Input:
*** Begin Patch
*** Update File: X-Codec-2.0/data_module.py
@@
-import os
-
-import re
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchaudio
-import numpy as np
-import pytorch_lightning as pl
-import random
-import librosa
-from os.path import basename, exists, join
-from torch.utils.data import Dataset, DataLoader
-import hydra
-import utils
-import torchaudio
-from transformers import AutoFeatureExtractor
-from torchaudio.transforms import Resample
-from tqdm import tqdm
-from torchaudio.transforms import Resample
+import io
+import json
+import os
+import random
+import tarfile
+from os.path import basename, exists, join
+
+import librosa
+import numpy as np
+import pytorch_lightning as pl
+import soundfile as sf
+import torch
+import torch.nn.functional as F
+import torchaudio
+from torch.utils.data import DataLoader, Dataset
+from torchaudio.transforms import Resample
+from tqdm import tqdm
+from transformers import AutoFeatureExtractor
+
+import hydra
+import utils
@@
     def get_loader(self, phase):
-        phase_cfg = self.cfg.dataset.get(phase)
-        batch_size = phase_cfg.batch_size
-        ds = FSDataset(phase, self.cfg)
-        # ds = FSDataset_add_STFT(phase, self.cfg)
+        phase_cfg = self.cfg.dataset.get(phase)
+        if phase_cfg is None:
+            raise ValueError(f"Missing dataset config for phase={phase}")
+        batch_size = phase_cfg.batch_size
+        dataset_cfg = self.cfg.dataset.get("dataset")
+        if dataset_cfg and "_target_" in dataset_cfg:
+            ds = hydra.utils.instantiate(dataset_cfg, phase=phase, cfg=self.cfg)
+        else:
+            ds = FSDataset(phase, self.cfg)
         dl = DataLoader(ds,
                         batch_size=batch_size,
                         shuffle=phase_cfg.shuffle,
                         num_workers=28,
                         collate_fn=ds.collate_fn,
@@
     def get_filelist(self, fpath):
         with open(fpath, 'r') as f:
             # flist = [l.strip() for l in f if l.strip()]
             flist = [l.strip().split('\t')[0] for l in f if l.strip()]
         return flist
@@
     def __getitem__(self, idx):
         # (  wavpath,fid) = self.filelist[idx]
         wavpath  = self.filelist[idx]
-        wavpath_full = join(self.cfg.preprocess.datasets.LibriSpeech.root, wavpath)
+        if os.path.isabs(wavpath):
+            wavpath_full = wavpath
+        else:
+            wavpath_full = join(self.cfg.preprocess.datasets.LibriSpeech.root, wavpath)
         # wav = self.load_wav(wavpath)
         # wav = torch.from_numpy(wav)

