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README.md

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+---
+tags:
+- pyannote
+- pyannote-audio
+- pyannote-audio-model
+- audio
+- voice
+- speech
+- speaker
+- speaker-segmentation
+- voice-activity-detection
+- overlapped-speech-detection
+- resegmentation
+license: mit
+inference: false
+extra_gated_prompt: "The collected information will help acquire a better knowledge of pyannote.audio userbase and help its maintainers apply for grants to improve it further. If you are an academic researcher, please cite the relevant papers in your own publications using the model. If you work for a company, please consider contributing back to pyannote.audio development (e.g. through unrestricted gifts). We also provide scientific consulting services around speaker diarization and machine listening."
+extra_gated_fields:
+  Company/university: text
+  Website: text
+  I plan to use this model for (task, type of audio data, etc): text
+---
+
+Using this open-source model in production?  
+Make the most of it thanks to our [consulting services](https://herve.niderb.fr/consulting.html).
+
+
+# 🎹 Speaker segmentation
+
+[Paper](http://arxiv.org/abs/2104.04045) | [Demo](https://huggingface.co/spaces/pyannote/pretrained-pipelines) | [Blog post](https://herve.niderb.fr/fastpages/2022/10/23/One-speaker-segmentation-model-to-rule-them-all)
+
+![Example](example.png)
+
+## Usage
+
+Relies on pyannote.audio 2.1.1: see [installation instructions](https://github.com/pyannote/pyannote-audio).
+
+```python
+# 1. visit hf.co/pyannote/segmentation and accept user conditions
+# 2. visit hf.co/settings/tokens to create an access token
+# 3. instantiate pretrained model
+from pyannote.audio import Model
+model = Model.from_pretrained("pyannote/segmentation", 
+                              use_auth_token="ACCESS_TOKEN_GOES_HERE")
+```
+
+### Voice activity detection
+
+```python
+from pyannote.audio.pipelines import VoiceActivityDetection
+pipeline = VoiceActivityDetection(segmentation=model)
+HYPER_PARAMETERS = {
+  # onset/offset activation thresholds
+  "onset": 0.5, "offset": 0.5,
+  # remove speech regions shorter than that many seconds.
+  "min_duration_on": 0.0,
+  # fill non-speech regions shorter than that many seconds.
+  "min_duration_off": 0.0
+}
+pipeline.instantiate(HYPER_PARAMETERS)
+vad = pipeline("audio.wav")
+# `vad` is a pyannote.core.Annotation instance containing speech regions
+```
+
+### Overlapped speech detection
+
+```python
+from pyannote.audio.pipelines import OverlappedSpeechDetection
+pipeline = OverlappedSpeechDetection(segmentation=model)
+pipeline.instantiate(HYPER_PARAMETERS)
+osd = pipeline("audio.wav")
+# `osd` is a pyannote.core.Annotation instance containing overlapped speech regions
+```
+
+### Resegmentation
+
+```python
+from pyannote.audio.pipelines import Resegmentation
+pipeline = Resegmentation(segmentation=model, 
+                          diarization="baseline")
+pipeline.instantiate(HYPER_PARAMETERS)
+resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline})
+# where `baseline` should be provided as a pyannote.core.Annotation instance
+```
+
+### Raw scores
+
+```python
+from pyannote.audio import Inference
+inference = Inference(model)
+segmentation = inference("audio.wav")
+# `segmentation` is a pyannote.core.SlidingWindowFeature
+# instance containing raw segmentation scores like the 
+# one pictured above (output)
+```
+
+
+## Citation
+
+```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},
+}
+```
+
+## Reproducible research 
+
+In order to reproduce the results of the paper ["End-to-end speaker segmentation for overlap-aware resegmentation
+"](https://arxiv.org/abs/2104.04045), use `pyannote/segmentation@Interspeech2021` with the following hyper-parameters:
+
+| Voice activity detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` |
+| ------------------------ | ------- | -------- | ----------------- | ------------------ |
+| AMI Mix-Headset          | 0.684   | 0.577    | 0.181             | 0.037              |
+| DIHARD3                  | 0.767   | 0.377    | 0.136             | 0.067              |
+| VoxConverse              | 0.767   | 0.713    | 0.182             | 0.501              |
+
+| Overlapped speech detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` |
+| --------------------------- | ------- | -------- | ----------------- | ------------------ |
+| AMI Mix-Headset             | 0.448   | 0.362    | 0.116             | 0.187              |
+| DIHARD3                     | 0.430   | 0.320    | 0.091             | 0.144              |
+| VoxConverse                 | 0.587   | 0.426    | 0.337             | 0.112              |
+
+| Resegmentation of VBx | `onset` | `offset` | `min_duration_on` | `min_duration_off` |
+| --------------------- | ------- | -------- | ----------------- | ------------------ |
+| AMI Mix-Headset       | 0.542   | 0.527    | 0.044             | 0.705              |
+| DIHARD3               | 0.592   | 0.489    | 0.163             | 0.182              |
+| VoxConverse           | 0.537   | 0.724    | 0.410             | 0.563              |
+
+Expected outputs (and VBx baseline) are also provided in the `/reproducible_research` sub-directories.
+

config.yaml

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+task:
+  _target_: pyannote.audio.tasks.Segmentation
+  duration: 5.0
+  max_num_speakers: 3
+model:
+  _target_: pyannote.audio.models.segmentation.PyanNet
+  sincnet:
+    stride: 10
+  lstm:
+    hidden_size: 128
+    num_layers: 4
+    bidirectional: true
+    monolithic: true
+  linear:
+    hidden_size: 128
+    num_layers: 2

pytorch_model.bin

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