Instruction:
## 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.


# AGENTS.md instructions for /home/ubuntu/maya3

<INSTRUCTIONS>
## 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.
</INSTRUCTIONS>


<environment_context>
  <cwd>/home/ubuntu/maya3</cwd>
  <approval_policy>on-request</approval_policy>
  <sandbox_mode>workspace-write</sandbox_mode>
  <network_access>restricted</network_access>
  <shell>bash</shell>
</environment_context>


<environment_context>
  <approval_policy>never</approval_policy>
  <sandbox_mode>danger-full-access</sandbox_mode>
  <network_access>enabled</network_access>
  <shell>bash</shell>
</environment_context>


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.


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.


**Investigating diarization issues**

I need to check if the diarization boundary occurred, but the chunker might not have split properly. I should look for the code responsible, like `chunk_cleaned_audio.py` or `split_by_speaker.py`. I’ll need a clear plan to locate the pipeline, fetch the audio chunk, and analyze it for diarization output issues. The user seems to be asking why there are two speakers and why the system failed to segment. I might utilize audio analysis and see if I can compute relevant features, even though listening isn't an option for me.


**Clarifying AGENTS instructions**

The developer mentioned that I don't need to re-read the root AGENTS content, but I still feel the need to check it for clarity. The user provided custom instructions, but I need to confirm the actual AGENTS details. It seems unclear, especially since the message titled "# AGENTS.md instructions for /home/ubuntu/maya3" refers to skills. I’ll proceed to use shell commands to list repo files and search for configurations related to R2. I plan to use several commands, including `ls` and `rg`, to find necessary details.


rg -n "(cloudflare|\bR2\b|r2\.cloudflarestorage|R2_|S3_ENDPOINT|AWS_ENDPOINT|CLEANED_BUCKET|cleaned bucket|cleaned_bucket|r2_endpoint|boto3)" . || true


find . -maxdepth 3 -name "AGENTS.md" -o -name "*.env*" -o -name "config*.yml" -o -name "config*.yaml" -o -name "*.toml" | head


Output:
Exit code: 0
Wall time: 0 seconds
Output:
dashboard-ui
data
docker-compose.yml
Dockerfile
massive_process.py
metadata.json
output
pipeline.py
__pycache__
R2_chapters_FINAL.csv
README.md
reports
requirements.txt
scripts
src
vast_ai.md
vast_ai_sdk.md


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 198
Output:
./requirements.txt:36:boto3
./Dockerfile:116:# Default command - Supabase queue mode with R2 upload
./Dockerfile:120:# deployments, which makes R2 appear "missing". Prefer sync export for reliability.
./scripts/audit_r2_outputs.py:3:Audit Cloudflare R2 output uploads for Maya3.
./scripts/audit_r2_outputs.py:27:    parser = argparse.ArgumentParser(description="Audit R2 outputs")
./scripts/audit_r2_outputs.py:36:    print(f"env_prefix (R2_PREFIX): {r2.prefix!r}")
./pipeline.py:873:        'source_url': metadata.get('source_url'),  # R2/YouTube URL for re-download
./pipeline.py:1016:                # Record the exact destination (including R2_PREFIX) even though
./pipeline.py:1018:                # locate outputs in R2 from Supabase without guessing prefixes.
./pipeline.py:1052:                # Step 1.5: Remove original audio from output (source is preserved in R2)
./pipeline.py:1057:                        logger.debug(f"Removed _original.flac from output (source in R2)")
./pipeline.py:1065:                # Step 3: Upload to R2 (if enabled)
./pipeline.py:1070:                        logger.info(f"R2 upload skipped (exists): {r2.uri(remote_key)}")
./pipeline.py:1074:                            raise RuntimeError(f"R2 upload failed for {r2.uri(remote_key)}")
./scripts/process_specific_videos.py:3:Process specific videos and optionally upload to R2.
./scripts/process_specific_videos.py:28:# Check if these videos have source audio in R2
./scripts/process_specific_videos.py:41:    # Check R2 source
./scripts/process_specific_videos.py:44:    print(f"  R2 source: {'EXISTS' if exists else 'NOT FOUND'} ({r2_key})")
./scripts/vast/deploy_from_offers.py:19:  - This script loads `.env` by default for Maya3 settings (URL, SUPABASE_ADMIN, R2_*, HF_TOKEN)
./scripts/vast/deploy_from_offers.py:203:        "R2_ENDPOINT_URL",
./scripts/vast/deploy_from_offers.py:204:        "R2_ACCESS_KEY_ID",
./scripts/vast/deploy_from_offers.py:205:        "R2_SECRET_ACCESS_KEY",
./scripts/vast/deploy_from_offers.py:215:        "-e R2_ENDPOINT_URL": os.environ["R2_ENDPOINT_URL"],
./scripts/vast/deploy_from_offers.py:216:        "-e R2_ACCESS_KEY_ID": os.environ["R2_ACCESS_KEY_ID"],
./scripts/vast/deploy_from_offers.py:217:        "-e R2_SECRET_ACCESS_KEY": os.environ["R2_SECRET_ACCESS_KEY"],
./scripts/vast/deploy_from_offers.py:224:    # by explicitly setting R2_PREFIX to empty.
./scripts/vast/deploy_from_offers.py:225:    env_map["-e R2_PREFIX"] = r2_prefix
./scripts/vast/deploy_from_offers.py:272:    p.add_argument("--r2-prefix", default="", help="R2_PREFIX to set (default: empty => upload to bucket root)")
./scripts/audit_completed_r2.py:3:Audit Supabase COMPLETED videos vs Cloudflare R2.
./scripts/audit_completed_r2.py:6:1) Every COMPLETED row has a non-empty R2 reference (typically `r2_tar_key`)
./scripts/audit_completed_r2.py:7:2) The referenced object exists in R2 (via S3 HEAD)
./scripts/audit_completed_r2.py:25:import boto3
./scripts/audit_completed_r2.py:90:def build_s3_client() -> "boto3.client":
./scripts/audit_completed_r2.py:91:    endpoint_url = os.environ.get("R2_ENDPOINT_URL") or os.environ.get("S3_API") or ""
./scripts/audit_completed_r2.py:92:    access_key_id = os.environ.get("R2_ACCESS_KEY_ID") or os.environ.get("AWS_ACCESS_KEY_ID") or ""
./scripts/audit_completed_r2.py:93:    secret_access_key = os.environ.get("R2_SECRET_ACCESS_KEY") or os.environ.get("AWS_SECRET_ACCESS_KEY") or ""
./scripts/audit_completed_r2.py:96:        raise RuntimeError("Missing R2_ENDPOINT_URL (or S3_API) in environment.")
./scripts/audit_completed_r2.py:98:        raise RuntimeError("Missing R2_ACCESS_KEY_ID (or AWS_ACCESS_KEY_ID) in environment.")
./scripts/audit_completed_r2.py:100:        raise RuntimeError("Missing R2_SECRET_ACCESS_KEY (or AWS_SECRET_ACCESS_KEY) in environment.")
./scripts/audit_completed_r2.py:102:    max_pool = int(os.environ.get("R2_MAX_POOL_CONNECTIONS", "128") or 128)
./scripts/audit_completed_r2.py:103:    connect_timeout = int(os.environ.get("R2_CONNECT_TIMEOUT_SEC", "10") or 10)
./scripts/audit_completed_r2.py:104:    read_timeout = int(os.environ.get("R2_READ_TIMEOUT_SEC", "60") or 60)
./scripts/audit_completed_r2.py:106:    return boto3.client(
./scripts/audit_completed_r2.py:188:    ap = argparse.ArgumentParser(description="Audit COMPLETED videos in Supabase vs object existence in R2.")
./scripts/audit_completed_r2.py:190:    ap.add_argument("--workers", type=int, default=int(os.environ.get("R2_AUDIT_WORKERS", "64") or 64))
./scripts/audit_completed_r2.py:234:    env_bucket = (os.environ.get("R2_BUCKET") or "").strip()
./docker-compose.yml:34:      # Persist output (optional if using R2)
./scripts/find_valid_videos.py:2:"""Find COMPLETED videos that have R2 source and local TAR files for validation."""
./scripts/find_valid_videos.py:18:# Get video IDs from R2
./scripts/find_valid_videos.py:19:print("Getting videos from R2 source bucket...")
./scripts/find_valid_videos.py:30:print(f"Found {len(r2_ids)} videos in R…7883 chars truncated…s.environ.get("R2_MAX_POOL_CONNECTIONS", "64") or 64),
./src/r2_client.py:144:        Upload a file to R2.
./src/r2_client.py:164:        Download a file from R2.
./src/r2_client.py:184:        """Upload JSON data directly to R2."""
./src/r2_client.py:201:        """Download JSON data from R2."""
./src/r2_client.py:325:    print("R2 Client Test")
./src/download.py:370:    """Extract video ID from various URL formats (YouTube, R2, etc)."""
./src/download.py:382:    # R2/S3 presigned URL format: extract from path before extension
./src/download.py:383:    # e.g., https://xxx.r2.cloudflarestorage.com/bucket/podcasts/---g_VL7ySo.webm?X-Amz-...
./src/download.py:384:    elif 'r2.cloudflarestorage.com' in video_url or 's3.amazonaws.com' in video_url:
./src/download.py:396:# Per-process cached R2 client for source/original videos (bucket: "test").
./src/download.py:397:# We keep this out of module imports to avoid paying boto3 init cost for
./src/download.py:398:# non-R2 workflows.
./src/download.py:399:_R2_SOURCE_CLIENT = None
./src/download.py:403:    """Lazy-init the R2 client used for reading source videos (bucket_type='source')."""
./src/download.py:404:    global _R2_SOURCE_CLIENT
./src/download.py:405:    if _R2_SOURCE_CLIENT is None:
./src/download.py:410:        # Avoid accidental prefixing from generic R2_PREFIX when accessing the source bucket.
./src/download.py:411:        # Use R2_SOURCE_PREFIX if provided; otherwise default to empty.
./src/download.py:412:        source_prefix = os.environ.get('R2_SOURCE_PREFIX', '')
./src/download.py:421:        _R2_SOURCE_CLIENT = R2Client(config=cfg, bucket_type='source')
./src/download.py:422:    return _R2_SOURCE_CLIENT
./src/download.py:432:    env_prefixes = os.environ.get('R2_SOURCE_PREFIXES', '').strip()
./src/download.py:462:    env_exts = os.environ.get('R2_SOURCE_EXTENSIONS', '').strip()
./src/download.py:493:    Resolve a video_id to an object key in the R2 source bucket.
./src/download.py:538:                logger.info(f"R2 source found: s3://{client.bucket}/{key}")
./src/download.py:543:        f"R2 source not found for {video_id}: tried {tried_count} keys "
./src/download.py:547:        f"R2 source not found: s3://{client.bucket}/{first_key}"
./src/download.py:552:    """Check if URL is an R2/S3 presigned URL."""
./src/download.py:553:    return 'r2.cloudflarestorage.com' in url or ('s3.' in url and 'amazonaws.com' in url)
./src/download.py:558:    Extract clean R2 bucket/key path from a presigned URL.
./src/download.py:561:        Input: https://xxx.r2.cloudflarestorage.com/test/podcasts/VIDEO.webm?X-Amz-...
./src/download.py:583:    Download audio directly from R2 presigned URL using ffmpeg.
./src/download.py:585:    === v2: R2 DIRECT DOWNLOAD ===
./src/download.py:586:    For pre-cached videos in R2, we bypass yt-dlp entirely and use ffmpeg
./src/download.py:596:    logger.info(f"⚡ R2 direct download: {video_id}")
./src/download.py:614:                'title': f'R2 Source: {video_id}',
./src/download.py:630:    # Step 1: Download raw audio from R2 using ffmpeg
./src/download.py:634:            logger.info(f"   R2 download attempt {attempt + 1}/{max_retries}")
./src/download.py:664:            logger.warning(f"   R2 download timeout, attempt {attempt + 1}")
./src/download.py:667:            logger.warning(f"   R2 download attempt {attempt + 1} failed: {e}")
./src/download.py:673:        raise DownloadError(f"R2 download failed after {max_retries} attempts: {last_error}")
./src/download.py:709:    video_title = f'R2 Source: {video_id}'
./src/download.py:873:    logger.info(f"   ✅ R2 download complete: {elapsed:.1f}s, {processed_duration:.0f}s audio")
./src/download.py:878:    # Extract clean R2 path from presigned URL
./src/download.py:895:        'source_url': r2_path,  # Clean R2 path: bucket/key (e.g., test/podcasts/VIDEO.webm)
./src/download.py:957:    Download audio from YouTube URL or R2 presigned URL and prepare for processing.
./src/download.py:967:    === v2: R2 URL SUPPORT ===
./src/download.py:968:    When video_url is an R2 presigned URL, we use ffmpeg directly instead of yt-dlp.
./src/download.py:976:        video_url: YouTube URL or R2 presigned URL
./src/download.py:998:    # === R2 SOURCE MODE (video_id -> R2 object) ===
./src/download.py:1000:    # When enabled, we resolve the video_id to an object in the R2 *source* bucket
./src/download.py:1012:            f"📦 R2 source resolved: s3://{client.bucket}/{remote_key} "
./src/download.py:1031:        logger.info(f"✅ Download (R2): {elapsed:.1f}s | Duration: {metadata.get('processed_duration', 0):.0f}s")
./src/download.py:1034:    # === v2: R2 URL HANDLING ===
./src/download.py:1035:    # R2 presigned URLs use ffmpeg directly (not yt-dlp)
./src/download.py:1037:        logger.info(f"📦 R2 source detected, using ffmpeg direct download")
./src/download.py:1048:        logger.info(f"✅ Download (R2 URL): {elapsed:.1f}s | Duration: {metadata.get('processed_duration', 0):.0f}s")


