Output:
Chunk ID: 5e48cd
Wall time: 0.0509 seconds
Process exited with code 0
Original token count: 1907
Output:
    30	            config=Config(signature_version='s3v4')
    31	        )
    32	        self.bucket = R2_BUCKET
    33
    34	    def find_tar_file(self, video_id: str, use_regex: bool = False) -> Optional[str]:
    35	        """
    36	        Find a tar file for the given video ID in R2.
    37
    38	        Args:
    39	            video_id: The YouTube video ID to search for
    40	            use_regex: If True, use regex matching (fallback for non-exact matches)
    41
    42	        Returns:
    43	            The S3 key of the found tar file, or None if not found
    44	        """
    45	        # Primary search: exact match with video_id in filename
    46	        paginator = self.s3_client.get_paginator('list_objects_v2')
    47
    48	        # Try exact prefix match first
    49	        exact_key = f"{video_id}.tar"
    50	        try:
    51	            self.s3_client.head_object(Bucket=self.bucket, Key=exact_key)
    52	            print(f"[R2] Found exact match: {exact_key}")
    53	            return exact_key
    54	        except:
    55	            pass
    56
    57	        # Search with video_id as prefix
    58	        for page in paginator.paginate(Bucket=self.bucket, Prefix=video_id):
    59	            for obj in page.get('Contents', []):
    60	                key = obj['Key']
    61	                if key.endswith('.tar'):
    62	                    print(f"[R2] Found tar file with prefix: {key}")
    63	                    return key
    64
    65	        # Fallback: regex search if enabled
    66	        if use_regex:
    67	            pattern = re.compile(rf".*{re.escape(video_id)}.*\.tar$", re.IGNORECASE)
    68	            for page in paginator.paginate(Bucket=self.bucket):
    69	                for obj in page.get('Contents', []):
    70	                    key = obj['Key']
    71	                    if pattern.match(key):
    72	                        print(f"[R2] Found via regex: {key}")
    73	                        return key
    74
    75	        print(f"[R2] No tar file found for video_id: {video_id}")
    76	        return None
    77
    78	    def download_tar(self, s3_key: str, local_path: str) -> str:
    79	        """
    80	        Download a tar file from R2 to local storage.
    81
    82	        Args:
    83	            s3_key: The S3 key of the tar file
    84	            local_path: Local directory to download to
    85
    86	        Returns:
    87	            Path to the downloaded tar file
    88	        """
    89	        os.makedirs(local_path, exist_ok=True)
    90	        filename = os.path.basename(s3_key)
    91	        local_file = os.path.join(local_path, filename)
    92
    93	        print(f"[R2] Downloading {s3_key} to {local_file}...")
    94	        self.s3_client.download_file(self.bucket, s3_key, local_file)
    95	        print(f"[R2] Download complete: {os.path.getsize(local_file) / 1024 / 1024:.2f} MB")
    96
    97	        return local_file
    98
    99	    def extract_tar(self, tar_path: str, extract_to: str) -> Tuple[str, dict]:
   100	        """
   101	        Extract a tar file and return paths to metadata and segments.
   102
   103	        Args:
   104	            tar_path: Path to the tar file
   105	            extract_to: Directory to extract to
   106
   107	        Returns:
   108	            Tuple of (segments_dir, metadata_dict)
   109	        """
   110	        os.makedirs(extract_to, exist_ok=True)
   111
   112	        print(f"[R2] Extracting {tar_path}...")
   113	        with tarfile.open(tar_path, 'r') as tar:
   114	            tar.extractall(extract_to)
   115
   116	        # Find metadata.json
   117	        metadata_path = None
   118	        segments_dir = None
   119
   120	        for root, dirs, files in os.walk(extract_to):
   121	            if 'metadata.json' in files:
   122	                metadata_path = os.path.join(root, 'metadata.json')
   123	            if 'segments' in dirs:
   124	                segments_dir = os.path.join(root, 'segments')
   125
   126	        # Load metadata
   127	        metadata = {}
   128	        if metadata_path:
   129	            with open(metadata_path, 'r') as f:
   130	                metadata = json.load(f)
   131	            print(f"[R2] Loaded metadata: {len(metadata.get('segments', []))} segments defined")
   132	        else:
   133	            print("[R2] Warning: metadata.json not found")
   134
   135	        if segments_dir and os.path.exists(segments_dir):
   136	            flac_files = list(Path(segments_dir).glob('*.flac'))
   137	            print(f"[R2] Found {len(flac_files)} FLAC segment files")
   138	        else:
   139	            print("[R2] Warning: segments directory not found")
   140	            # Try to find segments in the extracted root
   141	            flac_files = list(Path(extract_to).glob('**/*.flac'))
   142	            if flac_files:
   143	                segments_dir = str(flac_files[0].parent)
   144	                print(f"[R2] Found segments at: {segments_dir}")
   145
   146	        return segments_dir, metadata
   147
   148
   149	def download_video_segments(
   150	    video_id: str,
   151	    work_dir: Optional[str] = None,
   152	    use_regex_fallback: bool = True,
   153	    cleanup_tar: bool = True
   154	) -> Tuple[str, dict]:
   155	    """
   156	    Main function to download and extract audio segments for a video.
   157
   158	    Args:
   159	        video_id: YouTube video ID
   160	        work_dir: Working directory for downloads (default from config)
   161	        use_regex_fallback: Try regex matching if exact match fails
   162	        cleanup_tar: Remove tar file after extraction
   163
   164	    Returns:
   165	        Tuple of (segments_directory, metadata_dict)
   166	    """
   167	    if work_dir is None:
   168	        work_dir = DEFAULT_SETTINGS["work_dir"]
   169
   170	    video_work_dir = os.path.join(work_dir, video_id)
   171	    download_dir = os.path.join(video_work_dir, "download")
   172	    extract_dir = os.path.join(video_work_dir, "extracted")
   173
   174	    # Clean up any previous runs
   175	    if os.path.exists(video_work_dir):
   176	        shutil.rmtree(video_work_dir)
   177
   178	    client = R2StorageClient()
   179
   180	    # Find the tar file
   181	    s3_key = client.find_tar_file(video_id, use_regex=use_regex_fallback)
   182	    if not s3_key:
   183	        raise FileNotFoundError(f"No tar file found for video_id: {video_id}")
   184
   185	    # Download
   186	    tar_path = client.download_tar(s3_key, download_dir)
   187
   188	    # Extract
   189	    segments_dir, metadata = client.extract_tar(tar_path, extract_dir)
   190
   191	    # Cleanup tar if requested
   192	    if cleanup_tar and os.path.exists(tar_path):
   193	        os.remove(tar_path)
   194	        print(f"[R2] Cleaned up tar file")
   195
   196	    return segments_dir, metadata
   197
   198
   199	if __name__ == "__main__":
   200	    # Test download for the sample video
   201	    test_video_id = "pF_BQpHaIdU"
   202	    try:
   203	        segments_dir, metadata = download_video_segments(test_video_id)
   204	        print(f"\nSegments directory: {segments_dir}")
   205	        print(f"Metadata keys: {list(metadata.keys())}")
   206	    except Exception as e:
   207	        print(f"Error: {e}")


# Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

The `request_user_input` tool is unavailable in Default mode. If you call it while in Default mode, it will return an error.

If a decision is necessary and cannot be discovered from local context, ask the user directly. However, in Default mode you should strongly prefer executing the user's request rather than stopping to ask questions.

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) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) 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.


**Adding line numbers to table attempts**


nl -ba src/backend/supabase_client.py | sed -n '1,150p'


Output:
Chunk ID: ee463b
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 1021
Output:
     1	"""
     2	Supabase client module for fetching video metadata including language info.
     3	"""
     4	from typing import Optional, Dict, Any
     5	from supabase import create_client, Client
     6
     7	from .config import SUPABASE_URL, SUPABASE_ADMIN_KEY, LANGUAGE_MAP
     8
     9
    10	class SupabaseVideoClient:
    11	    """Client for fetching video information from Supabase."""
    12
    13	    def __init__(self):
    14	        self.client: Client = create_client(SUPABASE_URL, SUPABASE_ADMIN_KEY)
    15
    16	    def get_video_info(self, video_id: str) -> Optional[Dict[str, Any]]:
    17	        """
    18	        Fetch video information from Supabase.
    19
    20	        Args:
    21	            video_id: YouTube video ID
    22
    23	        Returns:
    24	            Dictionary with video info including language, or None if not found
    25	        """
    26	        try:
    27	            # Try different table names that might store video data
    28	            # Adjust table name based on your actual Supabase schema
    29	            tables_to_try = ['videos', 'video_metadata', 'youtube_videos', 'content']
    30
    31	            for table in tables_to_try:
    32	                try:
    33	                    response = self.client.table(table).select("*").eq("video_id", video_id).execute()
    34	                    if response.data:
    35	                        return response.data[0]
    36	                except Exception:
    37	                    continue
    38
    39	            # Try with 'id' column instead
    40	            for table in tables_to_try:
    41	                try:
    42	                    response = self.client.table(table).select("*").eq("id", video_id).execute()
    43	                    if response.data:
    44	                        return response.data[0]
    45	                except Exception:
    46	                    continue
    47
    48	            return None
    49
    50	        except Exception as e:
    51	            print(f"[Supabase] Error fetching video info: {e}")
    52	            return None
    53
    54	    def get_video_language(self, video_id: str, default: str = "Telugu") -> str:
    55	        """
    56	        Get the language for a video from Supabase.
    57
    58	        Args:
    59	            video_id: YouTube video ID
    60	            default: Default language if not found
    61
    62	        Returns:
    63	            Language name (e.g., "Telugu", "Hindi")
    64	        """
    65	        video_info = self.get_video_info(video_id)
    66
    67	        if video_info:
    68	            # Try common field names for language
    69	            for field in ['language', 'lang', 'audio_language', 'primary_language']:
    70	                if field in video_info and video_info[field]:
    71	                    lang_code = video_info[field]
    72	                    # Map language code to full name if needed
    73	                    return LANGUAGE_MAP.get(lang_code, lang_code)
    74
    75	        print(f"[Supabase] Language not found for {video_id}, using default: {default}")
    76	        return default
    77
    78
    79	def get_video_language(video_id: str, default: str = "Telugu") -> str:
    80	    """
    81	    Convenience function to get video language.
    82
    83	    Args:
    84	        video_id: YouTube video ID
    85	        default: Default language if not found
    86
    87	    Returns:
    88	        Language name
    89	    """
    90	    client = SupabaseVideoClient()
    91	    return client.get_video_language(video_id, default)
    92
    93
    94	if __name__ == "__main__":
    95	    # Test fetching language for sample video
    96	    test_video_id = "pF_BQpHaIdU"
    97	    language = get_video_language(test_video_id, default="Telugu")
    98	    print(f"Language for {test_video_id}: {language}")


