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import time |
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import os |
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import random |
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import numpy as np |
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import torch |
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import torch.utils.data |
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import commons |
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from mel_processing import spectrogram_torch, spec_to_mel_torch |
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from utils import load_wav_to_torch, load_filepaths_and_text, transform |
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"""Multi speaker version""" |
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class TextAudioSpeakerLoader(torch.utils.data.Dataset): |
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""" |
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1) loads audio, speaker_id, text pairs |
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2) normalizes text and converts them to sequences of integers |
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3) computes spectrograms from audio files. |
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""" |
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def __init__(self, audiopaths, hparams): |
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self.audiopaths = load_filepaths_and_text(audiopaths) |
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self.max_wav_value = hparams.data.max_wav_value |
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self.sampling_rate = hparams.data.sampling_rate |
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self.filter_length = hparams.data.filter_length |
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self.hop_length = hparams.data.hop_length |
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self.win_length = hparams.data.win_length |
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self.sampling_rate = hparams.data.sampling_rate |
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self.use_sr = hparams.train.use_sr |
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self.spec_len = hparams.train.max_speclen |
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self.spk_map = hparams.spk |
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random.seed(1234) |
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random.shuffle(self.audiopaths) |
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def get_audio(self, filename): |
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audio, sampling_rate = load_wav_to_torch(filename) |
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if sampling_rate != self.sampling_rate: |
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raise ValueError("{} SR doesn't match target {} SR".format( |
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sampling_rate, self.sampling_rate)) |
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audio_norm = audio / self.max_wav_value |
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audio_norm = audio_norm.unsqueeze(0) |
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spec_filename = filename.replace(".wav", ".spec.pt") |
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if os.path.exists(spec_filename): |
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spec = torch.load(spec_filename) |
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else: |
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spec = spectrogram_torch(audio_norm, self.filter_length, |
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self.sampling_rate, self.hop_length, self.win_length, |
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center=False) |
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spec = torch.squeeze(spec, 0) |
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torch.save(spec, spec_filename) |
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spk = filename.split(os.sep)[-2] |
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spk = torch.LongTensor([self.spk_map[spk]]) |
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c = torch.load(filename + ".soft.pt").squeeze(0) |
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c = torch.repeat_interleave(c, repeats=3, dim=1) |
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f0 = np.load(filename + ".f0.npy") |
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f0 = torch.FloatTensor(f0) |
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lmin = min(c.size(-1), spec.size(-1), f0.shape[0]) |
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assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape, filename) |
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assert abs(lmin - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape) |
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assert abs(lmin - c.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape) |
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spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin] |
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audio_norm = audio_norm[:, :lmin * self.hop_length] |
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_spec, _c, _audio_norm, _f0 = spec, c, audio_norm, f0 |
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while spec.size(-1) < self.spec_len: |
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spec = torch.cat((spec, _spec), -1) |
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c = torch.cat((c, _c), -1) |
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f0 = torch.cat((f0, _f0), -1) |
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audio_norm = torch.cat((audio_norm, _audio_norm), -1) |
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start = random.randint(0, spec.size(-1) - self.spec_len) |
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end = start + self.spec_len |
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spec = spec[:, start:end] |
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c = c[:, start:end] |
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f0 = f0[start:end] |
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audio_norm = audio_norm[:, start * self.hop_length:end * self.hop_length] |
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return c, f0, spec, audio_norm, spk |
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def __getitem__(self, index): |
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return self.get_audio(self.audiopaths[index][0]) |
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def __len__(self): |
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return len(self.audiopaths) |
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class EvalDataLoader(torch.utils.data.Dataset): |
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""" |
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1) loads audio, speaker_id, text pairs |
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2) normalizes text and converts them to sequences of integers |
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3) computes spectrograms from audio files. |
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""" |
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def __init__(self, audiopaths, hparams): |
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self.audiopaths = load_filepaths_and_text(audiopaths) |
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self.max_wav_value = hparams.data.max_wav_value |
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self.sampling_rate = hparams.data.sampling_rate |
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self.filter_length = hparams.data.filter_length |
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self.hop_length = hparams.data.hop_length |
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self.win_length = hparams.data.win_length |
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self.sampling_rate = hparams.data.sampling_rate |
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self.use_sr = hparams.train.use_sr |
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self.audiopaths = self.audiopaths[:5] |
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self.spk_map = hparams.spk |
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def get_audio(self, filename): |
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audio, sampling_rate = load_wav_to_torch(filename) |
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if sampling_rate != self.sampling_rate: |
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raise ValueError("{} SR doesn't match target {} SR".format( |
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sampling_rate, self.sampling_rate)) |
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audio_norm = audio / self.max_wav_value |
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audio_norm = audio_norm.unsqueeze(0) |
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spec_filename = filename.replace(".wav", ".spec.pt") |
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if os.path.exists(spec_filename): |
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spec = torch.load(spec_filename) |
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else: |
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spec = spectrogram_torch(audio_norm, self.filter_length, |
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self.sampling_rate, self.hop_length, self.win_length, |
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center=False) |
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spec = torch.squeeze(spec, 0) |
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torch.save(spec, spec_filename) |
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spk = filename.split(os.sep)[-2] |
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spk = torch.LongTensor([self.spk_map[spk]]) |
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c = torch.load(filename + ".soft.pt").squeeze(0) |
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c = torch.repeat_interleave(c, repeats=3, dim=1) |
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f0 = np.load(filename + ".f0.npy") |
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f0 = torch.FloatTensor(f0) |
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lmin = min(c.size(-1), spec.size(-1), f0.shape[0]) |
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assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape) |
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assert abs(f0.shape[0] - spec.shape[-1]) < 4, (c.size(-1), spec.size(-1), f0.shape) |
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spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin] |
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audio_norm = audio_norm[:, :lmin * self.hop_length] |
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return c, f0, spec, audio_norm, spk |
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def __getitem__(self, index): |
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return self.get_audio(self.audiopaths[index][0]) |
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def __len__(self): |
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return len(self.audiopaths) |
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