Output:
Exit code: 0
Wall time: 0 seconds
Output:


Output:
Exit code: 0
Wall time: 0 seconds
Output:
./.env
./.env.example


Summary: auto


rg -n "jp1imJD9w" -S output data reports . 2>/dev/null || true


find output data reports -maxdepth 4 -type d -name "*jp1imJD9w*" 2>/dev/null || true


find output data reports -maxdepth 8 -type f -name "*SPEAKER_00_0021_199.00-204.34.flac*" 2>/dev/null || true


Output:
Exit code: 0
Wall time: 0 seconds
Output:
-rw-rw-r-- 1 ubuntu ubuntu 3704 Jan  6 22:23 .env
-rw-rw-r-- 1 ubuntu ubuntu  470 Jan  6 22:23 .env.example


Output:
Exit code: 0
Wall time: 0 seconds
Output:


Output:
Exit code: 0
Wall time: 0 seconds
Output:


Output:
Exit code: 0
Wall time: 0 seconds
Output:


**Inspecting bucket configuration**

I need to check if the cleaned bucket is configured as `bucket_type='output'` instead of 'source.' It seems like the `r2_client` probably picks the bucket based on the `bucket_type`. So, I’ll take a closer look at the settings and see how everything is set up. This will help ensure I'm working with the right data source. Let's get this inspected!


rg -n "class R2Client|bucket_type|R2_BUCKET|R2_OUTPUT_BUCKET|R2_SOURCE_BUCKET|CLEANED" -n src/r2_client.py pipeline.py src/*.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
R2 Client Utilities for High-Throughput Pipeline Migration

Manages Cloudflare R2 buckets for:
- Production outputs (1-cleaned-data)
- Test outputs during migration (1-cleaned-data-test)
- Baseline snapshots for comparison (1-cleaned-data-baseline)

Usage:
    from src.r2_client import R2Client

    client = R2Client()
    client.upload_json("path/to/file.json", "video_id/metadata.json")
    client.download_json("video_id/metadata.json", "local/path.json")
"""

import os
import json
import logging
from pathlib import Path
from typing import Dict, Any, Optional, List
from dataclasses import dataclass

import boto3
from botocore.config import Config as BotoConfig

logger = logging.getLogger("R2Client")

@dataclass
class R2Config:
    """R2 bucket configuration."""
    endpoint_url: str
    access_key_id: str
    secret_access_key: str
    bucket: str
    prefix: str = ""

# Bucket configurations
BUCKETS = {
    'production': '1-cleaned-data',
    'test': '1-cleaned-data-test',
    'baseline': '1-cleaned-data-baseline',
    # Source/original videos (DO NOT DELETE). This bucket is expected to contain
    # the original YouTube files re-uploaded to R2 to avoid rate limits.
    'source': 'test',
}

def get_r2_config_from_env() -> R2Config:
    """Load R2 configuration from environment variables."""
    return R2Config(
        # Support a few common aliases used in various deploy templates.
        endpoint_url=os.environ.get('R2_ENDPOINT_URL') or os.environ.get('S3_API') or '',
        access_key_id=os.environ.get('R2_ACCESS_KEY_ID') or os.environ.get('AWS_ACCESS_KEY_ID') or '',
        secret_access_key=os.environ.get('R2_SECRET_ACCESS_KEY') or os.environ.get('AWS_SECRET_ACCESS_KEY') or '',
        bucket=os.environ.get('R2_BUCKET', 'test'),
        prefix=os.environ.get('R2_PREFIX', ''),
    )

class R2Client:
    """
    Cloudflare R2 client for pipeline data management.

    Supports multiple bucket targets for A/B testing during migration.
    """

    def __init__(
        self,
        config: Optional[R2Config] = None,
        bucket_type: str = 'test'
    ):
        """
        Initialize R2 client.

        Args:
            config: R2 configuration (loads from env if None)
            bucket_type: One of 'production', 'test', 'baseline'
        """
        self.config = config or get_r2_config_from_env()

        # Override bucket based on type
        if bucket_type in BUCKETS:
            self.bucket = BUCKETS[bucket_type]
        else:
            self.bucket = self.config.bucket

        # IMPORTANT: The output prefix (R2_PREFIX) should not accidentally apply
        # to the *source* bucket. Vast/RunPod templates often set R2_PREFIX for
        # uploads; for source reads we want R2_SOURCE_PREFIX (or empty).
        if bucket_type == 'source' and config is None:
            self.prefix = os.environ.get('R2_SOURCE_PREFIX', '') or ''
        else:
            self.prefix = self.config.prefix

        # Initialize boto3 client
        self._client = None

    @property
    def client(self):
        """Lazy-initialize boto3 client."""
        if self._client is None:
            if not self.config.endpoint_url:
                raise RuntimeError("R2 not configured: missing R2_ENDPOINT_URL (or S3_API)")
            if not self.config.access_key_id:
                raise RuntimeError("R2 not configured: missing R2_ACCESS_KEY_ID (or AWS_ACCESS_KEY_ID)")
            if not self.config.secret_access_key:
                raise RuntimeError("R2 not configured: missing R2_SECRET_ACCESS_KEY (or AWS_SECRET_ACCESS_KEY)")
            self._client = boto3.client(
                's3',
                endpoint_url=self.config.endpoint_url,
                aws_access_key_id=self.config.access_key_id,
                aws_secret_access_key=self.config.secret_access_key,
                config=BotoConfig(
                    signature_version='s3v4',
                    retries={'max_attempts': 3, 'mode': 'adaptive'},
                    # Prevent long hangs on HEAD/LIST during intermittent network stalls.
                    connect_timeout=int(os.environ.get("R2_CONNECT_TIMEOUT_SEC", "10") or 10),
                    read_timeout=int(os.environ.get("R2_READ_TIMEOUT_SEC", "60") or 60),
                    max_pool_connections=int(os.environ.get("R2_MAX_POOL_CONNECTIONS", "64") or 64),
                )
            )
        return self._client

    def full_key(self, key: str) -> str:
        """Public helper: return fully-qualified key (including configured prefix)."""
        return self._full_key(key)

    def uri(self, key: str) -> str:
        """Return an s3:// URI for a key (including configured prefix)."""
        return f"s3://{self.bucket}/{self._full_key(key)}"

    def _full_key(self, key: str) -> str:
        """Get full S3 key with prefix."""
        if self.prefix:
            return f"{self.prefix}/{key}"
        return key

    def upload_file(self, local_path: str, remote_key: str) -> bool:
        """
        Upload a file to R2.

        Args:
            local_path: Path to local file
            remote_key: Remote key (path in bucket)

        Returns:
            True if successful
        """
        try:
            full_key = self._full_key(remote_key)
            self.client.upload_file(local_path, self.bucket, full_key)
            logger.info(f"Uploaded {local_path} -> s3://{self.bucket}/{full_key}")
            return True
        except Exception as e:
            logger.error(f"Upload failed: {e}")
            return False

    def download_file(self, remote_key: str, local_path: str) -> bool:
        """
        Download a file from R2.

        Args:
            remote_key: Remote key (path in bucket)
            local_path: Path to save locally

        Returns:
            True if successful
        """
        try:
            full_key = self._full_key(remote_key)
            Path(local_path).parent.mkdir(parents=True, exist_ok=True)
            self.client.download_file(self.bucket, full_key, local_path)
            logger.info(f"Downloaded s3://{self.bucket}/{full_key} -> {local_path}")
            return True
        except Exception as e:
            logger.error(f"Download failed: {e}")
            return False

    def upload_json(self, data: Dict[str, Any], remote_key: str) -> bool:
        """Upload JSON data directly to R2."""
        try:
            full_key = self._full_key(remote_key)
            body = json.dumps(data, indent=2).encode('utf-8')
            self.client.put_object(
                Bucket=self.bucket,
                Key=full_key,
                Body=body,
                ContentType='application/json'
            )
            logger.info(f"Uploaded JSON -> s3://{self.bucket}/{full_key}")
            return True
        except Exception as e:
            logger.error(f"JSON upload failed: {e}")
            return False

    def download_json(self, remote_key: str) -> Optional[Dict[str, Any]]:
        """Download JSON data from R2."""
        try:
            full_key = self._full_key(remote_key)
            response = self.client.get_object(Bucket=self.bucket, Key=full_key)
            data = json.loads(response['Body'].read().decode('utf-8'))
            logger.info(f"Downloaded JSON <- s3://{self.bucket}/{full_key}")
            return data
        except Exception as e:
            logger.error(f"JSON download failed: {e}")
            return None

    def head_object(self, remote_key: str) -> Optional[Dict[str, Any]]:
        """Return S3 head_object response for a key, or None if missing/unreadable."""
        try:
            full_key = self._full_key(remote_key)
            return self.client.head_object(Bucket=self.bucket, Key=full_key)
        except Exception:
            return None

    def generate_presigned_get_url(self, remote_key: str, expires_in_sec: int = 3600) -> str:


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    def generate_presigned_get_url(self, remote_key: str, expires_in_sec: int = 3600) -> str:
        """
        Generate a presigned GET URL for an object.

        Note: This is a Class B request pattern (no bucket listing).
        """
        full_key = self._full_key(remote_key)
        return self.client.generate_presigned_url(
            'get_object',
            Params={'Bucket': self.bucket, 'Key': full_key},
            ExpiresIn=expires_in_sec,
        )

    def list_keys(self, prefix: str = "", limit: int = 1000) -> List[str]:
        """
        List keys in bucket with optional prefix.

        Args:
            prefix: Additional prefix to filter by
            limit: Maximum keys to return

        Returns:
            List of keys
        """
        try:
            full_prefix = self._full_key(prefix) if prefix else self.prefix
            response = self.client.list_objects_v2(
                Bucket=self.bucket,
                Prefix=full_prefix,
                MaxKeys=limit
            )

            keys = []
            for obj in response.get('Contents', []):
                key = obj['Key']
                # Remove prefix for cleaner output
                if self.prefix and key.startswith(self.prefix):
                    key = key[len(self.prefix):].lstrip('/')
                keys.append(key)

            return keys
        except Exception as e:
            logger.error(f"List failed: {e}")
            return []

    def exists(self, remote_key: str) -> bool:
        """Check if a key exists in the bucket."""
        try:
            full_key = self._full_key(remote_key)
            self.client.head_object(Bucket=self.bucket, Key=full_key)
            return True
        except:
            return False

    def delete(self, remote_key: str) -> bool:
        """Delete a key from the bucket."""
        try:
            # Safety guard: user explicitly forbids deletions from any bucket other
            # than 1-cleaned-data. Keep this invariant even if callers misuse API.
            if self.bucket != '1-cleaned-data':
                raise PermissionError(
                    f"Refusing to delete from bucket '{self.bucket}'. "
                    f"Only '1-cleaned-data' deletions are permitted."
                )
            full_key = self._full_key(remote_key)
            self.client.delete_object(Bucket=self.bucket, Key=full_key)
            logger.info(f"Deleted s3://{self.bucket}/{full_key}")
            return True
        except Exception as e:
            logger.error(f"Delete failed: {e}")
            return False

def copy_to_baseline(video_ids: List[str], source_type: str = 'production') -> int:
    """
    Copy video outputs from source bucket to baseline bucket.

    Args:
        video_ids: List of video IDs to copy
        source_type: Source bucket type ('production' or 'test')

    Returns:
        Number of successfully copied videos
    """
    source = R2Client(bucket_type=source_type)
    baseline = R2Client(bucket_type='baseline')

    copied = 0
    for video_id in video_ids:
        # Copy metadata.json
        metadata_key = f"{video_id}/metadata.json"
        data = source.download_json(metadata_key)
        if data and baseline.upload_json(data, metadata_key):
            copied += 1
            logger.info(f"Copied {video_id} to baseline")
        else:
            logger.warning(f"Failed to copy {video_id}")

    return copied

if __name__ == "__main__":
    # Quick test
    import sys

    print("R2 Client Test")
    print("-" * 40)

    client = R2Client(bucket_type='test')

    # List some keys
    print(f"Bucket: {client.bucket}")
    print(f"Prefix: {client.prefix}")

    keys = client.list_keys(limit=5)
    print(f"Sample keys: {keys[:5]}")


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/export.py:35:    r2_bucket_type: str = "production"
src/export.py:174:            r2 = R2Client(bucket_type=job.r2_bucket_type)
pipeline.py:1008:                    r2_bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'),
pipeline.py:1020:                    r2 = R2Client(bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'))
pipeline.py:1068:                    r2 = R2Client(bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'))
src/r2_client.py:59:        bucket=os.environ.get('R2_BUCKET', 'test'),
src/r2_client.py:64:class R2Client:
src/r2_client.py:74:        bucket_type: str = 'test'
src/r2_client.py:81:            bucket_type: One of 'production', 'test', 'baseline'
src/r2_client.py:86:        if bucket_type in BUCKETS:
src/r2_client.py:87:            self.bucket = BUCKETS[bucket_type]
src/r2_client.py:94:        if bucket_type == 'source' and config is None:
src/r2_client.py:304:    source = R2Client(bucket_type=source_type)
src/r2_client.py:305:    baseline = R2Client(bucket_type='baseline')
src/r2_client.py:328:    client = R2Client(bucket_type='test')
src/config.py:63:    r2_upload_bucket_type: str = "production"  # maps to 1-cleaned-data in src/r2_client.py
src/download.py:403:    """Lazy-init the R2 client used for reading source videos (bucket_type='source')."""
src/download.py:418:            bucket=base.bucket,  # overridden by bucket_type='source'
src/download.py:421:        _R2_SOURCE_CLIENT = R2Client(config=cfg, bucket_type='source')
src/download.py:1001:    # (bucket_type='source' -> bucket name "test") using HEAD requests only.
src/r2_client.py:59:        bucket=os.environ.get('R2_BUCKET', 'test'),
src/r2_client.py:64:class R2Client:
src/r2_client.py:74:        bucket_type: str = 'test'
src/r2_client.py:81:            bucket_type: One of 'production', 'test', 'baseline'
src/r2_client.py:86:        if bucket_type in BUCKETS:
src/r2_client.py:87:            self.bucket = BUCKETS[bucket_type]
src/r2_client.py:94:        if bucket_type == 'source' and config is None:
src/r2_client.py:304:    source = R2Client(bucket_type=source_type)
src/r2_client.py:305:    baseline = R2Client(bucket_type='baseline')
src/r2_client.py:328:    client = R2Client(bucket_type='test')


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 132
drwxrwxr-x  7 ubuntu ubuntu  4096 Jan  8 15:31 .
drwxrwxr-x 14 ubuntu ubuntu  4096 Jan  8 17:11 ..
-rw-rw-r--  1 ubuntu ubuntu 15729 Jan  8 16:04 audit_completed_r2.py
-rw-rw-r--  1 ubuntu ubuntu  1241 Jan  8 05:30 audit_r2_outputs.py
-rw-rw-r--  1 ubuntu ubuntu  2157 Jan  6 22:23 check_videos.py
drwxrwxr-x  3 ubuntu ubuntu  4096 Jan  8 06:02 deploy
drwxrwxr-x  2 ubuntu ubuntu  4096 Jan  8 00:31 docker
-rw-rw-r--  1 ubuntu ubuntu  2705 Jan  6 22:23 find_valid_videos.py
-rw-rw-r--  1 ubuntu ubuntu  2679 Jan  6 22:23 fix_rtf.py
drwxrwxr-x  2 ubuntu ubuntu  4096 Jan  8 00:31 monitor
-rw-rw-r--  1 ubuntu ubuntu  6123 Jan  6 22:23 monitor_analytics.py
-rw-rw-r--  1 ubuntu ubuntu  5100 Jan  6 22:23 prepare_benchmark.py
-rw-rw-r--  1 ubuntu ubuntu  1885 Jan  6 22:23 process_specific_videos.py
drwxrwxr-x  2 ubuntu ubuntu  4096 Jan  8 05:40 __pycache__
-rw-rw-r--  1 ubuntu ubuntu 10411 Jan  8 05:40 reset_for_production.py
-rw-rw-r--  1 ubuntu ubuntu  8164 Jan  6 22:23 run_analytics_migration.py
-rwxrwxr-x  1 ubuntu ubuntu   723 Jan  6 22:23 run_migration.sh
-rw-rw-r--  1 ubuntu ubuntu 13647 Jan  6 22:23 supabase_migrations.sql
-rw-rw-r--  1 ubuntu ubuntu  6091 Jan  6 22:23 sync_csv_to_supabase.py
-rw-rw-r--  1 ubuntu ubuntu  3682 Jan  6 22:23 test_analytics.py
drwxrwxr-x  3 ubuntu ubuntu  4096 Jan  8 06:46 vast


set -a; source .env; set +a; python - <<'PY'
from src.r2_client import R2Client

video_id = "--jp1imJD9w"
r2 = R2Client(bucket_type='production')
keys = r2.list_keys(prefix=f"{video_id}/", limit=2000)
print("bucket", r2.bucket)
print("prefix", r2.prefix)
print("found", len(keys))
for k in keys:
    if "SPEAKER_00_0021_199.00-204.34.flac" in k:
        print("MATCH", k)
PY


Output:
Exit code: 127
Wall time: 0 seconds
Output:
/bin/bash: line 1: python: command not found


Output:
Exit code: 0
Wall time: 0 seconds
Output:
Python 3.10.12


set -a; source .env; set +a; python3 - <<'PY'
from src.r2_client import R2Client

video_id = "--jp1imJD9w"
r2 = R2Client(bucket_type='production')
keys = r2.list_keys(prefix=f"{video_id}/", limit=2000)
print("bucket", r2.bucket)
print("prefix", r2.prefix)
print("found", len(keys))
for k in keys:
    if "SPEAKER_00_0021_199.00-204.34.flac" in k:
        print("MATCH", k)
PY


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
bucket 1-cleaned-data
prefix cleaned/trail
found 0


**Inspecting pipeline exports**

I'm considering the pipeline export process. It seems like the outputs could be tar files per video using the `r2_tar_key`, which might place chunk files inside the tar. However, the user mentions pulling up a specific chunk file from R2's cleaned bucket, which suggests it might exist as an individual object. I plan to check `pipeline.py` around line 1065 to review the upload step for R2, likely involving a `.tar` or zipped output.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    background_export = getattr(config, 'background_export', False)

    if export_enabled:
        log_stage(10, "Export", "START")

        upload_prefix = (getattr(config, 'r2_upload_prefix', '') or '').strip().strip('/')
        remote_key = f"{video_id}.tar" if not upload_prefix else f"{upload_prefix}/{video_id}.tar"
        if getattr(config, 'r2_upload_enabled', False):
            result['r2_upload_key'] = remote_key
        original_audio_path = Path(metadata.get('original_audio_path', ''))
        intro_offset = metadata.get('intro_skipped', 0.0)

        if background_export:
            # === BACKGROUND EXPORT MODE ===
            # Submit job to background manager, don't block GPU
            from src.export import ExportJob, get_export_manager
            from src.r2_client import R2Client

            export_manager = get_export_manager()
            if export_manager:
                job = ExportJob(
                    video_id=video_id,
                    output_dir=output_dir,
                    original_audio_path=original_audio_path,
                    segments=segments,
                    intro_offset=intro_offset,
                    r2_upload_enabled=getattr(config, 'r2_upload_enabled', False),
                    r2_upload_key=remote_key,
                    r2_bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'),
                    include_original_in_tar=getattr(config, 'include_original_in_tar', False),
                    delete_output_dir_after_upload=getattr(config, 'r2_upload_delete_local_output_dir', False),
                )
                export_manager.submit(job)
                log_stage(10, "Export", "QUEUED", f"background job submitted")
                result['export_mode'] = 'background'
                result['r2_upload_key'] = remote_key
                # Record the exact destination (including R2_PREFIX) even though
                # the upload happens asynchronously. This makes it possible to
                # locate outputs in R2 from Supabase without guessing prefixes.
                if getattr(config, 'r2_upload_enabled', False):
                    r2 = R2Client(bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'))
                    result.setdefault('export', {})
                    result['export'].update({
                        'r2_upload_bucket': r2.bucket,
                        'r2_upload_full_key': r2.full_key(remote_key),
                        'r2_upload_uri': r2.uri(remote_key),
                    })
            else:
                logger.warning("Background export manager not initialized, falling back to sync")
                background_export = False  # Fall through to sync mode

        if not background_export:
            # === SYNCHRONOUS EXPORT MODE ===
            from src.r2_client import R2Client
            from src.export import segment_audio_parallel

            with COMPUTE.monitor_stage("Export") as metrics:
                # Step 1: Segment original audio (parallel ffmpeg)
                segments_exported = 0
                if getattr(config, 'segment_audio', True) and original_audio_path.exists():
                    seg_start = time.time()
                    segments_exported = segment_audio_parallel(
                        audio_path=original_audio_path,
                        segments=segments,
                        output_dir=output_dir,
                        intro_offset=intro_offset,
                        max_workers=8,
                        output_format="flac"
                    )
                    seg_time = time.time() - seg_start
                    logger.info(f"📦 Segmented {segments_exported} clips in {seg_time:.1f}s")

                # Step 1.5: Remove original audio from output (source is preserved in R2)
                if not getattr(config, 'include_original_in_tar', False):
                    original_in_output = output_dir / f"{video_id}_original.flac"
                    if original_in_output.exists():
                        original_in_output.unlink()
                        logger.debug(f"Removed _original.flac from output (source in R2)")

                # Step 2: Create tar archive
                tar_path = output_dir.parent / f"{video_id}.tar"
                with tarfile.open(tar_path, "w") as tar:
                    tar.add(output_dir, arcname=video_id)
                tar_size_bytes = tar_path.stat().st_size if tar_path.exists() else 0

