| | --- |
| | tags: |
| | - pyannote |
| | - pyannote-audio |
| | - pyannote-audio-pipeline |
| | - audio |
| | - voice |
| | - speech |
| | - speaker |
| | - speaker-diarization |
| | - speaker-change-detection |
| | - voice-activity-detection |
| | - overlapped-speech-detection |
| | - automatic-speech-recognition |
| | license: mit |
| | 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 pipeline uses MIT license and will always remain open-source, we will occasionnally email you about premium pipelines and paid services around pyannote." |
| | extra_gated_fields: |
| | Company/university: text |
| | Website: text |
| | --- |
| | |
| | Using this open-source model in production? |
| | Consider switching to [pyannoteAI](https://www.pyannote.ai) for better and faster options. |
| |
|
| | # 🎹 Speaker diarization 3.1 |
| |
|
| | This pipeline is the same as [`pyannote/speaker-diarization-3.0`](https://hf.co/pyannote/speaker-diarization-3.1) except it removes the [problematic](https://github.com/pyannote/pyannote-audio/issues/1537) use of `onnxruntime`. |
| | Both speaker segmentation and embedding now run in pure PyTorch. This should ease deployment and possibly speed up inference. |
| | It requires pyannote.audio version 3.1 or higher. |
| |
|
| | It ingests mono audio sampled at 16kHz and outputs speaker diarization as an [`Annotation`](http://pyannote.github.io/pyannote-core/structure.html#annotation) instance: |
| |
|
| | - stereo or multi-channel audio files are automatically downmixed to mono by averaging the channels. |
| | - audio files sampled at a different rate are resampled to 16kHz automatically upon loading. |
| |
|
| | ## Requirements |
| |
|
| | 1. Install [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) `3.1` with `pip install pyannote.audio` |
| | 2. Accept [`pyannote/segmentation-3.0`](https://hf.co/pyannote/segmentation-3.0) user conditions |
| | 3. Accept [`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) user conditions |
| | 4. Create access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens). |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | # instantiate the pipeline |
| | from pyannote.audio import Pipeline |
| | pipeline = Pipeline.from_pretrained( |
| | "pyannote/speaker-diarization-3.1", |
| | use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE") |
| | |
| | # run the pipeline on an audio file |
| | diarization = pipeline("audio.wav") |
| | |
| | # dump the diarization output to disk using RTTM format |
| | with open("audio.rttm", "w") as rttm: |
| | diarization.write_rttm(rttm) |
| | ``` |
| |
|
| | ### Processing on GPU |
| |
|
| | `pyannote.audio` pipelines run on CPU by default. |
| | You can send them to GPU with the following lines: |
| |
|
| | ```python |
| | import torch |
| | pipeline.to(torch.device("cuda")) |
| | ``` |
| |
|
| | ### Processing from memory |
| |
|
| | Pre-loading audio files in memory may result in faster processing: |
| |
|
| | ```python |
| | waveform, sample_rate = torchaudio.load("audio.wav") |
| | diarization = pipeline({"waveform": waveform, "sample_rate": sample_rate}) |
| | ``` |
| |
|
| | ### Monitoring progress |
| |
|
| | Hooks are available to monitor the progress of the pipeline: |
| |
|
| | ```python |
| | from pyannote.audio.pipelines.utils.hook import ProgressHook |
| | with ProgressHook() as hook: |
| | diarization = pipeline("audio.wav", hook=hook) |
| | ``` |
| |
|
| | ### Controlling the number of speakers |
| |
|
| | In case the number of speakers is known in advance, one can use the `num_speakers` option: |
| |
|
| | ```python |
| | diarization = pipeline("audio.wav", num_speakers=2) |
| | ``` |
| |
|
| | One can also provide lower and/or upper bounds on the number of speakers using `min_speakers` and `max_speakers` options: |
| |
|
| | ```python |
| | diarization = pipeline("audio.wav", min_speakers=2, max_speakers=5) |
| | ``` |
| |
|
| | ## Benchmark |
| |
|
| | This pipeline has been benchmarked on a large collection of datasets. |
