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import librosa |
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from text_to_speech.utils.audio import librosa_wav2spec |
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from text_to_speech.utils.commons.hparams import hparams |
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import numpy as np |
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REGISTERED_VOCODERS = {} |
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def register_vocoder(name): |
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def _f(cls): |
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REGISTERED_VOCODERS[name] = cls |
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return cls |
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return _f |
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def get_vocoder_cls(vocoder_name): |
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return REGISTERED_VOCODERS.get(vocoder_name) |
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class BaseVocoder: |
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def spec2wav(self, mel): |
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""" |
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:param mel: [T, 80] |
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:return: wav: [T'] |
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""" |
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raise NotImplementedError |
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@staticmethod |
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def wav2spec(wav_fn): |
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""" |
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:param wav_fn: str |
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:return: wav, mel: [T, 80] |
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""" |
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wav_spec_dict = librosa_wav2spec(wav_fn, fft_size=hparams['fft_size'], |
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hop_size=hparams['hop_size'], |
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win_length=hparams['win_size'], |
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num_mels=hparams['audio_num_mel_bins'], |
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fmin=hparams['fmin'], |
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fmax=hparams['fmax'], |
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sample_rate=hparams['audio_sample_rate'], |
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loud_norm=hparams['loud_norm']) |
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wav = wav_spec_dict['wav'] |
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mel = wav_spec_dict['mel'] |
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return wav, mel |
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@staticmethod |
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def wav2mfcc(wav_fn): |
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fft_size = hparams['fft_size'] |
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hop_size = hparams['hop_size'] |
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win_length = hparams['win_size'] |
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sample_rate = hparams['audio_sample_rate'] |
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wav, _ = librosa.core.load(wav_fn, sr=sample_rate) |
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mfcc = librosa.feature.mfcc(y=wav, sr=sample_rate, n_mfcc=13, |
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n_fft=fft_size, hop_length=hop_size, |
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win_length=win_length, pad_mode="constant", power=1.0) |
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mfcc_delta = librosa.feature.delta(mfcc, order=1) |
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mfcc_delta_delta = librosa.feature.delta(mfcc, order=2) |
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mfcc = np.concatenate([mfcc, mfcc_delta, mfcc_delta_delta]).T |
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return mfcc |
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