Summary: auto


**Planning code snippet capture**


nl -ba src/backend/gemini_transcriber.py | sed -n '80,300p'


Output:
Chunk ID: 0e5ae3
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 2365
Output:
    80	        model_info = GEMINI_MODELS.get(model_name, {})
    81
    82	        # Build thinking config for Gemini 3 models
    83	        thinking_config = None
    84	        if model_info.get("supports_thinking") and config.thinking_level:
    85	            if model_name.startswith("gemini-3"):
    86	                thinking_config = types.ThinkingConfig(
    87	                    thinking_level=config.thinking_level.upper()
    88	                )
    89
    90	        gen_config = types.GenerateContentConfig(
    91	            temperature=config.temperature,
    92	            response_mime_type="application/json",
    93	            response_json_schema=TRANSCRIPTION_JSON_SCHEMA,
    94	            system_instruction=[
    95	                types.Part.from_text(text=get_transcription_prompt(config.language))
    96	            ]
    97	        )
    98
    99	        if thinking_config:
   100	            gen_config.thinking_config = thinking_config
   101
   102	        return gen_config
   103
   104	    def transcribe_audio(
   105	        self,
   106	        audio_path: str,
   107	        config: TranscriptionConfig
   108	    ) -> Dict[str, Any]:
   109	        """
   110	        Transcribe a single audio file.
   111
   112	        Args:
   113	            audio_path: Path to the audio file
   114	            config: Transcription configuration
   115
   116	        Returns:
   117	            Dictionary with transcription results
   118	        """
   119	        model_name = get_model_name(config.model)
   120
   121	        # Load audio
   122	        audio_bytes = self._load_audio_bytes(audio_path)
   123	        mime_type = self._get_mime_type(audio_path)
   124
   125	        # Build content
   126	        contents = [
   127	            types.Content(
   128	                role="user",
   129	                parts=[
   130	                    types.Part.from_bytes(
   131	                        mime_type=mime_type,
   132	                        data=audio_bytes
   133	                    ),
   134	                    types.Part.from_text(text=get_user_prompt())
   135	                ]
   136	            )
   137	        ]
   138
   139	        # Build config
   140	        gen_config = self._build_config(config)
   141
   142	        # Make API call
   143	        start_time = time.time()
   144	        try:
   145	            response = self.client.models.generate_content(
   146	                model=model_name,
   147	                contents=contents,
   148	                config=gen_config
   149	            )
   150	            processing_time = time.time() - start_time
   151
   152	            # Parse response
   153	            if response.text:
   154	                import json
   155	                result = json.loads(response.text)
   156	                result['_processing_time_sec'] = processing_time
   157	                result['_model'] = model_name
   158	                result['_thinking_level'] = config.thinking_level
   159	                # v4: derive romanized deterministically from native transcription
   160	                # Gemini no longer outputs romanized/code_switch - we produce them in code
   161	                native = result.get('transcription', '')
   162	                if native and 'romanized' not in result:
   163	                    result['romanized'] = romanize_for_alignment(native)
   164	                if 'code_switch' not in result:
   165	                    # Derive code_switch from tagged by stripping event tags
   166	                    tagged = result.get('tagged', '')
   167	                    result['code_switch'] = re.sub(r'\[[\w_]+\]', '', tagged).strip()
   168	                return result
   169	            else:
   170	                return {
   171	                    "error": "Empty response from model",
   172	                    "_processing_time_sec": processing_time,
   173	                    "_model": model_name
   174	                }
   175
   176	        except Exception as e:
   177	            processing_time = time.time() - start_time
   178	            return {
   179	                "error": str(e),
   180	                "_processing_time_sec": processing_time,
   181	                "_model": model_name
   182	            }
   183
   184	    def transcribe_chunk(
   185	        self,
   186	        chunk: AudioChunk,
   187	        config: TranscriptionConfig
