)
  Bench subset: `data/bench/maya_hf_v2_100` (12 languages x 100 samples). These runs include JSON dumps + eval.json for per-language WER.

  - IndicConformer 600M multilingual (HF): `outputs/bench/maya_hf_v2_100/indicconformer_600m_hf/results.jsonl`
    - Note: English segments failed with `KeyError:'joint_post_net_en'` (100 error rows).
  - Whisper large-v3: `outputs/bench/maya_hf_v2_100/whisper_large_v3/results.jsonl`
  - Voxtral-Small-24B-2507: `outputs/bench/maya_hf_v2_100/voxtral_small_vllm/results.jsonl`
    - Note: Voxtral only natively supports a small language set (hi/en/...) so non-supported languages likely degrade.
  - Gemma-3n-E2B-it: `outputs/bench/maya_hf_v2_100/gemma3n_e2b_vllm/results.jsonl`
  - OmniASR LLM 7B v2: `outputs/bench/maya_hf_v2_100/omniasr_llm_7b_v2/results.jsonl`
  - Seamless M4T v2 large: `outputs/bench/maya_hf_v2_100/seamless_m4t_v2_large/results.jsonl`

  ### Per-language WER (mean, %)
  Source: `eval.json` from each run. Note: IndicConformer HF failed on English (`n/a` below), so its overall WER is computed on 1100 segments.

  | Lang | IndicConformer-600M (HF) | Whisper large-v3 | Voxtral-Small | Gemma-3n-E2B | OmniASR LLM 7B v2 | Seamless M4T v2 large |
  | --- | --- | --- | --- | --- | --- | --- |
  | asm | 19.9 | 95.9 | 107.9 | 78.0 | 21.8 | 29.9 |
  | ben | 11.2 | 64.2 | 37.4 | 34.8 | 13.9 | 17.5 |
  | eng | n/a | 4.5 | 3.8 | 9.2 | 6.0 | 7.7 |
  | guj | 11.7 | 49.3 | 49.8 | 87.3 | 16.3 | 19.4 |
  | hin | 9.2 | 30.9 | 14.9 | 18.8 | 12.6 | 25.4 |
  | kan | 14.3 | 71.7 | 49.0 | 45.8 | 20.4 | 30.5 |
  | mal | 29.9 | 122.5 | 62.6 | 65.1 | 41.2 | 42.6 |
  | mar | 14.6 | 79.7 | 39.9 | 38.8 | 25.1 | 24.1 |
  | ory | 16.3 | 115.5 | 130.3 | 120.0 | 29.8 | 31.5 |
  | pan | 12.6 | 64.7 | 34.2 | 135.0 | 16.0 | 17.4 |
  | tam | 20.1 | 50.9 | 52.7 | 78.8 | 32.9 | 31.5 |
  | tel | 24.4 | 79.0 | 47.5 | 51.2 | 31.6 | 40.1 |
  | overall | 16.7 | 69.1 | 52.5 | 63.6 | 22.3 | 26.5 |


  go through each of these evaluations vs actual samples and see if you can notice any patterns between these evaluations. like are we doing any WER calculation mistakes, or is it too aggressive ? or is it not comparing right, or is there a pattern for particular language or something. think and provide your analysis on why these WER are so high. And add your analysis in results.md

first_user_message:
  ## Multilingual 100-sample Runs (Transcription Dumps)
  Bench subset: `data/bench/maya_hf_v2_100` (12 languages x 100 samples). These runs include JSON dumps + eval.json for per-language WER.

  - IndicConformer 600M multilingual (HF): `outputs/bench/maya_hf_v2_100/indicconformer_600m_hf/results.jsonl`
    - Note: English segments failed with `KeyError:'joint_post_net_en'` (100 error rows).
  - Whisper large-v3: `outputs/bench/maya_hf_v2_100/whisper_large_v3/results.jsonl`
  - Voxtral-Small-24B-2507: `outputs/bench/maya_hf_v2_100/voxtral_small_vllm/results.jsonl`
    - Note: Voxtral only natively supports a small language set (hi/en/...) so non-supported languages likely degrade.
  - Gemma-3n-E2B-it: `outputs/bench/maya_hf_v2_100/gemma3n_e2b_vllm/results.jsonl`
  - OmniASR LLM 7B v2: `outputs/bench/maya_hf_v2_100/omniasr_llm_7b_v2/results.jsonl`
  - Seamless M4T v2 large: `outputs/bench/maya_hf_v2_100/seamless_m4t_v2_large/results.jsonl`

  ### Per-language WER (mean, %)
  Source: `eval.json` from each run. Note: IndicConformer HF failed on English (`n/a` below), so its overall WER is computed on 1100 segments.

  | Lang | IndicConformer-600M (HF) | Whisper large-v3 | Voxtral-Small | Gemma-3n-E2B | OmniASR LLM 7B v2 | Seamless M4T v2 large |
  | --- | --- | --- | --- | --- | --- | --- |
  | asm | 19.9 | 95.9 | 107.9 | 78.0 | 21.8 | 29.9 |
  | ben | 11.2 | 64.2 | 37.4 | 34.8 | 13.9 | 17.5 |
  | eng | n/a | 4.5 | 3.8 | 9.2 | 6.0 | 7.7 |
  | guj | 11.7 | 49.3 | 49.8 | 87.3 | 16.3 | 19.4 |
  | hin | 9.2 | 30.9 | 14.9 | 18.8 | 12.6 | 25.4 |
  | kan | 14.3 | 71.7 | 49.0 | 45.8 | 20.4 | 30.5 |
  | mal | 29.9 | 122.5 | 62.6 | 65.1 | 41.2 | 42.6 |
  | mar | 14.6 | 79.7 | 39.9 | 38.8 | 25.1 | 24.1 |
  | ory | 16.3 | 115.5 | 130.3 | 120.0 | 29.8 | 31.5 |
  | pan | 12.6 | 64.7 | 34.2 | 135.0 | 16.0 | 17.4 |
  | tam | 20.1 | 50.9 | 52.7 | 78.8 | 32.9 | 31.5 |
  | tel | 24.4 | 79.0 | 47.5 | 51.2 | 31.6 | 40.1 |
  | overall | 16.7 | 69.1 | 52.5 | 63.6 | 22.3 | 26.5 |


  go through each of these evaluations vs actual samples and see if you can notice any patterns between these evaluations. like are we doing any WER calculation mistakes, or is it too aggressive ? or is it not comparing right, or is there a pattern for particular language or something. think and provide your analysis on why these WER are so high. And add your analysis in results.md


id: 019bd478-9096-7610-a669-ebd90e8931c7
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_prepare
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/19/rollout-2026-01-19T04-17-09-019bd478-9096-7610-a669-ebd90e8931c7.jsonl

title:
  We are on a plan of data preparation for codec training. plan is at [strategy.md](strategy.md) and objective - [objective.md](objective.md) 

  i want you to go through entire plan, pipeline implementation, and see if everything is implemented and is working as intended. test with a video download from R2 directly. and test the segments and read everything right from audio file by validating everything from audio file as well and see if everything tallies with what we are trying to do. and then see if we are missing anything as per plan. i also need you to go through the codec training code in x-codec-2.0 aswell and then ensure our data as .tar will be valid with it completely, or we can fix those scripts. in short, i need to be completely sure and reliable that my data is completely alright and there is no issue or failures, or regressions or problems with our pipeline here. and it's completely optimal about what we are trying to do here. validate with 1 videoID from R2, you can pick the videos from Supabase's table, run a video on this pipeline, and then if pipeline is validated, we can plan for optimising and parallelisation and scaling horizontally by docker and other methods later. first finish your complete effort in validating the pipeline by checking everything manually and ensuring each and every segment is read, validated, and i can rely the pipeline is robust and is working perfect. we can talk about scaling horizontally later.

first_user_message:
  We are on a plan of data preparation for codec training. plan is at [strategy.md](strategy.md) and objective - [objective.md](objective.md) 

  i want you to go through entire plan, pipeline implementation, and see if everything is implemented and is working as intended. test with a video download from R2 directly. and test the segments and read everything right from audio file by validating everything from audio file as well and see if everything tallies with what we are trying to do. and then see if we are missing anything as per plan. i also need you to go through the codec training code in x-codec-2.0 aswell and then ensure our data as .tar will be valid with it completely, or we can fix those scripts. in short, i need to be completely sure and reliable that my data is completely alright and there is no issue or failures, or regressions or problems with our pipeline here. and it's completely optimal about what we are trying to do here. validate with 1 videoID from R2, you can pick the videos from Supabase's table, run a video on this pipeline, and then if pipeline is validated, we can plan for optimising and parallelisation and scaling horizontally by docker and other methods later. first finish your complete effort in validating the pipeline by checking everything manually and ensuring each and every segment is read, validated, and i can rely the pipeline is robust and is working perfect. we can talk about scaling horizontally later.


id: 019bd497-695d-7781-9681-2ed21b834d45
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_prepare
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/19/rollout-2026-01-19T04-50-51-019bd497-695d-7781-9681-2ed21b834d45.jsonl

title:
  We are on a plan of data preparation for codec training. plan is at [strategy.md](strategy.md) and objective - [objective.md](objective.md) 

  i want you to go through entire plan, pipeline implementation, and see if everything is implemented and is working as intended. test with a video download from R2 directly. and test the segments and read everything right from audio file by validating everything from audio file as well and see if everything tallies with what we are trying to do. and then see if we are missing anything as per plan. i also need you to go through the codec training code in x-codec-2.0 aswell and then ensure our data as .tar will be valid with it completely, or we can fix those scripts. in short, i need to be completely sure and reliable that my data is completely alright and there is no issue or failures, or regressions or problems with our pipeline here. and it's completely optimal about what we are trying to do here. validate with 1 videoID from R2, you can pick the videos from Supabase's table, run a video on this pipeline, and then if pipeline is validated, we can plan for optimising and parallelisation and scaling horizontally by docker and other methods later. first finish your complete effort in validating the pipeline by checking everything manually and ensuring each and every segment is read, validated, and i can rely the pipeline is robust and is working perfect. we can talk about scaling horizontally later. original files are in `test` bucket and cleaned segmented flac .tar files segmented and metadata packed in videoID.tar are in `1-cleaned-data` bucket and note that there might be few prefixes. double check with supabase's r2_key column in Supabase. all credentials are in .env

first_user_message:
  We are on a plan of data preparation for codec training. plan is at [strategy.md](strategy.md) and objective - [objective.md](objective.md) 

  i want you to go through entire plan, pipeline implementation, and see if everything is implemented and is working as intended. test with a video download from R2 directly. and test the segments and read everything right from audio file by validating everything from audio file as well and see if everything tallies with what we are trying to do. and then see if we are missing anything as per plan. i also need you to go through the codec training code in x-codec-2.0 aswell and then ensure our data as .tar will be valid with it completely, or we can fix those scripts. in short, i need to be completely sure and reliable that my data is completely alright and there is no issue or failures, or regressions or problems with our pipeline here. and it's completely optimal about what we are trying to do here. validate with 1 videoID from R2, you can pick the videos from Supabase's table, run a video on this pipeline, and then if pipeline is validated, we can plan for optimising and parallelisation and scaling horizontally by docker and other methods later. first finish your complete effort in validating the pipeline by checking everything manually and ensuring each and every segment is read, validated, and i can rely the pipeline is robust and is working perfect. we can talk about scaling horizontally later. original files are in `test` bucket and cleaned segmented flac .tar files segmented and metadata packed in videoID.tar are in `1-cleaned-data` bucket and note that there might be few prefixes. double check with supabase's r2_key column in Supabase. all credentials are in .env


id: 019bd4b3-6bd4-7592-8e31-e835dc3ff9d8
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_prepare
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/19/rollout-2026-01-19T05-21-26-019bd4b3-6bd4-7592-8e31-e835dc3ff9d8.jsonl

title:
  We are on a plan of data preparation for codec training. plan is at [strategy.md](strategy.md) and objective - [objective.md](objective.md) 

  i want you to go through entire plan, pipeline implementation, and see if everything is implemented and is working as intended. test with a video download from R2 directly. and test the segments and read everything right from audio file by validating everything from audio file as well and see if everything tallies with what we are trying to do. and then see if we are missing anything as per plan. i also need you to go through the codec training code in x-codec-2.0 aswell and then ensure our data as .tar will be valid with it completely, or we can fix those scripts. in short, i need to be completely sure and reliable that my data is completely alright and there is no issue or failures, or regressions or problems with our pipeline here. and it's completely optimal about what we are trying to do here. validate with 1 videoID from R2, you can pick the videos from Supabase's table, run a video on this pipeline, and then if pipeline is validated, we can plan for optimising and parallelisation and scaling horizontally by docker and other methods later. first finish your complete effort in validating the pipeline by checking everything manually and ensuring each and every segment is read, validated, and i can rely the pipeline is robust and is working perfect. we can talk about scaling horizontally later. original files are in `test` bucket and cleaned segmented flac .tar files segmented and metadata packed in videoID.tar are in `1-cleaned-data` bucket and note that there might be few prefixes. double check with supabase's r2_key column in Supabase. all credentials are in .env

  DONOT make any changes to the training pipelines yet. we can do it later. now is only to validate if the current pipeline is working and each and every segment is validated, read, double checked and ensure i can safely rely the pipeline is working. once you give me this confidence, I will give the plan of scaling, or optimisations and etc later. but before that, ensure me this pipeline is fucking tight and insanely accurate.

first_user_message:
  We are on a plan of data preparation for codec training. plan is at [strategy.md](strategy.md) and objective - [objective.md](objective.md) 

  i want you to go through entire plan, pipeline implementation, and see if everything is implemented and is working as intended. test with a video download from R2 directly. and test the segments and read everything right from audio file by validating everything from audio file as well and see if everything tallies with what we are trying to do. and then see if we are missing anything as per plan. i also need you to go through the codec training code in x-codec-2.0 aswell and then ensure our data as .tar will be valid with it completely, or we can fix those scripts. in short, i need to be completely sure and reliable that my data is completely alright and there is no issue or failures, or regressions or problems with our pipeline here. and it's completely optimal about what we are trying to do here. validate with 1 videoID from R2, you can pick the videos from Supabase's table, run a video on this pipeline, and then if pipeline is validated, we can plan for optimising and parallelisation and scaling horizontally by docker and other methods later. first finish your complete effort in validating the pipeline by checking everything manually and ensuring each and every segment is read, validated, and i can rely the pipeline is robust and is working perfect. we can talk about scaling horizontally later. original files are in `test` bucket and cleaned segmented flac .tar files segmented and metadata packed in videoID.tar are in `1-cleaned-data` bucket and note that there might be few prefixes. double check with supabase's r2_key column in Supabase. all credentials are in .env

  DONOT make any changes to the training pipelines yet. we can do it later. now is only to validate if the current pipeline is working and each and every segment is validated, read, double checked and ensure i can safely rely the pipeline is working. once you give me this confidence, I will give the plan of scaling, or optimisations and etc later. but before that, ensure me this pipeline is fucking tight and insanely accurate.


id: 019bd4b9-d8ef-7931-8a2f-1dcf808e7db7
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_prepare
sandbox_policy: {"type":"read-only"}
approval_mode: on-request
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/19/rollout-2026-01-19T05-28-28-019bd4b9-d8ef-7931-8a2f-1dcf808e7db7.jsonl


id: 019bd535-3b72-7582-872d-d268a0abefe2
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_prepare
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/19/rollout-2026-01-19T07-43-14-019bd535-3b72-7582-872d-d268a0abefe2.jsonl

title:
  We are on a plan of data preparation for codec training. plan is at [strategy.md](strategy.md) and objective - [objective.md](objective.md) 

  i want you to go through entire plan, pipeline implementation, and see if everything is implemented and is working as intended. test with a video download from R2 directly. and test the segments and read everything right from audio file by validating everything from audio file as well and see if everything tallies with what we are trying to do. and then see if we are missing anything as per plan.  in short, i need to be completely sure and reliable that my data is completely alright and there is no issue or failures, or regressions or problems with our pipeline here. and it's completely optimal about what we are trying to do here. validate with 1 videoID , 100, 500 can be used for ETA estimates on 500k  videosfrom R2, you can pick the videos from Supabase's table, run a video on this pipeline, and then if pipeline is validated, we can plan for optimising and parallelisation and scaling horizontally by docker and other methods later. first finish your complete effort in validating the pipeline by checking everything manually and ensuring each and every segment is read, validated, and i can rely the pipeline is robust and is working perfect. we can talk about scaling horizontally later. original files are in `test` bucket and cleaned segmented flac .tar files segmented and metadata packed in videoID.tar are in `1-cleaned-data` bucket and note that there might be few prefixes. double check with supabase's r2_key column in Supabase. all credentials are in .env and i want you to validate both CPU paths and GPU path to make sure they both align and no regressions are introduced when implementing gpu path. 

  DONOT make any changes to the training pipelines yet. we can do it later. now is only to validate if the current pipeline is working and each and every segment is validated, read, double checked and ensure i can safely rely the pipeline is working. once you give me this confidence, I will give the plan of scaling, or optimisations and etc later. but before that, ensure me this pipeline is fucking tight and insanely accurate.

first_user_message:
  We are on a plan of data preparation for codec training. plan is at [strategy.md](strategy.md) and objective - [objective.md](objective.md) 

  i want you to go through entire plan, pipeline implementation, and see if everything is implemented and is working as intended. test with a video download from R2 directly. and test the segments and read everything right from audio file by validating everything from audio file as well and see if everything tallies with what we are trying to do. and then see if we are missing anything as per plan.  in short, i need to be completely sure and reliable that my data is completely alright and there is no issue or failures, or regressions or problems with our pipeline here. and it's completely optimal about what we are trying to do here. validate with 1 videoID , 100, 500 can be used for ETA estimates on 500k  videosfrom R2, you can pick the videos from Supabase's table, run a video on this pipeline, and then if pipeline is validated, we can plan for optimising and parallelisation and scaling horizontally by docker and other methods later. first finish your complete effort in validating the pipeline by checking everything manually and ensuring each and every segment is read, validated, and i can rely the pipeline is robust and is working perfect. we can talk about scaling horizontally later. original files are in `test` bucket and cleaned segmented flac .tar files segmented and metadata packed in videoID.tar are in `1-cleaned-data` bucket and note that there might be few prefixes. double check with supabase's r2_key column in Supabase. all credentials are in .env and i want you to validate both CPU paths and GPU path to make sure they both align and no regressions are introduced when implementing gpu path. 

  DONOT make any changes to the training pipelines yet. we can do it later. now is only to validate if the current pipeline is working and each and every segment is validated, read, double checked and ensure i can safely rely the pipeline is working. once you give me this confidence, I will give the plan of scaling, or optimisations and etc later. but before that, ensure me this pipeline is fucking tight and insanely accurate.


id: 019bd813-7b66-7733-923e-80ab15bd191d
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_prepare
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/19/rollout-2026-01-19T21-05-14-019bd813-7b66-7733-923e-80ab15bd191d.jsonl

title:
  Current performance. - Performance Analysis Complete                                                                                   
                                                                                                                    
    Key Findings                                                                                                    
    ┌─────────────────────┬───────────────────────────────────────────┐                                             
    │       Metric        │                   Value                   │                                             
    ├─────────────────────┼───────────────────────────────────────────┤                                             
    │ Measured Throughput │ 26.1 seg/s, 7.56 videos/min               │                                             
    ├─────────────────────┼───────────────────────────────────────────┤                                             
    │ GPU Utilization     │ <5% (only resampling uses GPU)            │                                             
    ├─────────────────────┼───────────────────────────────────────────┤                                             
    │ Primary Bottleneck  │ pyloudnorm LUFS computation (45% of time) │                                             
    ├─────────────────────┼───────────────────────────────────────────┤                                             
    │ GPU Benefit         │ ~2% improvement over CPU-only             │                                             
    └─────────────────────┴───────────────────────────────────────────┘                                             
    Per-Segment Breakdown                                                                                           
                                                                                                                    
    FLAC Decode:     17.9 ms (31.0%)                                                                                
    Metrics (LUFS):  26.1 ms (45.3%)  ← BOTTLENECK                                                                  
    GPU Resample:     0.5 ms (0.9%)   ← GPU barely used                                                             
    WAV Encode:      13.1 ms (22.8%)                                                                                
                                                                                                                    
    Optimal A100 Configuration                                                                                      
                                                                                                                    
    --gpu-batch-size 128 --gpu-decode-workers 12 --gpu-encode-workers 12                                            
                                                                                                                    
    This yields ~26-30 seg/s. Larger batch sizes and more workers don't help due to the sequential nature of metrics
     computation.                                                                                                   
                                                                                                                    
    Scaling Projections (500k videos)                                                                               
    ┌────────┬──────────────┬─────────┬───────────┐                                                                 
    │ Nodes  │   Instance   │  Time   │ Est. Cost │                                                                 
    ├────────┼──────────────┼─────────┼───────────┤                                                                 
    │ 1 A100 │ p4d.24xlarge │ 46 days │ ~$3,850   │                                                                 
    ├────────┼──────────────┼─────────┼───────────┤                                                                 
    │ 8 CPU  │ c6i.4xlarge  │ 6 days  │ ~$780     │                                                                 
    └────────┴──────────────┴─────────┴───────────┘                                                                 
    Recommendation                                                                                                  
                                                                                                                    
    The A100 GPU is overkill for this workload. The bottleneck is CPU-bound (pyloudnorm LUFS), and GPU resampling   
    provides negligible benefit.                                                                                    
                                                                                                                    
    For cost efficiency:                                                                                            
    - Use c6i.4xlarge (16 vCPUs, $0.68/hr) instead of A100                                                          
    - Scale horizontally with 4-8 nodes for faster completion                                                       
    - Expected 5× cost reduction with similar or better total throughput                                            
                                                                                        


  New branch git optimisations. for new approach. create this new optimisations and implement as per the plan or strategy and then do a brnchmark for 100 videos, 500 videos and provide scaling benchmarks for 500k videos with horizontal scaling into new instances suggestions.

first_user_message:
  Current performance. - Performance Analysis Complete                                                                                   
                                                                                                                    
    Key Findings                                                                                                    
    ┌─────────────────────┬───────────────────────────────────────────┐                                             
    │       Metric        │                   Value                   │                                             
    ├─────────────────────┼───────────────────────────────────────────┤                                             
    │ Measured Throughput │ 26.1 seg/s, 7.56 videos/min               │                                             
    ├─────────────────────┼───────────────────────────────────────────┤                                             
    │ GPU Utilization     │ <5% (only resampling uses GPU)            │                                             
    ├─────────────────────┼───────────────────────────────────────────┤                                             
    │ Primary Bottleneck  │ pyloudnorm LUFS computation (45% of time) │                                             
    ├─────────────────────┼───────────────────────────────────────────┤                                             
    │ GPU Benefit         │ ~2% improvement over CPU-only             │                                             
    └─────────────────────┴───────────────────────────────────────────┘                                             
    Per-Segment Breakdown                                                                                           
                                                                                                                    
    FLAC Decode:     17.9 ms (31.0%)                                                                                
    Metrics (LUFS):  26.1 ms (45.3%)  ← BOTTLENECK                                                                  
    GPU Resample:     0.5 ms (0.9%)   ← GPU barely used                                                             
    WAV Encode:      13.1 ms (22.8%)                                                                                
                                                                                                                    
    Optimal A100 Configuration                                                                                      
                                                                                                                    
    --gpu-batch-size 128 --gpu-decode-workers 12 --gpu-encode-workers 12                                            
                                                                                                                    
    This yields ~26-30 seg/s. Larger batch sizes and more workers don't help due to the sequential nature of metrics
     computation.                                                                                                   
                                                                                                                    
    Scaling Projections (500k videos)                                                                               
    ┌────────┬──────────────┬─────────┬───────────┐                                                                 
    │ Nodes  │   Instance   │  Time   │ Est. Cost │                                                                 
    ├────────┼──────────────┼─────────┼───────────┤                                                                 
    │ 1 A100 │ p4d.24xlarge │ 46 days │ ~$3,850   │                                                                 
    ├────────┼──────────────┼─────────┼───────────┤                                                                 
    │ 8 CPU  │ c6i.4xlarge  │ 6 days  │ ~$780     │                                                                 
    └────────┴──────────────┴─────────┴───────────┘                                                                 
    Recommendation                                                                                                  
                                                                                                                    
    The A100 GPU is overkill for this workload. The bottleneck is CPU-bound (pyloudnorm LUFS), and GPU resampling   
    provides negligible benefit.                                                                                    
                                                                                                                    
    For cost efficiency:                                                                                            
    - Use c6i.4xlarge (16 vCPUs, $0.68/hr) instead of A100                                                          
    - Scale horizontally with 4-8 nodes for faster completion                                                       
    - Expected 5× cost reduction with similar or better total throughput                                            
                                                                                        


  New branch git optimisations. for new approach. create this new optimisations and implement as per the plan or strategy and then do a brnchmark for 100 videos, 500 videos and provide scaling benchmarks for 500k videos with horizontal scaling into new instances suggestions.


id: 019bddd0-2fbc-78c0-ab49-d199e565eea1
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_prepare
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: c1067aba06c30e83dc80f5090b74dc81bff07fe2
git_branch: optimisations
cli_version: 0.81.0-alpha.8
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/20/rollout-2026-01-20T23-49-27-019bddd0-2fbc-78c0-ab49-d199e565eea1.jsonl

title:
  Can you summarize our entire data preparation pipeline in 5-6 sentences. You might want to continue this high level overview of maya3. 

  So, I am on a mission to Create a human-like quality TTS LLM model. And for that we need data.

