Using this open-source model in production?
Consider switching to
pyannoteAI
for better and faster options.
🎹 Speaker diarization 3.1
This pipeline is the same as
pyannote/speaker-diarization-3.0
except it removes the
problematic
use of
onnxruntime
.
Both speaker segmentation and embedding now run in pure PyTorch. This should ease deployment and possibly speed up inference.
It requires pyannote.audio version 3.1 or higher.
It ingests mono audio sampled at 16kHz and outputs speaker diarization as an
Annotation
instance:
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.
# instantiate the pipelinefrom pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
"pyannote/speaker-diarization-3.1",
use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")
# run the pipeline on an audio file
diarization = pipeline("audio.wav")
# dump the diarization output to disk using RTTM formatwithopen("audio.rttm", "w") as rttm:
diarization.write_rttm(rttm)
Processing on GPU
pyannote.audio
pipelines run on CPU by default.
You can send them to GPU with the following lines:
import torch
pipeline.to(torch.device("cuda"))
Processing from memory
Pre-loading audio files in memory may result in faster processing:
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pyannote speaker-diarization-3.1 online free url in huggingface.co:
speaker-diarization-3.1 is an open source model from GitHub that offers a free installation service, and any user can find speaker-diarization-3.1 on GitHub to install. At the same time, huggingface.co provides the effect of speaker-diarization-3.1 install, users can directly use speaker-diarization-3.1 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
speaker-diarization-3.1 install url in huggingface.co: