README.md

metadata

tags:
  - pyannote
  - pyannote-audio
  - pyannote-audio-model
  - audio
  - voice
  - speech
  - speaker
  - speaker-diarization
  - speaker-separation
  - speech-separation
license: mit
inference: false
extra_gated_prompt: >-
  The collected information will help acquire a better knowledge of
  pyannote.audio userbase and help its maintainers improve it further. Though
  this model uses MIT license and will always remain open-source, we will
  occasionnally email you about premium models and paid services around
  pyannote.
extra_gated_fields:
  Company/university: text
  Website: text

Using this open-source model in production?
Consider switching to pyannoteAI for better and faster options.

🎹 ToTaToNet / joint speaker diarization and speech separation

This model ingests 5 seconds of mono audio sampled at 16 kHz and outputs speaker diarization AND speech separation for up to 3 speakers.

It has been trained by Joonas Kalda with pyannote.audio 3.3.0 using the AMI dataset (single distant microphone, SDM). These paper and companion repository describe the approach in more details.

Requirements

1. Install pyannote.audio 3.3.0 with pip install pyannote.audio[separation]==3.3.0
2. Accept pyannote/separation-ami-1.0 user conditions
3. Create access token at hf.co/settings/tokens.

from pyannote.audio import Model
model = Model.from_pretrained(
    "pyannote/separation-ami-1.0",
    use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")

Usage

# model ingests 5s of mono audio sampled at 16kHz...
duration = 5.0
num_channels = 1
sample_rate = 16000

waveforms = torch.randn(batch_size, num_channels, duration * sample_rate)
waveforms.shape
# (batch_size, num_channels = 1, num_samples = 80000)

# ... and outputs both speaker diarization and separation
with torch.inference_mode():
    diarization, sources = model(waveform)

diarization.shape
# (batch_size, num_frames = 624, max_num_speakers = 3)
# with values between 0 (speaker inactive) and 1 (speaker active)

sources.shape
# (batch_size, num_samples = 80000, max_num_speakers = 3)

Limitations

This model cannot be used to perform speaker diarization and speech separation of full recordings on its own (it only processes 5s chunks): see pyannote/speech-separation-ami-1.0 pipeline that uses an additional speaker embedding model to do that.

Citations

@inproceedings{Kalda24,
  author={Joonas Kalda and Clément Pagés and Ricard Marxer and Tanel Alumäe and Hervé Bredin},
  title={{PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings}},
  year=2024,
  booktitle={Proc. Odyssey 2024},
}

@inproceedings{Bredin23,
  author={Hervé Bredin},
  title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}},
  year=2023,
  booktitle={Proc. INTERSPEECH 2023},
}