   188	    ) -> TranscriptionResult:
   189	        """
   190	        Transcribe an AudioChunk and return structured result.
   191
   192	        Args:
   193	            chunk: AudioChunk to transcribe
   194	            config: Transcription configuration
   195
   196	        Returns:
   197	            TranscriptionResult with full metadata
   198	        """
   199	        raw_result = self.transcribe_audio(chunk.file_path, config)
   200
   201	        error_msg = raw_result.get("error")
   202
   203	        # Handle errors
   204	        if error_msg:
   205	            transcription = TranscriptionOutput(
   206	                transcription=f"[ERROR: {error_msg}]",
   207	                code_switch=f"[ERROR: {error_msg}]",
   208	                romanized=f"[ERROR: {error_msg}]",
   209	                tagged=f"[ERROR: {error_msg}]",
   210	            )
   211	        else:
   212	            # Parse speaker metadata if present
   213	            speaker_data = raw_result.get('speaker')
   214	            speaker = SpeakerMeta(**speaker_data) if isinstance(speaker_data, dict) else None
   215
   216	            transcription = TranscriptionOutput(
   217	                transcription=raw_result.get('transcription', ''),
   218	                code_switch=raw_result.get('code_switch', ''),
   219	                romanized=raw_result.get('romanized', ''),
   220	                tagged=raw_result.get('tagged', ''),
   221	                speaker=speaker,
   222	                detected_language=raw_result.get('detected_language', ''),
   223	            )
   224
   225	        return TranscriptionResult(
   226	            segment_id=chunk.original_segment,
   227	            chunk_index=chunk.chunk_index,
   228	            total_chunks=chunk.total_chunks,
   229	            duration_sec=chunk.duration_sec,
   230	            language=config.language,
   231	            transcription=transcription,
   232	            model_used=raw_result.get('_model', config.model),
   233	            thinking_level=raw_result.get('_thinking_level'),
   234	            processing_time_sec=raw_result.get('_processing_time_sec')
   235	        )
   236
   237	    def transcribe_batch(
   238	        self,
   239	        chunks: List[AudioChunk],
   240	        config: TranscriptionConfig,
   241	        max_chunks: Optional[int] = None,
   242	        progress_callback: Optional[callable] = None
   243	    ) -> List[TranscriptionResult]:
   244	        """
   245	        Transcribe multiple audio chunks.
   246
   247	        Args:
   248	            chunks: List of AudioChunks to transcribe
   249	            config: Transcription configuration
   250	            max_chunks: Maximum chunks to process (for testing)
   251	            progress_callback: Optional callback(current, total) for progress updates
   252
   253	        Returns:
   254	            List of TranscriptionResults
   255	        """
   256	        if max_chunks:
   257	            chunks = chunks[:max_chunks]
   258
   259	        results = []
   260	        total = len(chunks)
   261
   262	        print(f"[Transcriber] Processing {total} chunks with {config.model}...")
   263
   264	        for i, chunk in enumerate(chunks):
   265	            if progress_callback:
   266	                progress_callback(i + 1, total)
   267	            else:
   268	                print(f"[Transcriber] Processing {i + 1}/{total}: {chunk.original_segment} "
   269	                      f"(chunk {chunk.chunk_index + 1}/{chunk.total_chunks})")
   270
   271	            result = self.transcribe_chunk(chunk, config)
   272	            results.append(result)
   273
   274	            # Brief pause to avoid rate limiting
   275	            if i < total - 1:
   276	                time.sleep(0.5)
   277
   278	        print(f"[Transcriber] Completed {len(results)} transcriptions")
   279	        return results
   280
   281
   282	def transcribe_segments(
   283	    segments: List[AudioChunk],
   284	    language: str = "Telugu",
   285	    model: str = "gemini-3-flash-preview",
   286	    thinking_level: str = "high",
   287	    max_segments: Optional[int] = None
   288	) -> List[TranscriptionResult]:
   289	    """
   290	    Convenience function to transcribe a list of audio segments.
   291
   292	    Args:
   293	        segments: List of AudioChunk objects
   294	        language: Primary language of the audio
   295	        model: Gemini model to use
   296	        thinking_level: Thinking level for Gemini 3 models
   297	        max_segments: Max segments to process (for testing)
   298
   299	    Returns:
   300	        List of TranscriptionResult objects