                # Step 3: Upload to R2 (if enabled)
                upload_ok = True
                if getattr(config, 'r2_upload_enabled', False):
                    r2 = R2Client(bucket_type=getattr(config, 'r2_upload_bucket_type', 'production'))
                    if getattr(config, 'r2_upload_skip_if_exists', False) and r2.exists(remote_key):
                        logger.info(f"R2 upload skipped (exists): {r2.uri(remote_key)}")
                    else:
                        upload_ok = r2.upload_file(str(tar_path), remote_key)
                        if not upload_ok:
                            raise RuntimeError(f"R2 upload failed for {r2.uri(remote_key)}")

                    # Delete local tar after upload
                    if getattr(config, 'r2_upload_delete_local_tar', True):
                        try:
                            tar_path.unlink()
                        except Exception:
                            pass

                    # Delete per-video output dir after successful upload (prevents disk fill).
                    if getattr(config, 'r2_upload_enabled', False) and getattr(config, 'r2_upload_delete_local_output_dir', False):
                        try:
                            import shutil
                            shutil.rmtree(output_dir, ignore_errors=True)
                        except Exception:
                            pass

            timings['export'] = metrics.duration
            compute_stats['export'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
            result['export'] = {
                'segments_exported': segments_exported,
                'tar_size_bytes': int(tar_size_bytes),
                'r2_upload_key': remote_key if getattr(config, 'r2_upload_enabled', False) else None,
                'r2_upload_bucket': r2.bucket if getattr(config, 'r2_upload_enabled', False) else None,
                'r2_upload_full_key': r2.full_key(remote_key) if getattr(config, 'r2_upload_enabled', False) else None,
                'r2_upload_uri': r2.uri(remote_key) if getattr(config, 'r2_upload_enabled', False) else None,
            }

            log_stage(10, "Export", "DONE", f"{timings['export']:.1f}s | {segments_exported} segs | {tar_size_bytes/(1024**2):.1f}MB")

            # Update timing_total
            total_time = time.time() - total_start
            result['timing'] = {k: round(v, 2) for k, v in timings.items()}
            result['timing_total'] = round(total_time, 2)
            result['compute_stats'] = compute_stats

            # Update metadata.json
            try:
                with open(output_dir / "metadata.json", 'w') as f:
                    json.dump(result, f, indent=2)
            except Exception:
                pass

    # Summary (after optional upload so timing_total is end-to-end)
    final_total = result.get('timing_total', round(total_time, 2))
    plog.done(
        video_id=video_id,


set -a; source .env; set +a; python3 - <<'PY'
from src.r2_client import R2Client
video_id='--jp1imJD9w'
r2=R2Client(bucket_type='production')
for p in [video_id, f"{video_id}.tar", "cleaned", ""]:
    keys=r2.list_keys(prefix=p, limit=50)
    print('prefix',repr(p),'->',len(keys))
    for k in keys[:10]:
        print(' ',k)
    print()
PY


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
prefix '--jp1imJD9w' -> 0

prefix '--jp1imJD9w.tar' -> 0

prefix 'cleaned' -> 0

prefix '' -> 50
  --Z4eGnAcPE.tar
  --f7Dr9NQHk.tar
  -11vKmnwmBI.tar
  -1BRVKD4t98.tar
  -1aOFdt6iY0.tar
  -2SASQMStwE.tar
  -2SrNk7yhmM.tar
  -2hrI1LeoUU.tar
  -3Fj9qZh988.tar
  -3Qtb7kTa9U.tar


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# Maya3 Pipeline Environment Variables
# Copy this file to .env and fill in your values

# Hugging Face (required for PyAnnote models)
HF_TOKEN=hf_your_token_here

# Supabase (required for queue management)
URL=https://your-project.supabase.co
SUPABASE_ADMIN=your_service_role_key_here

# Cloudflare R2 (required for audio source and output)
ACCOUNT_ID=your_cloudflare_account_id
API_TOKEN=your_r2_api_token

# Optional: RunPod deployment
RUNPOD_KEY=your_runpod_api_key


**Executing configuration setup**

I’m planning to use an empty prefix config before calling list_keys with the prefix string `cleaned/--jp1imJD9w`. I need to create the R2Config specifying the bucket as `1-cleaned-data` and the prefix as an empty string. I’ll utilize the environment for the endpoint and credentials, ensuring I don’t print any sensitive information. Let's go ahead and run this!


set -a; source .env; set +a; python3 - <<'PY'
import os
from src.r2_client import R2Client, R2Config

video_id='--jp1imJD9w'
base = R2Client(bucket_type='production')
# clone config but empty prefix
cfg = R2Config(
    endpoint_url=base.config.endpoint_url,
    access_key_id=base.config.access_key_id,
    secret_access_key=base.config.secret_access_key,
    bucket=base.bucket,
    prefix="",
)
r2 = R2Client(config=cfg, bucket_type=base.bucket)  # bucket_type ignored when not in BUCKETS? actually if bucket_type in BUCKETS then overrides; pass bucket name to avoid override

# Workaround: R2Client expects bucket_type str; if not in BUCKETS, uses config.bucket.
r2 = R2Client(config=cfg, bucket_type='custom')

candidates = [
    f"{video_id}.tar",
    f"cleaned/{video_id}.tar",
    f"cleaned/trail/{video_id}.tar",
    f"trail/{video_id}.tar",
    f"production/{video_id}.tar",
    f"cleaned/production/{video_id}.tar",
]

for key in candidates:
    ok = r2.exists(key)
    print(('FOUND' if ok else 'MISS').ljust(5), key)
PY


Output:
Exit code: 0
Wall time: 0.8 seconds
Output:
FOUND --jp1imJD9w.tar
MISS  cleaned/--jp1imJD9w.tar
MISS  cleaned/trail/--jp1imJD9w.tar
MISS  trail/--jp1imJD9w.tar
MISS  production/--jp1imJD9w.tar
MISS  cleaned/production/--jp1imJD9w.tar


**Planning file download**

Alright, the tar file sits at the bucket root, which simplifies things since our `R2_PREFIX` isn't needed. I can download it using a custom client with an empty prefix or adjust my commands accordingly. I’ll create a local folder called `tmp_debug/jp1imJD9w` and utilize `R2Client` to handle the download without deleting anything. The file might be large, like a few hundred MB. I plan to extract only necessary flac files to save time and space, checking the internal paths during extraction.


**Exploring segmentation code**

I’m going to check out the `src/export.py` file, focusing on the segmentation section. I want to understand how it's implemented and how it might relate to our current task. I’ll look for specific functions or logic that deal with segmentation, as that could help clarify what we need to do next. It feels like a good step to take in ensuring everything is correctly set up! Let’s see what I find in there.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Background Export Manager - Async audio segmentation and R2 upload.

Runs export jobs in background threads so GPU processing isn't blocked.
Properly handles shutdown and waits for in-flight jobs to complete.
"""

import os
import json
import time
import logging
import subprocess
import tarfile
from pathlib import Path
from typing import Dict, List, Optional, Any
from concurrent.futures import ThreadPoolExecutor, Future, as_completed
from dataclasses import dataclass, field
from threading import Lock
from queue import Queue

logger = logging.getLogger("FastPipelineV6.Export")

@dataclass
class ExportJob:
    """A single export job (segmentation + optional R2 upload)."""
    video_id: str
    output_dir: Path
    original_audio_path: Path
    segments: List[Dict]
    intro_offset: float = 0.0
    r2_upload_enabled: bool = False
    r2_upload_key: str = ""
    r2_bucket_type: str = "production"
    include_original_in_tar: bool = False  # Don't include _original.flac in TAR (source is in R2)
    submitted_at: float = field(default_factory=time.time)

@dataclass
class ExportResult:
    """Result of an export job."""
    video_id: str
    success: bool
    segments_exported: int = 0
    export_time_sec: float = 0.0
    r2_upload_time_sec: float = 0.0
    tar_size_bytes: int = 0
    error: Optional[str] = None

def segment_audio_parallel(
    audio_path: Path,
    segments: List[Dict],
    output_dir: Path,
    intro_offset: float = 0.0,
    max_workers: int = 8,
    output_format: str = "flac"
) -> int:
    """
    Cut audio into individual speaker segment files using parallel ffmpeg.

    Args:
        audio_path: Path to original quality audio (FLAC)
        segments: List of segment dicts with 'start', 'end', 'speaker', 'status'
        output_dir: Directory to write segment files
        intro_offset: Offset already applied to audio (for timestamp adjustment)
        max_workers: Number of parallel ffmpeg processes
        output_format: Output format (flac, wav, mp3)

    Returns:
        Number of segments exported
    """
    # Filter usable segments only
    usable = [s for s in segments if s.get('status') == 'usable']
    if not usable:
        logger.warning(f"No usable segments to export")
        return 0

    segments_dir = output_dir / "segments"
    segments_dir.mkdir(exist_ok=True)

    def cut_segment(seg: Dict, idx: int) -> Optional[str]:
        """Cut a single segment using ffmpeg."""
        try:
            speaker = seg.get('speaker', 'UNKNOWN')
            start = seg['start']
            end = seg['end']
            duration = end - start

            # Filename: SPEAKER_IDX_START-END.flac
            filename = f"{speaker}_{idx:04d}_{start:.2f}-{end:.2f}.{output_format}"
            out_path = segments_dir / filename

            cmd = [
                'ffmpeg', '-y', '-hide_banner', '-loglevel', 'error',
                '-ss', str(start),
                '-t', str(duration),
                '-i', str(audio_path),
                '-c:a', 'flac' if output_format == 'flac' else 'copy',
                str(out_path)
            ]

            result = subprocess.run(cmd, capture_output=True, timeout=30)
            if result.returncode != 0:
                logger.warning(f"ffmpeg failed for segment {idx}: {result.stderr.decode()[:100]}")
                return None

            return str(out_path)
        except Exception as e:
            logger.warning(f"Failed to cut segment {idx}: {e}")
            return None

    # Run parallel ffmpeg
    exported = 0
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = {
            executor.submit(cut_segment, seg, i): i
            for i, seg in enumerate(usable)
        }
        for future in as_completed(futures):
            result = future.result()
            if result:
                exported += 1

    return exported

def run_export_job(job: ExportJob) -> ExportResult:
    """
    Execute a complete export job (segmentation + tar + optional R2 upload).

    This runs in a background thread and should not block GPU processing.
    """
    start_time = time.time()
    result = ExportResult(video_id=job.video_id, success=False)

    try:
        # Step 1: Segment audio (parallel ffmpeg)
        if job.original_audio_path.exists():
            seg_start = time.time()
            result.segments_exported = segment_audio_parallel(
                audio_path=job.original_audio_path,
                segments=job.segments,
                output_dir=job.output_dir,
                intro_offset=job.intro_offset,
                max_workers=8,
                output_format="flac"
            )
            result.export_time_sec = time.time() - seg_start
            logger.info(f"[{job.video_id}] Segmented {result.segments_exported} clips in {result.export_time_sec:.1f}s")
        else:
            logger.warning(f"[{job.video_id}] Original audio not found: {job.original_audio_path}")

        # Step 2: Remove original audio from output dir if not needed in TAR
        # (Original is already preserved in R2 source bucket)
        if not job.include_original_in_tar:
            original_in_output = job.output_dir / f"{job.video_id}_original.flac"
            if original_in_output.exists():
                original_in_output.unlink()
                logger.debug(f"[{job.video_id}] Removed _original.flac from TAR (source in R2)")

        # Step 3: Create tar archive
        tar_path = job.output_dir.parent / f"{job.video_id}.tar"
        with tarfile.open(tar_path, "w") as tar:
            tar.add(job.output_dir, arcname=job.video_id)
        result.tar_size_bytes = tar_path.stat().st_size if tar_path.exists() else 0

        # Step 4: R2 upload (if enabled)
        if job.r2_upload_enabled and job.r2_upload_key:
            from src.r2_client import R2Client

            upload_start = time.time()
            r2 = R2Client(bucket_type=job.r2_bucket_type)
            upload_ok = r2.upload_file(str(tar_path), job.r2_upload_key)
            result.r2_upload_time_sec = time.time() - upload_start

            if not upload_ok:
                raise RuntimeError(f"R2 upload failed: {r2.uri(job.r2_upload_key)}")

            logger.info(
                f"[{job.video_id}] Uploaded: {r2.uri(job.r2_upload_key)} "
                f"in {result.r2_upload_time_sec:.1f}s"
            )

            # Delete local tar after successful upload
            try:
                tar_path.unlink()
            except:
                pass

        result.success = True

    except Exception as e:
        result.error = str(e)
        logger.error(f"[{job.video_id}] Export failed: {e}")

    return result

class BackgroundExportManager:
    """
    Manages background export jobs with proper lifecycle handling.

