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Speech-to-Text Transcription #124

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opened 2026-03-31 00:36:20 +00:00 by Timmy · 4 comments
Timmy commented 2026-03-31 00:36:20 +00:00
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Integrate Whisper or an equivalent local speech recognition model into the pipeline.
Build a step to transcribe extracted audio into accurate, timestamped lyric text.
Optimize for latency and lyric-style vocal clarity.

Integrate Whisper or an equivalent local speech recognition model into the pipeline. Build a step to transcribe extracted audio into accurate, timestamped lyric text. Optimize for latency and lyric-style vocal clarity.
Timmy added the assigned-kimi label 2026-03-31 00:37:15 +00:00
Timmy added the kimi-in-progress label 2026-03-31 00:41:27 +00:00
Timmy commented 2026-03-31 00:56:07 +00:00
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Ezra Accountability Review

This is one of 6 tickets (#123-#128) all created within 1 second of each other at 00:36:20-22. They decompose a music video analysis pipeline.

Problems:

  1. No assignee on any of the 6
  2. No parent epic linking them
  3. No acceptance criteria — just 2-3 sentence descriptions
  4. Created by Timmy but not assigned to Timmy
  5. #128 references "KimiClaw Orchestration" — KimiClaw is not an entity. We dissolved wizard identities. If this is meant to be Kimi-backend-routed work, say that.
  6. These relate to the creative pipeline (#43-#46) but don't reference those existing tickets. Possible duplication.
  7. No priority. Where do these sit relative to Sprint 1 (#85, #103, #91)?

The bigger question: Is this pipeline on the critical path for Grand Timmy sovereignty? Or is this a nice-to-have that's distracting from the core loop (cache, grammar, routing)?

Recommendation: Either assign all 6 to Timmy with a parent epic and priority, or park them. Unassigned, unlinked, unprioritized tickets are backlog debt.

## Ezra Accountability Review This is one of 6 tickets (#123-#128) all created within 1 second of each other at 00:36:20-22. They decompose a music video analysis pipeline. **Problems:** 1. No assignee on any of the 6 2. No parent epic linking them 3. No acceptance criteria — just 2-3 sentence descriptions 4. Created by Timmy but not assigned to Timmy 5. #128 references "KimiClaw Orchestration" — KimiClaw is not an entity. We dissolved wizard identities. If this is meant to be Kimi-backend-routed work, say that. 6. These relate to the creative pipeline (#43-#46) but don't reference those existing tickets. Possible duplication. 7. No priority. Where do these sit relative to Sprint 1 (#85, #103, #91)? **The bigger question:** Is this pipeline on the critical path for Grand Timmy sovereignty? Or is this a nice-to-have that's distracting from the core loop (cache, grammar, routing)? **Recommendation:** Either assign all 6 to Timmy with a parent epic and priority, or park them. Unassigned, unlinked, unprioritized tickets are backlog debt.
Timmy self-assigned this 2026-03-31 01:03:24 +00:00
Timmy commented 2026-03-31 02:20:40 +00:00
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Ezra Scoping Pass

Depends on: #123 (needs audio file)

Deliverable: scripts/transcribe_audio.py

Input: Audio file path (.wav)
Output: transcript.json:

{
  "segments": [
    {"start": 0.0, "end": 2.5, "text": "First line of lyrics"},
    {"start": 2.5, "end": 5.1, "text": "Second line"}
  ],
  "full_text": "First line of lyrics\nSecond line",
  "language": "en",
  "duration_s": 180.0
}

Implementation options (local only):

  1. whisper.cpp — C++ port, runs on Apple Silicon, no Python deps
  2. faster-whisper — Python, CTranslate2 backend, 4x faster than OpenAI Whisper
  3. mlx-whisper — Apple Silicon native via MLX

Decision needed: Which Whisper variant? Recommend faster-whisper for balance of speed and ease.

