claudetools/projects/radio-show/audio-processor/MAC_BUILD_TASK.md

Mac Build Task: Radio Show Audio Processor

Date: 2026-03-21 From: CachyOS workstation (acg-guru-5070) To: Mac Claude instance (Mikes-MacBook-Air, M4) Priority: High — this is blocked on the Linux workstation

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What We Need

Build a Mac-native version of the radio show audio processor that can run the transcription and voice profiling pipeline on Apple Silicon (M4). The Linux workstation's RTX 5070 Ti has a known GPU firmware bug that crashes after ~3 minutes of sustained compute, making GPU-accelerated transcription impossible until NVIDIA fixes it.

Why the Mac

The CachyOS workstation's NVIDIA RTX 5070 Ti Laptop GPU hits a GSP (GPU System Processor) firmware crash under sustained load. This is a known, unresolved bug across all RTX 50-series (Blackwell) GPUs on Linux:

• Error: NVRM: _issueRpcLarge: rpcSendMessage failed with status 0x00000062
• Triggers after ~3-5 minutes of continuous GPU compute
• GPU enters full ERR! state, requires hard reboot (warm reboot hangs)
• Cannot disable GSP on 50-series (open kernel module required, no NVreg_EnableGpuFirmware=0)
• NVIDIA internal bug #5953411 filed, no fix available
• Affects drivers 580.x, 590.x, 595.x (current: 595.45.04)
• Power management tweaks, persistence mode, clock locking — none helped
• See: session logs 2026-03-21-session.md and 2026-03-20-session.md for full diagnosis

The M4 MacBook Air with 16GB unified memory can run this workload on CPU or MPS backend without driver issues.

The Project

Location in repo: projects/radio-show/audio-processor/

Goal: Automated pipeline for processing "The Computer Guru Show" radio recordings. The immediate task is voice training — transcribing 9 archive episodes and building speaker embeddings to identify the host (Mike Swanson) vs. callers/guests/commercials.

What Already Works (built on Linux, may need Mac adaptation)

1. Voice Profiler (src/voice_profiler.py) — Uses WavLM (Microsoft microsoft/wavlm-base-sv) for speaker verification via x-vector embeddings. WORKING — 180 embeddings generated, composite built. Host voice scores 0.90-0.98 similarity, non-host 0.53-0.65. Threshold tuned to 0.83.

2. Transcriber (src/transcriber.py) — Uses faster-whisper with large-v3 model. On Linux it was configured for CUDA. Only 1 of 9 episodes transcribed before GPU died.

3. Config (config.yaml) — All pipeline settings, thresholds, paths.

4. Voice profiles — voice-profiles/mike-swanson/ has 180 .npy embedding files + composite.npy + profiles.json. These are numpy arrays, platform-independent.

5. One completed transcript — training-data/transcripts/2010-10-02-hr1/ (534 segments, transcript.json + .srt + .txt)

What Needs Doing on Mac

Primary task: Transcribe the remaining 8 episodes:

training-data/episodes/2011-06-04-hr1.mp3    (7.4MB, ~43 min)
training-data/episodes/2011-09-10-hr1.mp3    (11MB)
training-data/episodes/2014-s6e05.mp3        (9.5MB)
training-data/episodes/2015-s7e30.mp3        (9.0MB)
training-data/episodes/2016-s8e42.mp3        (19MB)
training-data/episodes/2017-s9e26.mp3        (48MB)
training-data/episodes/2018-s10e17.mp3       (21MB)
training-data/episodes/2018-s10e21.mp3       (21MB)

Output each to: training-data/transcripts/{episode-stem}/transcript.json (+ .srt, .txt)

Secondary: Verify voice profiles work on Mac — load the existing .npy embeddings and run similarity checks against the new transcripts.

Mac-Specific Build Notes

1. Do NOT try to port the Linux code directly. Build fresh for Mac hardware. The existing code has CUDA-specific paths (src/gpu.py sets LD_LIBRARY_PATH for CUDA 12), nvidia-specific device selection, etc. It's cleaner to build natively.

2. Reference the existing code for architecture and logic, especially:

   • src/voice_profiler.py — the WavLM embedding approach, similarity thresholds, profile structure
   • src/transcriber.py — the Whisper pipeline stages, output format (TranscriptSegment dataclass)
   • config.yaml — all the tuned parameters
   • README.md — full architecture doc for the 6-stage pipeline

3. Hardware target: Apple M4, 16GB unified memory

   • faster-whisper + ctranslate2 supports CPU on macOS (no MPS backend for ctranslate2)
   • large-v3 model needs ~3GB RAM — fits easily in 16GB
   • Expected speed: ~1x realtime on M4 CPU (43-min episode takes ~43 min)
   • Consider medium model if large-v3 is too slow — tradeoff is accuracy
   • PyTorch MPS backend works for pyannote.audio and WavLM (transformers)

4. Dependencies for Mac:

   brew install ffmpeg
   python3 -m venv .venv
   source .venv/bin/activate
   pip install faster-whisper pyannote.audio torch torchaudio pydub librosa scikit-learn ollama rich pyyaml
   

   • No CUDA packages needed — pip will pull CPU-only torch or MPS-enabled torch for macOS
   • pyannote.audio requires HuggingFace token (accept model license first): https://huggingface.co/pyannote/speaker-diarization-3.1

5. Ollama models available on Mac (per machine spec):

   • qwen3:14b — use for content analysis (Stage 6)
   • nomic-embed-text — for grepai, not needed for audio processing

6. Output compatibility: Keep the same output format (JSON with segments, timestamps, speaker labels) so the Linux workstation can consume the results after git pull.

Architecture Reference

Raw MP3 → 1. Transcribe (Whisper) → 2. Diarize (pyannote) → 3. Detect Segments
        → 4. Remove Commercials → 5. Split Segments → 6. Analyze (Ollama)

For now, only Stages 1-2 matter. Stages 3-6 can wait.

Key Thresholds (from working Linux version)

• Whisper model: large-v3
• Whisper language: en
• Voice profile host match threshold: 0.83
• Min/max speakers for diarization: 1-6
• WavLM model: microsoft/wavlm-base-sv (speaker verification, x-vector embeddings)

Data Flow

1. Training episodes are in training-data/episodes/ (already in git, 151MB total)
2. Voice profiles are in voice-profiles/mike-swanson/ (already in git)
3. Transcripts go to training-data/transcripts/{episode-stem}/
4. After Mac completes transcription, commit + push to Gitea
5. Linux workstation pulls results and continues with Stages 3-6

Session Logs for Context

Read these for the full story of what was built and why:

• session-logs/2026-03-21-session.md — Voice profiling results, GPU errors, transcription attempts, diagnosis
• session-logs/2026-03-20-session.md — Earlier session (may have additional audio processor context)

Success Criteria

1. All 8 remaining episodes transcribed with timestamps and segments
2. Transcripts in the same JSON format as training-data/transcripts/2010-10-02-hr1/transcript.json
3. Voice profiles load and produce reasonable similarity scores on Mac
4. Results committed to Gitea so Linux workstation can pull them