    Usage:
        manager = BackgroundExportManager(max_workers=4)
        manager.start()

        # Submit jobs (non-blocking)
        manager.submit(job1)
        manager.submit(job2)

        # At shutdown, wait for completion
        results = manager.shutdown(wait=True, timeout=300)
    """

    def __init__(self, max_workers: int = 4):
        self.max_workers = max_workers
        self.executor: Optional[ThreadPoolExecutor] = None
        self.pending_futures: Dict[str, Future] = {}
        self.results: Dict[str, ExportResult] = {}
        self.lock = Lock()
        self._started = False

    def start(self):
        """Start the background export manager."""
        if self._started:
            return
        self.executor = ThreadPoolExecutor(
            max_workers=self.max_workers,
            thread_name_prefix="export_worker"
        )
        self._started = True
        logger.info(f"BackgroundExportManager started with {self.max_workers} workers")

    def submit(self, job: ExportJob) -> bool:
        """
        Submit an export job to run in background.

        Returns True if submitted successfully, False otherwise.
        """
        if not self._started or not self.executor:
            logger.error("ExportManager not started")
            return False

        try:
            future = self.executor.submit(run_export_job, job)
            with self.lock:
                self.pending_futures[job.video_id] = future
            logger.debug(f"Submitted export job: {job.video_id}")
            return True
        except Exception as e:
            logger.error(f"Failed to submit export job: {e}")
            return False

    def get_pending_count(self) -> int:
        """Get number of pending export jobs."""
        with self.lock:
            return len(self.pending_futures)


rg -n "pyannote|diariz|speaker|overlap|segmen(t|ation)" pipeline.py src | head -200


rg -n "segments\s*=\s*\[|def .*segment|build_segments|merge" pipeline.py src | head -200


rg -n "status'\s*:\s*'usable'|status\s*==\s*'usable'" -S pipeline.py src | head -200


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 200
Output:
pipeline.py:32:- Chunk-based reassignment: Detects within-segment speaker changes
pipeline.py:41:- In-memory diarization chunks: No disk writes
pipeline.py:52:7. Conservative clustering (uses cached segment embeddings)
pipeline.py:81:from src.overlap_detection import detect_nonspeech_regions, split_on_overlaps, filter_by_quality
pipeline.py:82:# NOTE: segmentation module disabled - community-1 handles diarization+OSD in one pass
pipeline.py:83:# from src.segmentation import detect_speaker_changes_framelevel, get_frame_level_activations
pipeline.py:84:from src.diarization import create_chunks, run_diarization
pipeline.py:86:from src.clustering import merge_speakers, merge_adjacent_segments
pipeline.py:99:    build_segment_embeddings_from_cache,
pipeline.py:103:# Detect music/instruments to mark segments as clean/needs_demucs/heavy_music
pipeline.py:107:    build_segment_music_stats_batch
pipeline.py:114:    build_segment_music_stats_hybrid
pipeline.py:142:    3. OSD (GPU) - mark overlaps as unusable FIRST
pipeline.py:144:    5. Diarization (GPU) - speaker identification
pipeline.py:145:    6. Embeddings (GPU) - speaker vectors
pipeline.py:186:            # Extract duration from extra (e.g., "2.1s" or "2.1s | 45 segments")
pipeline.py:210:        # so end-to-end benchmark metrics include source I/O even when overlapped.
pipeline.py:242:        vad_segments = run_vad_parallel(audio_path, config, audio_buffer=audio_buffer)
pipeline.py:245:    log_stage(2, "VAD", "DONE", f"{timings['vad']:.1f}s | {len(vad_segments)} segments")
pipeline.py:250:    # Pass waveform tensors directly to diarization
pipeline.py:254:        chunks = create_chunks(audio_path, vad_segments, config,
pipeline.py:263:    # Previously: OSD ran diarization, then Diarization stage ran it again
pipeline.py:264:    # Now: Single diarization pass extracts BOTH segments AND overlaps
pipeline.py:268:    overlap_segments = []
pipeline.py:269:    nonspeech_segments = []
pipeline.py:272:        # Run unified diarization + overlap extraction
pipeline.py:273:        segments, overlap_segments = run_diarization(
pipeline.py:275:            extract_overlaps=config.detect_overlap
pipeline.py:279:        nonspeech_segments = detect_nonspeech_regions(vad_segments, total_duration)
pipeline.py:281:    timings['diarization'] = metrics.duration
pipeline.py:282:    compute_stats['diarization'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
pipeline.py:283:    log_stage(4, "Diarization+OSD", "DONE", f"{timings['diarization']:.1f}s | {len(segments)} segments")
pipeline.py:286:    # STAGE 4b: Split segments on overlaps (salvage clean portions)
pipeline.py:288:    # Previously: remove_overlap_regions() dropped entire segments if >50% overlap
pipeline.py:289:    # Now: split_on_overlaps() salvages clean portions around overlaps
pipeline.py:291:    if overlap_segments and config.detect_overlap:
pipeline.py:292:        original_count = len(segments)
pipeline.py:293:        segments = split_on_overlaps(segments, overlap_segments, min_segment=config.min_segment_duration)
pipeline.py:294:        logger.info(f"   Split overlaps: {original_count} → {len(segments)} segments (salvaged clean portions)")
pipeline.py:299:    # Mark low-quality segments as unusable to protect TTS training data
pipeline.py:301:    low_quality_segments = []
pipeline.py:304:            segments, low_quality_segments = filter_by_quality(
pipeline.py:305:                segments, audio_path,
pipeline.py:314:    # This ensures we don't waste compute on segments that will be filtered anyway
pipeline.py:321:    # AFTER:  Embeddings(8s) + Music(16s overlapped) ≈ 24s (25% faster)
pipeline.py:337:        vad_segments=vad_segments,
pipeline.py:382:                vad_segments=vad_segments,
pipeline.py:395:    # On 16GB GPUs (T4), overlapping PANNs model load with embedding inference can spike VRAM and OOM.
pipeline.py:415:    # Derive segment embeddings from cache (CPU only - instant!)
pipeline.py:417:        embeddings = build_segment_embeddings_from_cache(segments, embedding_cache)
pipeline.py:418:        logger.info(f"   Derived {len(embeddings)} segment embeddings from cache (CPU only)")
pipeline.py:419:    timings['segment_embeddings'] = metrics.duration
pipeline.py:420:    compute_stats['segment_embeddings'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
pipeline.py:432:    # Goal: Merge same-speaker fragments (CONSERVATIVE)
pipeline.py:436:        segments = merge_speakers(audio_path, segments, embeddings, config)
pipeline.py:444:    # BEFORE: Each segment required GPU inference for chunks + portions
pipeline.py:452:            # Compute speaker centroids from embeddings
pipeline.py:454:            speaker_embeddings = defaultdict(list)
pipeline.py:455:            for i, seg in enumerate(segments):
pipeline.py:456:                if i in embeddings and seg.get('speaker') not in ['OVERLAP', 'NON_SPEECH']:
pipeline.py:457:                    speaker_embeddings…7193 chars truncated…verlap_segs = [s for s in segments if s['speaker'] == 'OVERLAP']
pipeline.py:817:    nonspeech_segs = [s for s in segments if s['speaker'] == 'NON_SPEECH']
pipeline.py:818:    low_quality_segs = [s for s in segments if s.get('unusable_reason') == 'low_quality']
pipeline.py:819:    overlap_proximity_segs = [s for s in segments if s.get('unusable_reason') == 'overlap_proximity']  # v6.9
pipeline.py:822:    overlap_duration = sum(s['duration'] for s in overlap_segs)
pipeline.py:825:    overlap_proximity_duration = sum(s['duration'] for s in overlap_proximity_segs)  # v6.9
pipeline.py:826:    # Short (<min_tts_duration) speaker segments are DROPPED entirely (post-merge),
pipeline.py:827:    # so they won't appear in `segments`. We still surface them in stats.
pipeline.py:831:    # Prepare output segments with timestamps
pipeline.py:832:    # v7.6: Full precision timestamps (no rounding) for accurate segment cutting
pipeline.py:833:    output_segments = []
pipeline.py:836:    for seg in segments:
pipeline.py:837:        output_segments.append({
pipeline.py:842:            'speaker': seg['speaker'],
pipeline.py:852:            output_segments[-1]['music_stats'] = seg['music_stats']
pipeline.py:854:            output_segments[-1]['needs_demucs'] = True
pipeline.py:856:            output_segments[-1]['unusable_reason'] = seg['unusable_reason']
pipeline.py:880:        'num_speakers': len(all_speakers),
pipeline.py:881:        'speakers': all_speakers,
pipeline.py:882:        'total_segments': len(segments),
pipeline.py:883:        'segments': output_segments,
pipeline.py:885:            'usable_segments': len(usable_segs),
pipeline.py:888:            'overlap_segments': len(overlap_segs),
pipeline.py:889:            'overlap_duration': round(overlap_duration, 2),
pipeline.py:890:            'overlap_percentage': round(overlap_duration / total_duration * 100, 1) if total_duration > 0 else 0,
pipeline.py:891:            'nonspeech_segments': len(nonspeech_segs),
pipeline.py:895:            'low_quality_segments': len(low_quality_segs),
pipeline.py:897:            'too_short_segments': int(too_short_count),
pipeline.py:899:            # v6.9 overlap density filter
pipeline.py:900:            'overlap_proximity_segments': len(overlap_proximity_segs),
pipeline.py:901:            'overlap_proximity_duration': round(overlap_proximity_duration, 2),
pipeline.py:903:            'split_segments': chunk_reassignment_stats.segments_with_changes if chunk_reassignment_stats else 0,
pipeline.py:904:            'new_speakers_from_splits': chunk_reassignment_stats.new_speakers_created if chunk_reassignment_stats else 0,
pipeline.py:915:            'speech_segments': len(vad_segments),
pipeline.py:916:            'total_speech': round(sum(s['duration'] for s in vad_segments), 2),
pipeline.py:927:            'min_segment_duration': config.min_segment_duration,
pipeline.py:979:    export_enabled = getattr(config, 'r2_upload_enabled', False) or getattr(config, 'segment_audio', False)
pipeline.py:1004:                    segments=segments,
pipeline.py:1034:            from src.export import segment_audio_parallel
pipeline.py:1038:                segments_exported = 0
pipeline.py:1039:                if getattr(config, 'segment_audio', True) and original_audio_path.exists():
pipeline.py:1041:                    segments_exported = segment_audio_parallel(
pipeline.py:1043:                        segments=segments,
pipeline.py:1050:                    logger.info(f"📦 Segmented {segments_exported} clips in {seg_time:.1f}s")
pipeline.py:1094:                'segments_exported': segments_exported,
pipeline.py:1102:            log_stage(10, "Export", "DONE", f"{timings['export']:.1f}s | {segments_exported} segs | {tar_size_bytes/(1024**2):.1f}MB")
pipeline.py:1123:        speakers=result['num_speakers'],
pipeline.py:1192:    logger.info(f"   Min Segment: {config.min_segment_duration}s (for 0.4s events)")
pipeline.py:1346:        total_clean = sum(r['quality_stats'].get('music_detection', {}).get('segments_clean', 0) for r in successful)
pipeline.py:1347:        total_demucs = sum(r['quality_stats'].get('music_detection', {}).get('segments_needs_demucs', 0) for r in successful)
pipeline.py:1348:        total_heavy = sum(r['quality_stats'].get('music_detection', {}).get('segments_heavy_contamination',
pipeline.py:1349:                          r['quality_stats'].get('music_detection', {}).get('segments_heavy_music', 0)) for r in successful)
pipeline.py:1360:            logger.info(f"   Clean segments: {total_clean}")
pipeline.py:1369:                music_info = f" | 🎵 clean={md.get('segments_clean', 0)}, demucs={md.get('segments_needs_demucs', 0)}"
pipeline.py:1371:            logger.info(f"   • {r['video_id']}: {r['num_speakers']} speakers, "
src/embeddings.py:9:- Adaptive batching based on segment length
src/embeddings.py:11:- Memory-efficient processing order (short segments first)
src/embeddings.py:31:    window_overlap: float = 0.5