  1. My main source of data and only source of data is Youtube. And I've seggregated Youtube videos based on podcasts and non-podcasts. Like all podcast videos in each indic language like `telugu, hindi, kannada, tamil`, etc. Now, I assumed all podcasts are high quality microphone recorded audio and atleast 50% of it will be natively recorded in `44khz` atleast and may/maynot have been upsampled to `48khz`. So i assumed all or most of those podcasts will be atleast 44khz natively. So my `assumption-1` is `podcasts = quality`. 
  2. Hence i started filtering youtube channels first based on podcasts, then videos from those channels and filtered by, podcasts and all those channel’s each video is filtered by Gemini to accurately identify which among those are usable podcast videos assuming there might be so many other random videos as well. So, `Gemini-3-flash` filtered out most of those podcasts and now i have around 
  3. Now in R2, i have these indic youtube files saved as `YoutubeID.webm` and now. Now how did these Videos get saved in my `Cloudflare R2`, When i tried running it at scale in some automation jobs, then youtube detected it and blocked me from downloading youtube audios using `yt-dlp` . So here’s what i did. i bought proxies and downloaded the youtube video as residential proxy and reuploaded those youtube video URL → created a VPS CPU machines and then turned on proxy and then tried to extract best audio using `"bestaudio[ext=webm]/bestaudio[ext=opus]/bestaudio`  argument with yt-dlp. And then when i get the audio file in `webm`, i pushed it to cloudflare R2 in my account. Hence, i have all these respective audio `videoID.webm` formats. 
  4. Now, i have the data to get started. Now ofcourse these podcasts data is not directly usable, we will have to create a data cleaning pipeline - [Github Readme.md](https://www.notion.so/Github-Readme-md-2d76a437d35480288a25c4a2b3aafec8?pvs=21) 
  5. This pipeline is vastly inspired from kimi Audio. - [KimiAudio](https://www.notion.so/KimiAudio-2d76a437d35480d9a2c4d9b53ad832e2?pvs=21) 

  ![image.png](attachment:722c5013-436d-4d2a-ac37-dd00a0bcdaae:image.png)

  1. So, now i will have workers picking Each Yt ID.webm from cloudflare and then, running the entire single speaker segmentation pipeline using GPU. and then finally, i’ve chosen to save the cleaned data as Webdatasets as `.tar`  and `FLAC`  Codec. 
  2. each of videoID.webm in R2 in `test`  bucket will be orchestrated and cleaned and pushed back as `.tar`  and `FLAC`  files. buckets at - https://dash.cloudflare.com/cb908ed13329eb7b186e06ab51bda190/r2/overview
  3. PANN CNNs for music detection. Verified with UI. should ideally recheck when generating webdatasets. 
  4. Time to choose Codecs. and decide on their architectural advantages, capacities, best ones, given our huge data scale, which are suitable to be trainable on indic data for better reconstruction and stuff like that. For that we will now choose between codecs like Xcodec2.0, Mimi, SNAC, BiCodec, Encodec, and similar same family of codecs or vocoders or in simple words audio tokenizers.
  5. Cloudflare Bucket for cleaned single speaker segmented - https://dash.cloudflare.com/cb908ed13329eb7b186e06ab51bda190/r2/default/buckets/1-cleaned-data
  6. Now Supabase has the metadata for over like 500k videos across 12 different languages.  

  ## language breakdown with usable hours

  | Language | Videos | Audio Hours | Usable Hours |
  | --- | --- | --- | --- |
  | Telugu | 71,619 | 31,001.31 | 19,712.28 |
  | Malayalam | 65,273 | 29,108.54 | 18,183.70 |
  | English | 59,657 | 26,784.17 | 17,904.33 |
  | Hindi | 58,824 | 29,646.26 | 17,709.61 |
  | Punjabi | 55,761 | 27,515.76 | 18,064.84 |
  | Tamil | 50,199 | 18,085.98 | 11,257.98 |
  | Kannada | 45,689 | 21,857.12 | 13,578.46 |
  | Gujarati | 40,950 | 25,087.45 | 14,759.11 |
  | Bengali | 20,121 | 11,077.01 | 6,205.66 |
  | Odia | 19,280 | 8,943.51 | 5,009.52 |
  | Marathi | 16,221 | 7,980.74 | 5,130.47 |
  | Assamese | 3,793 | 2,136.10 | 1,369.66 |

  1. Now the dataset is of the format of videoID.tar where we have each tar for each videoID and all of them are inside cloudflare bucket named `1-cleaned-data` bucket and few videos are with a prefix inside same bucket. 
  2. Now it’s time for us to do a one last validation checks like calculating any missing metadata and then create shards of these datasets , yet still retain all pre-computed metadatas right inside the buckets itself. 
  - Repo’s to double check for pending validations, requirements or safety checks before creating packaged shards before we can upload them back to R2 and then pull back right before training.
      
      https://github.com/zhenye234/X-Codec-2.0
      https://github.com/inworld-ai/tts


  understand this pipeline now first, read the code files. understand the code. then outline it into 5-6 points.

first_user_message:
  Can you summarize our entire data preparation pipeline in 5-6 sentences. You might want to continue this high level overview of maya3. 

  So, I am on a mission to Create a human-like quality TTS LLM model. And for that we need data.

  1. My main source of data and only source of data is Youtube. And I've seggregated Youtube videos based on podcasts and non-podcasts. Like all podcast videos in each indic language like `telugu, hindi, kannada, tamil`, etc. Now, I assumed all podcasts are high quality microphone recorded audio and atleast 50% of it will be natively recorded in `44khz` atleast and may/maynot have been upsampled to `48khz`. So i assumed all or most of those podcasts will be atleast 44khz natively. So my `assumption-1` is `podcasts = quality`. 
  2. Hence i started filtering youtube channels first based on podcasts, then videos from those channels and filtered by, podcasts and all those channel’s each video is filtered by Gemini to accurately identify which among those are usable podcast videos assuming there might be so many other random videos as well. So, `Gemini-3-flash` filtered out most of those podcasts and now i have around 
  3. Now in R2, i have these indic youtube files saved as `YoutubeID.webm` and now. Now how did these Videos get saved in my `Cloudflare R2`, When i tried running it at scale in some automation jobs, then youtube detected it and blocked me from downloading youtube audios using `yt-dlp` . So here’s what i did. i bought proxies and downloaded the youtube video as residential proxy and reuploaded those youtube video URL → created a VPS CPU machines and then turned on proxy and then tried to extract best audio using `"bestaudio[ext=webm]/bestaudio[ext=opus]/bestaudio`  argument with yt-dlp. And then when i get the audio file in `webm`, i pushed it to cloudflare R2 in my account. Hence, i have all these respective audio `videoID.webm` formats. 
  4. Now, i have the data to get started. Now ofcourse these podcasts data is not directly usable, we will have to create a data cleaning pipeline - [Github Readme.md](https://www.notion.so/Github-Readme-md-2d76a437d35480288a25c4a2b3aafec8?pvs=21) 
  5. This pipeline is vastly inspired from kimi Audio. - [KimiAudio](https://www.notion.so/KimiAudio-2d76a437d35480d9a2c4d9b53ad832e2?pvs=21) 

  ![image.png](attachment:722c5013-436d-4d2a-ac37-dd00a0bcdaae:image.png)

  1. So, now i will have workers picking Each Yt ID.webm from cloudflare and then, running the entire single speaker segmentation pipeline using GPU. and then finally, i’ve chosen to save the cleaned data as Webdatasets as `.tar`  and `FLAC`  Codec. 
  2. each of videoID.webm in R2 in `test`  bucket will be orchestrated and cleaned and pushed back as `.tar`  and `FLAC`  files. buckets at - https://dash.cloudflare.com/cb908ed13329eb7b186e06ab51bda190/r2/overview
  3. PANN CNNs for music detection. Verified with UI. should ideally recheck when generating webdatasets. 
  4. Time to choose Codecs. and decide on their architectural advantages, capacities, best ones, given our huge data scale, which are suitable to be trainable on indic data for better reconstruction and stuff like that. For that we will now choose between codecs like Xcodec2.0, Mimi, SNAC, BiCodec, Encodec, and similar same family of codecs or vocoders or in simple words audio tokenizers.
  5. Cloudflare Bucket for cleaned single speaker segmented - https://dash.cloudflare.com/cb908ed13329eb7b186e06ab51bda190/r2/default/buckets/1-cleaned-data
  6. Now Supabase has the metadata for over like 500k videos across 12 different languages.  

  ## language breakdown with usable hours

  | Language | Videos | Audio Hours | Usable Hours |
  | --- | --- | --- | --- |
  | Telugu | 71,619 | 31,001.31 | 19,712.28 |
  | Malayalam | 65,273 | 29,108.54 | 18,183.70 |
  | English | 59,657 | 26,784.17 | 17,904.33 |
  | Hindi | 58,824 | 29,646.26 | 17,709.61 |
  | Punjabi | 55,761 | 27,515.76 | 18,064.84 |
  | Tamil | 50,199 | 18,085.98 | 11,257.98 |
  | Kannada | 45,689 | 21,857.12 | 13,578.46 |
  | Gujarati | 40,950 | 25,087.45 | 14,759.11 |
  | Bengali | 20,121 | 11,077.01 | 6,205.66 |
  | Odia | 19,280 | 8,943.51 | 5,009.52 |
  | Marathi | 16,221 | 7,980.74 | 5,130.47 |
  | Assamese | 3,793 | 2,136.10 | 1,369.66 |

  1. Now the dataset is of the format of videoID.tar where we have each tar for each videoID and all of them are inside cloudflare bucket named `1-cleaned-data` bucket and few videos are with a prefix inside same bucket. 
  2. Now it’s time for us to do a one last validation checks like calculating any missing metadata and then create shards of these datasets , yet still retain all pre-computed metadatas right inside the buckets itself. 
  - Repo’s to double check for pending validations, requirements or safety checks before creating packaged shards before we can upload them back to R2 and then pull back right before training.
      
      https://github.com/zhenye234/X-Codec-2.0
      https://github.com/inworld-ai/tts


  understand this pipeline now first, read the code files. understand the code. then outline it into 5-6 points.


id: 019be7a7-631b-7e13-8098-e8efb4ee9427
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_prepare
sandbox_policy: {"type":"read-only"}
approval_mode: on-request
git_sha: c1067aba06c30e83dc80f5090b74dc81bff07fe2
git_branch: optimisations
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/maya3_data_prep_workers.git
cli_version: 0.88.0-alpha.17
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/22/rollout-2026-01-22T21-41-05-019be7a7-631b-7e13-8098-e8efb4ee9427.jsonl


id: 019be7b1-4da1-77b1-8475-8089877aa25d
source: vscode
model_provider: openai
cwd: /home/ubuntu/xcodec2_16khz_indic
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: 96011790cdd9d971f34a2f8b9b27511e524a67b2
git_branch: main
git_origin_url: https://github.com/Maya-AI-ML-Trainings-Cookbooks/xcodec2_16khz_indic.git
cli_version: 0.88.0-alpha.17
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/22/rollout-2026-01-22T21-51-55-019be7b1-4da1-77b1-8475-8089877aa25d.jsonl

title:
  vast ai docs [vastai.md](vastai.md) and [vastai_sdk.md](vastai_sdk.md)  and API key is available in .env here. now can you find instances with 8*H200 or 8*H100 clusters with atleast like 5000mpbs or more and also, like 15TB storage atleast. SXM or something, or better infra. just see what instances are available for now. we can decide if they are still available and we can decide if we can rent that instance.

first_user_message:
  vast ai docs [vastai.md](vastai.md) and [vastai_sdk.md](vastai_sdk.md)  and API key is available in .env here. now can you find instances with 8*H200 or 8*H100 clusters with atleast like 5000mpbs or more and also, like 15TB storage atleast. SXM or something, or better infra. just see what instances are available for now. we can decide if they are still available and we can decide if we can rent that instance.


id: 019be856-621b-7182-9f11-cffaf82c0a3b
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya_transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.88.0-alpha.17
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/23/rollout-2026-01-23T00-52-13-019be856-621b-7182-9f11-cffaf82c0a3b.jsonl

title:
  So, my research topic was transcribing audio files for my TTS/ASR training and    
    especially on indic languages. Now my question or query to these models are       
    @User.md and all other files here are from each model and their research models   
    respectively. Now, your task is to read my question, without being very           
    discriminative, being honest, list down all hte positive points regarding the     
    types of transcripts we need to get from gemini-flash/pro resepctively, what is   
    htat best hybrid strategy that you are most looking forward to. like each model   
    can have an advantage of its own with their suggestions. so i want the most       
    cleverest answer and the WER has to be like less than 3% WER on the indic data.   
    We can test our pipeline on opensource datasets to ensure max robustness to       
    ensure as minimal WER as possible. unrealistically low. since we can make use of  
    the superior intelligent model on multi-linguality, let's actually make use of    
    it to our advantage along with a local  few models like whisper-large-v3 and      
    indicConformer so we can have a judge or ROVER of mixed and then ensure the most  
    out of the transcription so our data is lossless and model gets the best out of   
    training. get the best out of these strategies as an ensemble and teh path        
    through which gets me best WER and other formats so i get room for experiments    
    like code-mixed, language mixed, transliterated and etc. Give all your opinions   
    and strategies, validations, and situations where we might want to be really      
    careful so we get the lowest WER. donot write any new files. just provide your    
    side of views and explanations in the chat here, so it's easier for me to just    
    keep chatting with you regarding the plan.

first_user_message:
  So, my research topic was transcribing audio files for my TTS/ASR training and    
    especially on indic languages. Now my question or query to these models are       
    @User.md and all other files here are from each model and their research models   
    respectively. Now, your task is to read my question, without being very           
    discriminative, being honest, list down all hte positive points regarding the     
    types of transcripts we need to get from gemini-flash/pro resepctively, what is   
    htat best hybrid strategy that you are most looking forward to. like each model   
    can have an advantage of its own with their suggestions. so i want the most       
    cleverest answer and the WER has to be like less than 3% WER on the indic data.   
    We can test our pipeline on opensource datasets to ensure max robustness to       
    ensure as minimal WER as possible. unrealistically low. since we can make use of  
    the superior intelligent model on multi-linguality, let's actually make use of    
    it to our advantage along with a local  few models like whisper-large-v3 and      
    indicConformer so we can have a judge or ROVER of mixed and then ensure the most  
    out of the transcription so our data is lossless and model gets the best out of   
    training. get the best out of these strategies as an ensemble and teh path        
    through which gets me best WER and other formats so i get room for experiments    
    like code-mixed, language mixed, transliterated and etc. Give all your opinions   
    and strategies, validations, and situations where we might want to be really      
    careful so we get the lowest WER. donot write any new files. just provide your    
    side of views and explanations in the chat here, so it's easier for me to just    
    keep chatting with you regarding the plan.


id: 019be8e2-4a1e-7a03-a004-d29cdce175de
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya_transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.88.0-alpha.17
rollout_path:
  /home/ubuntu/.codex/sessions/2026/01/23/rollout-2026-01-23T03-25-02-019be8e2-4a1e-7a03-a004-d29cdce175de.jsonl

title:
  there is something that occupied the GPU, but nvidia-smi shows no processes. can you clear the GPU memory ? should be some zombied process

first_user_message:
  there is something that occupied the GPU, but nvidia-smi shows no processes. can you clear the GPU memory ? should be some zombied process


id: 019c25d9-da3d-73c3-a5a9-f57dafd29dd9
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.94.0-alpha.10
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/03/rollout-2026-02-03T23-32-40-019c25d9-da3d-73c3-a5a9-f57dafd29dd9.jsonl

title:
  Now that we have these transcriptions, what techniques do you suggest we do to validate these transcriptions. validate, improvise the prompt to see a better one, and then how can we be so sure to know the performance of the transcriptions. What do you generally suggest we can do here. Like Forced Aligners we can implement, get probability scores, and what else can we do here. Suggest the new validation and scoring pipeline first. we can decide on the other part next. what else can we do as validation steps. ideate. First read and understand the pipeline first. then ideate on validations of these transcriptions to ensure scoring and low-probability ones gets redone or improved.

first_user_message:
  Now that we have these transcriptions, what techniques do you suggest we do to validate these transcriptions. validate, improvise the prompt to see a better one, and then how can we be so sure to know the performance of the transcriptions. What do you generally suggest we can do here. Like Forced Aligners we can implement, get probability scores, and what else can we do here. Suggest the new validation and scoring pipeline first. we can decide on the other part next. what else can we do as validation steps. ideate. First read and understand the pipeline first. then ideate on validations of these transcriptions to ensure scoring and low-probability ones gets redone or improved.


id: 019c41d2-a046-7362-a4cf-30ae12dba8aa
source: vscode
model_provider: openai
cwd: /home/ubuntu/nemo_hindi
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.98.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/09/rollout-2026-02-09T09-54-08-019c41d2-a046-7362-a4cf-30ae12dba8aa.jsonl

title:
  Here is what i asked my another coding agent. 



  finish revamp with clean architecture mode implementation, then deploy to vast(all keys are in .env) serverless workers. then do stress testing simulating like actual customers to understand the working production server behaviour mocking and capture all metrics, so we can set scaling laws and move our production OpenAI transcription paid service to self hosten custom finetuned hindi model and serve any customer base with proper scaling laws set. assuming somewhere at 500 concurrent batching requests per GPU of 80GB GPU. Also while deploying check if region based deployment is available and check for closer to india or asia or anything closer. Also while implementation or planning, plan for both HTTP based and websocket based requests handlers. If we have vCPUs that take requests and sends batches of chunks to GPU and gets data back, or triton server, or whatever man. i just need high throughput mode with both HTTP streaming and websocket streaming. 

  Read the vast's serverless docs, requirements
  Plan the architecture that is failproof and high throughput mode with quality considered too.
  start working on it
  test it, deploy it to serverless
  smoke test
  stress test it and capture all major metrics(all) -> simulation tests that will be actual customers usage based
  then we'll discuss on scaling laws. 



  and after a while of trying it failed but got somewhere. and below is a handover summary from that agent. Can you now take over and finish the proper deployment, testing, stress testing, and capture metrics. below is it's summary. Also, all secrets are in .env FYI. docs are also in this repo for vast's serverless stack. understand the flow. and see the current implementation, make changes, or revamp as needed. 

  ## Summary

  ### What We Started With
  - A **fine-tuned Hindi ASR model** (Nemotron-0.6B, 8.8% WER) deployed on Modal
  - Prior benchmarks showing **A100-SXM4-80GB handles 50-100 concurrent streams** in the safe zone
  - Existing code in `nemotron_asr/` — a Modal-coupled WebSocket streaming server with event-driven batching, VAD, and centralized inference loop
  - The decision to move to **Vast.ai serverless** for production (cheaper, direct TCP, autoscaling)

  ### What We Want To Do
  1. Build a **production-ready ASR service** with both HTTP and WebSocket endpoints
  2. Deploy it on **Vast.ai serverless** with autoscaling (not bare metal instances)
  3. Run **stress tests simulating real customers** to capture all metrics
  4. Establish **scaling laws** so we can replace paid OpenAI transcription with our self-hosted model

  ### What We Built
  - `production/server.py` — Standalone FastAPI server (HTTP batch, HTTP SSE streaming, WebSocket streaming)
  - `production/worker.py` — Vast.ai PyWorker config (metrics reporting, benchmarks, autoscaling integration)
  - `production/onstart.sh` — Startup script that launches both model server and PyWorker
  - `production/Dockerfile.slim` — Slim image extending existing v5 image
  - `production/deploy.py` — Deployment automation (create/destroy endpoint+workergroup)
  - `production/test_smoke.py` — Smoke test (health, HTTP, WebSocket)
  - `production/test_stress.py` — Full stress test with percentile metrics, CSV/JSON output
  - PyWorker repo pushed to GitHub, Docker image on Docker Hub

  ### Vast.ai Serverless Deployment (Live)
  - Endpoint: `hindi-asr-prod` (id=11967)
  - Workergroup: 16773 (template hash `3ac272ae9dc0ae58d105a1d88145a325`)
  - Worker provisioned and managed by the serverless engine

  ### Issues We Hit (and Fixed)

  | Issue | Root Cause | Fix |
  |:---|:---|:---|
  | Created bare metal instance instead of serverless | My mistake — didn't follow docs | Destroyed instance, used `create endpoint` + `create workergroup` |
  | `PYWORKER_REPO` env var empty on instance | Account-level env vars don't auto-propagate; Vast.ai doesn't auto-bootstrap PyWorker for custom images | Handle PyWorker clone+run in our own `onstart.sh` |
  | `python-dateutil` downgraded to 2.6.1 | `vastai` pip package pulled old dateutil, broke matplotlib/NeMo | Install deps BEFORE starting server; pin `python-dateutil>=2.8.2` |
  | `cannot import name 'Worker' from 'vastai'` | Wrong pip package — `vastai` is CLI tool, `vastai_sdk` has the Worker class | Changed requirements.txt to `vastai_sdk` |
  | `KeyError: 'WORKER_PORT'` | `vastai_sdk` Worker expects env vars set by Vast.ai infra, which doesn't auto-set them for custom images | Manually set `WORKER_PORT=8000`, `VAST_TCP_PORT_8000` in onstart.sh; model server on 18000, PyWorker on 8000 |
  | `Object of type bytes is not JSON serializable` | Benchmark generator returned file tuple with bytes; PyWorker sends benchmarks as JSON | Changed to base64-encoded audio, new `/v1/audio/transcriptions/json` endpoint |
  | `'CacheAwareRNNTStreamingPipeline' has no attribute 'run'` | Streaming pipeline doesn't have `.run()` method (that's on the batch pipeline) | **Last fix** — replaced with `transcribe_pcm_bytes_streaming()` that uses the streaming session API |

  ### Where We Are Right Now
  - Worker is **recycling** with the latest fix (streaming inference for benchmarks)
  - Waiting to see if the PyWorker benchmark passes and the worker transitions to **"running"** (ready) status
  - Once ready: smoke test, stress test, metrics capture

  ### What's Left After Worker Goes Ready
  1. Smoke test (HTTP + WebSocket)
  2. Stress test sweep (c=1,5,10,25,50,75,100) capturing all latency/throughput metrics
  3. Analyze results, set scaling laws
  4. Update README with production benchmarks

first_user_message:
  Here is what i asked my another coding agent. 



  finish revamp with clean architecture mode implementation, then deploy to vast(all keys are in .env) serverless workers. then do stress testing simulating like actual customers to understand the working production server behaviour mocking and capture all metrics, so we can set scaling laws and move our production OpenAI transcription paid service to self hosten custom finetuned hindi model and serve any customer base with proper scaling laws set. assuming somewhere at 500 concurrent batching requests per GPU of 80GB GPU. Also while deploying check if region based deployment is available and check for closer to india or asia or anything closer. Also while implementation or planning, plan for both HTTP based and websocket based requests handlers. If we have vCPUs that take requests and sends batches of chunks to GPU and gets data back, or triton server, or whatever man. i just need high throughput mode with both HTTP streaming and websocket streaming. 

  Read the vast's serverless docs, requirements
  Plan the architecture that is failproof and high throughput mode with quality considered too.
  start working on it
  test it, deploy it to serverless
  smoke test
  stress test it and capture all major metrics(all) -> simulation tests that will be actual customers usage based
  then we'll discuss on scaling laws. 



  and after a while of trying it failed but got somewhere. and below is a handover summary from that agent. Can you now take over and finish the proper deployment, testing, stress testing, and capture metrics. below is it's summary. Also, all secrets are in .env FYI. docs are also in this repo for vast's serverless stack. understand the flow. and see the current implementation, make changes, or revamp as needed. 

  ## Summary

  ### What We Started With
  - A **fine-tuned Hindi ASR model** (Nemotron-0.6B, 8.8% WER) deployed on Modal
  - Prior benchmarks showing **A100-SXM4-80GB handles 50-100 concurrent streams** in the safe zone
  - Existing code in `nemotron_asr/` — a Modal-coupled WebSocket streaming server with event-driven batching, VAD, and centralized inference loop
  - The decision to move to **Vast.ai serverless** for production (cheaper, direct TCP, autoscaling)

  ### What We Want To Do
  1. Build a **production-ready ASR service** with both HTTP and WebSocket endpoints
  2. Deploy it on **Vast.ai serverless** with autoscaling (not bare metal instances)
  3. Run **stress tests simulating real customers** to capture all metrics
  4. Establish **scaling laws** so we can replace paid OpenAI transcription with our self-hosted model

  ### What We Built
  - `production/server.py` — Standalone FastAPI server (HTTP batch, HTTP SSE streaming, WebSocket streaming)
  - `production/worker.py` — Vast.ai PyWorker config (metrics reporting, benchmarks, autoscaling integration)
  - `production/onstart.sh` — Startup script that launches both model server and PyWorker
  - `production/Dockerfile.slim` — Slim image extending existing v5 image
  - `production/deploy.py` — Deployment automation (create/destroy endpoint+workergroup)
  - `production/test_smoke.py` — Smoke test (health, HTTP, WebSocket)
  - `production/test_stress.py` — Full stress test with percentile metrics, CSV/JSON output
  - PyWorker repo pushed to GitHub, Docker image on Docker Hub

  ### Vast.ai Serverless Deployment (Live)
  - Endpoint: `hindi-asr-prod` (id=11967)
  - Workergroup: 16773 (template hash `3ac272ae9dc0ae58d105a1d88145a325`)
  - Worker provisioned and managed by the serverless engine

  ### Issues We Hit (and Fixed)

  | Issue | Root Cause | Fix |
  |:---|:---|:---|
  | Created bare metal instance instead of serverless | My mistake — didn't follow docs | Destroyed instance, used `create endpoint` + `create workergroup` |
  | `PYWORKER_REPO` env var empty on instance | Account-level env vars don't auto-propagate; Vast.ai doesn't auto-bootstrap PyWorker for custom images | Handle PyWorker clone+run in our own `onstart.sh` |
  | `python-dateutil` downgraded to 2.6.1 | `vastai` pip package pulled old dateutil, broke matplotlib/NeMo | Install deps BEFORE starting server; pin `python-dateutil>=2.8.2` |
  | `cannot import name 'Worker' from 'vastai'` | Wrong pip package — `vastai` is CLI tool, `vastai_sdk` has the Worker class | Changed requirements.txt to `vastai_sdk` |
  | `KeyError: 'WORKER_PORT'` | `vastai_sdk` Worker expects env vars set by Vast.ai infra, which doesn't auto-set them for custom images | Manually set `WORKER_PORT=8000`, `VAST_TCP_PORT_8000` in onstart.sh; model server on 18000, PyWorker on 8000 |
  | `Object of type bytes is not JSON serializable` | Benchmark generator returned file tuple with bytes; PyWorker sends benchmarks as JSON | Changed to base64-encoded audio, new `/v1/audio/transcriptions/json` endpoint |
  | `'CacheAwareRNNTStreamingPipeline' has no attribute 'run'` | Streaming pipeline doesn't have `.run()` method (that's on the batch pipeline) | **Last fix** — replaced with `transcribe_pcm_bytes_streaming()` that uses the streaming session API |

  ### Where We Are Right Now
  - Worker is **recycling** with the latest fix (streaming inference for benchmarks)
  - Waiting to see if the PyWorker benchmark passes and the worker transitions to **"running"** (ready) status
  - Once ready: smoke test, stress test, metrics capture

  ### What's Left After Worker Goes Ready
  1. Smoke test (HTTP + WebSocket)
  2. Stress test sweep (c=1,5,10,25,50,75,100) capturing all latency/throughput metrics
  3. Analyze results, set scaling laws
  4. Update README with production benchmarks


id: 019c446b-65e8-7b62-bca4-03f4ffe31965
source: vscode
model_provider: openai
cwd: /home/ubuntu/nemo_hindi
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.98.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/09/rollout-2026-02-09T22-00-15-019c446b-65e8-7b62-bca4-03f4ffe31965.jsonl

title:
  Here is what i asked my another coding agent. 



  finish revamp with clean architecture mode implementation, then deploy to vast(all keys are in .env) serverless workers. then do stress testing simulating like actual customers to understand the working production server behaviour mocking and capture all metrics, so we can set scaling laws and move our production OpenAI transcription paid service to self hosten custom finetuned hindi model and serve any customer base with proper scaling laws set. assuming somewhere at 500 concurrent batching requests per GPU of 80GB GPU. Also while deploying check if region based deployment is available and check for closer to india or asia or anything closer. Also while implementation or planning, plan for both HTTP based and websocket based requests handlers. If we have vCPUs that take requests and sends batches of chunks to GPU and gets data back, or triton server, or whatever man. i just need high throughput mode with both HTTP streaming and websocket streaming. 