**Identifying code region for validation logic**


nl -ba src/validators/simple_validator.py | sed -n '360,530p'


Output:
Chunk ID: 936492
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 1965
Output:
   360	    if language == "en":
   361	        return cleaned
   362	    lang_config = SCRIPT_RANGES.get(language, SCRIPT_RANGES["te"])
   363	    result = []
   364	    for char in cleaned:
   365	        cp = ord(char)
   366	        is_native = any(start <= cp <= end for start, end in lang_config["ranges"])
   367	        if is_native or char in ' \t':
   368	            result.append(char)
   369	    return re.sub(r'\s+', ' ', ''.join(result)).strip()
   370
   371
   372	def validate_transcription(
   373	    audio_path: str,
   374	    transcription: str,
   375	    language: str = "te",
   376	    romanized_text: str = "",
   377	    check_audio: bool = True,
   378	    duration_sec: float = 0.0,
   379	) -> ValidationResult:
   380	    """
   381	    Validate a transcription (v4: uroman-based, stricter, structural checks).
   382
   383	    Flow:
   384	      Step 0: Structural sanity checks (instant, no ML)
   385	      Step 1: Character validation (instant)
   386	      Step 2: Native CTC forced alignment (language-specific wav2vec2)
   387	      Step 3: Romanized MMS forced alignment (uroman-derived, NOT Gemini's)
   388	      Step 4: Weighted combined score with per-word analysis
   389
   390	    Key v4 change: romanized_text parameter is IGNORED for MMS alignment.
   391	    Validator derives its own romanization via uroman from the native text.
   392	    This makes validation deterministic and independent of Gemini's output.
   393
   394	    Args:
   395	        audio_path: Path to audio file
   396	        transcription: Native script text
   397	        language: Language code (te, hi, ta, etc.)
   398	        romanized_text: IGNORED in v4 (kept for API compat). Validator uses uroman.
   399	        check_audio: Whether to run audio alignment checks
   400	        duration_sec: Audio duration (for structural checks, optional)
   401	    """
   402	    reasons = []
   403
   404	    # Clean text for alignment
   405	    clean_text = _strip_tags_and_punct(transcription)
   406	    # v5: transcription is code-mixed, so strip to native-only for CTC
   407	    native_only = _strip_to_native_only(transcription, language)
   408
   409	    # === STEP 0: Structural sanity checks (no ML, instant) ===
   410	    structural = structural_sanity_check(clean_text, language, duration_sec)
   411	    if not structural["pass"]:
   412	        return ValidationResult(
   413	            status="reject",
   414	            structural_pass=False,
   415	            structural_reasons=structural["reasons"],
   416	            reasons=[f"structural_fail: {', '.join(structural['reasons'])}"]
   417	        )
   418
   419	    # === STEP 1: Character validation (instant) ===
   420	    # v5: transcription is code-mixed, always allow English chars
   421	    char_check = check_characters(clean_text, language, allow_english=True)
   422
   423	    if not char_check["valid"]:
   424	        return ValidationResult(
   425	            status="reject",
   426	            char_valid=False,
   427	            invalid_chars=char_check["invalid_chars"],
   428	            script_ratio=char_check["script_ratio"],
   429	            reasons=["Invalid/alien characters found"]
   430	        )
   431
   432	    if char_check["script_ratio"] < 0.5:
   433	        reasons.append(f"Too few native chars ({char_check['script_ratio']:.0%})")
   434
   435	    # === STEP 1.5: Derive uroman romanization (deterministic) ===
   436	    # This replaces Gemini's creative romanization with stable, reproducible text
   437	    from src.romanization import romanize_for_alignment
   438	    uroman_roman = romanize_for_alignment(transcription)
   439
   440	    # === STEP 2: Native CTC alignment ===
   441	    native_ctc_score = 0.0
   442	    low_conf_words = []
   443	    low_conf_ratio = 0.0
   444
   445	    if check_audio and native_only:
   446	        try:
   447	            aligner = _get_ctc_aligner(language)
   448	            ctc_result = aligner.align(audio_path, native_only)
   449	            native_ctc_score = ctc_result.alignment_score
   450	            low_conf_words = ctc_result.low_confidence_words
   451	            low_conf_ratio = ctc_result.low_confidence_ratio
   452	        except Exception as e:
   453	            reasons.append(f"CTC alignment failed: {str(e)[:50]}")
   454
   455	    # === STEP 3: Romanized MMS alignment (uroman-derived) ===
   456	    roman_mms_score = 0.0
   457	    mms_min_word = 0.0
   458	    mms_boundary_avg = 0.0
   459	    mms_internal_below = 0.0
   460
   461	    if check_audio and uroman_roman:
   462	        try:
   463	            mms = _get_mms_aligner()
   464	            mms_result = mms.align(audio_path, uroman_roman)
   465	            roman_mms_score = mms_result.alignment_score
   466
   467	            # v4: Extract per-word metrics for stricter analysis
   468	            if hasattr(mms_result, 'word_scores') and mms_result.word_scores:
   469	                scores = mms_result.word_scores
   470	                if scores:
   471	                    mms_min_word = min(scores)
   472	                    # Boundary words: first + last
   473	                    boundary = []
   474	                    if len(scores) >= 1:
   475	                        boundary.append(scores[0])
   476	                    if len(scores) >= 2:
   477	                        boundary.append(scores[-1])
   478	                    mms_boundary_avg = sum(boundary) / len(boundary) if boundary else 0
   479	                    # Internal words below threshold
   480	                    internal = scores[1:-1] if len(scores) > 2 else []
   481	                    if internal:
   482	                        below = sum(1 for s in internal if s < 0.60)
   483	                        mms_internal_below = below / len(internal)
   484	        except Exception as e:
   485	            reasons.append(f"MMS alignment failed: {str(e)[:50]}")
   486
   487	    # === STEP 4: Combined scoring ===
   488	    if check_audio and native_ctc_score > 0 and roman_mms_score > 0:
   489	        combined_score, verdict = compute_combined_score(
   490	            native_ctc_score, roman_mms_score
   491	        )
   492	        if verdict == "review" and abs(native_ctc_score - roman_mms_score) > 0.25:
   493	            reasons.append(
   494	                f"Validator disagreement: CTC={native_ctc_score:.2f} "
   495	                f"MMS={roman_mms_score:.2f}"
   496	            )
   497	    elif check_audio and native_ctc_score > 0:
   498	        combined_score = native_ctc_score
   499	        if native_ctc_score >= 0.75:
   500	            verdict = "accept"
   501	        elif native_ctc_score >= 0.55:
   502	            verdict = "retry"
   503	        else:
   504	            verdict = "reject"
   505	        if roman_mms_score == 0 and uroman_roman:
   506	            reasons.append("MMS failed, using CTC only")
   507	    elif check_audio and roman_mms_score > 0:
   508	        combined_score = roman_mms_score
   509	        if roman_mms_score >= 0.75:
   510	            verdict = "accept"
   511	        elif roman_mms_score >= 0.55:
   512	            verdict = "retry"
   513	        else:
   514	            verdict = "reject"
   515	        reasons.append("CTC failed, using MMS only")
   516	    elif check_audio:
   517	        combined_score = 0.0
   518	        verdict = "review"
   519	        reasons.append("Both validators returned 0")
   520	    else:
   521	        combined_score = 0.0
   522	        verdict = "review" if reasons else "accept"
   523
   524	    # Character validation failures override
   525	    if not char_check["valid"]:
   526	        verdict = "reject"
   527
   528	    # Append score-based reasons
   529	    if verdict == "retry":
   530	        reasons.append(


**Planning line-number extraction**


nl -ba src/romanization.py | sed -n '1,140p'


