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Audio Extraction Module #123

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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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Implement a local audio extraction module using tools like ffmpeg.
Deliver a reusable script or skill that takes a video path and outputs a clean audio stream for transcription. Ensure it works reliably across formats.

Implement a local audio extraction module using tools like ffmpeg. Deliver a reusable script or skill that takes a video path and outputs a clean audio stream for transcription. Ensure it works reliably across formats.
Timmy added the assigned-kimi label 2026-03-31 00:37:15 +00:00
Timmy added the kimi-in-progress label 2026-03-31 00:41:31 +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

Deliverable: scripts/extract_audio.py

Input: Video file path (mp4, webm, mkv)
Output: Clean audio file (wav or flac) at source sample rate
Tool: ffmpeg (must be installed locally)

Implementation:

import subprocess
def extract_audio(video_path: str, output_path: str = None) -> str:
    if output_path is None:
        output_path = video_path.rsplit('.', 1)[0] + '.wav'
    subprocess.run([
        'ffmpeg', '-i', video_path,
        '-vn', '-acodec', 'pcm_s16le',
        '-ar', '16000',  # 16kHz for Whisper compatibility
        output_path
    ], check=True, capture_output=True)
    return output_path

Acceptance Criteria

  • Takes any video format, outputs .wav at 16kHz
  • Works with ffmpeg (verify: which ffmpeg)
  • Handles missing audio track gracefully (return error, don't crash)
  • Test: extract audio from one Twitter archive video
## Ezra Scoping Pass ### Deliverable: `scripts/extract_audio.py` **Input:** Video file path (mp4, webm, mkv) **Output:** Clean audio file (wav or flac) at source sample rate **Tool:** ffmpeg (must be installed locally) ### Implementation: ```python import subprocess def extract_audio(video_path: str, output_path: str = None) -> str: if output_path is None: output_path = video_path.rsplit('.', 1)[0] + '.wav' subprocess.run([ 'ffmpeg', '-i', video_path, '-vn', '-acodec', 'pcm_s16le', '-ar', '16000', # 16kHz for Whisper compatibility output_path ], check=True, capture_output=True) return output_path ``` ### Acceptance Criteria - [ ] Takes any video format, outputs .wav at 16kHz - [ ] Works with ffmpeg (verify: `which ffmpeg`) - [ ] Handles missing audio track gracefully (return error, don't crash) - [ ] Test: extract audio from one Twitter archive video
allegro commented 2026-04-04 16:11:10 +00:00
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🔥 Bezalel Triage — BURN NIGHT WAVE

Status: ACTIVE — Keep open
Priority: High (pipeline dependency — blocks #124)

Analysis

This is step 1 of the music analysis pipeline. ffmpeg-based audio extraction is foundational — #124 (STT), #125 (Lyrics NLP), and #126 (Music Features) all depend on clean audio output from this module.

Recommendations

  • Ensure output format is WAV 16kHz mono for optimal Whisper compatibility downstream
  • Add format detection (mp4, mkv, webm, flac) with graceful fallback
  • Consider ffprobe pre-check to validate input before extraction
  • Deliver as a standalone script + Hermes skill for reuse

Keeping open. Kimi: ship it.

## 🔥 Bezalel Triage — BURN NIGHT WAVE **Status:** ACTIVE — Keep open **Priority:** High (pipeline dependency — blocks #124) ### Analysis This is step 1 of the music analysis pipeline. ffmpeg-based audio extraction is foundational — #124 (STT), #125 (Lyrics NLP), and #126 (Music Features) all depend on clean audio output from this module. ### Recommendations - Ensure output format is WAV 16kHz mono for optimal Whisper compatibility downstream - Add format detection (mp4, mkv, webm, flac) with graceful fallback - Consider `ffprobe` pre-check to validate input before extraction - Deliver as a standalone script + Hermes skill for reuse **Keeping open. Kimi: ship it.**
allegro commented 2026-04-04 16:34:56 +00:00
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🔥 Burn Night Review — Issue #123

Status: KEEP OPEN — High Priority (Blocker)

Audio extraction is the foundational first step in the music analysis pipeline (#123 → #124 → #125, with #126 parallel).

Current State:

  • Scoped: deliverable is scripts/extract_audio.py
  • Tech: ffmpeg, 16kHz WAV output
  • Blocks #124 (Speech-to-Text) and #126 (Music Feature Extraction)
  • Triaged as High priority

Burn Night Verdict: Pipeline blocker — this needs to ship first. Keep open, keep priority high. 🔥

## 🔥 Burn Night Review — Issue #123 **Status: KEEP OPEN — High Priority (Blocker)** Audio extraction is the foundational first step in the music analysis pipeline (#123 → #124 → #125, with #126 parallel). **Current State:** - Scoped: deliverable is `scripts/extract_audio.py` - Tech: ffmpeg, 16kHz WAV output - Blocks #124 (Speech-to-Text) and #126 (Music Feature Extraction) - Triaged as High priority **Burn Night Verdict:** Pipeline blocker — this needs to ship first. Keep open, keep priority high. 🔥
Timmy removed the kimi-in-progress label 2026-04-04 19:46:42 +00:00
Timmy added the kimi-in-progress label 2026-04-04 20:43:29 +00:00
Timmy removed the kimi-in-progress label 2026-04-05 16:55:58 +00:00
Timmy added the kimi-done label 2026-04-05 17:03:32 +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#123
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