File size: 2,463 Bytes
1d5cf8b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | #!/usr/bin/env python3
"""Extract WeSpeaker ResNet34-LM embeddings through pyannote.audio.
Model: pyannote/wespeaker-voxceleb-resnet34-LM (supervised, ArcMargin/AAM-Softmax
with large-margin fine-tuning, VoxCeleb2 dev, 256-dim). This is the official
WeSpeaker ResNet34-LM checkpoint as packaged for pyannote.audio. One embedding
per file (window="whole"): 80-dim Kaldi fbank, per-utterance mean normalization,
ResNet34, temporal statistics pooling.
Install: pip install pyannote.audio==3.4.0
The released embeddings were extracted with pyannote.audio 3.4.0, torch 2.6.0
(CUDA 12.4) and checkpoint revision 837717ddb9ff5507820346191109dc79c958d614.
Usage:
python3 extract_wespeaker.py
python3 extract_wespeaker.py --audio-dir data/experiment2/audio \
--output-dir ../data/experiment2/embeddings
"""
import argparse
import torch
from extraction_utils import load_audio, extract_all
CHECKPOINT = "pyannote/wespeaker-voxceleb-resnet34-LM"
REVISION = "837717ddb9ff5507820346191109dc79c958d614"
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
parser.add_argument("--base-dir", default=None)
parser.add_argument("--output-dir", default=None)
parser.add_argument("--audio-dir", default=None,
help="folder with reference/ and comparison/ (default: data/audio)")
args = parser.parse_args()
from pyannote.audio import Inference, Model
from pyannote.audio.core.task import Problem, Resolution, Specifications
from torch.torch_version import TorchVersion
print(f"Loading {CHECKPOINT}@{REVISION} on {args.device}...")
# torch>=2.6 loads checkpoints with weights_only=True; allowlist the four
# non-tensor globals this checkpoint stores instead of disabling the check.
with torch.serialization.safe_globals([Specifications, Problem, Resolution, TorchVersion]):
model = Model.from_pretrained(f"{CHECKPOINT}@{REVISION}")
inference = Inference(model, window="whole", device=torch.device(args.device))
def model_fn(audio_path):
audio = load_audio(audio_path, target_sr=16000)
waveform = torch.from_numpy(audio).unsqueeze(0) # (1, T)
return inference({"waveform": waveform, "sample_rate": 16000})
extract_all(model_fn, "wespeaker_resnet34_lm", args.base_dir, args.output_dir,
args.audio_dir)
if __name__ == "__main__":
main()
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