--- title: pyannote-community canonical_url: "https://www.modelscope.ai/models/mrfakename/pyannote-community" md_url: "https://www.modelscope.ai/models/mrfakename/pyannote-community.md" repository: mrfakename/pyannote-community last_updated: 2025-12-16 license: cc-by-4.0 library_name: - pytorch frameworks: - pytorch downloads: 125 stars: 0 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 --- # pyannote-community > pyannote-community - An open-source model by mrfakename on ModelScope. community-1 speaker diarization mrfakename/pyannote-community is a machine learning model on ModelScope. licensed under cc-by-4.0. - **Repository**: mrfakename/pyannote-community - **License**: cc-by-4.0 - **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 - **Downloads**: 125 - **Stars**: 0 - **Last updated**: 2025-12-16 Source: https://www.modelscope.ai/models/mrfakename/pyannote-community --- # `community-1` speaker diarization This pipeline ingests mono audio sampled at 16kHz and outputs speaker diarization. - 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. The [main improvements brought by `Community-1`](https://www.pyannote.ai/blog/community-1) are: - [improved](#benchmark) speaker assignment and counting - simpler reconciliation with transcription timestamps with [*exclusive*](#exclusive-speaker-diarization) speaker diarization - easy [offline use](#offline-use) (i.e. without internet connection) - (optionally) [hosted](https://hf.co/pyannote/speaker-diarization-community-1-cloud) on pyannoteAI cloud ## Setup 1. `pip install pyannote.audio` 2. Accept user conditions 3. Create access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens). ## Quick start ```python # download the pipeline from Huggingface from pyannote.audio import Pipeline pipeline = Pipeline.from_pretrained( "pyannote/speaker-diarization-community-1", token="{huggingface-token}") # run the pipeline locally on your computer output = pipeline("audio.wav") # print the predicted speaker diarization for turn, speaker in output.speaker_diarization: print(f"{speaker} speaks between t={turn.start:.3f}s and t={turn.end:.3f}s") ``` ## Benchmark Out of the box, `Community-1` is much better than `speaker-diarization-3.1`. We report [diarization error rates](http://pyannote.github.io/pyannote-metrics/reference.html#diarization) (in %) on large collection of academic benchmarks (fully automatic processing, no forgiveness collar, nor skipping overlapping speech). | Benchmark (last updated in 2025-09) | `legacy` (3.1)| `community-1` | `precision-2` | | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------ | -------------------------------------------------| ------------------------------------------------ | | [AISHELL-4](https://arxiv.org/abs/2104.03603) | 12.2 | 11.7 | 11.4 | | [AliMeeting](https://www.openslr.org/119/) (channel 1) | 24.5 | 20.3 | 15.2 | | [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (IHM) | 18.8 | 17.0 | 12.9 | | [AMI](https://groups.inf.ed.ac.uk/ami/corpus/) (SDM) | 22.7 | 19.9 | 15.6 | | [AVA-AVD](https://arxiv.org/abs/2111.14448) | 49.7 | 44.6 | 37.1 | | [CALLHOME](https://catalog.ldc.upenn.edu/LDC2001S97) ([part 2](https://github.com/BUTSpeechFIT/CALLHOME_sublists/issues/1)) | 28.5 | 26.7 | 16.6 | | [DIHARD 3](https://catalog.ldc.upenn.edu/LDC2022S14) ([full](https://arxiv.org/abs/2012.01477)) | 21.4 | 20.2 | 14.7 | | [Ego4D](https://arxiv.org/abs/2110.07058) (dev.) | 51.2 | 46.8 | 39.0 | | [MSDWild](https://github.com/X-LANCE/MSDWILD) | 25.4 | 22.8 | 17.3 | | [RAMC](https://www.openslr.org/123/) | 22.2 | 20.8 | 10.5 | | [REPERE](https://www.islrn.org/resources/360-758-359-485-0/) (phase2) | 7.9 | 8.9 | 7.4 | | [VoxConverse](https://github.com/joonson/voxconverse) (v0.3) | 11.2 | 11.2 | 8.5 | `Precision-2` model is even better and can be tested like this: 1. Create an API key on [pyannoteAI dashboard]((https://dashboard.pyannote.ai)) (free credits included) 2. Change one line of code ```diff from pyannote.audio import Pipeline pipeline = Pipeline.from_pretrained( - 'pyannote/speaker-diarization-community-1', token="{huggingface-token}") + 'pyannote/speaker-diarization-precision-2', token="{pyannoteAI-api-key}") diarization = pipeline("audio.wav") # runs on pyannoteAI servers ``` ## 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") output = 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: output = 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 output = 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 output = pipeline("audio.wav", min_speakers=2, max_speakers=5) ``` ## Exclusive speaker diarization `Community-1` pretrained pipeline returns a new *exclusive* speaker diarization, on top of the regular speaker diarization, available as `output.exclusive_speaker_diarization`. This is a feature which is [backported from our latest commercial model](https://www.pyannote.ai/blog/precision-2) that simplifies the reconciliation between fine-grained speaker diarization timestamps and (sometimes not so precise) transcription timestamps. ## Offline use 1. In the terminal, copy the pipeline on disk: ```bash # make sure git-lfs is installed (https://git-lfs.com) git lfs install # create a directory on disk mkdir /path/to/directory # when prompted for a password, use an access token with write permissions. # generate one from your settings: https://huggingface.co/settings/tokens git clone https://hf.co/pyannote/speaker-diarization-community-1 /path/to/directory/pyannote-speaker-diarization-community-1 ``` 2. In Python, use the pipeline without internet connection: ```python # load pipeline from disk (works without internet connection) from pyannote.audio import Pipeline pipeline = Pipeline.from_pretrained('/path/to/directory/pyannote-speaker-diarization-community-1') # run the pipeline locally on your computer output = pipeline("audio.wav") ``` ## Citations 1. Speaker segmentation model ```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}, } ``` 2. Speaker embedding model ```bibtex @inproceedings{Wang2023, title={Wespeaker: A research and production oriented speaker embedding learning toolkit}, author={Wang, Hongji and Liang, Chengdong and Wang, Shuai and Chen, Zhengyang and Zhang, Binbin and Xiang, Xu and Deng, Yanlei and Qian, Yanmin}, booktitle={ICASSP 2023, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2023}, organization={IEEE} } ``` 3. Speaker clustering ```bibtex @article{Landini2022, author={Landini, Federico and Profant, J{\'a}n and Diez, Mireia and Burget, Luk{\'a}{\v{s}}}, title={{Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks}}, year={2022}, journal={Computer Speech \& Language}, } ``` ## Acknowledgment Training and tuning made possible thanks to [GENCI](https://www.genci.fr/) on the [**Jean Zay**](http://www.idris.fr/eng/jean-zay/) supercomputer.