| |
|
| | Processing is fully automatic: |
| |
|
| | - no manual voice activity detection (as is sometimes the case in the literature) |
| | - no manual number of speakers (though it is possible to provide it to the pipeline) |
| | - no fine-tuning of the internal models nor tuning of the pipeline hyper-parameters to each dataset |
| |
|
| | ... with the least forgiving diarization error rate (DER) setup (named _"Full"_ in [this paper](https://doi.org/10.1016/j.csl.2021.101254)): |
| |
|
| | - no forgiveness collar |
| | - evaluation of overlapped speech |
| |
|
| | | Benchmark | [DER%](. "Diarization error rate") | [FA%](. "False alarm rate") | [Miss%](. "Missed detection rate") | [Conf%](. "Speaker confusion rate") | Expected output | File-level evaluation | |
| | | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | --------------------------- | ---------------------------------- | ----------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | |
| | | [AISHELL-4](http://www.openslr.org/111/) | 12.2 | 3.8 | 4.4 | 4.0 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AISHELL.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AISHELL.SpeakerDiarization.Benchmark.test.eval) | |
| | | [AliMeeting (_channel 1_)](https://www.openslr.org/119/) | 24.4 | 4.4 | 10.0 | 10.0 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AliMeeting.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AliMeeting.SpeakerDiarization.Benchmark.test.eval) | |
| | | [AMI (_headset mix,_](https://groups.inf.ed.ac.uk/ami/corpus/) [_only_words_)](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 18.8 | 3.6 | 9.5 | 5.7 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AMI.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AMI.SpeakerDiarization.Benchmark.test.eval) | |
| | | [AMI (_array1, channel 1,_](https://groups.inf.ed.ac.uk/ami/corpus/) [_only_words)_](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 22.4 | 3.8 | 11.2 | 7.5 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AMI-SDM.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AMI-SDM.SpeakerDiarization.Benchmark.test.eval) | |
| | | [AVA-AVD](https://arxiv.org/abs/2111.14448) | 50.0 | 10.8 | 15.7 | 23.4 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AVA-AVD.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/AVA-AVD.SpeakerDiarization.Benchmark.test.eval) | |
| | | [DIHARD 3 (_Full_)](https://arxiv.org/abs/2012.01477) | 21.7 | 6.2 | 8.1 | 7.3 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/DIHARD.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/DIHARD.SpeakerDiarization.Benchmark.test.eval) | |
| | | [MSDWild](https://x-lance.github.io/MSDWILD/) | 25.3 | 5.8 | 8.0 | 11.5 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/MSDWILD.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/MSDWILD.SpeakerDiarization.Benchmark.test.eval) | |
| | | [REPERE (_phase 2_)](https://islrn.org/resources/360-758-359-485-0/) | 7.8 | 1.8 | 2.6 | 3.5 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/REPERE.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/REPERE.SpeakerDiarization.Benchmark.test.eval) | |
| | | [VoxConverse (_v0.3_)](https://github.com/joonson/voxconverse) | 11.3 | 4.1 | 3.4 | 3.8 | [RTTM](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/VoxConverse.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co/pyannote/speaker-diarization-3.1/blob/main/reproducible_research/VoxConverse.SpeakerDiarization.Benchmark.test.eval) | |
| |
|
| | ## Citations |
| |
|
| | ```bibtex |
| | @inproceedings{Plaquet23, |
| | author={Alexis Plaquet and Hervé Bredin}, |
| | title={{Powerset multi-class cross entropy loss for neural speaker diarization}}, |
| | year=2023, |
| | booktitle={Proc. INTERSPEECH 2023}, |
| | } |
| | ``` |
| |
|
| | ```bibtex |
| | @inproceedings{Bredin23, |
| | author={Hervé Bredin}, |
| | title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}}, |
| | year=2023, |
| | booktitle={Proc. INTERSPEECH 2023}, |
| | } |
| | ``` |
| |
|
