Automatic Speech Recognition
pyannote.audio
pyannote
pyannote-audio-pipeline
audio
voice
speech
speaker
speaker-segmentation
speaker-diarization
speaker-change-detection
voice-activity-detection
overlapped-speech-detection
Instructions to use pyannote/speaker-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use pyannote/speaker-segmentation with pyannote.audio:
from pyannote.audio import Pipeline pipeline = Pipeline.from_pretrained("pyannote/speaker-segmentation") # inference on the whole file pipeline("file.wav") # inference on an excerpt from pyannote.core import Segment excerpt = Segment(start=2.0, end=5.0) from pyannote.audio import Audio waveform, sample_rate = Audio().crop("file.wav", excerpt) pipeline({"waveform": waveform, "sample_rate": sample_rate}) - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - pyannote | |
| - pyannote-audio | |
| - pyannote-audio-pipeline | |
| - audio | |
| - voice | |
| - speech | |
| - speaker | |
| - speaker-segmentation | |
| - speaker-diarization | |
| - speaker-change-detection | |
| - voice-activity-detection | |
| - overlapped-speech-detection | |
| - automatic-speech-recognition | |
| datasets: | |
| - ami | |
| - dihard | |
| - voxconverse | |
| license: mit | |
| extra_gated_prompt: "The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers apply for grants to improve it further. If you are an academic researcher, please cite the relevant papers in your own publications using the model. If you work for a company, please consider contributing back to pyannote.audio development (e.g. through unrestricted gifts). We also provide scientific consulting services around speaker diarization and machine listening." | |
| extra_gated_fields: | |
| Company/university: text | |
| Website: text | |
| I plan to use this model for (task, type of audio data, etc): text | |
| Using this open-source model in production? | |
| Consider switching to [pyannoteAI](https://www.pyannote.ai) for better and faster options. | |
| # 🎹 Speaker segmentation | |
| Relies on pyannote.audio 2.1: see [installation instructions](https://github.com/pyannote/pyannote-audio#installation). | |
| ```python | |
| # 1. visit hf.co/pyannote/segmentation and accept user conditions | |
| # 2. visit hf.co/settings/tokens to create an access token | |
| # 3. instantiate pretrained speaker segmentation pipeline | |
| from pyannote.audio import Pipeline | |
| pipeline = Pipeline.from_pretrained("pyannote/speaker-segmentation") | |
| output = pipeline("audio.wav") | |
| for turn, _, speaker in output.itertracks(yield_label=True): | |
| # speaker speaks between turn.start and turn.end | |
| ... | |
| ``` | |
| ⚠️ This pipeline does not address [speaker diarization](https://huggingface.co/pyannote/speaker-diarization). | |
| ## Support | |
| For commercial enquiries and scientific consulting, please contact [me](mailto:herve@niderb.fr). | |
| For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository. | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{Bredin2021, | |
| Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}}, | |
| Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine}, | |
| Booktitle = {Proc. Interspeech 2021}, | |
| Address = {Brno, Czech Republic}, | |
| Month = {August}, | |
| Year = {2021}, | |
| ``` | |
| ```bibtex | |
| @inproceedings{Bredin2020, | |
| Title = {{pyannote.audio: neural building blocks for speaker diarization}}, | |
| Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe}, | |
| Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing}, | |
| Address = {Barcelona, Spain}, | |
| Month = {May}, | |
| Year = {2020}, | |
| } | |
| ``` | |