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--- |
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tags: |
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- pyannote |
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- pyannote-audio |
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- pyannote-audio-pipeline |
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- audio |
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- voice |
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- speech |
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- speaker |
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- speaker-diarization |
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- speaker-change-detection |
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- voice-activity-detection |
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- overlapped-speech-detection |
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- automatic-speech-recognition |
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license: mit |
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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." |
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extra_gated_fields: |
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Company/university: text |
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Website: text |
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--- |
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Using this open-source model in production? |
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Consider switching to [pyannoteAI](https://www.pyannote.ai) for better and faster options. |
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# 🎹 Speaker diarization 3.0 |
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This pipeline has been trained by Séverin Baroudi with [pyannote.audio](https://github.com/pyannote/pyannote-audio) `3.0.0` using a combination of the training sets of AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse. |
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It ingests mono audio sampled at 16kHz and outputs speaker diarization as an [`Annotation`](http://pyannote.github.io/pyannote-core/structure.html#annotation) instance: |
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* stereo or multi-channel audio files are automatically downmixed to mono by averaging the channels. |
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* audio files sampled at a different rate are resampled to 16kHz automatically upon loading. |
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## Requirements |
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1. Install [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) `3.0` with `pip install pyannote.audio` |
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2. Accept [`pyannote/segmentation-3.0`](https://hf.co/pyannote/segmentation-3.0) user conditions |
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3. Accept [`pyannote/speaker-diarization-3.0`](https://hf.co/pyannote-speaker-diarization-3.0) user conditions |
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4. Create access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens). |
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## Usage |
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```python |
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# instantiate the pipeline |
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from pyannote.audio import Pipeline |
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pipeline = Pipeline.from_pretrained( |
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"pyannote/speaker-diarization-3.0", |
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use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE") |
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# run the pipeline on an audio file |
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diarization = pipeline("audio.wav") |
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# dump the diarization output to disk using RTTM format |
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with open("audio.rttm", "w") as rttm: |
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diarization.write_rttm(rttm) |
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``` |
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### Processing on GPU |
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`pyannote.audio` pipelines run on CPU by default. |
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You can send them to GPU with the following lines: |
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```python |
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import torch |
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pipeline.to(torch.device("cuda")) |
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``` |
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Real-time factor is around 2.5% using one Nvidia Tesla V100 SXM2 GPU (for the neural inference part) and one Intel Cascade Lake 6248 CPU (for the clustering part). |
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In other words, it takes approximately 1.5 minutes to process a one hour conversation. |
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### Processing from memory |
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Pre-loading audio files in memory may result in faster processing: |
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```python |
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waveform, sample_rate = torchaudio.load("audio.wav") |
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diarization = pipeline({"waveform": waveform, "sample_rate": sample_rate}) |
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``` |
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### Monitoring progress |
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Hooks are available to monitor the progress of the pipeline: |
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```python |
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from pyannote.audio.pipelines.utils.hook import ProgressHook |
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with ProgressHook() as hook: |
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diarization = pipeline("audio.wav", hook=hook) |
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``` |
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### Controlling the number of speakers |
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In case the number of speakers is known in advance, one can use the `num_speakers` option: |
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```python |
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diarization = pipeline("audio.wav", num_speakers=2) |
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``` |
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One can also provide lower and/or upper bounds on the number of speakers using `min_speakers` and `max_speakers` options: |
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```python |
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diarization = pipeline("audio.wav", min_speakers=2, max_speakers=5) |
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``` |
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## Benchmark |
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This pipeline has been benchmarked on a large collection of datasets. |
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Processing is fully automatic: |
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* no manual voice activity detection (as is sometimes the case in the literature) |
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* no manual number of speakers (though it is possible to provide it to the pipeline) |
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* no fine-tuning of the internal models nor tuning of the pipeline hyper-parameters to each dataset |
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... with the least forgiving diarization error rate (DER) setup (named *"Full"* in [this paper](https://doi.org/10.1016/j.csl.2021.101254)): |
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* no forgiveness collar |
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* evaluation of overlapped speech |
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| Benchmark | [DER%](. "Diarization error rate") | [FA%](. "False alarm rate") | [Miss%](. "Missed detection rate") | [Conf%](. "Speaker confusion rate") | Expected output | File-level evaluation | |
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| ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | --------------------------- | ---------------------------------- | ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | |
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| [AISHELL-4](http://www.openslr.org/111/) | 12.3 | 3.8 | 4.4 | 4.1 | [RTTM](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AISHELL.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AISHELL.SpeakerDiarization.Benchmark.test.eval) | |
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| [AliMeeting (*channel 1*)](https://www.openslr.org/119/) | 24.3 | 4.4 | 10.0 | 9.9 | [RTTM](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AliMeeting.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AliMeeting.SpeakerDiarization.Benchmark.test.eval) | |
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| [AMI (*headset mix,*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*)](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 19.0 | 3.6 | 9.5 | 5.9 | [RTTM](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AMI.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AMI.SpeakerDiarization.Benchmark.test.eval) | |
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| [AMI (*array1, channel 1,*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words)*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 22.2 | 3.8 | 11.2 | 7.3 | [RTTM](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AMI-SDM.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AMI-SDM.SpeakerDiarization.Benchmark.test.eval) | |
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| [AVA-AVD](https://arxiv.org/abs/2111.14448) | 49.1 | 10.8 | 15.7| 22.5 | [RTTM](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AVA-AVD.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/AVA-AVD.SpeakerDiarization.Benchmark.test.eval) | |
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| [DIHARD 3 (*Full*)](https://arxiv.org/abs/2012.01477) | 21.7 | 6.2 | 8.1 | 7.3 | [RTTM](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/DIHARD.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/DIHARD.SpeakerDiarization.Benchmark.test.eval) | |
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| [MSDWild](https://x-lance.github.io/MSDWILD/) | 24.6 | 5.8 | 8.0 | 10.7 | [RTTM](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/MSDWILD.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/MSDWILD.SpeakerDiarization.Benchmark.test.eval) | |
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| [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-3.0.0/blob/main/reproducible_research/REPERE.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/REPERE.SpeakerDiarization.Benchmark.test.eval) | |
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| [VoxConverse (*v0.3*)](https://github.com/joonson/voxconverse) | 11.3 | 4.1 | 3.4 | 3.8 | [RTTM](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/VoxConverse.SpeakerDiarization.Benchmark.test.rttm) | [eval](https://huggingface.co./pyannote/speaker-diarization-3.0.0/blob/main/reproducible_research/VoxConverse.SpeakerDiarization.Benchmark.test.eval) | |
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## Citations |
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```bibtex |
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@inproceedings{Plaquet23, |
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author={Alexis Plaquet and Hervé Bredin}, |
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title={{Powerset multi-class cross entropy loss for neural speaker diarization}}, |
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year=2023, |
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booktitle={Proc. INTERSPEECH 2023}, |
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} |
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``` |
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```bibtex |
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@inproceedings{Bredin23, |
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author={Hervé Bredin}, |
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title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}}, |
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year=2023, |
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booktitle={Proc. INTERSPEECH 2023}, |
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} |
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``` |
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