  Read the vast's serverless docs, requirements
  Plan the architecture that is failproof and high throughput mode with quality considered too.
  start working on it
  test it, deploy it to serverless
  smoke test
  stress test it and capture all major metrics(all) -> simulation tests that will be actual customers usage based
  then we'll discuss on scaling laws. 



  and after a while of trying it failed but got somewhere. and below is a handover summary from that agent. Can you now take over and finish the proper deployment, testing, stress testing, and capture metrics. below is it's summary. Also, all secrets are in .env FYI. docs are also in this repo for vast's serverless stack. understand the flow. and see the current implementation, make changes, or revamp as needed. 

  ## Summary

  ### What We Started With
  - A **fine-tuned Hindi ASR model** (Nemotron-0.6B, 8.8% WER) deployed on Modal
  - Prior benchmarks showing **A100-SXM4-80GB handles 50-100 concurrent streams** in the safe zone
  - Existing code in `nemotron_asr/` — a Modal-coupled WebSocket streaming server with event-driven batching, VAD, and centralized inference loop
  - The decision to move to **Vast.ai serverless** for production (cheaper, direct TCP, autoscaling)

  ### What We Want To Do
  1. Build a **production-ready ASR service** with both HTTP and WebSocket endpoints
  2. Deploy it on **Vast.ai serverless** with autoscaling (not bare metal instances)
  3. Run **stress tests simulating real customers** to capture all metrics
  4. Establish **scaling laws** so we can replace paid OpenAI transcription with our self-hosted model

  ### What We Built
  - `production/server.py` — Standalone FastAPI server (HTTP batch, HTTP SSE streaming, WebSocket streaming)
  - `production/worker.py` — Vast.ai PyWorker config (metrics reporting, benchmarks, autoscaling integration)
  - `production/onstart.sh` — Startup script that launches both model server and PyWorker
  - `production/Dockerfile.slim` — Slim image extending existing v5 image
  - `production/deploy.py` — Deployment automation (create/destroy endpoint+workergroup)
  - `production/test_smoke.py` — Smoke test (health, HTTP, WebSocket)
  - `production/test_stress.py` — Full stress test with percentile metrics, CSV/JSON output
  - PyWorker repo pushed to GitHub, Docker image on Docker Hub

  ### Vast.ai Serverless Deployment (Live)
  - Endpoint: `hindi-asr-prod` (id=11967)
  - Workergroup: 16773 (template hash `3ac272ae9dc0ae58d105a1d88145a325`)
  - Worker provisioned and managed by the serverless engine

  ### Issues We Hit (and Fixed)

  | Issue | Root Cause | Fix |
  |:---|:---|:---|
  | Created bare metal instance instead of serverless | My mistake — didn't follow docs | Destroyed instance, used `create endpoint` + `create workergroup` |
  | `PYWORKER_REPO` env var empty on instance | Account-level env vars don't auto-propagate; Vast.ai doesn't auto-bootstrap PyWorker for custom images | Handle PyWorker clone+run in our own `onstart.sh` |
  | `python-dateutil` downgraded to 2.6.1 | `vastai` pip package pulled old dateutil, broke matplotlib/NeMo | Install deps BEFORE starting server; pin `python-dateutil>=2.8.2` |
  | `cannot import name 'Worker' from 'vastai'` | Wrong pip package — `vastai` is CLI tool, `vastai_sdk` has the Worker class | Changed requirements.txt to `vastai_sdk` |
  | `KeyError: 'WORKER_PORT'` | `vastai_sdk` Worker expects env vars set by Vast.ai infra, which doesn't auto-set them for custom images | Manually set `WORKER_PORT=8000`, `VAST_TCP_PORT_8000` in onstart.sh; model server on 18000, PyWorker on 8000 |
  | `Object of type bytes is not JSON serializable` | Benchmark generator returned file tuple with bytes; PyWorker sends benchmarks as JSON | Changed to base64-encoded audio, new `/v1/audio/transcriptions/json` endpoint |
  | `'CacheAwareRNNTStreamingPipeline' has no attribute 'run'` | Streaming pipeline doesn't have `.run()` method (that's on the batch pipeline) | **Last fix** — replaced with `transcribe_pcm_bytes_streaming()` that uses the streaming session API |

  ### Where We Are Right Now
  - Worker is **recycling** with the latest fix (streaming inference for benchmarks)
  - Waiting to see if the PyWorker benchmark passes and the worker transitions to **"running"** (ready) status
  - Once ready: smoke test, stress test, metrics capture

  ### What's Left After Worker Goes Ready
  1. Smoke test (HTTP + WebSocket)
  2. Stress test sweep (c=1,5,10,25,50,75,100) capturing all latency/throughput metrics
  3. Analyze results, set scaling laws
  4. Update README with production benchmarks



  and it replied with 

  I made the server/pyworker JSON-compatible, rebuilt/pushed the image, updated the Vast template/workergroup, and wired the tests to run against `/route` with auth. The current serverless worker is still provisioning (status `loading`), so full stress runs are pending.

  What changed (code + image)
  - JSON+multipart compatibility for batch + SSE endpoints in `production/server.py` so PyWorker can call `/v1/audio/transcriptions` and `/v1/audio/transcriptions/stream` with JSON payloads.
  - PyWorker now uses base64 JSON payloads, adds `/v1/audio/transcriptions/stream`, and uses a better workload estimator in `production/worker.py`.
  - Onstart no longer clones a repo; it runs the bundled worker and only installs deps if missing in `production/onstart.sh`.
  - Slim/full Dockerfiles now include `worker.py` and `vastai_sdk` deps in `production/Dockerfile.slim` and `production/Dockerfile`.
  - Deployment + tests now understand endpoint auth via `/route` in `production/deploy.py`, `production/test_smoke.py`, and `production/test_stress.py`.

  Image
  - Built and pushed `bharathkumar192/hindi-nemotron-asr:v6-production` with the updated server/pyworker.

  Current Vast state
  - Endpoint: `hindi-asr-prod` (id `11967`)
  - Workergroup: `16780` (template hash `3fc7205a26c3bd08fbd28f28ccca252a`)
  - Template (id `341822`) now uses:
    - image `bharathkumar192/hindi-nemotron-asr:v6-production`
    - env `-p 8000:8000`
    - filters: `gpu_ram>=40 num_gpus=1 reliability>0.95 direct_port_count>=1 disk_space>=30 inet_down>200`
  - Worker currently: `loading` (see `python3 production/deploy.py workers`).

  Smoke results (last completed run on previous worker)
  - Health: PASS
  - HTTP batch: PASS (~1.1s for 3s test audio)
  - SSE stream: PASS (~1.0s, 2 events)
  - WebSocket: FAIL (timeout; no direct port mapping for 18000)

  Why WebSocket failed in serverless
  - The current template exposes only port 8000 (PyWorker). `/ws` lives on the model server port (18000), which is not mapped, so WS connections time out.
  - To enable WS via serverless, the template must include `-p 18000:18000` and the search filters must require `direct_port_count>=2`. This is doable, but it caused unstable provisioning in this round.

  How to proceed once the worker is idle
  1) Smoke (serverless)
  ```
  python3 production/test_smoke.py --endpoint hindi-asr-prod --route-cost 10
  ```

  2) Stress (batch + SSE)
  ```
  python3 production/test_stress.py --endpoint hindi-asr-prod --mode http --levels 1,5,10,25,50
  python3 production/test_stress.py --endpoint hindi-asr-prod --mode sse  --levels 1,5,10,25,50
  ```
  Outputs go to `benchmark_results/*.csv` and `benchmark_results/*.json`.

  Region pinning (closer to India / Asia)
  - Vast supports `geolocation` in search params. Example:
    - `geolocation in [IN,SG,JP,HK,AE]`
  - You can apply this by updating the template search params:
  ```
  vastai update template 3fc7205a26c3bd08fbd28f28ccca252a \
    --search_params "gpu_ram>=40 num_gpus=1 reliability>0.95 rented=False geolocation in [IN,SG,JP,HK,AE] direct_port_count>=1 disk_space>=30 inet_down>200"
  ```

  If you want me to push WS support through serverless next, I’ll:
  1) Update the template env to `-p 8000:8000 -p 18000:18000`.
  2) Enforce `direct_port_count>=2`.
  3) Recreate workergroup and re-run smoke/stress with `--ws-port 18000`.

  Suggested next steps (pick one)
  1) Wait for the current worker to hit `idle`, then I’ll run smoke + stress and report metrics.
  2) Proceed with WebSocket-capable template/workergroup changes and retry provisioning.
  3) Roll back to a fully open search (no geolocation) to get a worker fast, then collect metrics immediately.


  and then i asked, check status

  And it replied with 
  Status right now:
  - Workers: 1
  - Instance ID: `31133036`
  - Status: `creating` (not ready yet)

  Want me to keep watching until it reaches `idle` and then run smoke/stress?


  and i asked, 

  what are the next steps. for our goal. here's what i asked. 

  Read the vast's serverless docs, requirements
  Plan the architecture that is failproof and high throughput mode with quality considered too.
  start working on it
  test it, deploy it to serverless
  smoke test
  stress test it and capture all major metrics(all) -> simulation tests that will be actual customers usage based
  then we'll discuss on scaling laws.


  and it replied with 

  Here are the concrete next steps toward your stated goal, in order. I’ve kept them actionable and tied to what’s already in the repo.

  Current state
  - Serverless worker is still `creating` (not ready yet). We can’t run smoke/stress until it’s `idle`.

  Next steps (exact order)
  1) Wait for worker to become ready  
     - I’ll keep polling `python3 production/deploy.py workers` until status is `idle`.

  2) Smoke test (serverless)  
     - Command:
       ```
       python3 production/test_smoke.py --endpoint hindi-asr-prod --route-cost 10
       ```
     - Confirms: auth flow, batch, SSE.  
     - WebSocket is not reachable unless we expose port 18000 (see step 6).

  3) Stress test (customer‑like load)  
     - HTTP batch:
       ```
       python3 production/test_stress.py --endpoint hindi-asr-prod --mode http --levels 1,5,10,25,50
       ```
     - SSE streaming:
       ```
       python3 production/test_stress.py --endpoint hindi-asr-prod --mode sse --levels 1,5,10,25,50
       ```
     - Outputs go to `benchmark_results/*.csv` and `benchmark_results/*.json`.

  4) Capture metrics and summarize  
     - I’ll pull the CSV/JSON results and summarize p50/p95/p99 latency, throughput, error rate, and worker load snapshots.

  5) Scaling laws draft  
     - Use the stress results to propose concurrency limits per GPU and utilization targets.

  6) WebSocket support (optional, but required if you want WS in prod)  
     - We must expose port 18000 in the template and require `direct_port_count>=2`.  
     - That requires template + workergroup update + new worker provisioning.  
     - Once enabled, smoke/stress will include:
       ```
       python3 production/test_smoke.py --endpoint hindi-asr-prod --ws-port 18000
       python3 production/test_stress.py --endpoint hindi-asr-prod --mode ws --ws-port 18000 --levels 1,5,10,25,50
       ```

  7) Region preference (Asia/India)
  - I can set `geolocation in [IN,SG,JP,HK,AE]` in the template search params, but that narrows supply and can slow provisioning. We can toggle it once baseline metrics are done.

  What I need from you
  - Confirm whether you want WebSocket enabled now (requires template/workergroup change + new worker provisioning), or do HTTP+SSE first to get metrics quickly.

  If you say “go ahead,” I’ll:
  - keep polling until the worker is idle,
  - run smoke and stress for HTTP+SSE immediately,
  - then report metrics and draft scaling‑law estimates.


  and i asked go, we can test teh websockets later.

  and it said back, 
  Worker is up and idle now. The serverless template was failing to start because the template’s `onstart` was set to `"bash /app/onstart.sh"` (exec-form string). I updated the template to use `/app/onstart.sh` and re-pointed the workergroup to the new template hash. After that, the worker loaded and stayed healthy.

  Smoke + stress tests (HTTP + SSE) are done (websockets skipped as requested). All runs were 0 errors.

  Key results (10s audio per stream):
  - HTTP p50 total latency: 0.7s (c=1), 1.1s (c=5), 1.9s (c=10), 3.7s (c=25), 7.0s (c=50)
  - SSE p50 total latency: 0.64s (c=1), 1.48s (c=5), 2.58s (c=10), 5.87s (c=25), 10.92s (c=50)
  - Throughput: ~38–42x realtime at c=50 for both HTTP and SSE

  Artifacts saved:
  - `benchmark_results/stress_results_20260209_195631.csv`
  - `benchmark_results/stress_results_20260209_195631.json`
  - `benchmark_results/stress_results_20260209_195730.csv`
  - `benchmark_results/stress_results_20260209_195730.json`

  Next steps (pick what you want me to do):
  1) Add “full metrics” capture by exposing `/metrics` via PyWorker (or mapping port 18000) so we can log GPU ms, VRAM, batch stats during stress tests.  
  2) WebSockets later: update template to `-p 18000:18000` and add `direct_port_count>=2`, then run `test_smoke.py` + `test_stress.py --mode ws`.  
  3) Region tuning: add `geolocation in [IN,SG,JP,HK,AE]` (or your preferred list) to search filters and re-run workergroup; we’ll verify offers exist so it doesn’t churn.  
  4) Scaling laws: use the new JSON outputs to fit throughput/latency curves and pick target concurrency per GPU.

  Tell me which option to execute next (or ask for a specific plot/summary from the JSON files).


  and i asked,  yes. let's do a thorough full metrics and stress testing first now. let's finish them now.

  and it replied with, 

  Full‑metrics instrumentation and stress tests are done. I updated the server/worker to expose `/metrics` through PyWorker, fixed the CUDA VRAM reporting (`total_memory`), rebuilt the image, redeployed, and ran full HTTP + SSE sweeps with metrics captured per level.

  Where the metrics live (latest runs):
  - HTTP: `benchmark_results/stress_results_20260209_202433.csv` and `benchmark_results/stress_results_20260209_202433.json`
  - SSE: `benchmark_results/stress_results_20260209_202718.csv` and `benchmark_results/stress_results_20260209_202718.json`

  Key results (RTX PRO 6000 WS, 10s audio per stream):
  - HTTP p50 total latency: 1.48s (c=1), 7.04s (c=25), 13.00s (c=50), 24.25s (c=100)
  - HTTP throughput: ~20–21x realtime at c=50–100; c=100 had 2/100 route failures (“All worker queues full”)
  - SSE p50 total latency: 1.51s (c=1), 11.02s (c=25), 20.21s (c=50), 34.65s (c=100)
  - SSE throughput: ~22–23x realtime at c=50–100; c=100 had 2/100 route failures
  - VRAM: ~10.43GB used, ~12.97GB reserved, 102GB total (now correctly reported)
  - `/metrics` GPU p50/p99 stays 0 for HTTP/SSE because those fields are tied to the WS batching loop; the JSON still captures all other server metrics + VRAM + HTTP counts

  What changed (code + deploy):
  - `/metrics` now supports POST and returns CUDA fields (`cuda_available`, `device_count`, `gpu_name`, VRAM used/reserved/total) from `production/server.py`
  - PyWorker now proxies `/metrics` in `production/worker.py`
  - `production/test_stress.py` now fetches metrics via serverless `/route`
  - Deployed image: `bharathkumar192/hindi-nemotron-asr:v9-production`
  - Workergroup template hash: `118364eef727b32735fa02ee2908b7a1`
  - Current worker: `31135755` (idle and healthy)

  If you want “GPU time per request” for HTTP/SSE (not just VRAM), I can add per‑request timing in the HTTP/SSE handlers and expose percentiles in `/metrics`.

  Next steps (pick one):
  1) Run longer‑duration stress (30s/60s) to simulate real customer sessions and capture drift.
  2) Add per‑request GPU timing for HTTP/SSE and re‑run the sweeps.
  3) Proceed to scaling‑law fitting using the new JSON outputs.




  Can you now do a thorough stress testing ensurimg we capture max metrics while doing so and on the way we should probaly ensure the WER also stays sane. and check for any issues where the current behaviour might fail in our production-ready application. We can work on websockets once the above is finished. first finish end to end metrics testing and auto-scaling rules are set and everything is done with http sse based worker.

first_user_message:
  Here is what i asked my another coding agent. 



  finish revamp with clean architecture mode implementation, then deploy to vast(all keys are in .env) serverless workers. then do stress testing simulating like actual customers to understand the working production server behaviour mocking and capture all metrics, so we can set scaling laws and move our production OpenAI transcription paid service to self hosten custom finetuned hindi model and serve any customer base with proper scaling laws set. assuming somewhere at 500 concurrent batching requests per GPU of 80GB GPU. Also while deploying check if region based deployment is available and check for closer to india or asia or anything closer. Also while implementation or planning, plan for both HTTP based and websocket based requests handlers. If we have vCPUs that take requests and sends batches of chunks to GPU and gets data back, or triton server, or whatever man. i just need high throughput mode with both HTTP streaming and websocket streaming. 

  Read the vast's serverless docs, requirements
  Plan the architecture that is failproof and high throughput mode with quality considered too.
  start working on it
  test it, deploy it to serverless
  smoke test
  stress test it and capture all major metrics(all) -> simulation tests that will be actual customers usage based
  then we'll discuss on scaling laws. 



  and after a while of trying it failed but got somewhere. and below is a handover summary from that agent. Can you now take over and finish the proper deployment, testing, stress testing, and capture metrics. below is it's summary. Also, all secrets are in .env FYI. docs are also in this repo for vast's serverless stack. understand the flow. and see the current implementation, make changes, or revamp as needed. 

  ## Summary

  ### What We Started With
  - A **fine-tuned Hindi ASR model** (Nemotron-0.6B, 8.8% WER) deployed on Modal
  - Prior benchmarks showing **A100-SXM4-80GB handles 50-100 concurrent streams** in the safe zone
  - Existing code in `nemotron_asr/` — a Modal-coupled WebSocket streaming server with event-driven batching, VAD, and centralized inference loop
  - The decision to move to **Vast.ai serverless** for production (cheaper, direct TCP, autoscaling)

  ### What We Want To Do
  1. Build a **production-ready ASR service** with both HTTP and WebSocket endpoints
  2. Deploy it on **Vast.ai serverless** with autoscaling (not bare metal instances)
  3. Run **stress tests simulating real customers** to capture all metrics
  4. Establish **scaling laws** so we can replace paid OpenAI transcription with our self-hosted model

  ### What We Built
  - `production/server.py` — Standalone FastAPI server (HTTP batch, HTTP SSE streaming, WebSocket streaming)
  - `production/worker.py` — Vast.ai PyWorker config (metrics reporting, benchmarks, autoscaling integration)
  - `production/onstart.sh` — Startup script that launches both model server and PyWorker
  - `production/Dockerfile.slim` — Slim image extending existing v5 image
  - `production/deploy.py` — Deployment automation (create/destroy endpoint+workergroup)
  - `production/test_smoke.py` — Smoke test (health, HTTP, WebSocket)
  - `production/test_stress.py` — Full stress test with percentile metrics, CSV/JSON output
  - PyWorker repo pushed to GitHub, Docker image on Docker Hub

  ### Vast.ai Serverless Deployment (Live)
  - Endpoint: `hindi-asr-prod` (id=11967)
  - Workergroup: 16773 (template hash `3ac272ae9dc0ae58d105a1d88145a325`)
  - Worker provisioned and managed by the serverless engine

  ### Issues We Hit (and Fixed)

  | Issue | Root Cause | Fix |
  |:---|:---|:---|
  | Created bare metal instance instead of serverless | My mistake — didn't follow docs | Destroyed instance, used `create endpoint` + `create workergroup` |
  | `PYWORKER_REPO` env var empty on instance | Account-level env vars don't auto-propagate; Vast.ai doesn't auto-bootstrap PyWorker for custom images | Handle PyWorker clone+run in our own `onstart.sh` |
  | `python-dateutil` downgraded to 2.6.1 | `vastai` pip package pulled old dateutil, broke matplotlib/NeMo | Install deps BEFORE starting server; pin `python-dateutil>=2.8.2` |
  | `cannot import name 'Worker' from 'vastai'` | Wrong pip package — `vastai` is CLI tool, `vastai_sdk` has the Worker class | Changed requirements.txt to `vastai_sdk` |
  | `KeyError: 'WORKER_PORT'` | `vastai_sdk` Worker expects env vars set by Vast.ai infra, which doesn't auto-set them for custom images | Manually set `WORKER_PORT=8000`, `VAST_TCP_PORT_8000` in onstart.sh; model server on 18000, PyWorker on 8000 |
  | `Object of type bytes is not JSON serializable` | Benchmark generator returned file tuple with bytes; PyWorker sends benchmarks as JSON | Changed to base64-encoded audio, new `/v1/audio/transcriptions/json` endpoint |
  | `'CacheAwareRNNTStreamingPipeline' has no attribute 'run'` | Streaming pipeline doesn't have `.run()` method (that's on the batch pipeline) | **Last fix** — replaced with `transcribe_pcm_bytes_streaming()` that uses the streaming session API |

  ### Where We Are Right Now
  - Worker is **recycling** with the latest fix (streaming inference for benchmarks)
  - Waiting to see if the PyWorker benchmark passes and the worker transitions to **"running"** (ready) status
  - Once ready: smoke test, stress test, metrics capture

  ### What's Left After Worker Goes Ready
  1. Smoke test (HTTP + WebSocket)
  2. Stress test sweep (c=1,5,10,25,50,75,100) capturing all latency/throughput metrics
  3. Analyze results, set scaling laws
  4. Update README with production benchmarks



  and it replied with 

  I made the server/pyworker JSON-compatible, rebuilt/pushed the image, updated the Vast template/workergroup, and wired the tests to run against `/route` with auth. The current serverless worker is still provisioning (status `loading`), so full stress runs are pending.

  What changed (code + image)
  - JSON+multipart compatibility for batch + SSE endpoints in `production/server.py` so PyWorker can call `/v1/audio/transcriptions` and `/v1/audio/transcriptions/stream` with JSON payloads.
  - PyWorker now uses base64 JSON payloads, adds `/v1/audio/transcriptions/stream`, and uses a better workload estimator in `production/worker.py`.
  - Onstart no longer clones a repo; it runs the bundled worker and only installs deps if missing in `production/onstart.sh`.
  - Slim/full Dockerfiles now include `worker.py` and `vastai_sdk` deps in `production/Dockerfile.slim` and `production/Dockerfile`.
  - Deployment + tests now understand endpoint auth via `/route` in `production/deploy.py`, `production/test_smoke.py`, and `production/test_stress.py`.

  Image
  - Built and pushed `bharathkumar192/hindi-nemotron-asr:v6-production` with the updated server/pyworker.

  Current Vast state
  - Endpoint: `hindi-asr-prod` (id `11967`)
  - Workergroup: `16780` (template hash `3fc7205a26c3bd08fbd28f28ccca252a`)
  - Template (id `341822`) now uses:
    - image `bharathkumar192/hindi-nemotron-asr:v6-production`
    - env `-p 8000:8000`
    - filters: `gpu_ram>=40 num_gpus=1 reliability>0.95 direct_port_count>=1 disk_space>=30 inet_down>200`
  - Worker currently: `loading` (see `python3 production/deploy.py workers`).

  Smoke results (last completed run on previous worker)
  - Health: PASS
  - HTTP batch: PASS (~1.1s for 3s test audio)
  - SSE stream: PASS (~1.0s, 2 events)
  - WebSocket: FAIL (timeout; no direct port mapping for 18000)

  Why WebSocket failed in serverless
  - The current template exposes only port 8000 (PyWorker). `/ws` lives on the model server port (18000), which is not mapped, so WS connections time out.
  - To enable WS via serverless, the template must include `-p 18000:18000` and the search filters must require `direct_port_count>=2`. This is doable, but it caused unstable provisioning in this round.

  How to proceed once the worker is idle
  1) Smoke (serverless)
  ```
  python3 production/test_smoke.py --endpoint hindi-asr-prod --route-cost 10
  ```

  2) Stress (batch + SSE)
  ```
  python3 production/test_stress.py --endpoint hindi-asr-prod --mode http --levels 1,5,10,25,50
  python3 production/test_stress.py --endpoint hindi-asr-prod --mode sse  --levels 1,5,10,25,50
  ```
  Outputs go to `benchmark_results/*.csv` and `benchmark_results/*.json`.

  Region pinning (closer to India / Asia)
  - Vast supports `geolocation` in search params. Example:
    - `geolocation in [IN,SG,JP,HK,AE]`
  - You can apply this by updating the template search params:
  ```
  vastai update template 3fc7205a26c3bd08fbd28f28ccca252a \
    --search_params "gpu_ram>=40 num_gpus=1 reliability>0.95 rented=False geolocation in [IN,SG,JP,HK,AE] direct_port_count>=1 disk_space>=30 inet_down>200"
  ```

  If you want me to push WS support through serverless next, I’ll:
  1) Update the template env to `-p 8000:8000 -p 18000:18000`.
  2) Enforce `direct_port_count>=2`.
  3) Recreate workergroup and re-run smoke/stress with `--ws-port 18000`.

  Suggested next steps (pick one)
  1) Wait for the current worker to hit `idle`, then I’ll run smoke + stress and report metrics.
  2) Proceed with WebSocket-capable template/workergroup changes and retry provisioning.
  3) Roll back to a fully open search (no geolocation) to get a worker fast, then collect metrics immediately.


  and then i asked, check status

  And it replied with 
  Status right now:
  - Workers: 1
  - Instance ID: `31133036`
  - Status: `creating` (not ready yet)

  Want me to keep watching until it reaches `idle` and then run smoke/stress?


  and i asked, 

  what are the next steps. for our goal. here's what i asked. 

