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"""Meta's w2vBERT based speaker embedding.""" |
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from typing import Optional |
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import torch |
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import librosa |
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import numpy as np |
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from transformers import AutoModel, AutoFeatureExtractor |
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class W2VBERTEmbedding: |
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def __init__(self, ckpt: str = "facebook/w2v-bert-2.0"): |
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self.processor = AutoFeatureExtractor.from_pretrained(ckpt) |
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self.model = AutoModel.from_pretrained(ckpt) |
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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self.model.to(self.device) |
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self.model.eval() |
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def get_speaker_embedding(self, wav: np.ndarray, sampling_rate: Optional[int] = None) -> np.ndarray: |
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if sampling_rate != self.processor.sampling_rate: |
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wav = librosa.resample(wav, orig_sr=sampling_rate, target_sr=self.processor.sampling_rate) |
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inputs = self.processor(wav, sampling_rate=self.processor.sampling_rate, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = self.model(**{k: v.to(self.device) for k, v in inputs.items()}) |
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return outputs.last_hidden_state.mean(1).cpu().numpy()[0] |
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class HuBERTXLEmbedding(W2VBERTEmbedding): |
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def __init__(self): |
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super().__init__("facebook/hubert-xlarge-ll60k") |
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class HuBERTLargeEmbedding(W2VBERTEmbedding): |
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def __init__(self): |
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super().__init__("facebook/hubert-large-ll60k") |
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class HuBERTBaseEmbedding(W2VBERTEmbedding): |
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def __init__(self): |
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super().__init__("facebook/hubert-base-ls960") |
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class Wav2VecEmbedding(W2VBERTEmbedding): |
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def __init__(self): |
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super().__init__("facebook/wav2vec2-large-xlsr-53") |
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class XLSR2BEmbedding(W2VBERTEmbedding): |
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def __init__(self): |
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super().__init__("facebook/wav2vec2-xls-r-2b") |
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class XLSR1BEmbedding(W2VBERTEmbedding): |
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def __init__(self): |
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super().__init__("facebook/wav2vec2-xls-r-1b") |
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class XLSR300MEmbedding(W2VBERTEmbedding): |
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def __init__(self): |
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super().__init__("facebook/wav2vec2-xls-r-300m") |
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