82 lines
3.2 KiB
Markdown
82 lines
3.2 KiB
Markdown
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# Live Streaming Microphone Transcription
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## Summary
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Add a `--stream` mode to `transcribe.py` that continuously captures audio from the microphone, detects speech segments using energy-based VAD, and transcribes each segment in near-real-time using the Cohere ASR model. Output scrolls as timestamped lines in the terminal. Ctrl+C stops the session.
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## Context
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- **Model**: CohereLabs/cohere-transcribe-03-2026, max 35s audio clips, 5s overlap for auto-chunking
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- **Inference speed**: ~0.4s for 5-10s audio on GPU (0.04-0.08x real-time)
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- **Microphone**: PD200X Podcast Microphone via PipeWire, 16kHz mono
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- **Existing code**: `transcribe.py` has `--mic` (fixed duration) and demo file modes
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## Architecture
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### Audio Capture
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`sounddevice.InputStream` with a callback streams 16kHz mono float32 audio into a thread-safe buffer. The callback appends raw samples; a separate consumer reads them.
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### Voice Activity Detection
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Energy-based VAD using RMS amplitude over 50ms frames (800 samples at 16kHz):
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- **Threshold**: Calibrated from ~0.5s of ambient silence at startup, with a sensible fallback (~-40 dBFS)
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- **State machine**: `SILENCE -> SPEAKING -> SILENCE`
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- SILENCE -> SPEAKING: RMS exceeds threshold for >= 3 consecutive frames (~150ms)
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- SPEAKING -> SILENCE: RMS stays below threshold for >= 0.8s
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- **Pre-roll**: ~0.3s of audio before speech onset is included to avoid clipping word beginnings
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- **Safety cap**: If speech exceeds 30s without a pause, force a chunk boundary (model max is 35s)
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### Threading Model
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Two threads communicating via `queue.Queue`:
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1. **Audio thread** (sounddevice callback + VAD logic): captures audio, runs VAD state machine, pushes completed speech segments onto the queue
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2. **Transcription thread**: pulls segments from the queue, runs `processor() -> model.generate() -> processor.decode()`, prints results
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No state carried between segments. Each is transcribed independently.
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### Output
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Timestamped lines printed to stdout as each segment is transcribed:
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```
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[00:03] Good morning, this is a test of the live captioning system.
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[00:08] The model seems to be picking up my voice pretty well.
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```
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### Shutdown
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Ctrl+C sets a stop flag via signal handler. The audio stream stops, any buffered speech is flushed and transcribed, then the program exits cleanly.
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## CLI Interface
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```
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uv run python transcribe.py --stream # stream, default language (en)
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uv run python transcribe.py --stream --lang ja # stream in Japanese
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uv run python transcribe.py --mic [duration] # existing fixed-duration mode
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uv run python transcribe.py # existing demo file mode
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```
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### Startup Sequence
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1. Print "Loading model..." and load model
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2. Record ~0.5s of ambient audio, compute silence threshold
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3. Print threshold info and "Listening... (Ctrl+C to stop)"
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4. Begin streaming
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## Dependencies
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No new dependencies. Uses: `sounddevice`, `numpy`, `threading`, `queue`, `signal`, `time` (all already available).
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## Code Organization
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All new logic in `transcribe.py`. File grows from ~50 to ~150-180 lines. No new files.
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## Constraints
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- Model max input: 35s per chunk (safety cap at 30s)
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- Sampling rate must be 16kHz
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- Single-channel (mono) audio only
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