         wav,sr=torchaudio.load(wavpath_full)
@@
         return out
+
+
+class ShardDataset(Dataset):
+    """Dataset for tar shards + JSONL manifest produced by prepare_shards.py."""
+
+    def __init__(self, phase, cfg):
+        self.phase = phase
+        self.cfg = cfg
+        self.phase_cfg = cfg.dataset.get(phase)
+        self.ocwd = hydra.utils.get_original_cwd()
+        self.sr = cfg.preprocess.audio.sr
+        self.min_audio_length = cfg.dataset.min_audio_length
+        self.feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0")
+
+        manifest = self.phase_cfg.get("manifest")
+        shards_root = self.phase_cfg.get("shards_root")
+        if not manifest or not shards_root:
+            raise ValueError("ShardDataset requires manifest and shards_root in dataset config.")
+
+        self.manifest_path = self._resolve_path(manifest)
+        self.shards_root = self._resolve_path(shards_root)
+        allowed = self.phase_cfg.get("allowed_sample_rates")
+        self.allowed_sample_rates = set(allowed) if allowed else {self.sr}
+
+        self.entries = []
+        with open(self.manifest_path, "r", encoding="utf-8") as fh:
+            for line in fh:
+                line = line.strip()
+                if not line:
+                    continue
+                entry = json.loads(line)
+                sample_rate = entry.get("sample_rate")
+                if sample_rate not in self.allowed_sample_rates:
+                    continue
+                self.entries.append(entry)
+
+        self._tar_handles = {}
+
+    def __len__(self):
+        return len(self.entries)
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["_tar_handles"] = {}
+        return state
+
+    def _resolve_path(self, path):
+        if os.path.isabs(path):
+            return path
+        return join(self.ocwd, path)
+
+    def _get_tar(self, tar_path):
+        tar = self._tar_handles.get(tar_path)
+        if tar is None:
+            tar = tarfile.open(tar_path, "r")
+            self._tar_handles[tar_path] = tar
+        return tar
+
+    def _load_audio(self, entry):
+        tar_path = join(self.shards_root, entry["shard"])
+        tar = self._get_tar(tar_path)
+        audio_name = entry["audio"]
+        try:
+            member = tar.getmember(audio_name)
+        except KeyError as exc:
+            raise KeyError(f"Missing {audio_name} in {tar_path}") from exc
+        audio_bytes = tar.extractfile(member).read()
+        audio, sr = sf.read(io.BytesIO(audio_bytes), dtype="float32")
+        if audio.ndim > 1:
+            audio = np.mean(audio, axis=1)
+        return audio, sr
+
+    def __getitem__(self, idx):
+        entry = self.entries[idx]
+        audio, sr = self._load_audio(entry)
+        wav = torch.from_numpy(audio)
+        if sr != self.sr:
+            wav = torchaudio.functional.resample(wav, sr, self.sr)
+
+        length = wav.shape[0]
+        if length < self.min_audio_length:
+            wav = F.pad(wav, (0, self.min_audio_length - length))
+            length = wav.shape[0]
+
+        start = random.randint(0, length - self.min_audio_length)
+        wav = wav[start : start + self.min_audio_length]
+
+        wav_pad = F.pad(wav, (160, 160))
+        feat = self.feature_extractor(
+            wav_pad, sampling_rate=self.sr, return_tensors="pt"
+        ).data["input_features"]
+
+        return {
+            "wav": wav,
+            "feat": feat,
+        }
+
+    def collate_fn(self, bs):
+        wavs = [b["wav"] for b in bs]
+        wavs = torch.stack(wavs)
+        feats = [b["feat"] for b in bs]
+        feats = torch.stack(feats)
+        return {
+            "wav": wavs,
+            "feats": feats,
+        }
*** End Patch


Output: {"output":"Success. Updated the following files:\nM X-Codec-2.0/data_module.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


**Confirming audio processing types**

Confirmed torchaudio functional resample works on 1D float tensors and returns float; also verified wav_pad is 1D tensor as feature extractor expects, matching original usage. Updated dataset config default to FSDataset and will apply the patch next.


Input:
*** Begin Patch
*** Update File: X-Codec-2.0/config/dataset/default.yaml
@@
-dataset:
-  _target_: data_module.FSDataset_add_STFT
+dataset:
+  _target_: data_module.FSDataset
*** End Patch


Output: {"output":"Success. Updated the following files:\nM X-Codec-2.0/config/dataset/default.yaml\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Summary: auto


**Designing new shards config file**

Planning the new Hydra config file for shards dataset to mirror the default dataset config, including dataset target, train/val sections with relevant keys, and duplicating dataset-level settings like min_audio_length, padding_idx, and frame_length to ensure completeness since shards.yaml won't merge defaults automatically.


**Including dataset-level defaults in shards config**

Decided to add padding_idx, frame_length, and min_audio_length with their default values into the shards config for completeness before creating the file.


Input:
*** Begin Patch
*** Add File: X-Codec-2.0/config/dataset/shards.yaml
+dataset:
+  _target_: data_module.ShardDataset
+
+train:
+  manifest: /path/to/manifests/telugu_train.jsonl
+  shards_root: /path/to/telugu_16
+  batch_size: 8
+  shuffle: true
+  allowed_sample_rates: [16000]
+
+val:
+  manifest: /path/to/manifests/telugu_val.jsonl
+  shards_root: /path/to/telugu_16
+  batch_size: 8
+  shuffle: false
+  allowed_sample_rates: [16000]
+
+test:
+  manifest: /path/to/manifests/telugu_val.jsonl
+  shards_root: /path/to/telugu_16
+  batch_size: 1
+  shuffle: false
+  allowed_sample_rates: [16000]
+
+padding_idx: 0
+frame_length: 200
+min_audio_length: 96000
*** End Patch


