segmentation 3.0

Providerfatymatariq
Categoryvoice-activity-detection
Licensemit
Downloads2.7K
Stars0

Overview

Segmentation 3.0 is a lightweight Voice Activity Detection (VAD) model designed to distinguish human speech from background noise in real-time audio streams. For developers building voice-enabled applications, this model serves as an efficient front-end filter to trigger downstream ASR (Automatic Speech Recognition) or TTS engines, significantly reducing unnecessary compute costs and latency. It is particularly suited for edge deployment or high-concurrency pipelines where minimal overhead is critical. Integration is straightforward due to its MIT license, allowing for flexible commercial deployment across diverse hardware environments without restrictive licensing hurdles.

Highlights

  • Efficient real-time voice activity detection for audio pipelines
  • Reduces ASR compute costs by filtering non-speech segments
  • Permissive MIT license for seamless commercial integration
  • Low-latency performance ideal for edge device deployment

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("fatymatariq/segmentation-3.0")
tokenizer = AutoTokenizer.from_pretrained("fatymatariq/segmentation-3.0")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download fatymatariq/segmentation-3.0

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download fatymatariq/segmentation-3.0 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('fatymatariq/segmentation-3.0')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/fatymatariq/segmentation-3.0

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/fatymatariq/segmentation-3.0

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('fatymatariq/segmentation-3.0')
tokenizer = AutoTokenizer.from_pretrained('fatymatariq/segmentation-3.0')

Full Documentation

来源: HuggingFace

---
tags:

  • pyannote

  • pyannote-audio

  • pyannote-audio-model

  • audio

  • voice

  • speech

  • speaker

  • speaker-diarization

  • speaker-change-detection

  • 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 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 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

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.

It has been trained by Séverin Baroudi with 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 by Alexis Plaquet also provides instructions on how to train or finetune such a model on your own data.

Requirements

1. Install pyannote.audio 3.0 with pip install pyannote.audio
2. Accept pyannote/segmentation-3.0 user conditions
3. Create access token at 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 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},
}
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