| """CLAP embedding. | |
| - feature dimension: 512 | |
| - source: https://huggingface.co/laion/larger_clap_music_and_speech | |
| """ | |
| from typing import Optional | |
| import torch | |
| import librosa | |
| import numpy as np | |
| from transformers import ClapModel, ClapProcessor | |
| class ClapSE: | |
| def __init__(self, ckpt: str = "laion/larger_clap_music_and_speech"): | |
| self.model = ClapModel.from_pretrained(ckpt) | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| self.model.to(self.device) | |
| self.model.eval() | |
| self.processor = ClapProcessor.from_pretrained(ckpt) | |
| def get_speaker_embedding(self, wav: np.ndarray, sampling_rate: Optional[int] = None) -> np.ndarray: | |
| if sampling_rate != self.processor.feature_extractor.sampling_rate: | |
| wav = librosa.resample(wav, orig_sr=sampling_rate, target_sr=self.processor.feature_extractor.sampling_rate) | |
| inputs = self.processor( | |
| audios=wav, sampling_rate=self.processor.feature_extractor.sampling_rate, return_tensors="pt" | |
| ) | |
| with torch.no_grad(): | |
| outputs = self.model.get_audio_features(**{k: v.to(self.device) for k, v in inputs.items()}) | |
| return outputs.cpu().numpy()[0] | |
| class ClapGeneralSE(ClapSE): | |
| def __init__(self): | |
| super().__init__(ckpt="laion/larger_clap_general") | |