mirror of
https://github.com/storytold/torchscript-models.git
synced 2026-10-19 07:42:59 +00:00
Brandon Thomas
242 lines
9.7 KiB
Python
242 lines
9.7 KiB
Python
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 utils import load_wav_to_torch, load_filepaths_and_text
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from text import text_to_sequence, cmudict
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class TextMelLoader(torch.utils.data.Dataset):
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"""
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1) loads audio,text pairs
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2) normalizes text and converts them to sequences of one-hot vectors
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3) computes mel-spectrograms from audio files.
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"""
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def __init__(self, audiopaths_and_text, hparams):
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self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
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self.text_cleaners = hparams.text_cleaners
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self.max_wav_value = hparams.max_wav_value
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self.sampling_rate = hparams.sampling_rate
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self.load_mel_from_disk = hparams.load_mel_from_disk
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self.add_noise = hparams.add_noise
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self.add_space = hparams.add_space
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if getattr(hparams, "cmudict_path", None) is not None:
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self.cmudict = cmudict.CMUDict(hparams.cmudict_path)
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self.stft = commons.TacotronSTFT(
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hparams.filter_length, hparams.hop_length, hparams.win_length,
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hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
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hparams.mel_fmax)
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random.seed(1234)
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random.shuffle(self.audiopaths_and_text)
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def get_mel_text_pair(self, audiopath_and_text):
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# separate filename and text
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audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
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text = self.get_text(text)
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mel = self.get_mel(audiopath)
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return (text, mel)
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def get_mel(self, filename):
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if not self.load_mel_from_disk:
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audio, sampling_rate = load_wav_to_torch(filename)
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if sampling_rate != self.stft.sampling_rate:
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raise ValueError("{} {} SR doesn't match target {} SR".format(
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sampling_rate, self.stft.sampling_rate))
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if self.add_noise:
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audio = audio + torch.rand_like(audio)
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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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melspec = self.stft.mel_spectrogram(audio_norm)
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melspec = torch.squeeze(melspec, 0)
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else:
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melspec = torch.from_numpy(np.load(filename))
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assert melspec.size(0) == self.stft.n_mel_channels, (
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'Mel dimension mismatch: given {}, expected {}'.format(
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melspec.size(0), self.stft.n_mel_channels))
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return melspec
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def get_text(self, text):
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if self.add_space:
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text = " " + text.strip() + " "
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text_norm = torch.IntTensor(
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text_to_sequence(text, self.text_cleaners, getattr(self, "cmudict", None)))
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return text_norm
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def __getitem__(self, index):
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return self.get_mel_text_pair(self.audiopaths_and_text[index])
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def __len__(self):
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return len(self.audiopaths_and_text)
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class TextMelCollate():
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""" Zero-pads model inputs and targets based on number of frames per step
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"""
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def __init__(self, n_frames_per_step=1):
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self.n_frames_per_step = n_frames_per_step
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def __call__(self, batch):
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"""Collate's training batch from normalized text and mel-spectrogram
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PARAMS
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------
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batch: [text_normalized, mel_normalized]
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"""
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# Right zero-pad all one-hot text sequences to max input length
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input_lengths, ids_sorted_decreasing = torch.sort(
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torch.LongTensor([len(x[0]) for x in batch]),
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dim=0, descending=True)
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max_input_len = input_lengths[0]
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text_padded = torch.LongTensor(len(batch), max_input_len)
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text_padded.zero_()
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for i in range(len(ids_sorted_decreasing)):
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text = batch[ids_sorted_decreasing[i]][0]
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text_padded[i, :text.size(0)] = text
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# Right zero-pad mel-spec
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num_mels = batch[0][1].size(0)
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max_target_len = max([x[1].size(1) for x in batch])
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if max_target_len % self.n_frames_per_step != 0:
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max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
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assert max_target_len % self.n_frames_per_step == 0
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# include mel padded
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mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
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mel_padded.zero_()
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output_lengths = torch.LongTensor(len(batch))
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for i in range(len(ids_sorted_decreasing)):
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mel = batch[ids_sorted_decreasing[i]][1]
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mel_padded[i, :, :mel.size(1)] = mel
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output_lengths[i] = mel.size(1)
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return text_padded, input_lengths, mel_padded, output_lengths
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"""Multi speaker version"""
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class TextMelSpeakerLoader(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 one-hot vectors
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3) computes mel-spectrograms from audio files.