Output:
Exit code: 0
Wall time: 0 seconds
Output:
pipeline.py:86:from src.clustering import merge_speakers, merge_adjacent_segments
pipeline.py:268:    overlap_segments = []
pipeline.py:269:    nonspeech_segments = []
pipeline.py:301:    low_quality_segments = []
pipeline.py:436:        segments = merge_speakers(audio_path, segments, embeddings, config)
pipeline.py:646:    # STAGE 8: Duration filtering for TTS (POST-adjacent-merge)
pipeline.py:662:        # Include low-quality segments as UNUSABLE boundaries so we never merge
pipeline.py:664:        speaker_segments_for_merge = list(segments)
pipeline.py:668:            speaker_segments_for_merge.append(seg)
pipeline.py:670:        # Treat overlap regions as hard boundaries so we never merge a "clean"
pipeline.py:673:            speaker_segments_for_merge.append({
pipeline.py:682:        speaker_segments_for_merge.sort(key=lambda x: x['start'])
pipeline.py:683:        merged_all_segments = merge_adjacent_segments(speaker_segments_for_merge, config)
pipeline.py:685:        merged_speaker_segments = [
pipeline.py:686:            s for s in merged_all_segments
pipeline.py:689:        overlap_output_segments = [s for s in merged_all_segments if s.get('speaker') == 'OVERLAP']
pipeline.py:695:            for seg in merged_speaker_segments:
pipeline.py:706:                    f"   Duration filter (post-merge): dropped {dropped_short_count} speaker segments "
pipeline.py:710:            merged_speaker_segments = kept
pipeline.py:712:        # Start final output segments from merged speaker segments + overlap boundaries
pipeline.py:713:        segments = merged_speaker_segments + overlap_output_segments
pipeline.py:716:        # Filter internal NON_SPEECH within (merged) speaker segments.
pipeline.py:718:        # NOTE: this is containment-based. If we merged same-speaker fragments
pipeline.py:826:    # Short (<min_tts_duration) speaker segments are DROPPED entirely (post-merge),
pipeline.py:833:    output_segments = []
pipeline.py:924:            'merge_threshold': config.cluster_merge_threshold,
pipeline.py:1191:    logger.info(f"   Merge Threshold: {config.cluster_merge_threshold}")
src/compute.py:486:def get_adaptive_batch_size(segment_lengths: list, base_batch: int, max_memory_mb: int = 500) -> int:
src/spectral_analysis.py:100:        segments = [waveform]
src/spectral_analysis.py:103:        segments = []
src/spectral_analysis.py:111:        segments = [waveform[:min(len(waveform), analysis_samples)]]
src/boundary_refinement.py:64:def find_gaps_within_segment(
src/boundary_refinement.py:131:def refine_segment_end(
src/boundary_refinement.py:219:def refine_all_segments(
src/supabase_client.py:411:                'Prefer': 'resolution=merge-duplicates,return=minimal'
src/unified_embeddings.py:427:    def get_segment_embedding(self, start: float, end: float) -> Optional[np.ndarray]:
src/unified_embeddings.py:638:def build_segment_embeddings_from_cache(
src/unified_embeddings.py:728:    refined_segments = []
src/unified_embeddings.py:1102:        Never merge across the burned region.
src/unified_embeddings.py:1136:    refined_segments = []
src/unified_embeddings.py:1137:    suspicious_segments = []
src/unified_embeddings.py:1255:        suspicious_regions = _merge_overlapping_regions(suspicious_regions)
src/unified_embeddings.py:1309:        contaminated_regions = _merge_overlapping_regions(contaminated_regions, gap_tolerance=0.3)
src/unified_embeddings.py:1408:def _merge_overlapping_regions(
src/unified_embeddings.py:1417:    merged = [sorted_regions[0]]
src/unified_embeddings.py:1420:        last_start, last_end = merged[-1]
src/unified_embeddings.py:1422:            merged[-1] = (last_start, max(last_end, end))
src/unified_embeddings.py:1424:            merged.append((start, end))
src/unified_embeddings.py:1426:    return merged
src/download.py:921:        'merge_output_format': 'mp4',
src/export.py:52:def segment_audio_parallel(
src/export.py:83:    def cut_segment(seg: Dict, idx: int) -> Optional[str]:
src/hybrid_music_detection.py:130:    def _load_ina_segmenter(self):
src/hybrid_music_detection.py:255:    def _run_ina_segmentation(self):
src/hybrid_music_detection.py:420:    def get_segment_stats(self, start: float, end: float) -> Dict[str, Any]:
src/hybrid_music_detection.py:502:def build_segment_music_stats_hybrid(
src/chunk_reassignment.py:635:    def process_segment(
src/chunk_reassignment.py:1030:    refined_segments = []
src/overlap_detection.py:74:        overlap_segments = []
src/overlap_detection.py:88:            overlap_segments = _merge_close_segments(overlap_segments, max_gap=0.1)
src/overlap_detection.py:94:        clean_segments = []
src/overlap_detection.py:113:        all_segments = []
src/overlap_detection.py:184:    segments = []
src/overlap_detection.py:225:        overlap_regions = _merge_close_segments(overlap_regions, max_gap=0.1)
src/overlap_detection.py:236:def _frames_to_segments(
src/overlap_detection.py:245:    segments = []
src/overlap_detection.py:286:def _pad_segments(segments: List[Dict], padding: float) -> List[Dict]:
src/overlap_detection.py:299:def _merge_close_segments(segments: List[Dict], max_gap: float = 0.2) -> List[Dict]:
src/overlap_detection.py:305:    merged = [sorted_segs[0].copy()]
src/overlap_detection.py:308:        last = merged[-1]
src/overlap_detection.py:316:            merged.append(seg.copy())
src/overlap_detection.py:318:    return merged
src/overlap_detection.py:422:def estimate_segment_quality(audio_chunk: np.ndarray, sr: int) -> Dict:
src/overlap_detection.py:628:def filter_overlap_sandwich_segments(
src/clustering.py:14:def speakers_overlap_in_time(segments: List[Dict], spk1: str, spk2: str, min_overlap: float = 0.2) -> bool:
src/clustering.py:31:def merge_speakers(
src/clustering.py:46:    logger.info(f"🔧 Merging speakers (threshold={config.cluster_merge_threshold})...")
src/clustering.py:84:    # Find merges using greedy approach
src/clustering.py:85:    merge_map = {s: s for s in speakers}
src/clustering.py:86:    merged_to = {}
src/clustering.py:101:    # Try to merge similar speakers
src/clustering.py:106:        if sim < config.cluster_merge_threshold:
src/clustering.py:117:        while root1 in merged_to:
src/clustering.py:118:            root1 = merged_to[root1]
src/clustering.py:121:        merge_map[spk2] = root1
src/clustering.py:122:        merged_to[spk2] = root1
src/clustering.py:125:    # Apply merges to segments
src/clustering.py:126:    unique_speakers = sorted(set(merge_map.values()))
src/clustering.py:130:        merged = merge_map.get(seg['speaker'], seg['speaker'])
src/clustering.py:131:        seg['speaker'] = name_map.get(merged, merged)
src/clustering.py:142:def merge_adjacent_segments(segments: List[Dict], config) -> List[Dict]:
src/clustering.py:153:      merged [start, end] span by extending `end` to the later segment.
src/clustering.py:163:    - Only merge two segments when BOTH are usable. Unusable segments act as
src/clustering.py:171:    merged = []
src/clustering.py:196:            merged.append(current)
src/clustering.py:201:        merged.append(current)
src/clustering.py:203:    logger.info(f"   Adjacent merge: {len(segments)} → {len(merged)} segments")
src/clustering.py:205:    return merged
src/config.py:169:    # === CLUSTERING (Conservative - no poison merges) ===
src/config.py:171:    cluster_merge_threshold: float = 0.80
src/config.py:172:    min_segments_for_merge: int = 2
src/config.py:243:    # === POST-PROCESSING (Adjacent merge) ===
src/config.py:245:    # Post-processing "adjacent merge" threshold for SAME-speaker segments.
src/config.py:247:    # between them is <= this value, we merge them into one continuous clip.
src/config.py:307:        assert 0 < self.cluster_merge_threshold <= 1.0, "merge threshold must be (0, 1]"
src/vad.py:169:    segments = []
src/vad.py:234:    all_segments = []
src/vad.py:305:    merged = _merge_adjacent_vad_segments(all_segments, merge_gap=0.1)
src/vad.py:307:    total_speech = sum(s['duration'] for s in merged)
src/vad.py:310:    logger.info(f"✅ VAD: {elapsed:.1f}s | {len(merged)} segments | {total_speech:.1f}s speech")
src/vad.py:313:    return merged
src/vad.py:348:def _merge_adjacent_vad_segments(segments: List[Dict], merge_gap: float = 0.1) -> List[Dict]:
src/vad.py:354:        merge_gap: Maximum gap (seconds) to merge across
src/vad.py:359:    merged = []
src/vad.py:366:        if gap < merge_gap:
src/vad.py:370:            merged.append(current)
src/vad.py:373:    merged.append(current)
src/vad.py:374:    return merged
src/vad.py:377:def find_silence_boundaries(vad_segments: List[Dict],
src/vad.py:429:def find_optimal_cut_point(vad_segments: List[Dict],
src/diarization.py:31:def find_vad_safe_cut(vad_segments: List[Dict], target_time: float, window: float = 30.0) -> float:
src/diarization.py:239:                segments = []
src/diarization.py:254:                segments = []
src/diarization.py:327:    all_segments = []
src/music_detection.py:135:    def __init__(self, audio_buffer, vad_segments, panns_model, config=None, device=None):
src/music_detection.py:425:    def get_segment_music_stats(self, start, end):
src/music_detection.py:499:def build_segment_music_stats_batch(segments, music_cache):
src/chunk_parallel.py:139:def _find_vad_safe_cut(vad_segments: List[Dict], target_time: float, window: float = 30.0) -> float:
src/chunk_parallel.py:228:        segments = []
src/chunk_parallel.py:476:    # Apply mapping and merge segments
src/chunk_parallel.py:491:    segments = []
src/chunk_parallel.py:617:    segments = []


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/unified_embeddings.py:893:                    'status': 'usable',
src/unified_embeddings.py:922:                        'status': 'usable',  # Still usable, just ambiguous
src/unified_embeddings.py:935:                'status': 'usable',
src/unified_embeddings.py:1373:                        'status': 'usable',
src/chunk_reassignment.py:622:                    'status': 'usable',


Summary: auto


**Looking into segmentation issues**

I’m considering modifications like adjusting chunk reassignment thresholds and adding detection for speaker changes using embeddings or frame-level diarization. The user asked why our system hasn't segmented two speakers effectively, so I need to debug this specific case first.

This might help identify the root cause; for instance, there could be cross-talk. It’s possible the diarizer output is correct, but merging segments may have incorrectly combined two speakers due to similarity thresholds or small gaps in the audio.


