Speaker diarization is the process of automatically partitioning the audio recording of a conversation into segments and labeling them by speaker, answering the question "who spoke when?". As the foundational layer of conversational AI, speaker diarization provides high-level insights for human-human and human-machine conversations, and unlocks a wide range of downstream applications: meeting transcription, call center analytics, voice agents, video dubbing.

Install pyannote.audio latest release available from with either uv (recommended) or pip:

$ uv add pyannote.audio
$ pip install pyannote.audio

Enjoy state-of-the-art speaker diarization:

# download pretrained pipeline from Huggingface
from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained('pyannote/speaker-diarization-community-1', token="HUGGINGFACE_TOKEN")

# perform speaker diarization locally
output = pipeline('/path/to/audio.wav')

# enjoy state-of-the-art speaker diarization
for turn, speaker in output.speaker_diarization:
    print(f"{speaker} speaks between t={turn.start}s and t={turn.end}s")

Read community-1 model card to make the most of it.

pyannoteAI research team trains cutting-edge speaker diarization models, thanks to Jean Zay 🇫🇷 supercomputer managed by GENCI 💚. They come in two flavors:

• pyannote.audio open models available on Huggingface and used by 140k+ developers over the world ;
• premium models available on pyannoteAI cloud (and on-premise for enterprise customers) that provide state-of-the-art speaker diarization as well as additional enterprise features.

Diarization error rate (in %, the lower, the better)

precision-2 premium model further improves accuracy, processing speed, as well as brings additional features.

Create a pyannoteAI account, change one line of code, and enjoy free cloud credits to try precision-2 premium diarization:

# perform premium speaker diarization on pyannoteAI cloud
pipeline = Pipeline.from_pretrained('pyannote/speaker-diarization-precision-2', token="PYANNOTEAI_API_KEY")
better_output = pipeline('/path/to/audio.wav')