hub: gate model

README.md

@@ -17,31 +17,37 @@ datasets:
 - voxconverse
 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
 ---
 
 # 🎹 Speaker segmentation
 
-![Example](example.png)
-
-Model from *[End-to-end speaker segmentation for overlap-aware resegmentation](http://arxiv.org/abs/2104.04045)*,  
-by Hervé Bredin and Antoine Laurent.
-
-[Online demo](https://huggingface.co/spaces/pyannote/pretrained-pipelines) is available as a Hugging Face Space.
-
-## Support
+[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)
 
-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.
+![Example](example.png)
 
 ## Usage
 
-Relies on pyannote.audio 2.0 currently in development: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation).
+Relies on pyannote.audio 2.1: see [installation instructions](https://github.com/pyannote/pyannote-audio).
+
+```python
+# 1. visit hf.co/pyannote/segmentation and accept user conditions (only if requested)
+# 2. visit hf.co/settings/tokens to create an access token (only if you had to go through 1.)
+# 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="pyannote/segmentation")
+pipeline = VoiceActivityDetection(segmentation=model)
 HYPER_PARAMETERS = {
   # onset/offset activation thresholds
   "onset": 0.5, "offset": 0.5,
@@ -59,7 +65,7 @@ vad = pipeline("audio.wav")
 
 ```python
 from pyannote.audio.pipelines import OverlappedSpeechDetection
-pipeline = OverlappedSpeechDetection(segmentation="pyannote/segmentation")
+pipeline = OverlappedSpeechDetection(segmentation=model)
 pipeline.instantiate(HYPER_PARAMETERS)
 osd = pipeline("audio.wav")
 # `osd` is a pyannote.core.Annotation instance containing overlapped speech regions
@@ -69,7 +75,7 @@ osd = pipeline("audio.wav")
 
 ```python
 from pyannote.audio.pipelines import Resegmentation
-pipeline = Resegmentation(segmentation="pyannote/segmentation", 
+pipeline = Resegmentation(segmentation=model, 
                           diarization="baseline")
 pipeline.instantiate(HYPER_PARAMETERS)
 resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline})
@@ -80,13 +86,41 @@ resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline})
 
 ```python
 from pyannote.audio import Inference
-inference = Inference("pyannote/segmentation")
+inference = Inference(model)
 segmentation = inference("audio.wav")
 # `segmentation` is a pyannote.core.SlidingWindowFeature
 # instance containing raw segmentation scores like the 
 # one pictured above (output)
 ```
 
+## Support
+
+For commercial enquiries and scientific consulting, please contact [me](mailto:[email protected]).  
+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.
+
+## 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
@@ -112,25 +146,3 @@ In order to reproduce the results of the paper ["End-to-end speaker segmentation
 
 Expected outputs (and VBx baseline) are also provided in the `/reproducible_research` sub-directories.
 
-## 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},
-}
-```