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"""CLAP embedding. |
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- feature dimension: 512 |
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- source: https://huggingface.co/laion/larger_clap_music_and_speech |
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""" |
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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 ClapModel, ClapProcessor |
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class ClapSE: |
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def __init__(self, ckpt: str = "laion/larger_clap_music_and_speech"): |
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self.model = ClapModel.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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self.processor = ClapProcessor.from_pretrained(ckpt) |
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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.feature_extractor.sampling_rate: |
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wav = librosa.resample(wav, orig_sr=sampling_rate, target_sr=self.processor.feature_extractor.sampling_rate) |
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inputs = self.processor( |
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audios=wav, sampling_rate=self.processor.feature_extractor.sampling_rate, return_tensors="pt" |
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) |
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with torch.no_grad(): |
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outputs = self.model.get_audio_features(**{k: v.to(self.device) for k, v in inputs.items()}) |
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return outputs.cpu().numpy()[0] |
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class ClapGeneralSE(ClapSE): |
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def __init__(self): |
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super().__init__(ckpt="laion/larger_clap_general") |
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