Output:
Chunk ID: 61b3be
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 1325
Output:
     1	"""
     2	Deterministic Romanization Module
     3	==================================
     4
     5	Converts native Indic script text to MMS_FA-compatible Latin romanization
     6	using the uroman library. This replaces Gemini-generated romanization with
     7	a deterministic, reproducible transform.
     8
     9	Why deterministic:
    10	  - Gemini's "romanized as pronounced" is creative and inconsistent across runs
    11	  - uroman produces identical output for identical input, every time
    12	  - MMS_FA was trained with uroman-style romanization, so alignment is optimal
    13
    14	MMS_FA normalization: [a-z' ] only, lowercase, collapsed spaces.
    15	Per: https://docs.pytorch.org/audio/2.8/tutorials/forced_alignment_for_multilingual_data_tutorial.html
    16
    17	Usage:
    18	    from src.romanization import romanize, romanize_for_alignment
    19
    20	    # Basic romanization
    21	    roman = romanize("నాకు కొన్ని యాడ్స్ గుర్తుంటాయి")
    22	    # -> "naaku konni yaadds gurtumttaayi"
    23
    24	    # MMS_FA-ready (stripped to [a-z' ] only)
    25	    aligned = romanize_for_alignment("నాకు కొన్ని, యాడ్స్!")
    26	    # -> "naaku konni yaadds"
    27	"""
    28	import re
    29	from typing import Optional
    30
    31	# Lazy-loaded uroman instance (heavy init: ~3s for data files)
    32	_uroman_instance = None
    33
    34
    35	def _get_uroman():
    36	    """Lazy load uroman. First call ~3s, subsequent calls instant."""
    37	    global _uroman_instance
    38	    if _uroman_instance is None:
    39	        import uroman
    40	        _uroman_instance = uroman.Uroman()
    41	    return _uroman_instance
    42
    43
    44	def romanize(text: str) -> str:
    45	    """
    46	    Romanize text from any script to Latin using uroman.
    47
    48	    Deterministic: same input always produces same output.
    49	    Handles Indic scripts (Telugu, Hindi, Tamil, etc.) + passes Latin through.
    50
    51	    Args:
    52	        text: Text in any script (Indic, Latin, mixed)
    53
    54	    Returns:
    55	        Romanized Latin text
    56	    """
    57	    if not text or not text.strip():
    58	        return ""
    59	    u = _get_uroman()
    60	    return u.romanize_string(text.strip())
    61
    62
    63	def romanize_for_alignment(text: str) -> str:
    64	    """
    65	    Romanize and normalize for MMS_FA forced alignment.
    66
    67	    MMS_FA expects: lowercase [a-z' ] only.
    68	    Steps:
    69	      1. Strip tags like [laugh], [UNK], [INAUDIBLE] etc.
    70	      2. Romanize via uroman
    71	      3. Lowercase
    72	      4. Strip everything except [a-z' ]
    73	      5. Collapse multiple spaces
    74	      6. Strip leading/trailing whitespace
    75
    76	    Args:
    77	        text: Native script text (may include punctuation, tags)
    78
    79	    Returns:
    80	        Normalized romanized text ready for MMS_FA alignment
    81	    """
    82	    if not text or not text.strip():
    83	        return ""
    84
    85	    # Step 1: Strip bracketed tags [laugh] [UNK] [INAUDIBLE] [NO_SPEECH] etc.
    86	    cleaned = re.sub(r'\[[\w_]+\]', '', text)
    87
    88	    # Step 2: Strip punctuation that would confuse uroman
    89	    # Keep native script chars + basic Latin + spaces
    90	    cleaned = re.sub(r'[।,.!?;:\'"()\-–—…]', ' ', cleaned)
    91
    92	    # Step 3: Romanize
    93	    roman = romanize(cleaned)
    94
    95	    # Step 4: MMS_FA normalization - lowercase, [a-z' ] only
    96	    roman = roman.lower()
    97	    roman = re.sub(r"[^a-z' ]", " ", roman)
    98
    99	    # Step 5: Collapse spaces
   100	    roman = re.sub(r'\s+', ' ', roman).strip()
   101
   102	    return roman
   103
   104
   105	def compute_romanization_cer(
   106	    native_text: str,
   107	    gemini_romanized: str
   108	) -> float:
   109	    """
   110	    Compute Character Error Rate between uroman-derived and Gemini-provided romanization.
   111
   112	    Low CER (<3%) = Gemini's romanization is consistent with deterministic version.
   113	    High CER (>10%) = Gemini hallucinated or used non-standard romanization.
   114
   115	    Useful for flagging romanization drift without blocking transcriptions.
   116
   117	    Args:
   118	        native_text: Native script text (source of truth)
   119	        gemini_romanized: Gemini's romanized output
   120
   121	    Returns:
   122	        CER as float (0.0 = identical, 1.0 = completely different)
   123	    """
   124	    # Normalize both to same format
   125	    uroman_roman = romanize_for_alignment(native_text)
   126	    gemini_norm = re.sub(r"[^a-z' ]", " ", gemini_romanized.lower())
   127	    gemini_norm = re.sub(r'\s+', ' ', gemini_norm).strip()
   128
   129	    if not uroman_roman:
   130	        return 1.0 if gemini_norm else 0.0
   131
   132	    # Simple character-level edit distance (Levenshtein)
   133	    # For CER we don't need a library - implement inline
   134	    ref = uroman_roman
   135	    hyp = gemini_norm
   136	    n = len(ref)
   137	    m = len(hyp)
   138
   139	    if n == 0:
   140	        return 1.0 if m > 0 else 0.0