**Examining speaker segmentation**

I’m realizing that two speakers might sound similar due to sharing the same gender, which could lead to clustering errors and segment mislabels. Ideally, even if there's a single segment returned, the diarization should still differentiate by time. If the diarizer fails, that’s an issue.

Post-processing might be merging adjacent segments indiscriminately, even across speaker boundaries. It should only merge segments with the same speaker label. If clustering causes different speakers to be labeled the same, it mixed them, which is plausible. I need to check how the pipeline handles this!


**Investigating overlapping speakers**

I’m considering that if two actual speakers are combined under one label, their segments could merge if separated by small gaps. However, the merge_adjacent_segments only works on segments with the same label and meets certain criteria. If speakers alternate with small gaps, they would be labeled the same after merge_speakers, leading to a multi-speaker segment.

I’ll need to check segments around 199-204 to see if they were initially separate but merged later. The mention of a "4th cleaned chunk" suggests a quick exchange between two speakers, and overlap detection might not catch it since it’s just a switch. Let’s inspect the metadata for those segments!


**Planning metadata extraction**

I need to extract metadata from a tar file, so the first step is to download the tar. I’ve found the remote key in the R2 cleaned bucket right at the root, which is great! I’ll use a Python snippet to download the file to `./tmp/r2debug/--jp1imJD9w.tar`, and I’ll set up the R2Client configuration with the proper prefix and bucket details.

It's important to be meticulous here, so let’s make a solid plan before proceeding with the download!


Explanation: Download the video’s exported TAR from the cleaned R2 bucket, inspect the segment metadata for the specific chunk, then reproduce/trace why segmentation/merging produced a multi-speaker segment.


Output: Plan updated


**Locating and downloading R2 object**

I'm focusing on step 1: locating the R2 object, which we have already identified with the key `--jp1imJD9w.tar`. It’s important to record this detail. The plan is to download it using a Python script while ensuring that we avoid logging any sensitive information. I'll track the progress of the download and will execute a shell command to carry this out. For now, everything is still in progress!


mkdir -p tmp/r2debug && set -a; source .env; set +a; python3 - <<'PY'
from pathlib import Path
from src.r2_client import R2Client, R2Config

video_id='--jp1imJD9w'
remote_key=f'{video_id}.tar'
local_path=Path('tmp/r2debug')/remote_key

base = R2Client(bucket_type='production')
cfg = R2Config(
    endpoint_url=base.config.endpoint_url,
    access_key_id=base.config.access_key_id,
    secret_access_key=base.config.secret_access_key,
    bucket=base.bucket,
    prefix='',
)
r2 = R2Client(config=cfg, bucket_type='custom')

print(f'Downloading s3://{r2.bucket}/{remote_key} -> {local_path}')
ok = r2.download_file(remote_key, str(local_path))
print('OK' if ok else 'FAIL')
PY


Output:
Exit code: 0
Wall time: 0.8 seconds
Output:
Downloading s3://1-cleaned-data/--jp1imJD9w.tar -> tmp/r2debug/--jp1imJD9w.tar
OK


**Checking download size**

I’m curious why the download finished quickly. Maybe it’s small because of caching? So, I’ll definitely check the size to see what's going on. It could give me insight into whether it was indeed a quick download due to being lightweight. There’s something satisfying about understanding these little details! Let’s confirm that size so I can be sure about the download's characteristics.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
-rw-rw-r-- 1 ubuntu ubuntu 16M Jan  8 19:59 tmp/r2debug/--jp1imJD9w.tar


**Updating plan progress**

I've completed step 1 of my update plan with a manageable 16MB tar file. Now, I'm working on step 2. I feel like I'm on track, but there's always a bit of uncertainty when moving from one step to the next. I hope everything goes smoothly as I transition into the next phase! Let's keep a close eye on the progress to ensure I'm staying on the right path.


Output: Plan updated


**Extracting tar files**

I'm planning to inspect the tar structure using `tar -tf` to locate the metadata and segments file. I'll execute `tar -tf tmp/r2debug/--jp1imJD9w.tar | head` to check the contents. I also need to find the flac path, so I’ll create a command to extract only the necessary files. It sounds like a good idea to do this step in parallel by listing the tar contents and grepping for flac.


tar -tf tmp/r2debug/--jp1imJD9w.tar | head -50


tar -tf tmp/r2debug/--jp1imJD9w.tar | rg "SPEAKER_00_0021_199\.00-204\.34\.flac|metadata\.json|segments/" | head -200


Output:
Exit code: 0
Wall time: 0 seconds
Output:
--jp1imJD9w/
--jp1imJD9w/metadata.json
--jp1imJD9w/segments/
--jp1imJD9w/segments/SPEAKER_00_0004_47.03-49.17.flac
--jp1imJD9w/segments/SPEAKER_00_0005_51.23-59.62.flac
--jp1imJD9w/segments/SPEAKER_00_0015_152.11-162.57.flac
--jp1imJD9w/segments/SPEAKER_00_0021_199.00-204.34.flac
--jp1imJD9w/segments/SPEAKER_00_0028_264.21-267.37.flac
--jp1imJD9w/segments/SPEAKER_00_0031_280.36-281.39.flac
--jp1imJD9w/segments/SPEAKER_00_0036_320.76-322.97.flac
--jp1imJD9w/segments/SPEAKER_00_0037_324.17-327.78.flac
--jp1imJD9w/segments/SPEAKER_00_0041_347.78-354.36.flac
--jp1imJD9w/segments/SPEAKER_00_0046_429.89-430.99.flac
--jp1imJD9w/segments/SPEAKER_00_0047_431.61-438.67.flac
--jp1imJD9w/segments/SPEAKER_00_0050_448.77-453.10.flac
--jp1imJD9w/segments/SPEAKER_00_0051_453.48-455.54.flac
--jp1imJD9w/segments/SPEAKER_00_0056_483.89-488.35.flac
--jp1imJD9w/segments/SPEAKER_00_0057_488.94-493.44.flac
--jp1imJD9w/segments/SPEAKER_00_0058_494.07-497.73.flac
--jp1imJD9w/segments/SPEAKER_01_0000_0.03-11.20.flac
--jp1imJD9w/segments/SPEAKER_01_0001_15.34-27.62.flac
--jp1imJD9w/segments/SPEAKER_01_0002_28.45-32.11.flac
--jp1imJD9w/segments/SPEAKER_01_0003_34.22-47.03.flac
--jp1imJD9w/segments/SPEAKER_01_0006_60.02-70.64.flac
--jp1imJD9w/segments/SPEAKER_01_0007_71.48-74.37.flac
--jp1imJD9w/segments/SPEAKER_01_0008_74.92-76.64.flac
--jp1imJD9w/segments/SPEAKER_01_0009_83.86-97.94.flac
--jp1imJD9w/segments/SPEAKER_01_0010_98.24-103.80.flac
--jp1imJD9w/segments/SPEAKER_01_0011_104.18-111.10.flac
--jp1imJD9w/segments/SPEAKER_01_0012_117.41-128.10.flac
--jp1imJD9w/segments/SPEAKER_01_0013_135.17-140.58.flac
--jp1imJD9w/segments/SPEAKER_01_0014_140.94-151.03.flac
--jp1imJD9w/segments/SPEAKER_01_0016_164.09-173.32.flac
--jp1imJD9w/segments/SPEAKER_01_0017_173.64-179.65.flac
--jp1imJD9w/segments/SPEAKER_01_0018_180.15-186.20.flac
--jp1imJD9w/segments/SPEAKER_01_0019_189.40-191.55.flac
--jp1imJD9w/segments/SPEAKER_01_0020_192.10-197.70.flac
--jp1imJD9w/segments/SPEAKER_01_0022_215.71-225.73.flac
--jp1imJD9w/segments/SPEAKER_01_0023_226.29-228.80.flac
--jp1imJD9w/segments/SPEAKER_01_0024_238.93-243.10.flac
--jp1imJD9w/segments/SPEAKER_01_0025_245.44-253.88.flac
--jp1imJD9w/segments/SPEAKER_01_0026_254.73-256.28.flac
--jp1imJD9w/segments/SPEAKER_01_0027_257.05-260.97.flac
--jp1imJD9w/segments/SPEAKER_01_0029_269.68-273.91.flac
--jp1imJD9w/segments/SPEAKER_01_0030_274.86-280.36.flac
--jp1imJD9w/segments/SPEAKER_01_0032_282.28-285.41.flac
--jp1imJD9w/segments/SPEAKER_01_0033_287.26-308.44.flac
--jp1imJD9w/segments/SPEAKER_01_0034_309.45-310.89.flac
--jp1imJD9w/segments/SPEAKER_01_0035_311.64-314.40.flac
--jp1imJD9w/segments/SPEAKER_01_0038_327.78-330.31.flac


