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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 3.1
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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`.
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Both speaker segmentation and embedding now run in pure PyTorch. This should ease deployment and possibly speed up inference.
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It requires pyannote.audio version 3.1 or higher.
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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) `3.1` 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-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) 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-3.1",
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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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### 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/) | 12.2 | 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) |
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| [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) |
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| [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) |
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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) | 22.4 | 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) |
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| [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) |
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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-3.1/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) |
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| [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) |
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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-3.1/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) |
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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-3.1/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) |
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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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