A Python toolkit for speaker-diarized transcription and transcript analysis. Built on faster-whisper and pyannote.audio; extract word-level, speaker-labeled CSVs from audio, then search, format, and chunk them (one step at a time).

Modules

Module	Description	Docs
extract	Transcribe audio with speaker diarization	→
format	Format CSV transcripts into readable scripts	→
chunk	Split audio into segments via YAML config	→
search	Fuzzy search transcripts by word or phrase	→

Installation

curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/beckettfrey/speech-mine
cd speech-mine
uv sync

See docs/installation.md for library dependency setup and HuggingFace token configuration.

Quick Start

Note

speech-mine is flexible and adapts to your use case. The commands below show a generalized example workflow. For more granular control, use the Python API directly.

# 1. (Optional) Chunk a long recording into segments
uv run speech-mine chunk recording.wav chunks.yaml chunks/

# 2. Extract a transcript
uv run speech-mine extract interview.mp3 output.csv \
  --hf-token YOUR_TOKEN \
  --num-speakers 2 \
  --compute-type float32

# 3. Format into a readable script
uv run speech-mine format output.csv script.txt

# 4. Search it
uv run speech-mine search "topic of interest" output.csv --pretty

# 5. (Optional) Chunk the recording again around segments of interest
uv run speech-mine chunk recording.wav segments.yaml clips/

Documentation

# Serve docs locally
uv run mkdocs serve

Or browse the docs/ folder directly.

License

MIT