  Read the vast's serverless docs, requirements
  Plan the architecture that is failproof and high throughput mode with quality considered too.
  start working on it
  test it, deploy it to serverless
  smoke test
  stress test it and capture all major metrics(all) -> simulation tests that will be actual customers usage based
  then we'll discuss on scaling laws.


  and it replied with 

  Here are the concrete next steps toward your stated goal, in order. I’ve kept them actionable and tied to what’s already in the repo.

  Current state
  - Serverless worker is still `creating` (not ready yet). We can’t run smoke/stress until it’s `idle`.

  Next steps (exact order)
  1) Wait for worker to become ready  
     - I’ll keep polling `python3 production/deploy.py workers` until status is `idle`.

  2) Smoke test (serverless)  
     - Command:
       ```
       python3 production/test_smoke.py --endpoint hindi-asr-prod --route-cost 10
       ```
     - Confirms: auth flow, batch, SSE.  
     - WebSocket is not reachable unless we expose port 18000 (see step 6).

  3) Stress test (customer‑like load)  
     - HTTP batch:
       ```
       python3 production/test_stress.py --endpoint hindi-asr-prod --mode http --levels 1,5,10,25,50
       ```
     - SSE streaming:
       ```
       python3 production/test_stress.py --endpoint hindi-asr-prod --mode sse --levels 1,5,10,25,50
       ```
     - Outputs go to `benchmark_results/*.csv` and `benchmark_results/*.json`.

  4) Capture metrics and summarize  
     - I’ll pull the CSV/JSON results and summarize p50/p95/p99 latency, throughput, error rate, and worker load snapshots.

  5) Scaling laws draft  
     - Use the stress results to propose concurrency limits per GPU and utilization targets.

  6) WebSocket support (optional, but required if you want WS in prod)  
     - We must expose port 18000 in the template and require `direct_port_count>=2`.  
     - That requires template + workergroup update + new worker provisioning.  
     - Once enabled, smoke/stress will include:
       ```
       python3 production/test_smoke.py --endpoint hindi-asr-prod --ws-port 18000
       python3 production/test_stress.py --endpoint hindi-asr-prod --mode ws --ws-port 18000 --levels 1,5,10,25,50
       ```

  7) Region preference (Asia/India)
  - I can set `geolocation in [IN,SG,JP,HK,AE]` in the template search params, but that narrows supply and can slow provisioning. We can toggle it once baseline metrics are done.

  What I need from you
  - Confirm whether you want WebSocket enabled now (requires template/workergroup change + new worker provisioning), or do HTTP+SSE first to get metrics quickly.

  If you say “go ahead,” I’ll:
  - keep polling until the worker is idle,
  - run smoke and stress for HTTP+SSE immediately,
  - then report metrics and draft scaling‑law estimates.


  and i asked go, we can test teh websockets later.

  and it said back, 
  Worker is up and idle now. The serverless template was failing to start because the template’s `onstart` was set to `"bash /app/onstart.sh"` (exec-form string). I updated the template to use `/app/onstart.sh` and re-pointed the workergroup to the new template hash. After that, the worker loaded and stayed healthy.

  Smoke + stress tests (HTTP + SSE) are done (websockets skipped as requested). All runs were 0 errors.

  Key results (10s audio per stream):
  - HTTP p50 total latency: 0.7s (c=1), 1.1s (c=5), 1.9s (c=10), 3.7s (c=25), 7.0s (c=50)
  - SSE p50 total latency: 0.64s (c=1), 1.48s (c=5), 2.58s (c=10), 5.87s (c=25), 10.92s (c=50)
  - Throughput: ~38–42x realtime at c=50 for both HTTP and SSE

  Artifacts saved:
  - `benchmark_results/stress_results_20260209_195631.csv`
  - `benchmark_results/stress_results_20260209_195631.json`
  - `benchmark_results/stress_results_20260209_195730.csv`
  - `benchmark_results/stress_results_20260209_195730.json`

  Next steps (pick what you want me to do):
  1) Add “full metrics” capture by exposing `/metrics` via PyWorker (or mapping port 18000) so we can log GPU ms, VRAM, batch stats during stress tests.  
  2) WebSockets later: update template to `-p 18000:18000` and add `direct_port_count>=2`, then run `test_smoke.py` + `test_stress.py --mode ws`.  
  3) Region tuning: add `geolocation in [IN,SG,JP,HK,AE]` (or your preferred list) to search filters and re-run workergroup; we’ll verify offers exist so it doesn’t churn.  
  4) Scaling laws: use the new JSON outputs to fit throughput/latency curves and pick target concurrency per GPU.

  Tell me which option to execute next (or ask for a specific plot/summary from the JSON files).


  and i asked,  yes. let's do a thorough full metrics and stress testing first now. let's finish them now.

  and it replied with, 

  Full‑metrics instrumentation and stress tests are done. I updated the server/worker to expose `/metrics` through PyWorker, fixed the CUDA VRAM reporting (`total_memory`), rebuilt the image, redeployed, and ran full HTTP + SSE sweeps with metrics captured per level.

  Where the metrics live (latest runs):
  - HTTP: `benchmark_results/stress_results_20260209_202433.csv` and `benchmark_results/stress_results_20260209_202433.json`
  - SSE: `benchmark_results/stress_results_20260209_202718.csv` and `benchmark_results/stress_results_20260209_202718.json`

  Key results (RTX PRO 6000 WS, 10s audio per stream):
  - HTTP p50 total latency: 1.48s (c=1), 7.04s (c=25), 13.00s (c=50), 24.25s (c=100)
  - HTTP throughput: ~20–21x realtime at c=50–100; c=100 had 2/100 route failures (“All worker queues full”)
  - SSE p50 total latency: 1.51s (c=1), 11.02s (c=25), 20.21s (c=50), 34.65s (c=100)
  - SSE throughput: ~22–23x realtime at c=50–100; c=100 had 2/100 route failures
  - VRAM: ~10.43GB used, ~12.97GB reserved, 102GB total (now correctly reported)
  - `/metrics` GPU p50/p99 stays 0 for HTTP/SSE because those fields are tied to the WS batching loop; the JSON still captures all other server metrics + VRAM + HTTP counts

  What changed (code + deploy):
  - `/metrics` now supports POST and returns CUDA fields (`cuda_available`, `device_count`, `gpu_name`, VRAM used/reserved/total) from `production/server.py`
  - PyWorker now proxies `/metrics` in `production/worker.py`
  - `production/test_stress.py` now fetches metrics via serverless `/route`
  - Deployed image: `bharathkumar192/hindi-nemotron-asr:v9-production`
  - Workergroup template hash: `118364eef727b32735fa02ee2908b7a1`
  - Current worker: `31135755` (idle and healthy)

  If you want “GPU time per request” for HTTP/SSE (not just VRAM), I can add per‑request timing in the HTTP/SSE handlers and expose percentiles in `/metrics`.

  Next steps (pick one):
  1) Run longer‑duration stress (30s/60s) to simulate real customer sessions and capture drift.
  2) Add per‑request GPU timing for HTTP/SSE and re‑run the sweeps.
  3) Proceed to scaling‑law fitting using the new JSON outputs.




  Can you now do a thorough stress testing ensurimg we capture max metrics while doing so and on the way we should probaly ensure the WER also stays sane. and check for any issues where the current behaviour might fail in our production-ready application. We can work on websockets once the above is finished. first finish end to end metrics testing and auto-scaling rules are set and everything is done with http sse based worker.


id: 019c4ed9-5f5b-7a30-ae55-b580f1e8cd85
source: vscode
model_provider: openai
cwd: /home/ubuntu/nemo_hindi
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.98.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/11/rollout-2026-02-11T22-36-34-019c4ed9-5f5b-7a30-ae55-b580f1e8cd85.jsonl

title:
  [1 tool called]

  Let me read the exact area in `TRTEncoderWrapper` where the method needs to be added.

  [1 tool called]

  Now I'll add the `cache_aware_stream_step` override right after the existing `forward` method (before `_trt_execute`). This is the critical fix.

  [1 tool called]

  Now let me verify syntax and that the method is properly placed.

  [1 tool called]

  Let me verify the fix is structurally correct by confirming `cache_aware_stream_step` is now a direct method on `TRTEncoderWrapper`, not delegated.

  [1 tool called]

  Good -- `cache_aware_stream_step` is at line 183, directly on `TRTEncoderWrapper`, before `forward` at 242. The `__getattr__` at 176 will never be hit for this method because Python finds it directly on the class first.

  [2 tools called]

  Deployed. Now I need to trigger a fresh container (old one with the bug may still be running). Let me check and force a restart.

  [1 tool called]

  There's an old container from 20:12 and a pending new one. Let me hit the health endpoint, which will route to whichever container is ready. The new container with the fix should start up (TRT engine is cached on volume, so no rebuild needed -- just load).

  [1 tool called]

  That container has uptime 4590s (from the old deployment). Let me check if the new pending container has started and verify the logs show TRT is actually being invoked now.

  [1 tool called]

  No new container logs yet. The old container is still serving. I need to stop the old containers so a fresh one with the fix starts.

  [2 tools called]

  Old containers are gone. Now let me trigger a fresh container start with the fix.

  [2 tools called]

  Empty response -- container startup timed out or returned empty. Let me try again -- it might have been a cold start.

  [1 tool called]

  Still empty. The container may be crashing during startup (the fix may be causing an error during warmup). Let me check the logs.

  [2 tools called]

  The TRT engine IS being called now (the fix works), but the warmup audio has time dimension 65 which exceeds the TRT profile max of 64. The TRT profile was built with `max time=64` but the warmup audio chunk is slightly larger. Two fixes needed:

  1. Increase the TRT profile max time dimension (need to rebuild engine)
  2. OR fall back to original encoder when input shape exceeds TRT profile bounds

  The faster fix is option 2 -- add a bounds check in `_trt_execute` that falls back gracefully, then also rebuild with a wider profile. Let me do the fallback fix first (immediate) and widen the profile (will rebuild on next cold start).

  [1 tool called]

  Now let me also check the TRT build profile to understand the bounds, then fix both `forward` (add fallback) and widen the profile.

  [2 tools called]

  The profile max for `audio_signal` time dimension is 64 (line 513), but warmup sends chunks that result in T=65. I need to:

  1. Widen the max time from 64 to 256 (generous headroom)
  2. Add a graceful fallback in `forward()` for any out-of-profile shapes
  3. Delete the cached engine so it rebuilds with the wider profile

  [1 tool called]

  Now add the graceful fallback in `forward()` for any shapes that still exceed the profile.

  [1 tool called]

  Now I need to delete the cached TRT engine on the volume so it rebuilds with the wider time profile. Let me add a version tag to the engine path so the old one isn't used.

  [3 tools called]

  Now trigger the container. First startup will rebuild the TRT engine with wider profile (~3 min on L40S), then it's cached.

  [1 tool called]

  Empty response again. Container might still be building. Let me wait and retry.

  [1 tool called]

  Container is up with `"encoder": "tensorrt"` and `uptime_s: 0.0` (fresh start). Let me check the logs to confirm the engine rebuilt and cache_aware_stream_step is now routing through TRT.

  [3 tools called]

  The v2 engine built successfully (187.4s, 2330MB) and **warmup completed** -- "All warmup iterations complete!" means the TRT encoder handled the warmup audio without crashes. The fix is working.

  [1 tool called]

  Now run the full benchmark. This is the moment of truth.

  [7 tools called]

  Results are flowing. **c=1: FT_p50=568ms, c=10: FT_p50=589ms** -- these are in the same ballpark as before. The encoder speedup manifests more at higher concurrency where batching multiplies the savings. Let me wait for more results.

  [2 tools called]

  c=100 results: **FT_p50=1061ms** (vs 1241ms before the fix -- 14% improvement). Let me wait for the higher concurrency levels.

  [1 tool called]

  The results are dramatically different now that TRT is actually executing. Let me compile the comparison.

  [1 tool called]

  All three tasks complete. Here's the full analysis.

  ---

  **The bug was confirmed and fixed. TRT is now actually executing. The results are night and day.**

  ## Before vs After Fix: Throughput Proves TRT is Real

  | Conc | Buggy TRT (=PyTorch) Thru | Fixed TRT Thru | Delta |
  |---:|---:|---:|---:|
  | 1 | 0.9x | 1.0x | same |
  | 50 | 23.5x | 23.7x | same |
  | 100 | 42.2x | 32.0x | batching dynamics |
  | 200 | 40.5x | **45.6x** | +13% |
  | 300 | 45.6x | **46.8x** | +3% |
  | 400 | 44.0x | **95.9x** | **+118%** |
  | 500 | 47.9x | **100.8x** | **+110%** |

  The **throughput at c=400 jumped from 44x to 96x** and **c=500 from 48x to 101x**. This is the real TRT executing -- previously it was capped because PyTorch eager was the bottleneck, and now TRT processes encoder batches 2-3x faster, so the system completes everything in less wall time at high concurrency.

  ## Quality Degradation Curve (the key constraint)

  | Conc | Old (buggy=PyTorch) WER | Fixed TRT WER | Notes |
  |---:|---:|---:|:---|
  | 1 | 33.3% | 37.5% | Single-sample variance |
  | 10 | 20.5% | 20.7% | Identical |
  | 50 | 33.0% | 33.7% | Identical |
  | 100 | 33.2% | 33.5% | Identical |
  | 200 | 33.2% | 33.8% | Identical |
  | 300 | 33.8% | 33.8% | **Exact match** |
  | 400 | 33.3% | **39.2%** | Degradation starts |
  | 500 | 33.3% | **50.2%** | Severe degradation |

  **Key finding**: WER stays flat 33-34% from c=1 to c=300 (matching the old deployment exactly), but degrades at 400+ streams. This is the **GPU saturation point** -- the encoder is faster (TRT), so more batches get processed, but at c=400+ the decoder (RNNT greedy) and async scheduling become the bottleneck. The p99 latency at c=400 hits 9 seconds, and at c=500 hits 15 seconds, which means some streams are starving.

  **The safe operating zone for TRT is c=1 to c=300 with flat WER, and the throughput at that range is now genuinely higher** (46.8x at c=300 vs the theoretical throughput). The big win is in wall time -- with TRT, the same 300 streams complete in less total time, meaning each L40S can cycle through more users per hour.

  **Production recommendation**: keep target_inputs=200, max_inputs=300 (same as torch.compile config) for zero-degradation quality, but the per-batch latency is now faster (TRT), which means lower TTFT and higher effective throughput at the same concurrency.
  From the benchmark that just completed (fixed TRT, L40S):

  | Concurrency | FT p50 | FT p99 | Total p50 | Wall Time |
  |---:|---:|---:|---:|---:|
  | 1 | 568ms | 568ms | 9,479ms | 9,518ms |
  | 10 | 589ms | 600ms | 12,531ms | 17,694ms |
  | 50 | 844ms | 964ms | 12,907ms | 24,708ms |
  | 100 | 1,061ms | 1,620ms | 21,771ms | 36,622ms |
  | 200 | 992ms | 1,681ms | 24,504ms | 51,308ms |
  | 300 | 1,140ms | 2,028ms | 39,974ms | 74,942ms |
  | 400 | 1,092ms | 8,999ms | 23,364ms | 48,805ms |
  | 500 | 995ms | 15,230ms | 28,483ms | 58,059ms |

  Side-by-side with the old torch.compile (which was actually vanilla PyTorch due to the same delegation bug):

  | Conc | torch.compile FT p50 | TRT Fixed FT p50 | torch.compile FT p99 | TRT Fixed FT p99 |
  |---:|---:|---:|---:|---:|
  | 1 | 575ms | 568ms | 575ms | 568ms |
  | 10 | 647ms | 589ms | 725ms | 600ms |
  | 50 | 643ms | 844ms | 827ms | 964ms |
  | 100 | 763ms | 1,061ms | 1,999ms | 1,620ms |
  | 200 | 1,034ms | 992ms | 3,961ms | 1,681ms |
  | 300 | 1,156ms | 1,140ms | 2,803ms | 2,028ms |

  At p50 the latencies are roughly comparable. The real TRT advantage shows in **p99** -- at c=200 it's **1,681ms vs 3,961ms** (2.4x better tail latency), and at c=300 it's **2,028ms vs 2,803ms**. The throughput difference is where TRT wins massively: **95.9x at c=400** vs 44x before, showing the encoder is genuinely processing batches faster.



  Now the modal service contains 2 different versions of apps. 1 has torch.compile version of encoder and the other has tensorRT version. and the latencies are mentioned. I now want you to go through both the implementations, and then do a thorough benchmarking about real-world customer like scenarios, ensuring everything works as intended in production. go till 500x concurrencies just for the sake of stress testing. and be sure to measure all scores. first see if the current benchmarking scritps are genuine, then go through the implementations, and then understand the situation here.

first_user_message:
  [1 tool called]

  Let me read the exact area in `TRTEncoderWrapper` where the method needs to be added.

  [1 tool called]

  Now I'll add the `cache_aware_stream_step` override right after the existing `forward` method (before `_trt_execute`). This is the critical fix.

  [1 tool called]

  Now let me verify syntax and that the method is properly placed.

  [1 tool called]

  Let me verify the fix is structurally correct by confirming `cache_aware_stream_step` is now a direct method on `TRTEncoderWrapper`, not delegated.

  [1 tool called]

  Good -- `cache_aware_stream_step` is at line 183, directly on `TRTEncoderWrapper`, before `forward` at 242. The `__getattr__` at 176 will never be hit for this method because Python finds it directly on the class first.

  [2 tools called]

  Deployed. Now I need to trigger a fresh container (old one with the bug may still be running). Let me check and force a restart.

  [1 tool called]

  There's an old container from 20:12 and a pending new one. Let me hit the health endpoint, which will route to whichever container is ready. The new container with the fix should start up (TRT engine is cached on volume, so no rebuild needed -- just load).

  [1 tool called]

  That container has uptime 4590s (from the old deployment). Let me check if the new pending container has started and verify the logs show TRT is actually being invoked now.

  [1 tool called]

  No new container logs yet. The old container is still serving. I need to stop the old containers so a fresh one with the fix starts.

  [2 tools called]

  Old containers are gone. Now let me trigger a fresh container start with the fix.

  [2 tools called]

  Empty response -- container startup timed out or returned empty. Let me try again -- it might have been a cold start.

  [1 tool called]

  Still empty. The container may be crashing during startup (the fix may be causing an error during warmup). Let me check the logs.

  [2 tools called]

  The TRT engine IS being called now (the fix works), but the warmup audio has time dimension 65 which exceeds the TRT profile max of 64. The TRT profile was built with `max time=64` but the warmup audio chunk is slightly larger. Two fixes needed:

  1. Increase the TRT profile max time dimension (need to rebuild engine)
  2. OR fall back to original encoder when input shape exceeds TRT profile bounds

  The faster fix is option 2 -- add a bounds check in `_trt_execute` that falls back gracefully, then also rebuild with a wider profile. Let me do the fallback fix first (immediate) and widen the profile (will rebuild on next cold start).

  [1 tool called]

  Now let me also check the TRT build profile to understand the bounds, then fix both `forward` (add fallback) and widen the profile.

  [2 tools called]

  The profile max for `audio_signal` time dimension is 64 (line 513), but warmup sends chunks that result in T=65. I need to:

  1. Widen the max time from 64 to 256 (generous headroom)
  2. Add a graceful fallback in `forward()` for any out-of-profile shapes
  3. Delete the cached engine so it rebuilds with the wider profile

  [1 tool called]

  Now add the graceful fallback in `forward()` for any shapes that still exceed the profile.

  [1 tool called]

  Now I need to delete the cached TRT engine on the volume so it rebuilds with the wider time profile. Let me add a version tag to the engine path so the old one isn't used.

  [3 tools called]

  Now trigger the container. First startup will rebuild the TRT engine with wider profile (~3 min on L40S), then it's cached.

  [1 tool called]

  Empty response again. Container might still be building. Let me wait and retry.

  [1 tool called]

  Container is up with `"encoder": "tensorrt"` and `uptime_s: 0.0` (fresh start). Let me check the logs to confirm the engine rebuilt and cache_aware_stream_step is now routing through TRT.

  [3 tools called]

  The v2 engine built successfully (187.4s, 2330MB) and **warmup completed** -- "All warmup iterations complete!" means the TRT encoder handled the warmup audio without crashes. The fix is working.

  [1 tool called]

  Now run the full benchmark. This is the moment of truth.

  [7 tools called]

  Results are flowing. **c=1: FT_p50=568ms, c=10: FT_p50=589ms** -- these are in the same ballpark as before. The encoder speedup manifests more at higher concurrency where batching multiplies the savings. Let me wait for more results.

  [2 tools called]

  c=100 results: **FT_p50=1061ms** (vs 1241ms before the fix -- 14% improvement). Let me wait for the higher concurrency levels.

  [1 tool called]

  The results are dramatically different now that TRT is actually executing. Let me compile the comparison.

  [1 tool called]

  All three tasks complete. Here's the full analysis.

  ---

  **The bug was confirmed and fixed. TRT is now actually executing. The results are night and day.**

  ## Before vs After Fix: Throughput Proves TRT is Real

  | Conc | Buggy TRT (=PyTorch) Thru | Fixed TRT Thru | Delta |
  |---:|---:|---:|---:|
  | 1 | 0.9x | 1.0x | same |
  | 50 | 23.5x | 23.7x | same |
  | 100 | 42.2x | 32.0x | batching dynamics |
  | 200 | 40.5x | **45.6x** | +13% |
  | 300 | 45.6x | **46.8x** | +3% |
  | 400 | 44.0x | **95.9x** | **+118%** |
  | 500 | 47.9x | **100.8x** | **+110%** |

  The **throughput at c=400 jumped from 44x to 96x** and **c=500 from 48x to 101x**. This is the real TRT executing -- previously it was capped because PyTorch eager was the bottleneck, and now TRT processes encoder batches 2-3x faster, so the system completes everything in less wall time at high concurrency.

  ## Quality Degradation Curve (the key constraint)

  | Conc | Old (buggy=PyTorch) WER | Fixed TRT WER | Notes |
  |---:|---:|---:|:---|
  | 1 | 33.3% | 37.5% | Single-sample variance |
  | 10 | 20.5% | 20.7% | Identical |
  | 50 | 33.0% | 33.7% | Identical |
  | 100 | 33.2% | 33.5% | Identical |
  | 200 | 33.2% | 33.8% | Identical |
  | 300 | 33.8% | 33.8% | **Exact match** |
  | 400 | 33.3% | **39.2%** | Degradation starts |
  | 500 | 33.3% | **50.2%** | Severe degradation |

  **Key finding**: WER stays flat 33-34% from c=1 to c=300 (matching the old deployment exactly), but degrades at 400+ streams. This is the **GPU saturation point** -- the encoder is faster (TRT), so more batches get processed, but at c=400+ the decoder (RNNT greedy) and async scheduling become the bottleneck. The p99 latency at c=400 hits 9 seconds, and at c=500 hits 15 seconds, which means some streams are starving.

  **The safe operating zone for TRT is c=1 to c=300 with flat WER, and the throughput at that range is now genuinely higher** (46.8x at c=300 vs the theoretical throughput). The big win is in wall time -- with TRT, the same 300 streams complete in less total time, meaning each L40S can cycle through more users per hour.

  **Production recommendation**: keep target_inputs=200, max_inputs=300 (same as torch.compile config) for zero-degradation quality, but the per-batch latency is now faster (TRT), which means lower TTFT and higher effective throughput at the same concurrency.
  From the benchmark that just completed (fixed TRT, L40S):

  | Concurrency | FT p50 | FT p99 | Total p50 | Wall Time |
  |---:|---:|---:|---:|---:|
  | 1 | 568ms | 568ms | 9,479ms | 9,518ms |
  | 10 | 589ms | 600ms | 12,531ms | 17,694ms |
  | 50 | 844ms | 964ms | 12,907ms | 24,708ms |
  | 100 | 1,061ms | 1,620ms | 21,771ms | 36,622ms |
  | 200 | 992ms | 1,681ms | 24,504ms | 51,308ms |
  | 300 | 1,140ms | 2,028ms | 39,974ms | 74,942ms |
  | 400 | 1,092ms | 8,999ms | 23,364ms | 48,805ms |
  | 500 | 995ms | 15,230ms | 28,483ms | 58,059ms |

  Side-by-side with the old torch.compile (which was actually vanilla PyTorch due to the same delegation bug):

  | Conc | torch.compile FT p50 | TRT Fixed FT p50 | torch.compile FT p99 | TRT Fixed FT p99 |
  |---:|---:|---:|---:|---:|
  | 1 | 575ms | 568ms | 575ms | 568ms |
  | 10 | 647ms | 589ms | 725ms | 600ms |
  | 50 | 643ms | 844ms | 827ms | 964ms |
  | 100 | 763ms | 1,061ms | 1,999ms | 1,620ms |
  | 200 | 1,034ms | 992ms | 3,961ms | 1,681ms |
  | 300 | 1,156ms | 1,140ms | 2,803ms | 2,028ms |

  At p50 the latencies are roughly comparable. The real TRT advantage shows in **p99** -- at c=200 it's **1,681ms vs 3,961ms** (2.4x better tail latency), and at c=300 it's **2,028ms vs 2,803ms**. The throughput difference is where TRT wins massively: **95.9x at c=400** vs 44x before, showing the encoder is genuinely processing batches faster.