**Planning line number check**


nl -ba transcription_prompt.txt | sed -n '1,170p'


Output:
Chunk ID: 52f6db
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 1357
Output:
     1	You are a verbatim speech-to-text transcription system. You are NOT a conversational assistant. Your output must precisely match the audio content.
     2
     3	TARGET: Telugu (te-IN)
     4
     5	CRITICAL RULES (violations cause rejection):
     6	1. NEVER TRANSLATE. This is transcription, not translation. If the speaker says English words, those are English. If the speaker says Telugu words, those are Telugu. Write what you HEAR, not what you think it means in another language.
     7	2. VERBATIM FIDELITY: Every repetition, filler, stammer, false start, hesitation - exactly as spoken.
     8	3. NO CORRECTION: Do not fix grammar, pronunciation, dialect, or word choice.
     9	4. NO HALLUCINATION: Never add words or phrases not in the audio. If audio cuts off mid-sentence, STOP where the audio stops. Do not complete anything. Output ONLY the JSON.
    10	5. UNCERTAINTY: If a word is unclear, write [UNK]. Use [INAUDIBLE] for unintelligible speech. Use [NO_SPEECH] for no speech (silence, noise, music only).
    11	6. BOUNDARY HANDLING: Audio is VAD-cut and may start/end mid-speech. Transcribe everything you can confidently hear. Only omit what is truly inaudible.
    12	7. LANGUAGE MISMATCH: Trust what you hear. If audio is clearly different from Telugu, transcribe in that language's script and set detected_language accordingly.
    13
    14	PUNCTUATION (prosody-based, not grammar):
    15	- Only: comma, period, ? and !
    16	- Insert from audible pauses/intonation only. No pause = no punctuation.
    17
    18	SCRIPT RULES FOR TELUGU:
    19	Don't over-split words. Preserve Sandhi/combined forms as spoken.
    20
    21	FIELD DERIVATION:
    22	"transcription" is the PRIMARY authoritative output. It IS code-mixed: each language in its own script.
    23	"tagged" is identical to transcription but with audio event markers inserted at their positions. Do NOT re-interpret the audio for tagged - just copy transcription and add tags.
    24
    25	OUTPUT FIELDS:
    26
    27	1. transcription (AUTHORITATIVE - native script)
    28	   Write Telugu words in Telugu script.
    29	   Keep English words in English (Latin script) exactly as spoken.
    30	   Keep Hindi words in Devanagari, Tamil words in Tamil script, etc.
    31	   Each language stays in its original script. Do NOT transliterate.
    32	   Example: speaker says 'salt biscuits manchidi' -> salt biscuits Telugu(manchidi)
    33	   Punctuation: period, comma, ? and ! only, from audible prosodic cues.
    34
    35	2. tagged (derived from transcription - code-mixed + event tags)
    36	   Same text as transcription with audio event tags inserted at their positions.
    37	   Do NOT change any words or scripts - just add the tags where events occur.
    38	   ONLY these tags, ONLY if clearly and prominently audible:
    39	   [laugh] [cough] [sigh] [breath] [singing] [noise] [music] [applause]
    40
    41	3. speaker (metadata from audio prosody)
    42	   emotion: neutral | happy | sad | angry | excited | surprised
    43	   speaking_style: conversational | narrative | excited | calm | emphatic | sarcastic | formal
    44	   pace: slow | normal | fast
    45	   accent: regional dialect/accent if confidently detectable, empty string otherwise.
    46
    47	4. detected_language
    48	   The language you actually hear spoken. If code-mixed, write the dominant language.
    49
    50
    51	--- USER PROMPT (sent alongside the audio bytes) ---
    52
    53	Transcribe this audio segment following the system instructions. Return a valid JSON object with all required fields.
    54
    55
    56	--- JSON SCHEMA (enforced via response_json_schema) ---
    57
    58	{
    59	  "type": "object",
    60	  "properties": {
    61	    "transcription": {
    62	      "type": "string",
    63	      "description": "Native script transcription with minimal punctuation"
    64	    },
    65	    "tagged": {
    66	      "type": "string",
    67	      "description": "Code-mixed transcription with audio event tags"
    68	    },
    69	    "speaker": {
    70	      "type": "object",
    71	      "description": "Speaker metadata",
    72	      "properties": {
    73	        "emotion": {
    74	          "type": "string",
    75	          "enum": ["neutral", "happy", "sad", "angry", "excited", "surprised"]
    76	        },
    77	        "speaking_style": {
    78	          "type": "string",
    79	          "enum": ["conversational", "narrative", "excited", "calm", "emphatic", "sarcastic", "formal"]
    80	        },
    81	        "pace": {
    82	          "type": "string",
    83	          "enum": ["slow", "normal", "fast"]
    84	        },
    85	        "accent": {
    86	          "type": "string",
    87	          "description": "Regional accent/dialect or empty string"
    88	        }
    89	      },
    90	      "required": ["emotion", "speaking_style", "pace"],
    91	      "additionalProperties": false
    92	    },
    93	    "detected_language": {
    94	      "type": "string",
    95	      "description": "Language actually spoken in the audio"
    96	    }
    97	  },
    98	  "required": ["transcription", "tagged", "speaker", "detected_language"],
    99	  "additionalProperties": false
   100	}
   101
   102
   103
   104	- Supported languages: Hindi, Marathi, Telugu, Tamil, Kannada, Malayalam, Gujarati, Punjabi, Bengali, Assamese, Odia, English


**Confirming file absence with search**


rg -n "transcription_prompt\.txt" -S .; echo EXIT:$?


