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| import soundfile as sf | |
| import torch | |
| import torch.multiprocessing | |
| import torch.multiprocessing | |
| from numpy import trim_zeros | |
| from speechbrain.pretrained import EncoderClassifier | |
| from Preprocessing.AudioPreprocessor import AudioPreprocessor | |
| class ProsodicConditionExtractor: | |
| def __init__(self, sr, device=torch.device("cpu")): | |
| self.ap = AudioPreprocessor(input_sr=sr, output_sr=16000, melspec_buckets=80, hop_length=256, n_fft=1024, cut_silence=False) | |
| # https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb | |
| self.speaker_embedding_func_ecapa = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", | |
| run_opts={"device": str(device)}, | |
| savedir="Models/SpeakerEmbedding/speechbrain_speaker_embedding_ecapa") | |
| # https://huggingface.co/speechbrain/spkrec-xvect-voxceleb | |
| self.speaker_embedding_func_xvector = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb", | |
| run_opts={"device": str(device)}, | |
| savedir="Models/SpeakerEmbedding/speechbrain_speaker_embedding_xvector") | |
| def extract_condition_from_reference_wave(self, wave, already_normalized=False): | |
| if already_normalized: | |
| norm_wave = wave | |
| else: | |
| norm_wave = self.ap.audio_to_wave_tensor(normalize=True, audio=wave) | |
| norm_wave = torch.tensor(trim_zeros(norm_wave.numpy())) | |
| spk_emb_ecapa = self.speaker_embedding_func_ecapa.encode_batch(wavs=norm_wave.unsqueeze(0)).squeeze() | |
| spk_emb_xvector = self.speaker_embedding_func_xvector.encode_batch(wavs=norm_wave.unsqueeze(0)).squeeze() | |
| combined_utt_condition = torch.cat([spk_emb_ecapa.cpu(), | |
| spk_emb_xvector.cpu()], dim=0) | |
| return combined_utt_condition | |
| if __name__ == '__main__': | |
| wave, sr = sf.read("../audios/1.wav") | |
| ext = ProsodicConditionExtractor(sr=sr) | |
| print(ext.extract_condition_from_reference_wave(wave=wave).shape) | |