Output: {"output":"Success. Updated the following files:\nA X-Codec-2.0/config/dataset/shards.yaml\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Verifying soundfile dependency**

Checking if soundfile is listed in requirements since it's used in the pipeline; it’s likely already included but confirming to ensure no missing dependency issues.


rg -n "soundfile" -S X-Codec-2.0/requirements.txt


Output:
Exit code: 0
Wall time: 0 seconds
Output:
108:soundfile==0.12.1


**Planning minimal smoke test for dataset**

I'm preparing a minimal Python script to instantiate the new dataset using an OmegaConf config and load one sample to verify it works, while noting that the feature extractor will download a model from HuggingFace and may take some time.


**Deciding against runtime dataset test to avoid large model download**

I'm opting to skip instantiating the dataset fully because the feature extractor triggers a large model download; instead, I'll verify dataset code by importing the module to catch syntax errors without triggering network activity.


python3 - <<'PY'
import data_module
print('data_module imported')
PY


Output:
Exit code: 1
Wall time: 0.2 seconds
Output:
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/ubuntu/maya3_prepare/X-Codec-2.0/data_module.py", line 10, in <module>
    import pytorch_lightning as pl
ModuleNotFoundError: No module named 'pytorch_lightning'


**Noting missing PyTorch Lightning dependency**

The environment lacks pytorch_lightning, so I can't import or run related code; I'll confirm syntax via compilation attempts and note this limitation in the final summary.


Review strategy vs current pipeline scripts to map requirements and identify initial gaps.

Pick a Supabase video, download its cleaned tar from R2, and validate metadata/segment audio alignment.

Run the sharding pipeline on that video and verify shard/manifest/audio outputs match expectations.

Check X-Codec-2.0 data loading compatibility with tar shards; implement minimal fixes or adapters as needed.

Explanation: Added a shard-based dataset loader to X-Codec-2.0 and fixed dataset config target; pipeline validation complete.


Output: Plan updated


**Reviewing plan versus implementation**

I identified updates to the plan: several scripts are consolidated under one, the language lookup file isn't used in the pipeline though planned, and most expected features—like multi-resolution buckets, silence-aware chunking, and quality metrics—are implemented with minor differences in metric calculations.


**Identifying pipeline divergences and pending validations**

I found nuances where chunking allows segments >60s to contribute smaller windows, diverging from the strict drop >60s plan. Also, key plan items like language lookup use, large smoke tests, and upload verification remain unvalidated. I noted metadata handling quirks, manifest filtering needs, and dataset config updates, with import/runtime testing still pending.


Output:
Exit code: 128
Wall time: 0 seconds
Output:
fatal: not a git repository (or any of the parent directories): .git


**Summarizing pipeline validation and results**

The pipeline processed a Telugu video ID yielding 22 usable segments matching metadata perfectly, with negligible duration differences and consistent sample rates. `prepare_shards.py` produced samples at various rates with some short segments dropped, and effective Nyquist range identified; file changes and test outputs will be referenced without exposing ephemeral paths.


**Adding shard dataset loader and fixing config**

Added a shard-based dataset loader to X-Codec-2.0 and fixed dataset config targets so training code can consume `prepare_shards.py` tar outputs directly without extraction. Modified `data_module.py` to instantiate datasets from config with support for absolute paths.

**Pipeline Validation**

- Verified tar for video ID `U2c2AMyTYRo` from R2: 22 usable segments perfectly match 22 FLAC files; max duration difference 10 microseconds.
- Ran `prepare_shards.py` producing outputs in `/tmp/pipeline_test` and downloads in `/tmp/xcodec_work/downloads`.
- Generated manifests include 21 samples at 16kHz and 16 samples at 24/32/44kHz; 12 short slices (<3s) dropped; 5 segments chunked; native sample rate bucket is `32-44k`.
- Confirmed shard and JSONL sidecar consistency with correct sample rates and durations.