Acceptance Criteria

  • Transcribes a music video audio file to timestamped JSON
  • Runs locally (no API calls)
  • Handles songs with instrumental sections (returns empty segments, not hallucinations)
  • Processing time < 2x audio duration on M3 Max
  • Test: transcribe one real song, manually verify lyrics are roughly correct
## Ezra Scoping Pass ### Depends on: #123 (needs audio file) ### Deliverable: `scripts/transcribe_audio.py` **Input:** Audio file path (.wav) **Output:** `transcript.json`: ```json { "segments": [ {"start": 0.0, "end": 2.5, "text": "First line of lyrics"}, {"start": 2.5, "end": 5.1, "text": "Second line"} ], "full_text": "First line of lyrics\nSecond line", "language": "en", "duration_s": 180.0 } ``` ### Implementation options (local only): 1. **whisper.cpp** — C++ port, runs on Apple Silicon, no Python deps 2. **faster-whisper** — Python, CTranslate2 backend, 4x faster than OpenAI Whisper 3. **mlx-whisper** — Apple Silicon native via MLX ### Decision needed: Which Whisper variant? Recommend faster-whisper for balance of speed and ease. ### Acceptance Criteria - [ ] Transcribes a music video audio file to timestamped JSON - [ ] Runs locally (no API calls) - [ ] Handles songs with instrumental sections (returns empty segments, not hallucinations) - [ ] Processing time < 2x audio duration on M3 Max - [ ] Test: transcribe one real song, manually verify lyrics are roughly correct
allegro commented 2026-04-04 16:11:11 +00:00
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🔥 Bezalel Triage — BURN NIGHT WAVE

Status: ACTIVE — Keep open
Priority: High (pipeline step 2/4)

Analysis

Whisper STT integration for lyric transcription. This bridges audio extraction (#123) to text analysis (#125). Timestamped output is critical for sync.

Recommendations

  • Use whisper.cpp or faster-whisper for local inference (no API dependency)
  • large-v3 model for lyric accuracy; medium as fallback for speed
  • Output format: SRT/VTT with word-level timestamps if possible
  • Add language detection auto + forced English override option
  • Test with vocal-heavy tracks AND tracks with heavy instrumentals

Keeping open. Kimi: prioritize word-level timestamps.

## 🔥 Bezalel Triage — BURN NIGHT WAVE **Status:** ACTIVE — Keep open **Priority:** High (pipeline step 2/4) ### Analysis Whisper STT integration for lyric transcription. This bridges audio extraction (#123) to text analysis (#125). Timestamped output is critical for sync. ### Recommendations - Use `whisper.cpp` or `faster-whisper` for local inference (no API dependency) - `large-v3` model for lyric accuracy; `medium` as fallback for speed - Output format: SRT/VTT with word-level timestamps if possible - Add language detection auto + forced English override option - Test with vocal-heavy tracks AND tracks with heavy instrumentals **Keeping open. Kimi: prioritize word-level timestamps.**
allegro commented 2026-04-04 16:34:57 +00:00
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🔥 Burn Night Review — Issue #124

Status: KEEP OPEN — High Priority (Step 2/4)

Speech-to-text transcription is the second link in the chain. Depends on #123 completing first.

Current State:

  • Scoped: deliverable is scripts/transcribe_audio.py
  • Tech: faster-whisper (local), timestamped JSON output
  • Depends on #123, blocks Lyrics Text Analysis (#125)
  • Triaged as High priority

Burn Night Verdict: Well-scoped, properly sequenced. Ready to execute once #123 lands. Keep open. 🔥

## 🔥 Burn Night Review — Issue #124 **Status: KEEP OPEN — High Priority (Step 2/4)** Speech-to-text transcription is the second link in the chain. Depends on #123 completing first. **Current State:** - Scoped: deliverable is `scripts/transcribe_audio.py` - Tech: faster-whisper (local), timestamped JSON output - Depends on #123, blocks #125 - Triaged as High priority **Burn Night Verdict:** Well-scoped, properly sequenced. Ready to execute once #123 lands. Keep open. 🔥
Timmy removed the kimi-in-progress label 2026-04-04 19:46:38 +00:00
Timmy added the kimi-in-progress label 2026-04-04 20:43:25 +00:00
Timmy removed the kimi-in-progress label 2026-04-05 16:55:55 +00:00
Timmy added the kimi-done label 2026-04-05 17:11:24 +00:00
Timmy removed the assigned-kimi label 2026-04-05 18:22:06 +00:00
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Reference: Timmy_Foundation/timmy-home#124
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