  Now the modal service contains 2 different versions of apps. 1 has torch.compile version of encoder and the other has tensorRT version. and the latencies are mentioned. I now want you to go through both the implementations, and then do a thorough benchmarking about real-world customer like scenarios, ensuring everything works as intended in production. go till 500x concurrencies just for the sake of stress testing. and be sure to measure all scores. first see if the current benchmarking scritps are genuine, then go through the implementations, and then understand the situation here.


id: 019c53da-3bb2-7870-93fb-7475b8dced60
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.99.0-alpha.23
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/12/rollout-2026-02-12T21-55-37-019c53da-3bb2-7870-93fb-7475b8dced60.jsonl

title:
  on a scale of 1-10. how good do you think our current transcription system + validation works. and is it sufficient to train large TTS and ASR systems ? if not what's that missing piece. if yes, how good. and if we are missing out on any corner cases, explain them too. considering indic languages, we cannot have a consensus decision makers. single source of controlled & deterministic transcription from gemini-3-flash with thinking-low, and temperature-0 and post validation with CTC on native "and" roman MFA. anything below 70% should be retried, with gemini-3-pro and if even after retry, if still <70% ignore / mark for review. the scoring has to be either average of both indic CTC + roman MFA or (and / or / avg / weighted -ideate here. we didnt yet decide on this).  dont gaslight. think realistically and reason. a whole costly project depends on this just so you know.

first_user_message:
  on a scale of 1-10. how good do you think our current transcription system + validation works. and is it sufficient to train large TTS and ASR systems ? if not what's that missing piece. if yes, how good. and if we are missing out on any corner cases, explain them too. considering indic languages, we cannot have a consensus decision makers. single source of controlled & deterministic transcription from gemini-3-flash with thinking-low, and temperature-0 and post validation with CTC on native "and" roman MFA. anything below 70% should be retried, with gemini-3-pro and if even after retry, if still <70% ignore / mark for review. the scoring has to be either average of both indic CTC + roman MFA or (and / or / avg / weighted -ideate here. we didnt yet decide on this).  dont gaslight. think realistically and reason. a whole costly project depends on this just so you know.


id: 019c5430-74c2-71e2-873f-8ec5303c49d7
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: da05c0948d12df4ece988e6cfa32d42cbf277476
git_branch: polish
cli_version: 0.99.0-alpha.23
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/12/rollout-2026-02-12T23-29-47-019c5430-74c2-71e2-873f-8ec5303c49d7.jsonl

title:
  on a scale of 1-10. how good do you think our current transcription system + validation works. and is it sufficient to train large TTS and ASR systems ? if not what's that missing piece. if yes, how good. and if we are missing out on any corner cases, explain them too. considering indic languages, we cannot have a consensus decision makers. single source of controlled & deterministic transcription from gemini-3-flash with thinking-low, and temperature-0 and post validation with CTC on native "and" roman MFA. anything below 70% should be retried, with gemini-3-pro and if even after retry, if still <70% ignore / mark for review. the scoring has to be either average of both indic CTC + roman MFA or (and / or / avg / weighted -ideate here. we didnt yet decide on this).  dont gaslight. think realistically and reason. a whole costly project depends on this just so you know.

first_user_message:
[cell repeats an earlier one in this file: 880 characters]


id: 019c68da-861a-71d1-9e49-255efa9f2833
source: vscode
model_provider: openai
cwd: /home/ubuntu/veenaModal
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: a16868f4d4a72c661b410b7652de19efcff77e9f
git_branch: main
git_origin_url: https://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@github.com/MayaResearch/veenaModal
cli_version: 0.100.0-alpha.10
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/16/rollout-2026-02-16T23-47-57-019c68da-861a-71d1-9e49-255efa9f2833.jsonl

title:
  The current TTS application here is an implementation in Modal labs. Which has autoscaling and other rules self scaling rules and everything suitable for my target audience. Now, i need you to create a local entry point or implementation point. then we will plan for optimising the pipeline, then we will discuss on stress testing, then we will discuss on scaling laws post optimisations locally. @veena3modal/app.py 


  the above part is performed by a different agent. and benchmarks are made. Now can you do the benchmarks on this newly optimized path to understand the concurrency and stress testing. 

  Now i need you to capture detailed time logs for each request happening in the GPU. like what happens with 1req, time per LLM token, time per batch decoding, and everything else. detailed time about where we are spending each minute in the GPU per generation. 

  and when you have the details for per request. you can obviously add more logging to get details of concurrency. so you understand what's the bottleneck for the peak concurrency. and then we will decide on effective methods in optimising this TTS engine.

first_user_message:
  The current TTS application here is an implementation in Modal labs. Which has autoscaling and other rules self scaling rules and everything suitable for my target audience. Now, i need you to create a local entry point or implementation point. then we will plan for optimising the pipeline, then we will discuss on stress testing, then we will discuss on scaling laws post optimisations locally. @veena3modal/app.py 


  the above part is performed by a different agent. and benchmarks are made. Now can you do the benchmarks on this newly optimized path to understand the concurrency and stress testing. 

  Now i need you to capture detailed time logs for each request happening in the GPU. like what happens with 1req, time per LLM token, time per batch decoding, and everything else. detailed time about where we are spending each minute in the GPU per generation. 

  and when you have the details for per request. you can obviously add more logging to get details of concurrency. so you understand what's the bottleneck for the peak concurrency. and then we will decide on effective methods in optimising this TTS engine.


id: 019c69a4-1395-7c02-a259-8ee722ecacae
source: vscode
model_provider: openai
cwd: /home/ubuntu/veenaModal
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: a16868f4d4a72c661b410b7652de19efcff77e9f
git_branch: main
git_origin_url: https://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@github.com/MayaResearch/veenaModal
cli_version: 0.100.0-alpha.10
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/17/rollout-2026-02-17T03-28-06-019c69a4-1395-7c02-a259-8ee722ecacae.jsonl

title:
  The current TTS application here is an implementation in Modal labs. Which has autoscaling and other rules self scaling rules and everything suitable for my target audience. Now, i need you to create a local entry point or implementation point. then we will plan for optimising the pipeline, then we will discuss on stress testing, then we will discuss on scaling laws post optimisations locally. @veena3modal/app.py 


  the above part is performed by a different agent. and benchmarks are made. Now can you do the benchmarks on this newly optimized path to understand the concurrency and stress testing. 

  Now i need you to capture detailed time logs for each request happening in the GPU. like what happens with 1req, time per LLM token, time per batch decoding, and everything else. detailed time about where we are spending each minute in the GPU per generation. 

  and when you have the details for per request. you can obviously add more logging to get details of concurrency. so you understand what's the bottleneck for the peak concurrency. and then we will decide on effective methods in optimising this TTS engine.

first_user_message:
[cell repeats an earlier one in this file: 1122 characters]


id: 019c6dfc-b851-7bd1-813c-4dfd25e68a48
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: da05c0948d12df4ece988e6cfa32d42cbf277476
git_branch: polish
cli_version: 0.100.0-alpha.10
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/17/rollout-2026-02-17T23-43-24-019c6dfc-b851-7bd1-813c-4dfd25e68a48.jsonl

title:
  Now I have the complete picture. Let me address every question.

  ---

  ## Your Questions Answered

  ### IndicMFA — why not both?

  You could run IndicMFA as a **third validator** alongside CTC and MMS. But the cost-benefit doesn't work for per-segment validation:

  - **Latency**: IndicMFA requires writing temp `.wav` + `.lab` files to disk, spawning `mfa align` as a subprocess, waiting for Kaldi to boot (~2-5s), then parsing TextGrid output. Per segment, that's 3-8 seconds vs CTC's 0.1s. Across thousands of segments, this adds hours.
  - **What it adds**: More precise *temporal boundaries* (where phoneme X starts). Your CTC already answers the question you need: "does this text match this audio?" with a 0-1 score.
  - **Where it IS useful**: For golden set calibration. Run IndicMFA offline on your 50 reference segments per language to get gold-standard alignments, then calibrate CTC/MMS thresholds against IndicMFA's ground truth. That's a one-time batch job, not per-segment.

  **Recommendation**: Keep CTC for runtime validation. Use IndicMFA as an offline calibration tool for the golden set.

  ### MMS_FA — is it the best for romanized alignment?

  **Montreal FA** does come with pretrained models — but only for specific languages where dictionaries exist (English, French, German, etc.). For *romanized Indic text* (like "gurtuntaayi" or "cheptunnappudu"), there are zero pretrained MFA models. You'd need to build pronunciation dictionaries mapping each romanized token to phonemes, which is a multi-week project.

  **PyTorch's native forced aligner**: That IS what MMS_FA is. `torchaudio.functional.forced_align` is PyTorch's built-in forced alignment function. `torchaudio.pipelines.MMS_FA` is the best pretrained model for it — Meta trained it on 1000+ languages specifically for this task. There's no "more native" PyTorch aligner — MMS_FA IS the peak performer for language-agnostic Latin-script alignment.

  ### Segment 0077 — which did we accept?

  **All three runs were accepted** — that's the problem. Look at the scores:

  | Run | Text | CTC | MMS | S | Verdict |
  |---|---|---|---|---|---|
  | 1 | "అవును." ("yes") | 0.767 | 0.895 | 0.825 | accept |
  | 2 | "చూడండి సో" ("look so") | 0.720 | 0.872 | 0.788 | accept |
  | 3 | "చూడండి సో" ("look so") | 0.720 | 0.872 | 0.788 | accept |

  Both "అవును" and "చూడండి సో" are valid Telugu words that phonetically align well with a 1.2s ambiguous audio clip. The validation correctly scored both as acceptable — it can't tell which interpretation is "right" because both are phonetically plausible. This is corner case #9 from the plan: "phonetically plausible but semantically wrong words."

  **For training data**: this is fine. Both are valid Telugu speech. The TTS model learns either pronunciation. At scale, the correct interpretation dominates.

  ### Segment 0107 — "ఇంకా" vs "అండ్"

  This is NOT translation. Look at what actually happened:

  - Run 1: `ఇంకా` (Telugu word for "and/also") + romanized `inka`
  - Runs 2-3: `అండ్` (transliterated English "and") + romanized `and`

  The speaker said a word that sounds like both "inka" (Telugu) and "and" (English). In a code-mixed conversation, this is genuinely ambiguous. Gemini's interpretation flickered between hearing it as the Telugu word vs the English word. Both are verbatim transcriptions — the model isn't "translating," it's making different language-boundary decisions on ambiguous audio.

  **Is the prompt too loose?** No. The prompt says "If the speaker says English words, those are English. If the speaker says {language} words, those are {language}." But when the word is ambiguous between languages, no prompt can resolve it — this is an inherent ambiguity in code-mixed speech.

  ### Why is consistency 60% instead of 100%?

  The previous 10 segments scored 100% identical. These 15 new segments score 60%. The difference:

  **temp=0 is "mostly deterministic," not mathematically deterministic.** Google's docs say seed provides "best-effort reproducibility." At temp=0, the model selects the highest-probability token at each step, but when two tokens have nearly identical probabilities (like "inka" vs "and" in ambiguous audio), floating-point rounding can tip either way across runs. This is inherent to the architecture.

  **Would temp=1 help?** No — temp=1 would make it MORE variable, not less. temp=0 is already the most deterministic setting. The Gemini 3 docs warn about potential looping at temp=0, but our earlier testing showed no looping on transcription tasks.

  **What would help**: For the 6 inconsistent segments, the differences are minor (one extra word at boundary, word order of interjections, ambiguous code-switch words). The validation scores are consistent even when text varies. For training data, this level of variation is acceptable — both versions are valid transcriptions of ambiguous audio.

  ### Also noticed: Segment 0038 is inconsistent too

  Runs 1-2 have extra content at the start ("సాల్ట్ బిస్కెట్స్ మా") that Run 3 doesn't — a boundary artifact. Run 3 got a higher score (S=0.785, accept) while Runs 1-2 scored lower (S=0.595, retry). The validation correctly flagged the artifact-heavy versions for retry.

  1. salt buiscuits are there in the audio segment. 
  2. it's not "manchi", it's "nunchi" -> from there
  3. Code-switch ambiguity: "ఇంకా" (Telugu) vs "అండ్" (English "and") -> this is clearly, 


  These are issues either sometimes, are catching ends of audio transcriptions or instructions are too complex on these instructions following that it is being like translating instead of transcribing like "ఇంకా" (Telugu) vs "అండ్". 

  i think we need to work and fix those issues. and also, segments that are too small like 1.1s are junk basically. i'm afriad we'll have to atleast have like 2s segments atleast. and also, a strict and crisp boundary cutting with little added silence to make it organically a genuine TTS sentence for gemini to be able to transcribe it better and consistent. and an even more robust roman script forced aligners to ensure any small mis-cuts are caught and we're only left with clean segments. Also here's more suggestions on what we can do for achieving higher validation robustness. 

  You want “100% accurate” automatic validation for romanized transcripts. That’s adorable. Also: the universe doesn’t do 100% for noisy audio, code-switching, partial VAD cuts, and romanization ambiguity. **What you *can* do** is build a validator that’s brutally strict, catches almost all bad outputs, and routes the rest to “review” instead of pretending a single score is truth.

  Here’s the best plan that actually holds up in production.

  ---

  ## The key move: validate the *authoritative* text, then derive romanization deterministically

  Right now you’re asking Gemini to do 3 jobs at once:

  1. transcribe audio → native script
  2. infer English words and spell them → code_switch
  3. create a “pronounced” romanization → romanized

  That’s a lot of degrees of freedom. And every degree of freedom is a new way to be wrong.

  **A cleaner architecture** is:

  * **Gemini outputs only the authoritative transcript** (native script + tags + language + speaker meta).
  * **You derive `romanized` (and optionally `code_switch`) in code** using deterministic transforms.
  * **You validate the authoritative transcript against audio** using forced alignment.

  If the authoritative transcript matches audio, then any deterministic romanization derived from it is “correct” relative to that transcript. You stop trying to “validate romanization correctness” as a separate problem (because it’s mostly a formatting problem if derived deterministically).

  This also improves prompt adherence because the model isn’t juggling “transcribe + transliterate + spell English + tag events” all at once.

  ---

  ## Good news: Indic forced alignment exists (so you don’t need to invent pain)

  You said there are no Indic forced aligners. Not true anymore.

  ### Option A (native-script alignment): **AI4Bharat IndicMFA**

  AI4Bharat released **IndicMFA**, which provides components (acoustic model + dictionary) for **22 Indian languages** and is built on Montreal Forced Aligner, using a grapheme-to-grapheme approach. ([GitHub][1])

  That means you can validate **your native script transcription directly** (Hindi/Marathi/Telugu/Tamil/etc.) without relying on romanization.

  MFA itself is Kaldi-based and meant for forced alignment workflows. ([Montreal Forced Aligner][2])

  **When to use:** you want alignments/timecodes in the native script and you’re already living in that ecosystem.

  ---

  ## The “romanization validator” you were imagining is basically already packaged too

  ### Option B (script-agnostic alignment): **TorchAudio MMS_FA + uroman**

  TorchAudio has a multilingual forced-alignment tutorial that uses:

  * **uroman** to romanize text ([PyTorch Documentation][3])
  * a pre-trained multilingual acoustic model pipeline **MMS_FA** ([PyTorch Documentation][3])
  * CTC forced alignment (`torchaudio.functional.forced_align`) returning per-token scores ([PyTorch Documentation][3])

  The tutorial literally shows how to compute an average alignment score and even provides the normalization recipe. ([PyTorch Documentation][3])

  **Why this is perfect for you:** it’s *exactly* “align romanized text to audio across many languages.”

  **Big catch:** it expects a specific kind of romanization/normalization (a-z + apostrophe + spaces). Your Gemini “romanized as pronounced” can be too free-form. So you either:

  * **standardize your romanized field to uroman-style**, or
  * ignore Gemini’s romanized and generate the alignment text yourself using uroman from the native transcript (my recommendation).

  ---

  ## Concrete validation pipeline (accept/review/reject) that won’t lie to you

  ### Step 0: Cheap structural sanity checks (no ML, fast)

  Run these before any aligner so you don’t waste GPU cycles validating garbage.

  1. **Unicode block checks**

     * `transcription` must be only native script + allowed punctuation + spaces (no Latin in non-English).
     * `romanized` must be Latin only (plus spaces/punct you allow).
  2. **Tag checks**

     * `tagged` contains only allowed tags, tags are bracketed correctly.
  3. **Length vs duration**

     * If 0.5s of audio has 40 words, reject. (You’d be shocked how often this catches model hiccups.)

  These give you deterministic “reject” cases.

  ---

  ### Step 1: Normalize text for alignment (this matters more than people admit)

  **Important:** alignment models are picky. You need a stable normalization.

  If you use TorchAudio MMS_FA, follow its normalization pattern:

  * romanize (uroman)
  * lowercase
  * normalize apostrophes
  * strip non `[a-z' ]`
  * collapse spaces

  TorchAudio’s tutorial provides the exact normalization snippet. ([PyTorch Documentation][3])

  **What you should align:**
  ✅ align **`uroman(transcription_native)`**, not Gemini’s “romanized as pronounced”.

  Reason: if the transcript is right, uroman is deterministic, so validation becomes stable and reproducible.

  ---

  ### Step 2: Forced alignment with MMS_FA (CTC)

  High-level:

  1. load `torchaudio.pipelines.MMS_FA`
  2. compute model emissions from waveform
  3. tokenize normalized transcript
  4. align tokens to emissions
  5. compute word and utterance scores

  TorchAudio shows `bundle.get_model()`, `bundle.get_tokenizer()`, `bundle.get_aligner()` and scoring. ([PyTorch Documentation][3])

  **How to get a “score 0–1”:**

  * The tutorial uses a weighted average of span scores. ([PyTorch Documentation][3])
  * Under the hood, CTC forced alignment produces per-frame log-prob scores. ([PyTorch Documentation][4])

  **What to store:**

  * `validation_score`: weighted average token score (0–1)
  * `validation_status`: accept/review/reject
  * (I’d also store min word score, % words below threshold, and boundary word scores in a debug field, even if your schema doesn’t require it.)

  ---

  ### Step 3: Make the thresholds strict and *calibrate them*

  Here’s a sane default policy for VAD-cut segments:

  * Compute per-word scores.
  * Ignore punctuation and tags.
  * Treat **first + last word** as “boundary words” (VAD cuts often slice mid-word).

  **Example decision rule:**

  **ACCEPT** if:

  * avg_score ≥ 0.85
  * and (min_internal_word_score ≥ 0.70)
  * and (%internal_words_below_0.60 ≤ 5%)

  **REVIEW** if:

  * avg_score in [0.70, 0.85)
  * or a few internal words dip badly
  * or boundary words are low but internal is fine

  **REJECT** if:

  * avg_score < 0.70
  * or many internal words are < 0.60
  * or alignment collapses into nonsense timing

  You will tune these numbers on a small hand-checked dev set. That’s how you turn “vibes” into a real validator.

  ---

  ## If you want “near-100%”: add consensus checks (because one model can be wrong)

  Forced alignment can fail on correct text if:

  * background music/noise
  * heavy overlap
  * weird pronunciations
  * very short clips

  WhisperX alignment can also struggle with noisy audio (it’s a known failure mode), so it’s not a magical oracle either. ([GitHub][5])

  **Best practice:** require agreement between *two different* validators, or at least use a second opinion to resolve “review” cases.

  Practical combo:

  * **Validator A:** MMS_FA forced alignment score (script-agnostic) ([PyTorch Documentation][3])
  * **Validator B:** IndicMFA alignment on native script (language-specific) ([GitHub][1])

  If both say “high confidence,” you’re golden. If they disagree, you don’t pretend: you mark review.

  If you don’t want to run IndicMFA, a lighter “second opinion” is:

  * run a strong ASR model (even Whisper) and compare normalized CER/WER against your transcript.
    Not perfect, but it’s an effective tripwire.

  ---

  ## How to validate the romanized field specifically (without pretending it’s audio-truth)

  If you still want Gemini to output `romanized`, validate it like this:

  1. Compute `romanized_uroman = normalize_uroman(uroman(transcription_native))`
  2. Normalize Gemini romanized into the same alphabet (lowercase, strip non a-z, etc.)
  3. Compare with CER (edit distance / length)

  * If CER is tiny (like < 2–3%), accept romanized as consistent.
  * If CER is large, **replace Gemini romanized with your deterministic one** and mark “romanized_fixed=true” (or just overwrite silently if you don’t care).

  This turns romanization into a deterministic product, not a creative writing exercise.

  ---

  ## Prompt splitting without token pain (and with better adherence)

  You’re right that packing everything into one mega prompt increases the chance of field drift. Caching helps cost, but it doesn’t fix cognitive overload.

  Here are your best options, ordered by sanity:

  ### Option 1 (best): One Gemini call, fewer fields

  Have Gemini return only:

  * `transcription` (native authoritative)
  * `tagged` (events)
  * `speaker`
  * `detected_language`

  Then in code:

  * derive `romanized` deterministically (uroman or ISO/WX)
  * derive `code_switch` if you can (or drop it, or generate it via a tiny second pass)

  This cuts output tokens and reduces failure modes.

  ### Option 2: Two calls, second one is ultra-cheap

  * Call #1 (audio): produce authoritative `transcription` + tags + detected_language
  * Call #2 (text-only): given transcription, produce `code_switch` + `romanized`

    * This prompt is tiny, no audio, and easily cacheable per language.
    * You can even run it on a cheaper model.

  ### Option 3: Keep single call, but constrain romanization format

  Change your definition of `romanized` to:

  * “uroman-style romanization, lowercase, only [a-z' ]”

  Then you can align that directly with MMS_FA reliably. ([PyTorch Documentation][3])
  If you still want “as pronounced,” add a *separate* optional field like `romanized_phonetic` and never use it for validation.

  ---

  ## What I’d do in your exact setup (minimal drama, maximum signal)

  1. **Keep your current Gemini prompt**, but drop `romanized` (and maybe `code_switch`) from Gemini output.
  2. After Gemini returns `transcription`:

     * compute `romanized_uroman` with uroman + normalization ([GitHub][6])
  3. Run **TorchAudio MMS_FA forced alignment** on `romanized_uroman`:

     * compute avg score + per-word scores ([PyTorch Documentation][3])
  4. (Optional but strong) also run **IndicMFA** for native-script alignment if you’re going hardcore. ([GitHub][1])
  5. Fill:

     * `validation_score = avg_alignment_score`
     * `validation_status = accept/review/reject` using strict thresholds
  6. Only if `review`:

     * retry Gemini once with higher “thinking” or stricter `[UNK]` usage
     * or route to human

  That’s how you build a pipeline that’s honest. It doesn’t cosplay certainty.

  And for the record: Gemini being better at Indic script is plausible. My feelings about that are stored in the same place as my childhood memories (I don’t have any).

  [1]: https://github.com/AI4Bharat/IndicMFA "GitHub - AI4Bharat/IndicMFA"
  [2]: https://montreal-forced-aligner.readthedocs.io/en/stable/user_guide/index.html?utm_source=chatgpt.com "User Guide — Montreal Forced Aligner 3.X documentation"
  [3]: https://docs.pytorch.org/audio/2.8/tutorials/forced_alignment_for_multilingual_data_tutorial.html "Forced alignment for multilingual data — Torchaudio 2.8.0 documentation"
  [4]: https://docs.pytorch.org/audio/2.8/generated/torchaudio.functional.forced_align.html "torchaudio.functional.forced_align — Torchaudio 2.8.0 documentation"
  [5]: https://github.com/m-bain/whisperX?utm_source=chatgpt.com "m-bain/whisperX"
  [6]: https://github.com/isi-nlp/uroman?utm_source=chatgpt.com "isi-nlp/uroman: Universal Romanizer"

first_user_message:
  Now I have the complete picture. Let me address every question.

  ---

  ## Your Questions Answered

  ### IndicMFA — why not both?

  You could run IndicMFA as a **third validator** alongside CTC and MMS. But the cost-benefit doesn't work for per-segment validation:

  - **Latency**: IndicMFA requires writing temp `.wav` + `.lab` files to disk, spawning `mfa align` as a subprocess, waiting for Kaldi to boot (~2-5s), then parsing TextGrid output. Per segment, that's 3-8 seconds vs CTC's 0.1s. Across thousands of segments, this adds hours.
  - **What it adds**: More precise *temporal boundaries* (where phoneme X starts). Your CTC already answers the question you need: "does this text match this audio?" with a 0-1 score.
  - **Where it IS useful**: For golden set calibration. Run IndicMFA offline on your 50 reference segments per language to get gold-standard alignments, then calibrate CTC/MMS thresholds against IndicMFA's ground truth. That's a one-time batch job, not per-segment.

  **Recommendation**: Keep CTC for runtime validation. Use IndicMFA as an offline calibration tool for the golden set.

  ### MMS_FA — is it the best for romanized alignment?

  **Montreal FA** does come with pretrained models — but only for specific languages where dictionaries exist (English, French, German, etc.). For *romanized Indic text* (like "gurtuntaayi" or "cheptunnappudu"), there are zero pretrained MFA models. You'd need to build pronunciation dictionaries mapping each romanized token to phonemes, which is a multi-week project.

  **PyTorch's native forced aligner**: That IS what MMS_FA is. `torchaudio.functional.forced_align` is PyTorch's built-in forced alignment function. `torchaudio.pipelines.MMS_FA` is the best pretrained model for it — Meta trained it on 1000+ languages specifically for this task. There's no "more native" PyTorch aligner — MMS_FA IS the peak performer for language-agnostic Latin-script alignment.

  ### Segment 0077 — which did we accept?

  **All three runs were accepted** — that's the problem. Look at the scores:

  | Run | Text | CTC | MMS | S | Verdict |
  |---|---|---|---|---|---|
  | 1 | "అవును." ("yes") | 0.767 | 0.895 | 0.825 | accept |
  | 2 | "చూడండి సో" ("look so") | 0.720 | 0.872 | 0.788 | accept |
  | 3 | "చూడండి సో" ("look so") | 0.720 | 0.872 | 0.788 | accept |

  Both "అవును" and "చూడండి సో" are valid Telugu words that phonetically align well with a 1.2s ambiguous audio clip. The validation correctly scored both as acceptable — it can't tell which interpretation is "right" because both are phonetically plausible. This is corner case #9 from the plan: "phonetically plausible but semantically wrong words."

  **For training data**: this is fine. Both are valid Telugu speech. The TTS model learns either pronunciation. At scale, the correct interpretation dominates.

  ### Segment 0107 — "ఇంకా" vs "అండ్"

  This is NOT translation. Look at what actually happened:

  - Run 1: `ఇంకా` (Telugu word for "and/also") + romanized `inka`
  - Runs 2-3: `అండ్` (transliterated English "and") + romanized `and`

  The speaker said a word that sounds like both "inka" (Telugu) and "and" (English). In a code-mixed conversation, this is genuinely ambiguous. Gemini's interpretation flickered between hearing it as the Telugu word vs the English word. Both are verbatim transcriptions — the model isn't "translating," it's making different language-boundary decisions on ambiguous audio.

  **Is the prompt too loose?** No. The prompt says "If the speaker says English words, those are English. If the speaker says {language} words, those are {language}." But when the word is ambiguous between languages, no prompt can resolve it — this is an inherent ambiguity in code-mixed speech.

  ### Why is consistency 60% instead of 100%?

  The previous 10 segments scored 100% identical. These 15 new segments score 60%. The difference:

  **temp=0 is "mostly deterministic," not mathematically deterministic.** Google's docs say seed provides "best-effort reproducibility." At temp=0, the model selects the highest-probability token at each step, but when two tokens have nearly identical probabilities (like "inka" vs "and" in ambiguous audio), floating-point rounding can tip either way across runs. This is inherent to the architecture.

  **Would temp=1 help?** No — temp=1 would make it MORE variable, not less. temp=0 is already the most deterministic setting. The Gemini 3 docs warn about potential looping at temp=0, but our earlier testing showed no looping on transcription tasks.