**Gaps/Risks vs plan**

- Segments longer than 60s are chunked and retained at 16kHz rather than dropped as planned; behavior decision needed.
- Manifests combine multiple sample rates; training with 16k-only audio requires filtering or per-bucket manifests.
- Pipeline does not create `language_lookup.json` though a script exists; pipeline relies on DB queries.
- Upload verification not performed; larger multi-language smoke tests pending.

**X-Codec-2.0 compatibility**

- `X-Codec-2.0/data_module.py` updated to instantiate datasets via config and includes new `ShardDataset` for tar shards.
- Fixed dataset target in `X-Codec-2.0/config/dataset/default.yaml` from missing class to existing `FSDataset`.
- Added new `X-Codec-2.0/config/dataset/shards.yaml` for training directly from shard manifests.

**Tests**

- Ran `python3 scripts/prepare_shards.py` on sample video and checked outputs.
- Verified shard manifests and audio presence with ad-hoc scripts.
- Could not run training or dataset instantiation tests due to missing `pytorch_lightning`.
- Did not test audio decoding in dataset because of large W2V-BERT download.

**Next Steps**

1. Run larger multi-video smoke tests (50–100 per language) including bandwidth validation and `prepare_shards.py --verify-upload`.
2. Decide on enforcing drop of >60s segments prior to chunking and generating per-bucket manifests or filters.
3. Test X-Codec training with `dataset=shards` after installing dependencies.


Added a shard-based dataset loader to X-Codec-2.0 and fixed the dataset config target so training can consume `prepare_shards.py` tar outputs directly without extraction; also made the DataModule honor the config-defined dataset and accept absolute filelist paths. Changes are in `X-Codec-2.0/data_module.py`, `X-Codec-2.0/config/dataset/default.yaml`, and the new `X-Codec-2.0/config/dataset/shards.yaml`.

**Pipeline Validation**
- R2 sample used: `U2c2AMyTYRo` (telugu). Tar at `/tmp/xcodec_work/downloads/U2c2AMyTYRo.tar` contains 22 segment FLACs and 22 usable metadata entries; max duration diff vs metadata is ~1e-5s; no missing matches.
- Ran `scripts/prepare_shards.py` on that video; outputs in `/tmp/pipeline_test` and stats in `/tmp/pipeline_test/stats/telugu_stats.json`.
- Sharding results: 21 samples in 16k, 16 samples in each 24/32/44 bucket; 12 slices dropped <3s; 5 segments chunked; native SR bucket `32-44k`.
- Verified shard/JSONL consistency for `/tmp/pipeline_test/telugu_16` and `/tmp/pipeline_test/telugu_24`: all entries present in tar, sample rates correct.

**Gaps/Risks vs Strategy**
- Long segments >60s are chunked and kept in 16k if chunking is on; plan says drop >60s entirely—decide the intended behavior.
- Manifests mix all sample rates; 16k-only training must filter by `sample_rate` or use per-bucket manifests.
- `language_lookup.json` is not used in the pipeline (DB is queried directly); offline runs still need wiring if that’s desired.
- Upload integrity checks and larger smoke tests (50–100 videos per language, bandwidth distribution) remain unvalidated.

**X-Codec-2.0 Alignment**
- `X-Codec-2.0/data_module.py` now instantiates the dataset from config and adds `ShardDataset` to read tar shards + JSONL manifests.
- `X-Codec-2.0/config/dataset/default.yaml` target fixed to `FSDataset` (previous target did not exist).
- Added `X-Codec-2.0/config/dataset/shards.yaml` as a template for shard-based training.

**Tests**
- `python3 scripts/prepare_shards.py --language telugu --limit 1 --output-dir /tmp/pipeline_test --tmp-dir /tmp/xcodec_work --download-workers 4 --segment-workers 4`
- Ad-hoc Python validation scripts to compare tar audio vs metadata and to verify shard/JSONL integrity.
- `python3 - <<'PY' import data_module` failed because `pytorch_lightning` is not installed in this environment.

Next steps:
1. Run a broader smoke test (50–100 videos per language) and validate bandwidth distribution; then use `--verify-upload` after an R2 upload.
2. Decide whether to enforce dropping >60s pre-chunk and whether to emit per-bucket manifests.
3. Smoke-train X-Codec with `dataset=shards` once training deps are available, using `/tmp/pipeline_test` as a local sanity check.