--- 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}, } ```