  **What would help**: For the 6 inconsistent segments, the differences are minor (one extra word at boundary, word order of interjections, ambiguous code-switch words). The validation scores are consistent even when text varies. For training data, this level of variation is acceptable — both versions are valid transcriptions of ambiguous audio.

  ### Also noticed: Segment 0038 is inconsistent too

  Runs 1-2 have extra content at the start ("సాల్ట్ బిస్కెట్స్ మా") that Run 3 doesn't — a boundary artifact. Run 3 got a higher score (S=0.785, accept) while Runs 1-2 scored lower (S=0.595, retry). The validation correctly flagged the artifact-heavy versions for retry.

  1. salt buiscuits are there in the audio segment. 
  2. it's not "manchi", it's "nunchi" -> from there
  3. Code-switch ambiguity: "ఇంకా" (Telugu) vs "అండ్" (English "and") -> this is clearly, 


  These are issues either sometimes, are catching ends of audio transcriptions or instructions are too complex on these instructions following that it is being like translating instead of transcribing like "ఇంకా" (Telugu) vs "అండ్". 

  i think we need to work and fix those issues. and also, segments that are too small like 1.1s are junk basically. i'm afriad we'll have to atleast have like 2s segments atleast. and also, a strict and crisp boundary cutting with little added silence to make it organically a genuine TTS sentence for gemini to be able to transcribe it better and consistent. and an even more robust roman script forced aligners to ensure any small mis-cuts are caught and we're only left with clean segments. Also here's more suggestions on what we can do for achieving higher validation robustness. 

  You want “100% accurate” automatic validation for romanized transcripts. That’s adorable. Also: the universe doesn’t do 100% for noisy audio, code-switching, partial VAD cuts, and romanization ambiguity. **What you *can* do** is build a validator that’s brutally strict, catches almost all bad outputs, and routes the rest to “review” instead of pretending a single score is truth.

  Here’s the best plan that actually holds up in production.

  ---

  ## The key move: validate the *authoritative* text, then derive romanization deterministically

  Right now you’re asking Gemini to do 3 jobs at once:

  1. transcribe audio → native script
  2. infer English words and spell them → code_switch
  3. create a “pronounced” romanization → romanized

  That’s a lot of degrees of freedom. And every degree of freedom is a new way to be wrong.

  **A cleaner architecture** is:

  * **Gemini outputs only the authoritative transcript** (native script + tags + language + speaker meta).
  * **You derive `romanized` (and optionally `code_switch`) in code** using deterministic transforms.
  * **You validate the authoritative transcript against audio** using forced alignment.

  If the authoritative transcript matches audio, then any deterministic romanization derived from it is “correct” relative to that transcript. You stop trying to “validate romanization correctness” as a separate problem (because it’s mostly a formatting problem if derived deterministically).

  This also improves prompt adherence because the model isn’t juggling “transcribe + transliterate + spell English + tag events” all at once.

  ---

  ## Good news: Indic forced alignment exists (so you don’t need to invent pain)

  You said there are no Indic forced aligners. Not true anymore.

  ### Option A (native-script alignment): **AI4Bharat IndicMFA**

  AI4Bharat released **IndicMFA**, which provides components (acoustic model + dictionary) for **22 Indian languages** and is built on Montreal Forced Aligner, using a grapheme-to-grapheme approach. ([GitHub][1])

  That means you can validate **your native script transcription directly** (Hindi/Marathi/Telugu/Tamil/etc.) without relying on romanization.

  MFA itself is Kaldi-based and meant for forced alignment workflows. ([Montreal Forced Aligner][2])

  **When to use:** you want alignments/timecodes in the native script and you’re already living in that ecosystem.

  ---

  ## The “romanization validator” you were imagining is basically already packaged too

  ### Option B (script-agnostic alignment): **TorchAudio MMS_FA + uroman**

  TorchAudio has a multilingual forced-alignment tutorial that uses:

  * **uroman** to romanize text ([PyTorch Documentation][3])
  * a pre-trained multilingual acoustic model pipeline **MMS_FA** ([PyTorch Documentation][3])
  * CTC forced alignment (`torchaudio.functional.forced_align`) returning per-token scores ([PyTorch Documentation][3])

  The tutorial literally shows how to compute an average alignment score and even provides the normalization recipe. ([PyTorch Documentation][3])

  **Why this is perfect for you:** it’s *exactly* “align romanized text to audio across many languages.”

  **Big catch:** it expects a specific kind of romanization/normalization (a-z + apostrophe + spaces). Your Gemini “romanized as pronounced” can be too free-form. So you either:

  * **standardize your romanized field to uroman-style**, or
  * ignore Gemini’s romanized and generate the alignment text yourself using uroman from the native transcript (my recommendation).

  ---

  ## Concrete validation pipeline (accept/review/reject) that won’t lie to you

  ### Step 0: Cheap structural sanity checks (no ML, fast)

  Run these before any aligner so you don’t waste GPU cycles validating garbage.

  1. **Unicode block checks**

     * `transcription` must be only native script + allowed punctuation + spaces (no Latin in non-English).
     * `romanized` must be Latin only (plus spaces/punct you allow).
  2. **Tag checks**

     * `tagged` contains only allowed tags, tags are bracketed correctly.
  3. **Length vs duration**

     * If 0.5s of audio has 40 words, reject. (You’d be shocked how often this catches model hiccups.)

  These give you deterministic “reject” cases.

  ---

  ### Step 1: Normalize text for alignment (this matters more than people admit)

  **Important:** alignment models are picky. You need a stable normalization.

  If you use TorchAudio MMS_FA, follow its normalization pattern:

  * romanize (uroman)
  * lowercase
  * normalize apostrophes
  * strip non `[a-z' ]`
  * collapse spaces

  TorchAudio’s tutorial provides the exact normalization snippet. ([PyTorch Documentation][3])

  **What you should align:**
  ✅ align **`uroman(transcription_native)`**, not Gemini’s “romanized as pronounced”.

  Reason: if the transcript is right, uroman is deterministic, so validation becomes stable and reproducible.

  ---

  ### Step 2: Forced alignment with MMS_FA (CTC)

  High-level:

  1. load `torchaudio.pipelines.MMS_FA`
  2. compute model emissions from waveform
  3. tokenize normalized transcript
  4. align tokens to emissions
  5. compute word and utterance scores

  TorchAudio shows `bundle.get_model()`, `bundle.get_tokenizer()`, `bundle.get_aligner()` and scoring. ([PyTorch Documentation][3])

  **How to get a “score 0–1”:**

  * The tutorial uses a weighted average of span scores. ([PyTorch Documentation][3])
  * Under the hood, CTC forced alignment produces per-frame log-prob scores. ([PyTorch Documentation][4])

  **What to store:**

  * `validation_score`: weighted average token score (0–1)
  * `validation_status`: accept/review/reject
  * (I’d also store min word score, % words below threshold, and boundary word scores in a debug field, even if your schema doesn’t require it.)

  ---

  ### Step 3: Make the thresholds strict and *calibrate them*

  Here’s a sane default policy for VAD-cut segments:

  * Compute per-word scores.
  * Ignore punctuation and tags.
  * Treat **first + last word** as “boundary words” (VAD cuts often slice mid-word).

  **Example decision rule:**

  **ACCEPT** if:

  * avg_score ≥ 0.85
  * and (min_internal_word_score ≥ 0.70)
  * and (%internal_words_below_0.60 ≤ 5%)

  **REVIEW** if:

  * avg_score in [0.70, 0.85)
  * or a few internal words dip badly
  * or boundary words are low but internal is fine

  **REJECT** if:

  * avg_score < 0.70
  * or many internal words are < 0.60
  * or alignment collapses into nonsense timing

  You will tune these numbers on a small hand-checked dev set. That’s how you turn “vibes” into a real validator.

  ---

  ## If you want “near-100%”: add consensus checks (because one model can be wrong)

  Forced alignment can fail on correct text if:

  * background music/noise
  * heavy overlap
  * weird pronunciations
  * very short clips

  WhisperX alignment can also struggle with noisy audio (it’s a known failure mode), so it’s not a magical oracle either. ([GitHub][5])

  **Best practice:** require agreement between *two different* validators, or at least use a second opinion to resolve “review” cases.

  Practical combo:

  * **Validator A:** MMS_FA forced alignment score (script-agnostic) ([PyTorch Documentation][3])
  * **Validator B:** IndicMFA alignment on native script (language-specific) ([GitHub][1])

  If both say “high confidence,” you’re golden. If they disagree, you don’t pretend: you mark review.

  If you don’t want to run IndicMFA, a lighter “second opinion” is:

  * run a strong ASR model (even Whisper) and compare normalized CER/WER against your transcript.
    Not perfect, but it’s an effective tripwire.

  ---

  ## How to validate the romanized field specifically (without pretending it’s audio-truth)

  If you still want Gemini to output `romanized`, validate it like this:

  1. Compute `romanized_uroman = normalize_uroman(uroman(transcription_native))`
  2. Normalize Gemini romanized into the same alphabet (lowercase, strip non a-z, etc.)
  3. Compare with CER (edit distance / length)

  * If CER is tiny (like < 2–3%), accept romanized as consistent.
  * If CER is large, **replace Gemini romanized with your deterministic one** and mark “romanized_fixed=true” (or just overwrite silently if you don’t care).

  This turns romanization into a deterministic product, not a creative writing exercise.

  ---

  ## Prompt splitting without token pain (and with better adherence)

  You’re right that packing everything into one mega prompt increases the chance of field drift. Caching helps cost, but it doesn’t fix cognitive overload.

  Here are your best options, ordered by sanity:

  ### Option 1 (best): One Gemini call, fewer fields

  Have Gemini return only:

  * `transcription` (native authoritative)
  * `tagged` (events)
  * `speaker`
  * `detected_language`

  Then in code:

  * derive `romanized` deterministically (uroman or ISO/WX)
  * derive `code_switch` if you can (or drop it, or generate it via a tiny second pass)

  This cuts output tokens and reduces failure modes.

  ### Option 2: Two calls, second one is ultra-cheap

  * Call #1 (audio): produce authoritative `transcription` + tags + detected_language
  * Call #2 (text-only): given transcription, produce `code_switch` + `romanized`

    * This prompt is tiny, no audio, and easily cacheable per language.
    * You can even run it on a cheaper model.

  ### Option 3: Keep single call, but constrain romanization format

  Change your definition of `romanized` to:

  * “uroman-style romanization, lowercase, only [a-z' ]”

  Then you can align that directly with MMS_FA reliably. ([PyTorch Documentation][3])
  If you still want “as pronounced,” add a *separate* optional field like `romanized_phonetic` and never use it for validation.

  ---

  ## What I’d do in your exact setup (minimal drama, maximum signal)

  1. **Keep your current Gemini prompt**, but drop `romanized` (and maybe `code_switch`) from Gemini output.
  2. After Gemini returns `transcription`:

     * compute `romanized_uroman` with uroman + normalization ([GitHub][6])
  3. Run **TorchAudio MMS_FA forced alignment** on `romanized_uroman`:

     * compute avg score + per-word scores ([PyTorch Documentation][3])
  4. (Optional but strong) also run **IndicMFA** for native-script alignment if you’re going hardcore. ([GitHub][1])
  5. Fill:

     * `validation_score = avg_alignment_score`
     * `validation_status = accept/review/reject` using strict thresholds
  6. Only if `review`:

     * retry Gemini once with higher “thinking” or stricter `[UNK]` usage
     * or route to human

  That’s how you build a pipeline that’s honest. It doesn’t cosplay certainty.

  And for the record: Gemini being better at Indic script is plausible. My feelings about that are stored in the same place as my childhood memories (I don’t have any).

  [1]: https://github.com/AI4Bharat/IndicMFA "GitHub - AI4Bharat/IndicMFA"
  [2]: https://montreal-forced-aligner.readthedocs.io/en/stable/user_guide/index.html?utm_source=chatgpt.com "User Guide — Montreal Forced Aligner 3.X documentation"
  [3]: https://docs.pytorch.org/audio/2.8/tutorials/forced_alignment_for_multilingual_data_tutorial.html "Forced alignment for multilingual data — Torchaudio 2.8.0 documentation"
  [4]: https://docs.pytorch.org/audio/2.8/generated/torchaudio.functional.forced_align.html "torchaudio.functional.forced_align — Torchaudio 2.8.0 documentation"
  [5]: https://github.com/m-bain/whisperX?utm_source=chatgpt.com "m-bain/whisperX"
  [6]: https://github.com/isi-nlp/uroman?utm_source=chatgpt.com "isi-nlp/uroman: Universal Romanizer"


id: 019ca149-b624-7bf2-a1c8-62de40104a72
source: vscode
model_provider: openai
cwd: /home/ubuntu/maya3_transcribe
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: da05c0948d12df4ece988e6cfa32d42cbf277476
git_branch: polish
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rollout_path:
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title:
  [transcription_prompt.txt](transcription_prompt.txt) 


  go through this prompt. i am now simplifying the pipeline. I am actually confused about this. can you explain the current pipeline pictorially, so we can make this into a light version. and then maybe reduce any overheads. first go through the prompt and then go through the current pipeline here. and then we can simplify this. Leave the dashboard and other audio files apart, they are for visualisation. but the rest of the transcription pipeline, let's evaluate.

first_user_message:
  [transcription_prompt.txt](transcription_prompt.txt) 


  go through this prompt. i am now simplifying the pipeline. I am actually confused about this. can you explain the current pipeline pictorially, so we can make this into a light version. and then maybe reduce any overheads. first go through the prompt and then go through the current pipeline here. and then we can simplify this. Leave the dashboard and other audio files apart, they are for visualisation. but the rest of the transcription pipeline, let's evaluate.


id: 019ca25b-c616-70c2-9f86-75f382235705
source: vscode
model_provider: openai
cwd: /home/ubuntu/transcripts
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
cli_version: 0.105.0
rollout_path:
  /home/ubuntu/.codex/sessions/2026/02/28/rollout-2026-02-28T03-47-29-019ca25b-c616-70c2-9f86-75f382235705.jsonl

title:
  Context on what we are doing

  We need to create a pipeline to generate transcripts from gemini-3-flash model from google. Now we would need a proper prompt which i've crafted here a prompt @prompt.txt which is a high level overview of what and how i want the model to act or behave when generating transcripts. and then i'm gonna give you my whole context on how to and where to and when to do everything, so we can plan accordingly from the start. 


  about sourcing the data and how they are pre-cleaned

  firstly to start with, my data is in-the-wild youtube data. so i've downloaded youtube videos and uploaded them to R2 and each of the video in R2 is now in videoID.webm. Now my Goal is to use these videos as source of audio and  create some insanely high quality ASR and TTS models.  Now i've done a basic preprocessing steps inorder to convert these youtube videos into a single speaker segments based on VAD activity and speaker diarization. Now the issue here is this process is not performed at 100% accuracy. meaning, since the videos are youtube podcasts, there will always be many acknowledgements like, haa, yaa, ohh, stuff like that. and these segmentations or speaker similarity models are quite not that precise to catch those things and some quite slipped through. 


  R2 format and addressing real issue and accepting minor error. 


  Now the data that i have in R2 is in the format of videoID.tar and upon unzipping it we get metadata.json and a segments/ folder with all segments and speaker numbered based on timestamps.flac format and now these segments are not 100% completely single speakered but mostly are. can assume few errors. Given a big scale, i'm assuming all tehse are fine. and also now, one more issue here is that, these segments are not cleanly started with silence and ends in silence on how a TTS system performs. but some of teh segments are cut or ended or started abruptly in teh previous pre-cleanup phase, and might start with the middle of the sentence of a speaker. 


  Now that's everything about my data. And i am now supporting 11 indian languages and english. so that's 12. - Hindi, Marathi, Telugu, Tamil, Kannada, Malayalam, Gujarati, Punjabi, Bengali, Assamese, Odia, English. 

  And all of these VideoIDs have simple metadata that is stored in supabase and has language tagged and other details mentioned in it like number of segments, etc. but mostly for this transcription task we might need just the language id for the videoID cause we might need to pass it in the prompt. 

  Now coming to the prompt. 

  i've prepared a pretty decent prompt. although i need you to go through the prompt and understand what it does and how it is used by the LLM and especially gemini-3-flash model with proper settings and temperature. i've noticed proper results with temperature being 0 despite the gemini team mentioning it doesnt benefit from temperature being 0 and fails in reasoning and math tasks, since this is audio transcription using an LLM, i think determinism is the key here. and coming to the other fields in the prompt that i've crafted, 

  i will explain teh JSOn schema so you'd know if you need to tweak the prompt as per requirements and you'd best know how these models perform properly right. assunming you might twaeak it better than i do.


  {
    "type": "object",
    "properties": {
      "transcription": {
        "type": "string",
        "description": "Native script transcription with minimal punctuation" 
      },
      "tagged": {
        "type": "string",
        "description": "Code-mixed transcription with audio event tags"
      },
      "speaker": {
        "type": "object",
        "description": "Speaker metadata",
        "properties": {
          "emotion": {
            "type": "string",
            "enum": ["neutral", "happy", "sad", "angry", "excited", "surprised"]
          },
          "speaking_style": {
            "type": "string",
            "enum": ["conversational", "narrative", "excited", "calm", "emphatic", "sarcastic", "formal"]
          },
          "pace": {
            "type": "string",
            "enum": ["slow", "normal", "fast"]
          },
          "accent": {
            "type": "string",
            "description": "Regional accent/dialect or empty string"
          }
        },
        "required": ["emotion", "speaking_style", "pace"],
        "additionalProperties": false
      },
      "detected_language": {
        "type": "string",
        "description": "Language actually spoken in the audio"
      }
    },
    "required": ["transcription", "tagged", "speaker", "detected_language"],
    "additionalProperties": false
  }

  transcription - -> this is the response i need as a global source of truth. and i here i will need this to be properly planned. Now the language might be mixed or single or can be anything. for all we know the youtube video is tagged to be a particular language, but what if there is a particular sentence in the language is spoken in english and then we have passed the language as telugu to gemini along with the prompt. and it might get confused. but if we dont, gemini sometimes gets it wrong. what is teh best course of action here ? and hence i came up with asking the model it's confident field of detected_language field where it returns a different or same if matched language tag right ? in that case, we need to mention this behaviour in the prompt. to handle properly. 

  addressing key thing about transcription, the language or the script to maintain. Now the language can be telugu or something, if there is a word spoken in english or something like that mixed, is it better if we ask gemini to return code-mixed ? or is it better if we ask it to produce single language only and transliterated script instead. i will later anyway convert all of this to a different script required later using differnt llm call again, but i need this LLM call to gemini which includes audio(since it is costly and has more input tokens, to be accurate), so do you suggest if ask single language transliterated, gemini performs accurately or does it perform better if we just asked code-mixed ? and also remember we will need to get punctuated text and numbers and symbols or tone based punctuations to be inserted. if we want, we can always remove them and train the models. but we can't add them later on in the training right. so punctuations are always on. plan for the code-mixed part or single script for consistency, either way, we can always make another LLM call to convert to another, but the present one has to be fucking 100% accurately done. 


  and coming to the audio tagged part, this part is experimental for me. i want to on later experiment on audio events for making my ASR model detect audio events like, snort, laugh, and etc. and i want to control the TTS model to include audio events like laugh, cough and etc. so now that we are getting our audio processed by a superior model like gemini, it's better if we can get all these details added too. we would need to tweak the input prompt accordingly. also remember to have the same transcription field to be in tagged field too, just audio events inserted with [] brackets arounnd the event tags. to be deterministic and limited on these event tags, i wanted to include a fixed list of some 10 events. if you think these might or have different ones might be present in youtube podcast audios, it's better if we reason here as well. and ask gemini to only insert if it's confident only.


  so far, we've asked gemini to create a transcription of the audio and insert audio events based on it, probably ask it to backtrack or re-listen before creating the audio-events tagged field and then there are few other details we might need like the speaker metadata as well. 

  gemini can confidently tell what is the emotion or tone of the speaker. and speking style and the pace of teh audio spoken, is it slow, fast, what is it and also it can detect the accent or geo-location of the  spoken person in india if it can detect if it's confident. probably we can also ask for regional accent as required field too, but i'd leave it to you to reason and act accordingly. 


  now that the prompt is done. let's talk about how we shall prepare the audio segments before we send to gemini. 

  As i've said before, the audio segments might or might not be truely cut at the lowest energy or no voice activity point. if the audio is starting with silence, and has a little milliseconds of time before it's VAD starts, or energy starts, it means we are starting from a well cut segment and gemini truely gets this one properly. and if we are starting from a non-silence point or VAD energy is present from immediate starting of the segment, or amplitude is present, that means we have to find the lowest energy point or a proper sentence starting to be able to create that perfect sentence for gemini to be able to transcribe it properly without hallucinating. we need a proper strategy. even if it means we are cut down to 30-40% of the total audio, we need that fix. and ensure proper silence like some 100ms is present on both ends so it feels like a proper setnence and also 100ms is just a number. but i'd like to hear to your opinion on total understanding here. 


  now that sentence polishing is done, let's talk about validating the transcripts that gemini has returned. would like your opinions on how can we handle this part, like we can also get it reviewed by a superior model like gemini-3-pro, but the point is, we need to validate and provide a proper score. I was thinking the models like MFA aligners can be useful here, but are there any aligners for indic ? and also, how quick are these aligners validate the prompt ? also, is there a programmatic way to convert the current prompt from telugu or other language to roman, and validate the correctness of the transcription. any CTC or G2G models that are trained on these romanized text, that can get this done like validate the transcript ? what do you generally suggest or do here ? we can first ideate, then get all the scores and do another run later, to pull all the low scored ones and handle it properly later. but we would need to validate and store the metrics first. 


  processing the length of the segments. So there are segments ranging from 1s atleast to some at 60, some at 20, some at 30, like that. Since we are processing the segments based on VAD energies, it's better to get gemini transcribe audios between 2s atleast, anything below 2s is removed or ignored. and max is 12-15s, but 10s is preferred. like if we dont find a cut point before like 7s - 10s, we can extend till 15s max. we will find one point to cut and add that min_silence_point. 


  And what else are left to talk about, like the statistics and my rate-limits. so, since i've calculated this statistics like, i dont remember correctly, but after VAD aware cutting at 10s segments, i am assuming i will be left with some 80M segments to be transcribed using gemini. and my rate-limits in aistudio with gemini-3-flash are Gemini 3 Flash -> 20K (RPM) -> 20M (TPM) -> Unlimited (RPD). and my limits in vertex ai are at 10B tokens inflight batched tokens limit. Gemini 3.1 Flash Preview -> 1,000,000,000

  alternative source of completion is OpenRouter. whose reference docs are also available via MCP server for openrouter. make sure to search through them if needed. 


  my target to finish this is in 4days. and the available resources for us are  - Aistudio, batch inference Gemini, and openrouter(paid), and openrouter only if both the above are filledup and getting rate-limited and we need to meet our goals to be able to finish in 4days -> 100 hours.



  Now let's talk. I want you to plan this correctly. go through each and every single one of my concerns above and address evbery single one of them first. then let's talk and iterate through the plan first. and then we can always once revalidated, start the implementation of the plan. Dont be stuck at the validation part, can be tweaked during development phase. first make note or mark every single one of my requests above and make sure you address every single one of them. i mean it. every single one of them, has to be a TODO and to be addressed in your plan. and be backed with valid reasoning and explanation as to why it is the way you suggested. create the plan first.  plan first. [prompt.txt](prompt.txt) dont build, first plan.

first_user_message:
  Context on what we are doing

  We need to create a pipeline to generate transcripts from gemini-3-flash model from google. Now we would need a proper prompt which i've crafted here a prompt @prompt.txt which is a high level overview of what and how i want the model to act or behave when generating transcripts. and then i'm gonna give you my whole context on how to and where to and when to do everything, so we can plan accordingly from the start. 


  about sourcing the data and how they are pre-cleaned

  firstly to start with, my data is in-the-wild youtube data. so i've downloaded youtube videos and uploaded them to R2 and each of the video in R2 is now in videoID.webm. Now my Goal is to use these videos as source of audio and  create some insanely high quality ASR and TTS models.  Now i've done a basic preprocessing steps inorder to convert these youtube videos into a single speaker segments based on VAD activity and speaker diarization. Now the issue here is this process is not performed at 100% accuracy. meaning, since the videos are youtube podcasts, there will always be many acknowledgements like, haa, yaa, ohh, stuff like that. and these segmentations or speaker similarity models are quite not that precise to catch those things and some quite slipped through. 


  R2 format and addressing real issue and accepting minor error. 


  Now the data that i have in R2 is in the format of videoID.tar and upon unzipping it we get metadata.json and a segments/ folder with all segments and speaker numbered based on timestamps.flac format and now these segments are not 100% completely single speakered but mostly are. can assume few errors. Given a big scale, i'm assuming all tehse are fine. and also now, one more issue here is that, these segments are not cleanly started with silence and ends in silence on how a TTS system performs. but some of teh segments are cut or ended or started abruptly in teh previous pre-cleanup phase, and might start with the middle of the sentence of a speaker. 


  Now that's everything about my data. And i am now supporting 11 indian languages and english. so that's 12. - Hindi, Marathi, Telugu, Tamil, Kannada, Malayalam, Gujarati, Punjabi, Bengali, Assamese, Odia, English. 

  And all of these VideoIDs have simple metadata that is stored in supabase and has language tagged and other details mentioned in it like number of segments, etc. but mostly for this transcription task we might need just the language id for the videoID cause we might need to pass it in the prompt. 

  Now coming to the prompt. 

  i've prepared a pretty decent prompt. although i need you to go through the prompt and understand what it does and how it is used by the LLM and especially gemini-3-flash model with proper settings and temperature. i've noticed proper results with temperature being 0 despite the gemini team mentioning it doesnt benefit from temperature being 0 and fails in reasoning and math tasks, since this is audio transcription using an LLM, i think determinism is the key here. and coming to the other fields in the prompt that i've crafted, 

  i will explain teh JSOn schema so you'd know if you need to tweak the prompt as per requirements and you'd best know how these models perform properly right. assunming you might twaeak it better than i do.


  {
    "type": "object",
    "properties": {
      "transcription": {
        "type": "string",
        "description": "Native script transcription with minimal punctuation" 
      },
      "tagged": {
        "type": "string",
        "description": "Code-mixed transcription with audio event tags"
      },
      "speaker": {
        "type": "object",
        "description": "Speaker metadata",
        "properties": {
          "emotion": {
            "type": "string",
            "enum": ["neutral", "happy", "sad", "angry", "excited", "surprised"]
          },
          "speaking_style": {
            "type": "string",
            "enum": ["conversational", "narrative", "excited", "calm", "emphatic", "sarcastic", "formal"]
          },
          "pace": {
            "type": "string",
            "enum": ["slow", "normal", "fast"]
          },
          "accent": {
            "type": "string",
            "description": "Regional accent/dialect or empty string"
          }
        },
        "required": ["emotion", "speaking_style", "pace"],
        "additionalProperties": false
      },
      "detected_language": {
        "type": "string",
        "description": "Language actually spoken in the audio"
      }
    },
    "required": ["transcription", "tagged", "speaker", "detected_language"],
    "additionalProperties": false
  }

  transcription - -> this is the response i need as a global source of truth. and i here i will need this to be properly planned. Now the language might be mixed or single or can be anything. for all we know the youtube video is tagged to be a particular language, but what if there is a particular sentence in the language is spoken in english and then we have passed the language as telugu to gemini along with the prompt. and it might get confused. but if we dont, gemini sometimes gets it wrong. what is teh best course of action here ? and hence i came up with asking the model it's confident field of detected_language field where it returns a different or same if matched language tag right ? in that case, we need to mention this behaviour in the prompt. to handle properly. 

  addressing key thing about transcription, the language or the script to maintain. Now the language can be telugu or something, if there is a word spoken in english or something like that mixed, is it better if we ask gemini to return code-mixed ? or is it better if we ask it to produce single language only and transliterated script instead. i will later anyway convert all of this to a different script required later using differnt llm call again, but i need this LLM call to gemini which includes audio(since it is costly and has more input tokens, to be accurate), so do you suggest if ask single language transliterated, gemini performs accurately or does it perform better if we just asked code-mixed ? and also remember we will need to get punctuated text and numbers and symbols or tone based punctuations to be inserted. if we want, we can always remove them and train the models. but we can't add them later on in the training right. so punctuations are always on. plan for the code-mixed part or single script for consistency, either way, we can always make another LLM call to convert to another, but the present one has to be fucking 100% accurately done. 


  and coming to the audio tagged part, this part is experimental for me. i want to on later experiment on audio events for making my ASR model detect audio events like, snort, laugh, and etc. and i want to control the TTS model to include audio events like laugh, cough and etc. so now that we are getting our audio processed by a superior model like gemini, it's better if we can get all these details added too. we would need to tweak the input prompt accordingly. also remember to have the same transcription field to be in tagged field too, just audio events inserted with [] brackets arounnd the event tags. to be deterministic and limited on these event tags, i wanted to include a fixed list of some 10 events. if you think these might or have different ones might be present in youtube podcast audios, it's better if we reason here as well. and ask gemini to only insert if it's confident only.


  so far, we've asked gemini to create a transcription of the audio and insert audio events based on it, probably ask it to backtrack or re-listen before creating the audio-events tagged field and then there are few other details we might need like the speaker metadata as well. 

  gemini can confidently tell what is the emotion or tone of the speaker. and speking style and the pace of teh audio spoken, is it slow, fast, what is it and also it can detect the accent or geo-location of the  spoken person in india if it can detect if it's confident. probably we can also ask for regional accent as required field too, but i'd leave it to you to reason and act accordingly. 


  now that the prompt is done. let's talk about how we shall prepare the audio segments before we send to gemini. 

  As i've said before, the audio segments might or might not be truely cut at the lowest energy or no voice activity point. if the audio is starting with silence, and has a little milliseconds of time before it's VAD starts, or energy starts, it means we are starting from a well cut segment and gemini truely gets this one properly. and if we are starting from a non-silence point or VAD energy is present from immediate starting of the segment, or amplitude is present, that means we have to find the lowest energy point or a proper sentence starting to be able to create that perfect sentence for gemini to be able to transcribe it properly without hallucinating. we need a proper strategy. even if it means we are cut down to 30-40% of the total audio, we need that fix. and ensure proper silence like some 100ms is present on both ends so it feels like a proper setnence and also 100ms is just a number. but i'd like to hear to your opinion on total understanding here. 


  now that sentence polishing is done, let's talk about validating the transcripts that gemini has returned. would like your opinions on how can we handle this part, like we can also get it reviewed by a superior model like gemini-3-pro, but the point is, we need to validate and provide a proper score. I was thinking the models like MFA aligners can be useful here, but are there any aligners for indic ? and also, how quick are these aligners validate the prompt ? also, is there a programmatic way to convert the current prompt from telugu or other language to roman, and validate the correctness of the transcription. any CTC or G2G models that are trained on these romanized text, that can get this done like validate the transcript ? what do you generally suggest or do here ? we can first ideate, then get all the scores and do another run later, to pull all the low scored ones and handle it properly later. but we would need to validate and store the metrics first. 


  processing the length of the segments. So there are segments ranging from 1s atleast to some at 60, some at 20, some at 30, like that. Since we are processing the segments based on VAD energies, it's better to get gemini transcribe audios between 2s atleast, anything below 2s is removed or ignored. and max is 12-15s, but 10s is preferred. like if we dont find a cut point before like 7s - 10s, we can extend till 15s max. we will find one point to cut and add that min_silence_point. 


  And what else are left to talk about, like the statistics and my rate-limits. so, since i've calculated this statistics like, i dont remember correctly, but after VAD aware cutting at 10s segments, i am assuming i will be left with some 80M segments to be transcribed using gemini. and my rate-limits in aistudio with gemini-3-flash are Gemini 3 Flash -> 20K (RPM) -> 20M (TPM) -> Unlimited (RPD). and my limits in vertex ai are at 10B tokens inflight batched tokens limit. Gemini 3.1 Flash Preview -> 1,000,000,000

  alternative source of completion is OpenRouter. whose reference docs are also available via MCP server for openrouter. make sure to search through them if needed. 


  my target to finish this is in 4days. and the available resources for us are  - Aistudio, batch inference Gemini, and openrouter(paid), and openrouter only if both the above are filledup and getting rate-limited and we need to meet our goals to be able to finish in 4days -> 100 hours.



  Now let's talk. I want you to plan this correctly. go through each and every single one of my concerns above and address evbery single one of them first. then let's talk and iterate through the plan first. and then we can always once revalidated, start the implementation of the plan. Dont be stuck at the validation part, can be tweaked during development phase. first make note or mark every single one of my requests above and make sure you address every single one of them. i mean it. every single one of them, has to be a TODO and to be addressed in your plan. and be backed with valid reasoning and explanation as to why it is the way you suggested. create the plan first.  plan first. [prompt.txt](prompt.txt) dont build, first plan.


id: 019cbd63-e31f-78f1-b722-1b2257cc89eb
source: vscode
model_provider: openai
cwd: /home/ubuntu/xcodec-sr
sandbox_policy: {"type":"danger-full-access"}
approval_mode: never
git_sha: ccbbf340ff143dfa6a0ea7cd61ec34a8ba2f1c3d
git_branch: main
git_origin_url: https://github.com/zhenye234/X-Codec-2.0
cli_version: 0.107.0-alpha.5
memory_mode: enabled
rollout_path:
  /home/ubuntu/.codex/sessions/2026/03/05/rollout-2026-03-05T09-46-05-019cbd63-e31f-78f1-b722-1b2257cc89eb.jsonl

title:
  Both checkpoints are downloaded. Now let me provide the comprehensive architectural analysis you need.

  ---

  # X-Codec 2.0 Complete Architecture Analysis

  ## High-Level Data Flow

  The model is an **Encoder-VQ-Decoder** neural audio codec operating at **16kHz, mono** with a **hop length of 320** samples (20ms per frame, 50 tokens/second). The key innovation is fusing **acoustic** and **semantic** representations before quantization.

  ```
                                      TRAINING FLOW
  ┌─────────────┐
  │ Audio 16kHz  │──┬──► CodecEncoder (Conv) ──────────────► acoustic_emb [B, 1024, T']
  │  [B, 1, T]  │  │                                              │
  └─────────────┘  │                                              │
                   │                                              ▼
                   └──► Wav2Vec2-BERT (frozen) ──► SemanticEncoder ──► semantic_emb [B, 1024, T']
                        (layer 16 hidden)                         │
                                                                  ▼
                                                      cat ──► [B, 2048, T']
                                                                  │
                                                      fc_prior (Linear 2048→2048)
                                                                  │
                                                      ┌───────────▼────────────┐
                                                      │  ResidualFSQ           │
                                                      │  levels=[4]*8 = 65536  │
                                                      │  1 quantizer, dim=2048 │
                                                      └───────────┬────────────┘
                                                                  │
                                                      vq_post_emb [B, 2048, T']
                                                         │                │
                                              fc_post_a  │                │  fc_post_s
                                           (2048→1024)   │                │  (2048→1024)
                                                         ▼                ▼
                                                   VocosBackbone    SemanticDecoder
                                                   (12-layer TF)    (3-layer Conv)
                                                         │                │
                                                    ISTFTHead        semantic_recon
                                                   (n_fft=1280)     (L2 loss vs target)
                                                         │
                                                    Recon Audio
                                                    [B, 1, T]
  ```

  Where `T' = T / 320` (the temporal compression ratio).

  ---

  ## 1. CODEC ENCODER (`CodecEncoder`)

  **File**: `vq/codec_encoder.py` lines 29-99  
  **Purpose**: Compress raw waveform into a dense acoustic embedding  
  **Architecture origin**: BigCodec encoder with Snake activations + alias-free processing

  ### Input/Output
  - **Input**: `[B, 1, T]` — raw mono audio at 16kHz
  - **Output**: `[B, T/320, 1024]` — acoustic embedding (permuted to `[B, T', H]`)

  ### Layer-by-layer Breakdown

  ```
  Layer 0: WNConv1d(1 → 48, kernel=7, pad=3)         # Weight-normed, initial projection
           ↓ [B, 48, T]

  Layer 1: EncoderBlock(48→96, stride=2)              # Downsample 2x
           ↓ [B, 96, T/2]

  Layer 2: EncoderBlock(96→192, stride=2)             # Downsample 2x
           ↓ [B, 192, T/4]

  Layer 3: EncoderBlock(192→384, stride=4)            # Downsample 4x
           ↓ [B, 384, T/16]

  Layer 4: EncoderBlock(384→768, stride=4)            # Downsample 4x
           ↓ [B, 768, T/64]

  Layer 5: EncoderBlock(768→1536, stride=5)           # Downsample 5x
           ↓ [B, 1536, T/320]

  Final:   SnakeBeta(1536) → WNConv1d(1536→1024, k=3, p=1)
           ↓ [B, 1024, T/320]

  Permute: .permute(0, 2, 1) → [B, T/320, 1024]
  ```

  **Total downsampling**: `2 × 2 × 4 × 4 × 5 = 320` (matches STFT hop_length)

  ### EncoderBlock Internal Structure

  Each `EncoderBlock(dim_in→dim_out, stride)` contains:

  ```
  3x ResidualUnit(dim_in/2, dilations=[1, 3, 9])
      Each ResidualUnit:
          SnakeBeta(dim) → WNConv1d(dim, dim, k=7, dilation=d) → SnakeBeta(dim) → WNConv1d(dim, dim, k=1)
          + residual connection

  SnakeBeta(dim_in/2)
  WNConv1d(dim_in/2 → dim_out, kernel=2*stride, stride=stride)     # Strided conv for downsampling
  ```

  The dilations `[1, 3, 9]` give effective receptive fields of `[7, 19, 55]` per residual unit, capturing multi-scale temporal patterns.

  ### SnakeBeta Activation

  ```
  SnakeBeta(x) = x + (1/β) * sin²(x * α)
  ```
  - `α` (frequency) and `β` (magnitude) are **learnable per-channel** parameters
  - Initialized in log-space (`alpha_logscale=True`) so `α = exp(param)`, `β = exp(param)`
  - Adds periodic inductive bias suited for audio signals

  ### Alias-Free Activation (`Activation1d`)

  Every SnakeBeta in the encoder is wrapped in `Activation1d`:

  ```
  x → UpSample1d(2x, Kaiser-sinc filter) → SnakeBeta(x) → DownSample1d(2x, Kaiser-sinc LPF)
  ```

  This prevents aliasing artifacts when applying nonlinear activations to bandlimited signals. The Kaiser-sinc filters are designed with cutoff=0.25 (relative to Nyquist) and half-width=0.3.

  ---

  ## 2. SEMANTIC BRANCH

  ### 2a. Wav2Vec2-BERT (`facebook/w2v-bert-2.0`)

  **Frozen** pretrained model. 580M parameters, trained on 4.5M hours of 143+ languages.

  - **Input**: Feature-extracted audio via `AutoFeatureExtractor` (pads 160 samples on each side)
  - **Output used**: `hidden_states[16]` — the 16th transformer layer's hidden state
  - Shape: `[B, T', 1024]` where `T'` matches the codec frame rate

  ### 2b. SemanticEncoder

  **File**: `vq/module.py` lines 308-374  
  **Purpose**: Adapt Wav2Vec2-BERT features into the codec's representation space

  ```
  Conv1d(1024→1024, k=3, p=1, no bias)      # Initial projection
      ↓
  Residual Block:
      ReLU → Conv1d(1024→1024, k=3, p=1) → ReLU → Conv1d(1024→1024, k=3, p=1)
      + skip connection
      ↓
  Conv1d(1024→1024, k=3, p=1, no bias)      # Final projection
  ```

  - Input: `[B, 1024, T']` (Wav2Vec2 layer-16 features, transposed)
  - Output: `[B, 1024, T']` (adapted semantic embedding)

  ### 2c. SemanticDecoder

  **File**: `vq/module.py` lines 376-420  
  **Mirror architecture** of SemanticEncoder, reconstructs semantic features from VQ output for the semantic reconstruction loss.

  ---

  ## 3. FUSION & QUANTIZATION

  ### 3a. Feature Concatenation & Projection

  ```python
  vq_emb = torch.cat([semantic_target_processed, vq_emb], dim=1)  # [B, 2048, T']
  vq_emb = fc_prior(vq_emb.transpose(1, 2)).transpose(1, 2)      # Linear(2048→2048)
  ```

  The `fc_prior` is a single `nn.Linear(2048, 2048)` that mixes the concatenated acoustic + semantic embeddings.

  ### 3b. Finite Scalar Quantization (ResidualFSQ)

  **File**: `vq/codec_decoder_vocos.py` lines 389-393  
  **From**: `vector_quantize_pytorch.ResidualFSQ`

  ```python
  ResidualFSQ(
      dim=2048,
      levels=[4, 4, 4, 4, 4, 4, 4, 4],  # 8 scalars, each with 4 levels
      num_quantizers=1                     # Single quantizer (no residual stages)
  )
  ```

  **How FSQ works**:
  - Projects the 2048-dim vector into 8 scalar dimensions
  - Each scalar is quantized to one of 4 levels (via rounding)
  - Total codebook size: `4^8 = 65,536` entries
  - **No codebook collapse** — achieves ~99% codebook utilization
  - Uses straight-through gradient estimator for backprop
  - **No commitment loss** needed (FSQ returns `None` for vq_loss)

  **Input**: `[B, T', 2048]` (permuted before passing)  
  **Output**: `vq_post_emb [B, 2048, T']`, `vq_code [B, T', 1]` (single integer code per frame)

  ### 3c. Post-VQ Projections

  ```python
  fc_post_a = nn.Linear(2048, 1024)   # Project VQ output for acoustic decoder
  fc_post_s = nn.Linear(2048, 1024)   # Project VQ output for semantic decoder
  ```

  ---

  ## 4. VOCOS DECODER

  ### 4a. VocosBackbone

  **File**: `vq/codec_decoder_vocos.py` lines 294-358  
  **Purpose**: Transform quantized embeddings into STFT-domain features

  ```
  Input: [B, T', 1024] (from fc_post_a output)

  Layer 1: Transpose → Conv1d(1024→1024, k=7, p=3) → embed
           ↓ [B, 1024, T']

  Layer 2: prior_net — 2× ResnetBlock(1024→1024, dropout=0.1)
           ↓ [B, 1024, T']

  Layer 3: Transpose → 12× TransformerBlock(dim=1024, heads=16, RoPE dim=64)
           ↓ [B, T', 1024]

  Layer 4: Transpose → post_net — 2× ResnetBlock(1024→1024, dropout=0.1)
           ↓ [B, 1024, T']

  Layer 5: Transpose → LayerNorm(1024)
           ↓ [B, T', 1024]
  ```

  ### ResnetBlock (within VocosBackbone)

  ```
  x → GroupNorm(32 groups) → Swish → Conv1d(k=3) → GroupNorm(32 groups) → Swish → Dropout(0.1) → Conv1d(k=3)
    + residual shortcut
  ```

  ### TransformerBlock

  **File**: `vq/bs_roformer5.py` lines 91-110  
  **Architecture**: Pre-norm Transformer with RoPE + Flash Attention + SiLU MLP

  ```
  x → RMSNorm(1024) → Attention → + residual
    → RMSNorm(1024) → MLP       → + residual
  ```

  **Attention** (lines 43-88):
  ```
  Input: [B, T', 1024]

  c_attn: Linear(1024 → 3072, no bias)    # Joint Q/K/V projection
      → rearrange to q,k,v: [B, 16 heads, T', 64]

  RoPE: RotaryPositionalEmbeddings(dim=64) applied to Q and K
      (from torchtune — standard RoPE with complex rotation)

  Flash Attention: scaled_dot_product_attention(q, k, v, causal=False)
      → [B, 16, T', 64]

  rearrange → [B, T', 1024]
  c_proj: Linear(1024 → 1024, no bias)    # Output projection
  ```

  **MLP** (lines 28-40):
  ```
  fc1: Linear(1024 → 4096, no bias)       # 4× expansion
  SiLU activation                          # x * sigmoid(x)
  fc2: Linear(4096 → 1024, no bias)       # Project back
  ```

  **RMSNorm** (lines 14-24):
  ```
  output = x * rsqrt(mean(x²) + eps) * weight     # Simpler than LayerNorm, no mean subtraction
  ```

  ### 4b. ISTFTHead

  **File**: `vq/codec_decoder_vocos.py` lines 104-149  
  **Purpose**: Convert transformer output to time-domain audio via inverse STFT

  ```
  Input: [B, T', 1024]

  Linear(1024 → 1282)              # n_fft + 2 = 1280 + 2
      ↓ [B, T', 1282]

  Transpose → [B, 1282, T']

  chunk(2, dim=1):
      mag: [B, 641, T']            # Magnitude (n_fft/2 + 1 bins)
      p:   [B, 641, T']            # Phase

  mag = exp(mag)                   # Log-magnitude → linear magnitude
  mag = clip(mag, max=1e2)         # Safety clamp

  Complex spectrum: S = mag * (cos(p) + j*sin(p))    # Polar → rectangular

  ISTFT:
      n_fft=1280, hop_length=320, win_length=1280
      "same" padding (not center)
      Hann window
      Overlap-add → [B, T]
  ```

  The ISTFT uses a custom implementation (lines 17-85) that supports "same" padding for neural vocoding, unlike PyTorch's native `torch.istft` which only supports "center" padding.

  **Output**: `[B, 1, T]` — reconstructed waveform

  ---

  ## 5. DISCRIMINATORS (Training Only)

  ### 5a. Multi-Period Discriminator (MPD)

  **File**: `module/mpd.py`  
  5 parallel discriminators with periods `[2, 3, 5, 7, 11]`

  Each `HiFiGANPeriodDiscriminator(period=p)`:
  ```
  Input: [B, 1, T]

  Reshape: [B, 1, T/p, p]         # Fold waveform by period

  5× Conv2d layers (downsampling along time, keeping period dim):
      Conv2d(in→out, kernel=(5,1), stride=(3,1)) → LeakyReLU(0.1)
      channels: 16 → 64 → 256 → 512 → 512 → 512

  Output Conv2d(512→1, kernel=(2,1))
  Flatten → scalar prediction per frame
  ```

  Returns intermediate feature maps for feature matching loss.

  ### 5b. Multi-STFT Spectral Discriminator

  **File**: `module/mstft.py`  
  8 parallel discriminators with FFT sizes: `[78, 126, 206, 334, 542, 876, 1418, 2296]`

  These are **deliberately non-standard FFT sizes** tailored for the transformer decoder's characteristics.

  Each `NLayerSpecDiscriminator`:
  ```
  Input: STFT magnitude [B, 1, F, T']    (computed externally per FFT size)

  Conv2d(1→32, k=5, stride=2) → LeakyReLU(0.2)

  3× downsampling:
      Conv2d(in→out, k=2*scale+1, stride=scale) → LeakyReLU(0.2)
      scales: [2, 2, 2]
      channels: 32 → 64 → 128 → 256

  Conv2d(256→512, k=3) → LeakyReLU(0.2)
  Conv2d(512→1, k=3)   # Final prediction
  ```

  ---

  ## 6. LOSS FUNCTIONS

  ### Generator Total Loss:
  ```
  L_gen = 15 * L_mel + 1 * L_adv + 1 * L_fm + 5 * L_semantic + L_vq
  ```

  | Loss | Weight | Description |
  |------|--------|-------------|
  | `L_mel` | 15.0 | Multi-resolution mel spectrogram loss (L1 on mel spectrograms at multiple scales) |
  | `L_adv` | 1.0 | LS-GAN adversarial loss from both MPD + Spec discriminators |
  | `L_fm` | 1.0 | Feature matching loss (L1 on discriminator intermediate features) |
  | `L_semantic` | 5.0 | L2 between reconstructed semantic features and original Wav2Vec2 features |
  | `L_vq` | — | FSQ quantization loss (None for FSQ, handled internally) |

  ### Discriminator Loss:
  ```
  L_disc = 1.0 * (L_real + L_fake)
  ```
  Standard LS-GAN: `L_real = mean((D(y) - 1)²)`, `L_fake = mean(D(ŷ)²)`

  ---

  ## 7. KEY DIMENSIONS SUMMARY

  | Component | Input Shape | Output Shape | Parameters |
  |-----------|------------|--------------|------------|
  | **CodecEncoder** | `[B, 1, T]` | `[B, T/320, 1024]` | ~8M |
  | **Wav2Vec2-BERT** (frozen) | `[B, feat, T_feat]` | `[B, T', 1024]` | ~580M (frozen) |
  | **SemanticEncoder** | `[B, 1024, T']` | `[B, 1024, T']` | ~6M |
  | **fc_prior** | `[B, T', 2048]` | `[B, T', 2048]` | ~4.2M |
  | **ResidualFSQ** | `[B, T', 2048]` | `[B, T', 2048]` + codes | ~small |
  | **fc_post_a** | `[B, T', 2048]` | `[B, T', 1024]` | ~2.1M |
  | **VocosBackbone** | `[B, T', 1024]` | `[B, T', 1024]` | ~100M |
  | **ISTFTHead** | `[B, T', 1024]` | `[B, 1, T]` | ~1.3M |
  | **SemanticDecoder** | `[B, 1024, T']` | `[B, 1024, T']` | ~6M |
  | **fc_post_s** | `[B, T', 2048]` | `[B, T', 1024]` | ~2.1M |
  | **MPD** (5 period discs) | `[B, 1, T]` | per-disc preds | ~7M |
  | **SpecDisc** (8 STFT discs) | `[B, 1, T]` | per-disc preds | ~15M |

  ---

  ## 8. CRITICAL RESOLUTION-RELEVANT DETAILS

  These are the elements that directly constrain or determine the output resolution:

  1. **Temporal compression = 320x**: Set by `up_ratios=[2,2,4,4,5]` in encoder. At 16kHz, this yields 50 frames/sec.

  2. **ISTFT reconstruction**: `n_fft=1280`, `hop_length=320`, `win_length=1280`. The ISTFTHead outputs `n_fft + 2 = 1282` values per frame, split into 641 magnitude + 641 phase bins. This means the maximum representable frequency is `16000/2 = 8kHz` (Nyquist at 16kHz SR).

  3. **The bottleneck**: The VQ layer operates at `[B, 2048, T/320]`. Every 320 input samples are compressed to a single 2048-dim vector, then quantized to one of 65,536 codes.

  4. **Transformer receptive field**: 12 layers of self-attention with RoPE can attend to the entire sequence, giving global context at the compressed frame rate.

  5. **No upsampling convolutions in decoder**: Unlike traditional vocoders (HiFi-GAN), the decoder uses ISTFT for waveform synthesis, so the temporal upsampling is implicit in the ISTFT operation. The transformer operates entirely at the compressed rate `T/320`.

  6. **Frequency resolution**: With `n_fft=1280` and `sr=16000`, frequency bin resolution = `16000/1280 = 12.5 Hz`. The 641 bins span `[0, 8000] Hz`.

  ---

  ## 9. INFERENCE PIPELINE (for context)

  From `inference.py`, the exact inference steps are:

  ```python
  # 1. Pad audio to multiple of 320
  wav = F.pad(wav, (0, 320 - wav.shape[1] % 320))

  # 2. Extract features for Wav2Vec2
  feat = feature_extractor(F.pad(wav, (160, 160)), sr=16000)

  # 3. Encode acoustic
  vq_emb = encoder(wav.unsqueeze(1))                    # [B, T/320, 1024]
  vq_emb = vq_emb.transpose(1, 2)                       # [B, 1024, T/320]

  # 4. Extract semantic
  semantic = semantic_model(feat).hidden_states[16]      # [B, T', 1024]
  semantic = SemanticEncoder(semantic.transpose(1, 2))   # [B, 1024, T']

  # 5. Fuse & quantize
  combined = cat([semantic, vq_emb], dim=1)              # [B, 2048, T']
  combined = fc_prior(combined.T(1,2)).T(1,2)            # [B, 2048, T']
  _, vq_code, _ = decoder(combined, vq=True)             # Quantize → codes

  # 6. Decode from codes
  vq_post_emb = decoder.quantizer.get_output_from_indices(vq_code.T(1,2))
  vq_post_emb = fc_post_a(vq_post_emb.T(1,2)).T(1,2)   # [B, 1024, T']
  recon = decoder(vq_post_emb.T(1,2), vq=False)         # VocosBackbone → ISTFT
  ```

  ---

  This gives you the complete context of every layer, shape, activation, and design choice in X-Codec 2.0. The critical bottlenecks for resolution scaling are: (a) the 320x compression ratio in the encoder, (b) the ISTFT head's `n_fft=1280` tied to 16kHz, and (c) the VQ operating at a fixed 2048-dim per frame at 50 fps. When discussing super-resolution with another model, these three constraints are the primary targets to modify or bypass.