Output:
Exit code: 0
Wall time: 0 seconds
Output:
--jp1imJD9w/metadata.json
--jp1imJD9w/segments/
--jp1imJD9w/segments/SPEAKER_00_0004_47.03-49.17.flac
--jp1imJD9w/segments/SPEAKER_00_0005_51.23-59.62.flac
--jp1imJD9w/segments/SPEAKER_00_0015_152.11-162.57.flac
--jp1imJD9w/segments/SPEAKER_00_0021_199.00-204.34.flac
--jp1imJD9w/segments/SPEAKER_00_0028_264.21-267.37.flac
--jp1imJD9w/segments/SPEAKER_00_0031_280.36-281.39.flac
--jp1imJD9w/segments/SPEAKER_00_0036_320.76-322.97.flac
--jp1imJD9w/segments/SPEAKER_00_0037_324.17-327.78.flac
--jp1imJD9w/segments/SPEAKER_00_0041_347.78-354.36.flac
--jp1imJD9w/segments/SPEAKER_00_0046_429.89-430.99.flac
--jp1imJD9w/segments/SPEAKER_00_0047_431.61-438.67.flac
--jp1imJD9w/segments/SPEAKER_00_0050_448.77-453.10.flac
--jp1imJD9w/segments/SPEAKER_00_0051_453.48-455.54.flac
--jp1imJD9w/segments/SPEAKER_00_0056_483.89-488.35.flac
--jp1imJD9w/segments/SPEAKER_00_0057_488.94-493.44.flac
--jp1imJD9w/segments/SPEAKER_00_0058_494.07-497.73.flac
--jp1imJD9w/segments/SPEAKER_01_0000_0.03-11.20.flac
--jp1imJD9w/segments/SPEAKER_01_0001_15.34-27.62.flac
--jp1imJD9w/segments/SPEAKER_01_0002_28.45-32.11.flac
--jp1imJD9w/segments/SPEAKER_01_0003_34.22-47.03.flac
--jp1imJD9w/segments/SPEAKER_01_0006_60.02-70.64.flac
--jp1imJD9w/segments/SPEAKER_01_0007_71.48-74.37.flac
--jp1imJD9w/segments/SPEAKER_01_0008_74.92-76.64.flac
--jp1imJD9w/segments/SPEAKER_01_0009_83.86-97.94.flac
--jp1imJD9w/segments/SPEAKER_01_0010_98.24-103.80.flac
--jp1imJD9w/segments/SPEAKER_01_0011_104.18-111.10.flac
--jp1imJD9w/segments/SPEAKER_01_0012_117.41-128.10.flac
--jp1imJD9w/segments/SPEAKER_01_0013_135.17-140.58.flac
--jp1imJD9w/segments/SPEAKER_01_0014_140.94-151.03.flac
--jp1imJD9w/segments/SPEAKER_01_0016_164.09-173.32.flac
--jp1imJD9w/segments/SPEAKER_01_0017_173.64-179.65.flac
--jp1imJD9w/segments/SPEAKER_01_0018_180.15-186.20.flac
--jp1imJD9w/segments/SPEAKER_01_0019_189.40-191.55.flac
--jp1imJD9w/segments/SPEAKER_01_0020_192.10-197.70.flac
--jp1imJD9w/segments/SPEAKER_01_0022_215.71-225.73.flac
--jp1imJD9w/segments/SPEAKER_01_0023_226.29-228.80.flac
--jp1imJD9w/segments/SPEAKER_01_0024_238.93-243.10.flac
--jp1imJD9w/segments/SPEAKER_01_0025_245.44-253.88.flac
--jp1imJD9w/segments/SPEAKER_01_0026_254.73-256.28.flac
--jp1imJD9w/segments/SPEAKER_01_0027_257.05-260.97.flac
--jp1imJD9w/segments/SPEAKER_01_0029_269.68-273.91.flac
--jp1imJD9w/segments/SPEAKER_01_0030_274.86-280.36.flac
--jp1imJD9w/segments/SPEAKER_01_0032_282.28-285.41.flac
--jp1imJD9w/segments/SPEAKER_01_0033_287.26-308.44.flac
--jp1imJD9w/segments/SPEAKER_01_0034_309.45-310.89.flac
--jp1imJD9w/segments/SPEAKER_01_0035_311.64-314.40.flac
--jp1imJD9w/segments/SPEAKER_01_0038_327.78-330.31.flac
--jp1imJD9w/segments/SPEAKER_01_0039_330.82-337.62.flac
--jp1imJD9w/segments/SPEAKER_01_0040_338.87-347.78.flac
--jp1imJD9w/segments/SPEAKER_01_0042_354.46-370.00.flac
--jp1imJD9w/segments/SPEAKER_01_0048_441.89-444.94.flac
--jp1imJD9w/segments/SPEAKER_01_0049_445.60-448.40.flac
--jp1imJD9w/segments/SPEAKER_01_0052_455.54-459.17.flac
--jp1imJD9w/segments/SPEAKER_01_0053_459.89-467.90.flac
--jp1imJD9w/segments/SPEAKER_01_0054_477.01-479.12.flac
--jp1imJD9w/segments/SPEAKER_01_0055_479.69-483.05.flac
--jp1imJD9w/segments/SPEAKER_01_0059_498.22-521.61.flac
--jp1imJD9w/segments/SPEAKER_01_0060_522.55-527.12.flac
--jp1imJD9w/segments/SPEAKER_01_0061_527.66-529.69.flac
--jp1imJD9w/segments/SPEAKER_01_0062_530.30-534.90.flac
--jp1imJD9w/segments/SPEAKER_01_0063_535.48-542.48.flac
--jp1imJD9w/segments/SPEAKER_01_0064_544.42-547.41.flac
--jp1imJD9w/segments/SPEAKER_01_0065_547.95-551.34.flac
--jp1imJD9w/segments/SPEAKER_01_0066_551.98-554.80.flac
--jp1imJD9w/segments/SPEAKER_01_0067_560.96-565.30.flac
--jp1imJD9w/segments/SPEAKER_01_0068_577.21-586.79.flac
--jp1imJD9w/segments/SPEAKER_01_0069_587.13-597.97.flac
--jp1imJD9w/segments/SPEAKER_01_0070_598.62-603.49.flac
--jp1imJD9w/segments/SPEAKER_01_0071_603.79-605.80.flac
--jp1imJD9w/segments/SPEAKER_01_0072_608.29-612.70.flac
--jp1imJD9w/segments/SPEAKER_01_0073_613.32-618.00.flac
--jp1imJD9w/segments/SPEAKER_01_0074_618.85-620.70.flac
--jp1imJD9w/segments/SPEAKER_01_0075_627.37-633.03.flac
--jp1imJD9w/segments/SPEAKER_01_0076_633.62-635.10.flac
--jp1imJD9w/segments/SPEAKER_01_0077_640.13-642.90.flac
--jp1imJD9w/segments/SPEAKER_01_0078_644.54-648.30.flac
--jp1imJD9w/segments/SPEAKER_02_0043_373.07-375.42.flac
--jp1imJD9w/segments/SPEAKER_02_0044_384.28-386.62.flac
--jp1imJD9w/segments/SPEAKER_02_0045_421.72-423.97.flac


mkdir -p tmp/r2debug/extracted && tar -xf tmp/r2debug/--jp1imJD9w.tar -C tmp/r2debug/extracted --wildcards '--jp1imJD9w/metadata.json' '--jp1imJD9w/segments/SPEAKER_00_0021_199.00-204.34.flac'


Output:
Exit code: 64
Wall time: 0 seconds
Output:
tar: unrecognized option '--jp1imJD9w/metadata.json'
Try 'tar --help' or 'tar --usage' for more information.


**Rethinking tar command**

I’m exploring the GNU tar command and its use of wildcards. It seems like I don’t need the `--wildcards` option for the command `tar -xf file -C dir path1 path2`, which is a relief! However, my quoting inadvertently included `--wildcards`, causing paths to start with `--`, and tar thinks these are options. I’ll need to include `--` before my file list. So, I’ll rerun the command like this: `tar -xf ... -C ... -- --jp1imJD9w/metadata.json ...`. Let’s get to it!


mkdir -p tmp/r2debug/extracted && tar -xf tmp/r2debug/--jp1imJD9w.tar -C tmp/r2debug/extracted -- --jp1imJD9w/metadata.json --jp1imJD9w/segments/SPEAKER_00_0021_199.00-204.34.flac


Output:
Exit code: 0
Wall time: 0 seconds
Output:


**Inspecting segment metadata**

I’m planning to inspect metadata for a specific segment, starting around index 199. I’ll parse JSON to find that segment and use a Python snippet to read the file, printing the relevant segment along with some surrounding context. I want to keep the output minimal and avoid a huge transcript, focusing on important details like speaker status and any reassignment details. I’ll also check for any overlapping segments nearby. Let’s go ahead and run this!


python3 - <<'PY'
import json
from pathlib import Path

meta_path = Path('tmp/r2debug/extracted/--jp1imJD9w/metadata.json')
meta = json.loads(meta_path.read_text())

segs = meta.get('segments', [])
# segments in metadata already output segments (after merge and post-processing).

# Find target
start_t, end_t = 199.00, 204.34
hits = [i for i,s in enumerate(segs) if abs(s.get('start',-1)-start_t)<1e-2 and abs(s.get('end',-1)-end_t)<1e-2 and s.get('speaker')=='SPEAKER_00']
print('target_hits', hits)

# Print neighboring segments in time window
window_start, window_end = 180, 230
near = [(i,s) for i,s in enumerate(segs) if s.get('end',0)>=window_start and s.get('start',0)<=window_end]
near_sorted = sorted(near, key=lambda x: x[1]['start'])
print('segments_in_window', len(near_sorted))
for i,s in near_sorted:
    print(f"{i:04d} {s['speaker']:10s} {s['start']:.2f}-{s['end']:.2f} dur={s['duration']:.2f} status={s.get('status')} reason={s.get('unusable_reason')}")

# High-level stats
print('\nnum_speakers', meta.get('num_speakers'))
qs = meta.get('quality_stats', {})
print('quality_stats keys', list(qs.keys()))
print('overlap stats', qs.get('overlap', {}))
print('split stats', qs.get('split_stats', qs.get('chunk_reassignment', {})))
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
target_hits [41]
segments_in_window 16
0036 SPEAKER_01 180.15-186.20 dur=6.05 status=usable reason=None
0037 NON_SPEECH 189.00-189.40 dur=0.40 status=unusable reason=non_speech
0038 SPEAKER_01 189.40-191.55 dur=2.14 status=usable reason=None
0039 NON_SPEECH 191.60-192.10 dur=0.50 status=unusable reason=non_speech
0040 SPEAKER_01 192.10-197.70 dur=5.60 status=usable reason=None
0041 SPEAKER_00 199.00-204.34 dur=5.33 status=usable reason=None
0042 OVERLAP    204.54-206.02 dur=1.48 status=unusable reason=overlap
0043 OVERLAP    206.41-208.18 dur=1.77 status=unusable reason=overlap
0044 SPEAKER_00 208.18-210.97 dur=2.79 status=unusable reason=overlap_proximity
0045 OVERLAP    210.97-211.59 dur=0.62 status=unusable reason=overlap
0046 SPEAKER_00 211.59-213.92 dur=2.33 status=unusable reason=overlap_proximity
0047 OVERLAP    213.92-215.05 dur=1.13 status=unusable reason=overlap
0048 OVERLAP    215.29-215.71 dur=0.42 status=unusable reason=overlap
0049 SPEAKER_01 215.71-225.73 dur=10.03 status=usable reason=None
0050 NON_SPEECH 225.80-226.30 dur=0.50 status=unusable reason=non_speech
0051 SPEAKER_01 226.29-228.80 dur=2.51 status=usable reason=None

num_speakers 3
quality_stats keys ['usable_segments', 'usable_duration', 'usable_percentage', 'overlap_segments', 'overlap_duration', 'overlap_percentage', 'nonspeech_segments', 'nonspeech_duration', 'nonspeech_percentage', 'low_quality_segments', 'low_quality_duration', 'too_short_segments', 'too_short_duration', 'overlap_proximity_segments', 'overlap_proximity_duration', 'split_segments', 'new_speakers_from_splits', 'chunk_reassignment_detail', 'micro_contamination', 'music_detection', 'boundary_refinement']
overlap stats {}
split stats {}


Summary: auto


python3 - <<'PY'
import json
from pathlib import Path
meta=json.loads(Path('tmp/r2debug/extracted/--jp1imJD9w/metadata.json').read_text())
detail=meta.get('quality_stats',{}).get('chunk_reassignment_detail')
print('has_chunk_reassignment_detail', bool(detail))
if detail:
    # detail may be list/dict; introspect
    print('type', type(detail).__name__)
    if isinstance(detail, dict):
        print('keys', list(detail.keys())[:20])
        # try common fields
        for k in ['segments_with_changes','new_speakers_created','changes']:
            if k in detail:
                print(k, detail[k])
    elif isinstance(detail, list):
        print('len', len(detail))

# Try to find any entries referencing 199-204
needle='199'
import re

text=json.dumps(detail) if detail is not None else ''
# show small excerpt around first match
m=re.search(r'.{0,120}199\.0.{0,120}', text)
print('match_excerpt', m.group(0) if m else None)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
has_chunk_reassignment_detail True
type dict
keys ['segments_analyzed', 'segments_with_changes', 'total_split_points', 'validated_split_points', 'rejected_by_centroid_gate', 'new_speakers_created', 'portions_unusable', 'severe_triggers', 'lookahead_triggers', 'kept_original_speaker', 'time_sec']
segments_with_changes 2
new_speakers_created 0
match_excerpt None


**Inspecting trigger values**

I'm planning to check and print out the values for the keys that hold lists, like `severe_triggers` and `lookahead_triggers`. This step seems important because it’ll help me understand what data is associated with those keys. I want to ensure I’m getting the correct values and they’re formatted properly. This way, I can move forward without confusion. Let's see what we find!


python3 - <<'PY'
import json
from pathlib import Path

detail=json.loads(Path('tmp/r2debug/extracted/--jp1imJD9w/metadata.json').read_text()).get('quality_stats',{}).get('chunk_reassignment_detail',{})
for k,v in detail.items():
    if isinstance(v, (list,dict)):
        if isinstance(v, list):
            print(k, 'list', len(v))
        else:
            print(k, 'dict', len(v))
    else:
        print(k, v)

# Print the triggers entries (first few)
for k in ['severe_triggers','lookahead_triggers','rejected_by_centroid_gate','kept_original_speaker']:
    v=detail.get(k)
    if isinstance(v,list) and v:
        print('\n',k,'sample:')
        for item in v[:5]:
            print(item)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
segments_analyzed 107
segments_with_changes 2
total_split_points 11
validated_split_points 2
rejected_by_centroid_gate 9
new_speakers_created 0
portions_unusable 0
severe_triggers 0
lookahead_triggers 11
kept_original_speaker 0
time_sec 0.02