"""Meta's w2vBERT based speaker embedding.""" from typing import Optional import torch import librosa import numpy as np from transformers import AutoModel, AutoFeatureExtractor ############ # W2V BERT # ############ class W2VBERTEmbedding: def __init__(self, ckpt: str = "facebook/w2v-bert-2.0"): self.processor = AutoFeatureExtractor.from_pretrained(ckpt) self.model = AutoModel.from_pretrained(ckpt) 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.cpu().numpy()[0] ########## # HuBERT # ########## class HuBERTXLEmbedding(W2VBERTEmbedding): def __init__(self): super().__init__("facebook/hubert-xlarge-ll60k") class HuBERTLargeEmbedding(W2VBERTEmbedding): def __init__(self): super().__init__("facebook/hubert-large-ll60k") class HuBERTBaseEmbedding(W2VBERTEmbedding): def __init__(self): super().__init__("facebook/hubert-base-ls960") ########### # wav2vec # ########### class Wav2VecEmbedding(W2VBERTEmbedding): def __init__(self): super().__init__("facebook/wav2vec2-large-xlsr-53") ######### # XLS-R # ######### class XLSR2BEmbedding(W2VBERTEmbedding): def __init__(self): super().__init__("facebook/wav2vec2-xls-r-2b") class XLSR1BEmbedding(W2VBERTEmbedding): def __init__(self): super().__init__("facebook/wav2vec2-xls-r-1b") class XLSR300MEmbedding(W2VBERTEmbedding): def __init__(self): super().__init__("facebook/wav2vec2-xls-r-300m")