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"""
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def __init__(self, audiopaths_sid_text, hparams):
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self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
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self.text_cleaners = hparams.text_cleaners
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self.max_wav_value = hparams.max_wav_value
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self.sampling_rate = hparams.sampling_rate
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self.load_mel_from_disk = hparams.load_mel_from_disk
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self.add_noise = hparams.add_noise
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self.add_space = hparams.add_space
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self.min_text_len = getattr(hparams, "min_text_len", 1)
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self.max_text_len = getattr(hparams, "max_text_len", 190)
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if getattr(hparams, "cmudict_path", None) is not None:
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self.cmudict = cmudict.CMUDict(hparams.cmudict_path)
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self.stft = commons.TacotronSTFT(
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hparams.filter_length, hparams.hop_length, hparams.win_length,
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hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
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hparams.mel_fmax)
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self._filter_text_len()
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random.seed(1234)
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random.shuffle(self.audiopaths_sid_text)
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def _filter_text_len(self):
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audiopaths_sid_text_new = []
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for audiopath, sid, text in self.audiopaths_sid_text:
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if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
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audiopaths_sid_text_new.append([audiopath, sid, text])
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self.audiopaths_sid_text = audiopaths_sid_text_new
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def get_mel_text_speaker_pair(self, audiopath_sid_text):
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# separate filename, speaker_id and text
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audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
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text = self.get_text(text)
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mel = self.get_mel(audiopath)
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sid = self.get_sid(sid)
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return (text, mel, sid)
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def get_mel(self, filename):
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if not self.load_mel_from_disk:
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audio, sampling_rate = load_wav_to_torch(filename)
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if sampling_rate != self.stft.sampling_rate:
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raise ValueError("{} {} SR doesn't match target {} SR".format(
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sampling_rate, self.stft.sampling_rate))
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if self.add_noise:
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audio = audio + torch.rand_like(audio)
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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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melspec = self.stft.mel_spectrogram(audio_norm)
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melspec = torch.squeeze(melspec, 0)
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else:
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melspec = torch.from_numpy(np.load(filename))
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assert melspec.size(0) == self.stft.n_mel_channels, (
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'Mel dimension mismatch: given {}, expected {}'.format(
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melspec.size(0), self.stft.n_mel_channels))
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return melspec
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def get_text(self, text):
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if self.add_space:
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text = " " + text.strip() + " "
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text_norm = torch.IntTensor(
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text_to_sequence(text, self.text_cleaners, getattr(self, "cmudict", None)))
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return text_norm
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def get_sid(self, sid):
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sid = torch.IntTensor([int(sid)])
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return sid
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def __getitem__(self, index):
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return self.get_mel_text_speaker_pair(self.audiopaths_sid_text[index])
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def __len__(self):
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return len(self.audiopaths_sid_text)
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class TextMelSpeakerCollate():
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""" Zero-pads model inputs and targets based on number of frames per step
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"""
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def __init__(self, n_frames_per_step=1):
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self.n_frames_per_step = n_frames_per_step
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def __call__(self, batch):
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"""Collate's training batch from normalized text and mel-spectrogram
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PARAMS
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------
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batch: [text_normalized, mel_normalized]
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"""
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# Right zero-pad all one-hot text sequences to max input length
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input_lengths, ids_sorted_decreasing = torch.sort(
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torch.LongTensor([len(x[0]) for x in batch]),
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dim=0, descending=True)
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max_input_len = input_lengths[0]
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text_padded = torch.LongTensor(len(batch), max_input_len)
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text_padded.zero_()
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for i in range(len(ids_sorted_decreasing)):
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text = batch[ids_sorted_decreasing[i]][0]
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text_padded[i, :text.size(0)] = text
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# Right zero-pad mel-spec
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num_mels = batch[0][1].size(0)
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max_target_len = max([x[1].size(1) for x in batch])
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if max_target_len % self.n_frames_per_step != 0:
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max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
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assert max_target_len % self.n_frames_per_step == 0
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# include mel padded & sid
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mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
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mel_padded.zero_()
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output_lengths = torch.LongTensor(len(batch))
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sid = torch.LongTensor(len(batch))
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for i in range(len(ids_sorted_decreasing)):
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mel = batch[ids_sorted_decreasing[i]][1]
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mel_padded[i, :, :mel.size(1)] = mel
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output_lengths[i] = mel.size(1)
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sid[i] = batch[ids_sorted_decreasing[i]][2]
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return text_padded, input_lengths, mel_padded, output_lengths, sid
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