diarize¶
Speaker diarization for Python — answers "who spoke when?" in any audio file.
Runs on CPU. No GPU, no API keys, no account signup. Apache 2.0 licensed.
Install¶
Requires Python 3.9+. All models download automatically on first use.
diarize installs a compatible torch/torchaudio range automatically.
from diarize import diarize
result = diarize("meeting.wav")
for seg in result.segments:
print(f" [{seg.start:.1f}s - {seg.end:.1f}s] {seg.speaker}")
How diarize compares¶
| diarize | pyannote (free) | pyannote (commercial) | |
|---|---|---|---|
| License | Apache 2.0 | CC-BY-4.0 | Commercial |
| GPU required | No | No (7x slower on CPU) | No |
| HuggingFace account | No | Yes | Yes |
| Auto speaker count | Yes | Yes | Yes |
| DER (VoxConverse dev) | ~4.8% | ~11.2% | ~8.5% |
| CPU speed (RTF) | 0.12 | 0.86 | --- |
DER and speed numbers for pyannote are from their benchmark page. The diarize number is from the VoxConverse dev evaluation described in Benchmarks.
Next Steps¶
- How It Works --- pipeline architecture and algorithms
- Benchmarks --- VoxConverse, AMI, speed comparison, limitations
- API Reference --- full auto-generated API documentation
License¶
Apache 2.0 License. All dependencies are permissively licensed (MIT, Apache 2.0, BSD).