Update README.md

README.md

@@ -15,115 +15,3 @@ tags:
 - 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 improve it further. Though this model uses MIT license and will always remain open-source, we will occasionnally email you about premium models and paid services around pyannote."
-extra_gated_fields:
-  Company/university: text
-  Website: text
----
-
-Using this open-source model in production?  
-Consider switching to [pyannoteAI](https://www.pyannote.ai) for better and faster options.
-
-# 🎹 "Powerset" speaker segmentation
-
-This model ingests 10 seconds of mono audio sampled at 16kHz and outputs speaker diarization as a (num_frames, num_classes) matrix where the 7 classes are _non-speech_, _speaker #1_, _speaker #2_, _speaker #3_, _speakers #1 and #2_, _speakers #1 and #3_, and _speakers #2 and #3_.
-
-![Example output](example.png)
-
-```python
-# waveform (first row)
-duration, sample_rate, num_channels = 10, 16000, 1
-waveform = torch.randn(batch_size, num_channels, duration * sample_rate) 
-
-# powerset multi-class encoding (second row)
-powerset_encoding = model(waveform)
-
-# multi-label encoding (third row)
-from pyannote.audio.utils.powerset import Powerset
-max_speakers_per_chunk, max_speakers_per_frame = 3, 2
-to_multilabel = Powerset(
-    max_speakers_per_chunk, 
-    max_speakers_per_frame).to_multilabel
-multilabel_encoding = to_multilabel(powerset_encoding)
-```
-
-The various concepts behind this model are described in details in this [paper](https://www.isca-speech.org/archive/interspeech_2023/plaquet23_interspeech.html).
-
-It has been trained by Séverin Baroudi with [pyannote.audio](https://github.com/pyannote/pyannote-audio) `3.0.0` using the combination of the training sets of AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse.
-
-This [companion repository](https://github.com/FrenchKrab/IS2023-powerset-diarization/) by [Alexis Plaquet](https://frenchkrab.github.io/) also provides instructions on how to train or finetune such a model on your own data.
-
-## Requirements
-
-1. Install [`pyannote.audio`](https://github.com/pyannote/pyannote-audio) `3.0` with `pip install pyannote.audio`
-2. Accept [`pyannote/segmentation-3.0`](https://hf.co/pyannote/segmentation-3.0) user conditions
-3. Create access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens).
-
-
-## Usage
-
-```python
-# instantiate the model
-from pyannote.audio import Model
-model = Model.from_pretrained(
-  "pyannote/segmentation-3.0", 
-  use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")
-```
-
-### Speaker diarization
-
-This model cannot be used to perform speaker diarization of full recordings on its own (it only processes 10s chunks). 
-
-See [pyannote/speaker-diarization-3.0](https://hf.co/pyannote/speaker-diarization-3.0) pipeline that uses an additional speaker embedding model to perform full recording speaker diarization.
-
-### Voice activity detection
-
-```python
-from pyannote.audio.pipelines import VoiceActivityDetection
-pipeline = VoiceActivityDetection(segmentation=model)
-HYPER_PARAMETERS = {
-  # 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)
-HYPER_PARAMETERS = {
-  # remove overlapped speech regions shorter than that many seconds.
-  "min_duration_on": 0.0,
-  # fill non-overlapped speech regions shorter than that many seconds.
-  "min_duration_off": 0.0
-}
-pipeline.instantiate(HYPER_PARAMETERS)
-osd = pipeline("audio.wav")
-# `osd` is a pyannote.core.Annotation instance containing overlapped speech regions
-```
-
-## Citations
-
-```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},
-}
-```
-
-```bibtex
-@inproceedings{Bredin23,
-  author={Hervé Bredin},
-  title={{pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe}},
-  year=2023,
-  booktitle={Proc. INTERSPEECH 2023},
-}
-```