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"""Meta's w2vBERT based speaker embedding.
- feature dimension: 1024
- source: https://huggingface.co/facebook/w2v-bert-2.0
"""
from typing import Optional
import torch
import librosa
import numpy as np
from transformers import Wav2Vec2BertModel, AutoFeatureExtractor
class W2VBertSE:
def __init__(self):
self.processor = AutoFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0")
self.model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0")
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
self.model.eval()
def get_speaker_embedding(self, wav: np.ndarray, sampling_rate: Optional[int] = None) -> np.ndarray:
# audio file is decoded on the fly
if sampling_rate != self.processor.sampling_rate:
wav = librosa.resample(wav, orig_sr=sampling_rate, target_sr=self.processor.sampling_rate)
inputs = self.processor(wav, sampling_rate=self.processor.sampling_rate, return_tensors="pt")
with torch.no_grad():
outputs = self.model(**{k: v.to(self.device) for k, v in inputs.items()})
return outputs.last_hidden_state.mean(1).cpu().numpy()[0]
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