| --- |
| tags: |
| - pyannote |
| - pyannote-audio |
| - pyannote-audio-pipeline |
| - audio |
| - voice |
| - speech |
| - speaker |
| - speaker-diarization |
| - speaker-change-detection |
| - voice-activity-detection |
| - overlapped-speech-detection |
| datasets: |
| - ami |
| - dihard |
| - voxconverse |
| - aishell |
| - repere |
| - voxceleb |
| license: mit |
| --- |
| |
| # 🎹 Speaker diarization |
|
|
| Relies on pyannote.audio 2.0: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation). |
|
|
|
|
| ## TL;DR |
|
|
| ```python |
| # load the pipeline from Hugginface Hub |
| from pyannote.audio import Pipeline |
| pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2022.07") |
| |
| # apply the pipeline to 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) |
| ``` |
|
|
| ## Advanced usage |
|
|
| If the number of speakers is known in advance, you can include the num_speakers parameter in the parameters dictionary: |
| |
| ```python |
| handler = EndpointHandler() |
| diarization = handler({"inputs": base64_audio, "parameters": {"num_speakers": 2}}) |
| ``` |
| |
| You can also provide lower and/or upper bounds on the number of speakers using the min_speakers and max_speakers parameters: |
| |
| ```python |
| handler = EndpointHandler() |
| diarization = handler({"inputs": base64_audio, "parameters": {"min_speakers": 2, "max_speakers": 5}}) |
| ``` |
| |
| If you're feeling adventurous, you can experiment with various pipeline hyperparameters. |
| For instance, you can use a more aggressive voice activity detection by increasing the value of segmentation_onset threshold: |
| |
| ```python |
| hparams = handler.pipeline.parameters(instantiated=True) |
| hparams["segmentation_onset"] += 0.1 |
| handler.pipeline.instantiate(hparams) |
| ``` |
| To apply the updated handler for the API inference that can handle the number of speakers, use the following code: |
| ```python |
| from typing import Dict |
| from pyannote.audio import Pipeline |
| import torch |
| import base64 |
| import numpy as np |
| |
| SAMPLE_RATE = 16000 |
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| # load the model |
| self.pipeline = Pipeline.from_pretrained("KIFF/pyannote-speaker-diarization-endpoint") |
| |
| def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]: |
| """ |
| Args: |
| data (:obj:): |
| includes the deserialized audio file as bytes |
| Return: |
| A :obj:`dict`:. base64 encoded image |
| """ |
| # process input |
| inputs = data.pop("inputs", data) |
| parameters = data.pop("parameters", None) # min_speakers=2, max_speakers=5 |
| |
| # decode the base64 audio data |
| audio_data = base64.b64decode(inputs) |
| audio_nparray = np.frombuffer(audio_data, dtype=np.int16) |
| |
| # prepare pynannote input |
| audio_tensor= torch.from_numpy(audio_nparray).float().unsqueeze(0) |
| pyannote_input = {"waveform": audio_tensor, "sample_rate": SAMPLE_RATE} |
| |
| # apply pretrained pipeline |
| # pass inputs with all kwargs in data |
| if parameters is not None: |
| diarization = self.pipeline(pyannote_input, **parameters) |
| else: |
| diarization = self.pipeline(pyannote_input) |
| |
| # postprocess the prediction |
| processed_diarization = [ |
| {"label": str(label), "start": str(segment.start), "stop": str(segment.end)} |
| for segment, _, label in diarization.itertracks(yield_label=True) |
| ] |
| |
| return {"diarization": processed_diarization} |
| ``` |
| ## Benchmark |
| |
| ### Real-time factor |
|
|
| Real-time factor is around 5% using one Nvidia Tesla V100 SXM2 GPU (for the neural inference part) and one Intel Cascade Lake 6248 CPU (for the clustering part). |
|
|
| In other words, it takes approximately 3 minutes to process a one hour conversation. |
|
|
| ### Accuracy |
|
|
| This pipeline is benchmarked on a growing 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/) | 14.61 | 3.31 | 4.35 | 6.95 | [RTTM](reproducible_research/AISHELL.SpeakerDiarization.Full.test.rttm) | [eval](reproducible_research/AISHELL.SpeakerDiarization.Full.test.eval) | |
| | [AMI *Mix-Headset*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 18.21 | 3.28 | 11.07 | 3.87 | [RTTM](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.rttm) | [eval](reproducible_research/2022.07/AMI.SpeakerDiarization.only_words.test.eval) | |
| | [AMI *Array1-01*](https://groups.inf.ed.ac.uk/ami/corpus/) [*only_words*](https://github.com/BUTSpeechFIT/AMI-diarization-setup) | 29.00 | 2.71 | 21.61 | 4.68 | [RTTM](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.rttm) | [eval](reproducible_research/2022.07/AMI-SDM.SpeakerDiarization.only_words.test.eval) | |
| | [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) [*Part2*](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1) | 30.24 | 3.71 | 16.86 | 9.66 | [RTTM](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.rttm) | [eval](reproducible_research/2022.07/CALLHOME.SpeakerDiarization.CALLHOME.test.eval) | |
| | [DIHARD 3 *Full*](https://arxiv.org/abs/2012.01477) | 20.99 | 4.25 | 10.74 | 6.00 | [RTTM](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.rttm) | [eval](reproducible_research/2022.07/DIHARD.SpeakerDiarization.Full.test.eval) | |
| | [REPERE *Phase 2*](https://islrn.org/resources/360-758-359-485-0/) | 12.62 | 1.55 | 3.30 | 7.76 | [RTTM](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.rttm) | [eval](reproducible_research/2022.07/REPERE.SpeakerDiarization.Full.test.eval) | |
| | [VoxConverse *v0.0.2*](https://github.com/joonson/voxconverse) | 12.76 | 3.45 | 3.85 | 5.46 | [RTTM](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.rttm) | [eval](reproducible_research/2022.07/VoxConverse.SpeakerDiarization.VoxConverse.test.eval) | |
|
|
|
|
| ## 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. |
|
|
|
|
| ## Citations |
|
|
| ```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}, |
| } |
| ``` |
|
|