Output:
Chunk ID: 9fc922
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 2
Output:
EXIT:1


**Planning detailed pipeline explanation**


I reviewed the prompt and the transcription pipeline code (excluding dashboard/visualization files).

**Prompt Reality Check**
`transcription_prompt.txt` is clear and strict (verbatim, code-mixed script fidelity, 4 JSON fields) at [transcription_prompt.txt:1](/home/ubuntu/maya3_transcribe/transcription_prompt.txt:1), [transcription_prompt.txt:21](/home/ubuntu/maya3_transcribe/transcription_prompt.txt:21), [transcription_prompt.txt:58](/home/ubuntu/maya3_transcribe/transcription_prompt.txt:58).

Important: this file is not what runtime uses. Runtime prompt/schema come from:
- [transcription_schema.py:169](/home/ubuntu/maya3_transcribe/src/backend/transcription_schema.py:169)
- [transcription_schema.py:252](/home/ubuntu/maya3_transcribe/src/backend/transcription_schema.py:252)
- wired in [gemini_transcriber.py:93](/home/ubuntu/maya3_transcribe/src/backend/gemini_transcriber.py:93)

**Current Pipeline (Pictorial)**
```text
video_id
  |
  v
R2 download + extract tar
  |  (always wipes local work dir first)
  v
segment stats scan
  |
  v
language lookup (Supabase fallback probing multiple tables)
  |
  v
audio processing
  |- skip < min duration
  |- split > 10s (energy-valley chunking)
  v
audio polishing + SNR/duration gate
  |
  v
Gemini transcription (sequential, 0.5s sleep/chunk)
  |- system prompt + schema
  |- returns transcription/tagged/speaker/detected_language
  |- derives romanized + code_switch in code
  v
validation (if enabled)
  |- structural + char checks
  |- CTC native align
  |- MMS roman align
  |- combined score
  |- optional retry with Pro model + re-validate
  v
save partial JSON every batch
  v
save final JSON + cleanup models
```

Key anchors:
- Orchestration steps: [pipeline.py:219](/home/ubuntu/maya3_transcribe/pipeline.py:219), [pipeline.py:252](/home/ubuntu/maya3_transcribe/pipeline.py:252), [pipeline.py:271](/home/ubuntu/maya3_transcribe/pipeline.py:271), [pipeline.py:301](/home/ubuntu/maya3_transcribe/pipeline.py:301)
- R2 behavior: [r2_storage.py:34](/home/ubuntu/maya3_transcribe/src/backend/r2_storage.py:34), [r2_storage.py:176](/home/ubuntu/maya3_transcribe/src/backend/r2_storage.py:176)
- Supabase probing: [supabase_client.py:29](/home/ubuntu/maya3_transcribe/src/backend/supabase_client.py:29)
- Split/scan decode paths: [audio_processor.py:60](/home/ubuntu/maya3_transcribe/src/backend/audio_processor.py:60), [audio_processor.py:127](/home/ubuntu/maya3_transcribe/src/backend/audio_processor.py:127), [audio_processor.py:277](/home/ubuntu/maya3_transcribe/src/backend/audio_processor.py:277)
- Sequential tx + sleep: [gemini_transcriber.py:237](/home/ubuntu/maya3_transcribe/src/backend/gemini_transcriber.py:237), [gemini_transcriber.py:276](/home/ubuntu/maya3_transcribe/src/backend/gemini_transcriber.py:276)
- Validation + retry: [pipeline.py:327](/home/ubuntu/maya3_transcribe/pipeline.py:327), [pipeline.py:344](/home/ubuntu/maya3_transcribe/pipeline.py:344)
- Duplicate romanization path: [gemini_transcriber.py:163](/home/ubuntu/maya3_transcribe/src/backend/gemini_transcriber.py:163), [simple_validator.py:390](/home/ubuntu/maya3_transcribe/src/validators/simple_validator.py:390), [simple_validator.py:437](/home/ubuntu/maya3_transcribe/src/validators/simple_validator.py:437)
- uroman heavy first init: [romanization.py:31](/home/ubuntu/maya3_transcribe/src/romanization.py:31)
- Partial rewrite per batch: [pipeline.py:424](/home/ubuntu/maya3_transcribe/pipeline.py:424), [pipeline.py:471](/home/ubuntu/maya3_transcribe/pipeline.py:471)

**Where Overhead Is Highest**
1. Sequential Gemini calls + fixed sleep.
2. Dual validation (CTC + MMS) on every segment.
3. Retry with Pro model for retry/reject segments.
4. Re-download/re-extract every run (no local reuse).
5. Repeated audio decode passes (stats + processing + splitting/polish).
6. Romanization done twice (transcriber and validator).

**Light Version Proposal**
```text
video_id/local_segments
  |
  v
(optional) fetch/reuse segments cache
  |
  v
basic chunking only (skip polish by default)
  |
  v
parallel Gemini transcription (N workers, no fixed sleep)
  |
  v
optional quick validation only (structural+chars)
  |
  v
append-only output (jsonl) + one final consolidated json
```

If you want, I can implement a `--lite` mode next with:
1. `no-polish`, `no-validate`, `validation_action=flag`, `no-retry`
2. parallel transcribe workers
3. cached local extracted segments
4. remove duplicate romanization work in lite path