  Now that you have all the context on entire architecture of the model and inference as well. Now the outputs of this model is in 16khz. which sounds a little muffled or tunnely, cause ofcourse it's 16khz and not super quality. i ofcourse dont wanna add any bottleneck to overall decoding inference as i am going to use this in voice agents and a TTS platform, so would want any diffusion style decoding bottlenecks and need smooth and effective methods and worked and what's novel and where does research leads us and what new methods can i try and what worked and did anyone try stuff like this that isnt diffusion ? or if diffusion is the solution, are there any methods i can use to not add any latency or overhead or not-so much extra compute requirements than the orignal decoder requirements i mean it comes at a price, but not drastically overheads. and also, if it requires retraining the entire decoder module or parts or it, i am ready to do it. have the hifi-tts2 data encoded and ready since we wont be touching teh encoder layers, i can reuse the pre-encoded data and can run experimetns. please direct me towards effective reserach methods i can implement and take over the upsampling problem with high fidelity and superior quality. and if controllability with gating for conditional 16/24/32/48 is possible, that's still an option preferrably willing to explore.


  above is the prompt for different agents that i provided. Now, instructions for you. 

  You are my Architecture Integrator + Principal Engineer.

  Goal:
  I will provide 3–4 architecture plans from different agents. Your job is to produce ONE upgraded ensemble architecture that accounts for EVERY suggestion from every plan, with explicit traceability and robust evaluation. No suggestion may be ignored.

  Operating rules:
  1) You must create a “Suggestion Inventory” that captures every distinct suggestion/upgrade/idea/concern from every plan. Assign each a unique ID (e.g., A1, A2… for Plan A; B1… for Plan B; etc.).
  2) You must produce a “Coverage Matrix” table where each suggestion ID is marked as:
     - ACCEPTED (fully included),
     - MODIFIED (included but changed),
     - REJECTED (not included),
     - DEFERRED (not now, but tracked)
     AND include a clear reason + implications.
  3) You must identify conflicts between plans (e.g., different storage choices, different scaling strategies). For each conflict, propose a resolution and justify it with tradeoffs.
  4) Your final architecture must be internally consistent and implementable. If you keep multiple options, you must specify decision triggers (what evidence/metrics decide which option wins).
  5) You must create Architecture Decision Records (ADRs) for every key decision (datastore, queue, caching, auth, deployment, observability, model serving, etc.).
  6) You must produce:
     - Final Ensembled Architecture (diagram in text form is fine),
     - Data flow (request → processing → storage),
     - Key components and responsibilities,
     - Failure modes + mitigations,
     - Security & privacy considerations,
     - Performance & cost considerations.
  7) You must produce a step-by-step Implementation Plan with phases, milestones, and checklists. Each checklist item must reference suggestion IDs it satisfies.
  8) You must produce a Robust Evaluation Plan:
     - unit/integration/e2e tests,
     - load tests + SLOs,
     - chaos/failure injection tests,
     - observability signals (logs/metrics/traces),
     - acceptance criteria per component.
  9) You must highlight “Unknowns / Questions” as a bounded list, but you are not allowed to stop. Make best-effort assumptions and clearly label them.
  10) Output must be structured exactly as:
     Section 1: Inputs summary
     Section 2: Suggestion Inventory (by plan)
     Section 3: Coverage Matrix (table)
     Section 4: Conflicts & Resolutions
     Section 5: Ensembled Architecture (final)
     Section 6: ADRs (key decisions)
     Section 7: Implementation Plan (phased + checklists w/ suggestion IDs)
     Section 8: Robust Evaluation Plan (metrics + tests + SLOs)
     Section 9: Risks, Failure modes, Mitigations
     Section 10: Assumptions & Open questions

  Now wait for my inputs. When I paste plans, do NOT summarize them loosely. Extract every actionable suggestion and preserve traceability.

  All plans are in @SRPlans

first_user_message:
  Both checkpoints are downloaded. Now let me provide the comprehensive architectural analysis you need.

  ---

  # X-Codec 2.0 Complete Architecture Analysis

  ## High-Level Data Flow

  The model is an **Encoder-VQ-Decoder** neural audio codec operating at **16kHz, mono** with a **hop length of 320** samples (20ms per frame, 50 tokens/second). The key innovation is fusing **acoustic** and **semantic** representations before quantization.

  ```
                                      TRAINING FLOW
  ┌─────────────┐
  │ Audio 16kHz  │──┬──► CodecEncoder (Conv) ──────────────► acoustic_emb [B, 1024, T']
  │  [B, 1, T]  │  │                                              │
  └─────────────┘  │                                              │
                   │                                              ▼
                   └──► Wav2Vec2-BERT (frozen) ──► SemanticEncoder ──► semantic_emb [B, 1024, T']
                        (layer 16 hidden)                         │
                                                                  ▼
                                                      cat ──► [B, 2048, T']
                                                                  │
                                                      fc_prior (Linear 2048→2048)
                                                                  │
                                                      ┌───────────▼────────────┐
                                                      │  ResidualFSQ           │
                                                      │  levels=[4]*8 = 65536  │
                                                      │  1 quantizer, dim=2048 │
                                                      └───────────┬────────────┘
                                                                  │
                                                      vq_post_emb [B, 2048, T']
                                                         │                │
                                              fc_post_a  │                │  fc_post_s
                                           (2048→1024)   │                │  (2048→1024)
                                                         ▼                ▼
                                                   VocosBackbone    SemanticDecoder
                                                   (12-layer TF)    (3-layer Conv)
                                                         │                │
                                                    ISTFTHead        semantic_recon
                                                   (n_fft=1280)     (L2 loss vs target)
                                                         │
                                                    Recon Audio
                                                    [B, 1, T]
  ```

  Where `T' = T / 320` (the temporal compression ratio).

  ---

  ## 1. CODEC ENCODER (`CodecEncoder`)

  **File**: `vq/codec_encoder.py` lines 29-99  
  **Purpose**: Compress raw waveform into a dense acoustic embedding  
  **Architecture origin**: BigCodec encoder with Snake activations + alias-free processing

  ### Input/Output
  - **Input**: `[B, 1, T]` — raw mono audio at 16kHz
  - **Output**: `[B, T/320, 1024]` — acoustic embedding (permuted to `[B, T', H]`)

  ### Layer-by-layer Breakdown

  ```
  Layer 0: WNConv1d(1 → 48, kernel=7, pad=3)         # Weight-normed, initial projection
           ↓ [B, 48, T]

  Layer 1: EncoderBlock(48→96, stride=2)              # Downsample 2x
           ↓ [B, 96, T/2]

  Layer 2: EncoderBlock(96→192, stride=2)             # Downsample 2x
           ↓ [B, 192, T/4]

  Layer 3: EncoderBlock(192→384, stride=4)            # Downsample 4x
           ↓ [B, 384, T/16]

  Layer 4: EncoderBlock(384→768, stride=4)            # Downsample 4x
           ↓ [B, 768, T/64]

  Layer 5: EncoderBlock(768→1536, stride=5)           # Downsample 5x
           ↓ [B, 1536, T/320]

  Final:   SnakeBeta(1536) → WNConv1d(1536→1024, k=3, p=1)
           ↓ [B, 1024, T/320]

  Permute: .permute(0, 2, 1) → [B, T/320, 1024]
  ```

  **Total downsampling**: `2 × 2 × 4 × 4 × 5 = 320` (matches STFT hop_length)

  ### EncoderBlock Internal Structure

  Each `EncoderBlock(dim_in→dim_out, stride)` contains:

  ```
  3x ResidualUnit(dim_in/2, dilations=[1, 3, 9])
      Each ResidualUnit:
          SnakeBeta(dim) → WNConv1d(dim, dim, k=7, dilation=d) → SnakeBeta(dim) → WNConv1d(dim, dim, k=1)
          + residual connection

  SnakeBeta(dim_in/2)
  WNConv1d(dim_in/2 → dim_out, kernel=2*stride, stride=stride)     # Strided conv for downsampling
  ```

  The dilations `[1, 3, 9]` give effective receptive fields of `[7, 19, 55]` per residual unit, capturing multi-scale temporal patterns.

  ### SnakeBeta Activation

  ```
  SnakeBeta(x) = x + (1/β) * sin²(x * α)
  ```
  - `α` (frequency) and `β` (magnitude) are **learnable per-channel** parameters
  - Initialized in log-space (`alpha_logscale=True`) so `α = exp(param)`, `β = exp(param)`
  - Adds periodic inductive bias suited for audio signals

  ### Alias-Free Activation (`Activation1d`)

  Every SnakeBeta in the encoder is wrapped in `Activation1d`:

  ```
  x → UpSample1d(2x, Kaiser-sinc filter) → SnakeBeta(x) → DownSample1d(2x, Kaiser-sinc LPF)
  ```

  This prevents aliasing artifacts when applying nonlinear activations to bandlimited signals. The Kaiser-sinc filters are designed with cutoff=0.25 (relative to Nyquist) and half-width=0.3.

  ---

  ## 2. SEMANTIC BRANCH

  ### 2a. Wav2Vec2-BERT (`facebook/w2v-bert-2.0`)

  **Frozen** pretrained model. 580M parameters, trained on 4.5M hours of 143+ languages.

  - **Input**: Feature-extracted audio via `AutoFeatureExtractor` (pads 160 samples on each side)
  - **Output used**: `hidden_states[16]` — the 16th transformer layer's hidden state
  - Shape: `[B, T', 1024]` where `T'` matches the codec frame rate

  ### 2b. SemanticEncoder

  **File**: `vq/module.py` lines 308-374  
  **Purpose**: Adapt Wav2Vec2-BERT features into the codec's representation space

  ```
  Conv1d(1024→1024, k=3, p=1, no bias)      # Initial projection
      ↓
  Residual Block:
      ReLU → Conv1d(1024→1024, k=3, p=1) → ReLU → Conv1d(1024→1024, k=3, p=1)
      + skip connection
      ↓
  Conv1d(1024→1024, k=3, p=1, no bias)      # Final projection
  ```

  - Input: `[B, 1024, T']` (Wav2Vec2 layer-16 features, transposed)
  - Output: `[B, 1024, T']` (adapted semantic embedding)

  ### 2c. SemanticDecoder

  **File**: `vq/module.py` lines 376-420  
  **Mirror architecture** of SemanticEncoder, reconstructs semantic features from VQ output for the semantic reconstruction loss.

  ---

  ## 3. FUSION & QUANTIZATION

  ### 3a. Feature Concatenation & Projection

  ```python
  vq_emb = torch.cat([semantic_target_processed, vq_emb], dim=1)  # [B, 2048, T']
  vq_emb = fc_prior(vq_emb.transpose(1, 2)).transpose(1, 2)      # Linear(2048→2048)
  ```

  The `fc_prior` is a single `nn.Linear(2048, 2048)` that mixes the concatenated acoustic + semantic embeddings.

  ### 3b. Finite Scalar Quantization (ResidualFSQ)

  **File**: `vq/codec_decoder_vocos.py` lines 389-393  
  **From**: `vector_quantize_pytorch.ResidualFSQ`

  ```python
  ResidualFSQ(
      dim=2048,
      levels=[4, 4, 4, 4, 4, 4, 4, 4],  # 8 scalars, each with 4 levels
      num_quantizers=1                     # Single quantizer (no residual stages)
  )
  ```

  **How FSQ works**:
  - Projects the 2048-dim vector into 8 scalar dimensions
  - Each scalar is quantized to one of 4 levels (via rounding)
  - Total codebook size: `4^8 = 65,536` entries
  - **No codebook collapse** — achieves ~99% codebook utilization
  - Uses straight-through gradient estimator for backprop
  - **No commitment loss** needed (FSQ returns `None` for vq_loss)

  **Input**: `[B, T', 2048]` (permuted before passing)  
  **Output**: `vq_post_emb [B, 2048, T']`, `vq_code [B, T', 1]` (single integer code per frame)

  ### 3c. Post-VQ Projections

  ```python
  fc_post_a = nn.Linear(2048, 1024)   # Project VQ output for acoustic decoder
  fc_post_s = nn.Linear(2048, 1024)   # Project VQ output for semantic decoder
  ```

  ---

  ## 4. VOCOS DECODER

  ### 4a. VocosBackbone

  **File**: `vq/codec_decoder_vocos.py` lines 294-358  
  **Purpose**: Transform quantized embeddings into STFT-domain features

  ```
  Input: [B, T', 1024] (from fc_post_a output)

  Layer 1: Transpose → Conv1d(1024→1024, k=7, p=3) → embed
           ↓ [B, 1024, T']

  Layer 2: prior_net — 2× ResnetBlock(1024→1024, dropout=0.1)
           ↓ [B, 1024, T']

  Layer 3: Transpose → 12× TransformerBlock(dim=1024, heads=16, RoPE dim=64)
           ↓ [B, T', 1024]

  Layer 4: Transpose → post_net — 2× ResnetBlock(1024→1024, dropout=0.1)
           ↓ [B, 1024, T']

  Layer 5: Transpose → LayerNorm(1024)
           ↓ [B, T', 1024]
  ```

  ### ResnetBlock (within VocosBackbone)

  ```
  x → GroupNorm(32 groups) → Swish → Conv1d(k=3) → GroupNorm(32 groups) → Swish → Dropout(0.1) → Conv1d(k=3)
    + residual shortcut
  ```

  ### TransformerBlock

  **File**: `vq/bs_roformer5.py` lines 91-110  
  **Architecture**: Pre-norm Transformer with RoPE + Flash Attention + SiLU MLP

  ```
  x → RMSNorm(1024) → Attention → + residual
    → RMSNorm(1024) → MLP       → + residual
  ```

  **Attention** (lines 43-88):
  ```
  Input: [B, T', 1024]

  c_attn: Linear(1024 → 3072, no bias)    # Joint Q/K/V projection
      → rearrange to q,k,v: [B, 16 heads, T', 64]

  RoPE: RotaryPositionalEmbeddings(dim=64) applied to Q and K
      (from torchtune — standard RoPE with complex rotation)

  Flash Attention: scaled_dot_product_attention(q, k, v, causal=False)
      → [B, 16, T', 64]

  rearrange → [B, T', 1024]
  c_proj: Linear(1024 → 1024, no bias)    # Output projection
  ```

  **MLP** (lines 28-40):
  ```
  fc1: Linear(1024 → 4096, no bias)       # 4× expansion
  SiLU activation                          # x * sigmoid(x)
  fc2: Linear(4096 → 1024, no bias)       # Project back
  ```

  **RMSNorm** (lines 14-24):
  ```
  output = x * rsqrt(mean(x²) + eps) * weight     # Simpler than LayerNorm, no mean subtraction
  ```

  ### 4b. ISTFTHead

  **File**: `vq/codec_decoder_vocos.py` lines 104-149  
  **Purpose**: Convert transformer output to time-domain audio via inverse STFT

  ```
  Input: [B, T', 1024]

  Linear(1024 → 1282)              # n_fft + 2 = 1280 + 2
      ↓ [B, T', 1282]

  Transpose → [B, 1282, T']

  chunk(2, dim=1):
      mag: [B, 641, T']            # Magnitude (n_fft/2 + 1 bins)
      p:   [B, 641, T']            # Phase

  mag = exp(mag)                   # Log-magnitude → linear magnitude
  mag = clip(mag, max=1e2)         # Safety clamp

  Complex spectrum: S = mag * (cos(p) + j*sin(p))    # Polar → rectangular

  ISTFT:
      n_fft=1280, hop_length=320, win_length=1280
      "same" padding (not center)
      Hann window
      Overlap-add → [B, T]
  ```

  The ISTFT uses a custom implementation (lines 17-85) that supports "same" padding for neural vocoding, unlike PyTorch's native `torch.istft` which only supports "center" padding.

  **Output**: `[B, 1, T]` — reconstructed waveform

  ---

  ## 5. DISCRIMINATORS (Training Only)

  ### 5a. Multi-Period Discriminator (MPD)

  **File**: `module/mpd.py`  
  5 parallel discriminators with periods `[2, 3, 5, 7, 11]`

  Each `HiFiGANPeriodDiscriminator(period=p)`:
  ```
  Input: [B, 1, T]

  Reshape: [B, 1, T/p, p]         # Fold waveform by period

  5× Conv2d layers (downsampling along time, keeping period dim):
      Conv2d(in→out, kernel=(5,1), stride=(3,1)) → LeakyReLU(0.1)
      channels: 16 → 64 → 256 → 512 → 512 → 512

  Output Conv2d(512→1, kernel=(2,1))
  Flatten → scalar prediction per frame
  ```

  Returns intermediate feature maps for feature matching loss.

  ### 5b. Multi-STFT Spectral Discriminator

  **File**: `module/mstft.py`  
  8 parallel discriminators with FFT sizes: `[78, 126, 206, 334, 542, 876, 1418, 2296]`

  These are **deliberately non-standard FFT sizes** tailored for the transformer decoder's characteristics.

  Each `NLayerSpecDiscriminator`:
  ```
  Input: STFT magnitude [B, 1, F, T']    (computed externally per FFT size)

  Conv2d(1→32, k=5, stride=2) → LeakyReLU(0.2)

  3× downsampling:
      Conv2d(in→out, k=2*scale+1, stride=scale) → LeakyReLU(0.2)
      scales: [2, 2, 2]
      channels: 32 → 64 → 128 → 256

  Conv2d(256→512, k=3) → LeakyReLU(0.2)
  Conv2d(512→1, k=3)   # Final prediction
  ```

  ---

  ## 6. LOSS FUNCTIONS

  ### Generator Total Loss:
  ```
  L_gen = 15 * L_mel + 1 * L_adv + 1 * L_fm + 5 * L_semantic + L_vq
  ```

  | Loss | Weight | Description |
  |------|--------|-------------|
  | `L_mel` | 15.0 | Multi-resolution mel spectrogram loss (L1 on mel spectrograms at multiple scales) |
  | `L_adv` | 1.0 | LS-GAN adversarial loss from both MPD + Spec discriminators |
  | `L_fm` | 1.0 | Feature matching loss (L1 on discriminator intermediate features) |
  | `L_semantic` | 5.0 | L2 between reconstructed semantic features and original Wav2Vec2 features |
  | `L_vq` | — | FSQ quantization loss (None for FSQ, handled internally) |

  ### Discriminator Loss:
  ```
  L_disc = 1.0 * (L_real + L_fake)
  ```
  Standard LS-GAN: `L_real = mean((D(y) - 1)²)`, `L_fake = mean(D(ŷ)²)`

  ---

  ## 7. KEY DIMENSIONS SUMMARY

  | Component | Input Shape | Output Shape | Parameters |
  |-----------|------------|--------------|------------|
  | **CodecEncoder** | `[B, 1, T]` | `[B, T/320, 1024]` | ~8M |
  | **Wav2Vec2-BERT** (frozen) | `[B, feat, T_feat]` | `[B, T', 1024]` | ~580M (frozen) |
  | **SemanticEncoder** | `[B, 1024, T']` | `[B, 1024, T']` | ~6M |
  | **fc_prior** | `[B, T', 2048]` | `[B, T', 2048]` | ~4.2M |
  | **ResidualFSQ** | `[B, T', 2048]` | `[B, T', 2048]` + codes | ~small |
  | **fc_post_a** | `[B, T', 2048]` | `[B, T', 1024]` | ~2.1M |
  | **VocosBackbone** | `[B, T', 1024]` | `[B, T', 1024]` | ~100M |
  | **ISTFTHead** | `[B, T', 1024]` | `[B, 1, T]` | ~1.3M |
  | **SemanticDecoder** | `[B, 1024, T']` | `[B, 1024, T']` | ~6M |
  | **fc_post_s** | `[B, T', 2048]` | `[B, T', 1024]` | ~2.1M |
  | **MPD** (5 period discs) | `[B, 1, T]` | per-disc preds | ~7M |
  | **SpecDisc** (8 STFT discs) | `[B, 1, T]` | per-disc preds | ~15M |

  ---

  ## 8. CRITICAL RESOLUTION-RELEVANT DETAILS

  These are the elements that directly constrain or determine the output resolution:

  1. **Temporal compression = 320x**: Set by `up_ratios=[2,2,4,4,5]` in encoder. At 16kHz, this yields 50 frames/sec.

  2. **ISTFT reconstruction**: `n_fft=1280`, `hop_length=320`, `win_length=1280`. The ISTFTHead outputs `n_fft + 2 = 1282` values per frame, split into 641 magnitude + 641 phase bins. This means the maximum representable frequency is `16000/2 = 8kHz` (Nyquist at 16kHz SR).

  3. **The bottleneck**: The VQ layer operates at `[B, 2048, T/320]`. Every 320 input samples are compressed to a single 2048-dim vector, then quantized to one of 65,536 codes.

  4. **Transformer receptive field**: 12 layers of self-attention with RoPE can attend to the entire sequence, giving global context at the compressed frame rate.

  5. **No upsampling convolutions in decoder**: Unlike traditional vocoders (HiFi-GAN), the decoder uses ISTFT for waveform synthesis, so the temporal upsampling is implicit in the ISTFT operation. The transformer operates entirely at the compressed rate `T/320`.

  6. **Frequency resolution**: With `n_fft=1280` and `sr=16000`, frequency bin resolution = `16000/1280 = 12.5 Hz`. The 641 bins span `[0, 8000] Hz`.

  ---

  ## 9. INFERENCE PIPELINE (for context)

  From `inference.py`, the exact inference steps are:

  ```python
  # 1. Pad audio to multiple of 320
  wav = F.pad(wav, (0, 320 - wav.shape[1] % 320))

  # 2. Extract features for Wav2Vec2
  feat = feature_extractor(F.pad(wav, (160, 160)), sr=16000)

  # 3. Encode acoustic
  vq_emb = encoder(wav.unsqueeze(1))                    # [B, T/320, 1024]
  vq_emb = vq_emb.transpose(1, 2)                       # [B, 1024, T/320]

  # 4. Extract semantic
  semantic = semantic_model(feat).hidden_states[16]      # [B, T', 1024]
  semantic = SemanticEncoder(semantic.transpose(1, 2))   # [B, 1024, T']

  # 5. Fuse & quantize
  combined = cat([semantic, vq_emb], dim=1)              # [B, 2048, T']
  combined = fc_prior(combined.T(1,2)).T(1,2)            # [B, 2048, T']
  _, vq_code, _ = decoder(combined, vq=True)             # Quantize → codes

  # 6. Decode from codes
  vq_post_emb = decoder.quantizer.get_output_from_indices(vq_code.T(1,2))
  vq_post_emb = fc_post_a(vq_post_emb.T(1,2)).T(1,2)   # [B, 1024, T']
  recon = decoder(vq_post_emb.T(1,2), vq=False)         # VocosBackbone → ISTFT
  ```

  ---

  This gives you the complete context of every layer, shape, activation, and design choice in X-Codec 2.0. The critical bottlenecks for resolution scaling are: (a) the 320x compression ratio in the encoder, (b) the ISTFT head's `n_fft=1280` tied to 16kHz, and (c) the VQ operating at a fixed 2048-dim per frame at 50 fps. When discussing super-resolution with another model, these three constraints are the primary targets to modify or bypass.

  Now that you have all the context on entire architecture of the model and inference as well. Now the outputs of this model is in 16khz. which sounds a little muffled or tunnely, cause ofcourse it's 16khz and not super quality. i ofcourse dont wanna add any bottleneck to overall decoding inference as i am going to use this in voice agents and a TTS platform, so would want any diffusion style decoding bottlenecks and need smooth and effective methods and worked and what's novel and where does research leads us and what new methods can i try and what worked and did anyone try stuff like this that isnt diffusion ? or if diffusion is the solution, are there any methods i can use to not add any latency or overhead or not-so much extra compute requirements than the orignal decoder requirements i mean it comes at a price, but not drastically overheads. and also, if it requires retraining the entire decoder module or parts or it, i am ready to do it. have the hifi-tts2 data encoded and ready since we wont be touching teh encoder layers, i can reuse the pre-encoded data and can run experimetns. please direct me towards effective reserach methods i can implement and take over the upsampling problem with high fidelity and superior quality. and if controllability with gating for conditional 16/24/32/48 is possible, that's still an option preferrably willing to explore.


  above is the prompt for different agents that i provided. Now, instructions for you. 

  You are my Architecture Integrator + Principal Engineer.

  Goal:
  I will provide 3–4 architecture plans from different agents. Your job is to produce ONE upgraded ensemble architecture that accounts for EVERY suggestion from every plan, with explicit traceability and robust evaluation. No suggestion may be ignored.

  Operating rules:
  1) You must create a “Suggestion Inventory” that captures every distinct suggestion/upgrade/idea/concern from every plan. Assign each a unique ID (e.g., A1, A2… for Plan A; B1… for Plan B; etc.).
  2) You must produce a “Coverage Matrix” table where each suggestion ID is marked as:
     - ACCEPTED (fully included),
     - MODIFIED (included but changed),
     - REJECTED (not included),
     - DEFERRED (not now, but tracked)
     AND include a clear reason + implications.
  3) You must identify conflicts between plans (e.g., different storage choices, different scaling strategies). For each conflict, propose a resolution and justify it with tradeoffs.
  4) Your final architecture must be internally consistent and implementable. If you keep multiple options, you must specify decision triggers (what evidence/metrics decide which option wins).
  5) You must create Architecture Decision Records (ADRs) for every key decision (datastore, queue, caching, auth, deployment, observability, model serving, etc.).
  6) You must produce:
     - Final Ensembled Architecture (diagram in text form is fine),
     - Data flow (request → processing → storage),
     - Key components and responsibilities,
     - Failure modes + mitigations,
     - Security & privacy considerations,
     - Performance & cost considerations.
  7) You must produce a step-by-step Implementation Plan with phases, milestones, and checklists. Each checklist item must reference suggestion IDs it satisfies.
  8) You must produce a Robust Evaluation Plan:
     - unit/integration/e2e tests,
     - load tests + SLOs,
     - chaos/failure injection tests,
     - observability signals (logs/metrics/traces),
     - acceptance criteria per component.
  9) You must highlight “Unknowns / Questions” as a bounded list, but you are not allowed to stop. Make best-effort assumptions and clearly label them.
  10) Output must be structured exactly as:
     Section 1: Inputs summary
     Section 2: Suggestion Inventory (by plan)
     Section 3: Coverage Matrix (table)
     Section 4: Conflicts & Resolutions
     Section 5: Ensembled Architecture (final)
     Section 6: ADRs (key decisions)
     Section 7: Implementation Plan (phased + checklists w/ suggestion IDs)
     Section 8: Robust Evaluation Plan (metrics + tests + SLOs)
     Section 9: Risks, Failure modes, Mitigations
     Section 10: Assumptions & Open questions

  Now wait for my inputs. When I paste plans, do NOT summarize them loosely. Extract every actionable suggestion and preserve traceability.

  All plans are in @SRPlans


