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data_utils.py
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data_utils.py
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import random
import os
import re
import numpy as np
import torch
import torch.utils.data
import librosa
import layers
from utils import load_wav_to_torch, load_filepaths_and_text
from text import text_to_sequence, cmudict
from yin import compute_yin
class TextMelLoader(torch.utils.data.Dataset):
"""
1) loads audio, text and speaker ids
2) normalizes text and converts them to sequences of one-hot vectors
3) computes mel-spectrograms and f0s from audio files.
"""
def __init__(self, audiopaths_and_text, hparams, speaker_ids=None, output_directory=None):
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
self.text_cleaners = hparams.text_cleaners
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.stft = layers.TacotronSTFT(
hparams.filter_length, hparams.hop_length, hparams.win_length,
hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
hparams.mel_fmax)
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.f0_min = hparams.f0_min
self.f0_max = hparams.f0_max
self.harm_thresh = hparams.harm_thresh
self.p_arpabet = hparams.p_arpabet
self.cmudict = None
if hparams.cmudict_path is not None:
self.cmudict = cmudict.CMUDict(hparams.cmudict_path)
self.speaker_ids = speaker_ids
if speaker_ids is None:
self.speaker_ids = self.create_speaker_lookup_table(self.audiopaths_and_text)
# print speaker_lookup_table
if not (output_directory is None) and not (self.speaker_ids is None):
speaker_id_path = os.path.join(output_directory, 'speaker_ids.txt')
with open(speaker_id_path, 'w', encoding='utf-8') as f:
for key, value in self.speaker_ids.items():
f.write('{}: {}\n'.format(key, value))
# random.seed(1234)
# random.shuffle(self.audiopaths_and_text)
def create_speaker_lookup_table(self, audiopaths_and_text):
speaker_list = [x[2] for x in audiopaths_and_text]
speaker_ids = np.sort(np.unique(speaker_list))
d = {speaker_ids[i]: i for i in range(len(speaker_ids))}
return d
def get_f0(self, audio, sampling_rate=22050, frame_length=1024,
hop_length=256, f0_min=100, f0_max=300, harm_thresh=0.1):
f0, harmonic_rates, argmins, times = compute_yin(
audio, sampling_rate, frame_length, hop_length, f0_min, f0_max,
harm_thresh)
pad = int((frame_length / hop_length) / 2)
f0 = [0.0] * pad + f0 + [0.0] * pad
f0 = np.array(f0, dtype=np.float32)
return f0
def get_data(self, audiopath_and_text):
audiopath, text, speaker, lang_code = audiopath_and_text
lang_code = int(lang_code)
text = self.get_text(text, lang_code)
mel, f0 = self.get_mel_and_f0(audiopath)
speaker_id = self.get_speaker_id(speaker)
return (text, mel, speaker_id, f0)
def get_speaker_id(self, speaker_id):
return torch.IntTensor([self.speaker_ids[speaker_id]])
def get_mel_and_f0(self, filepath):
audio, sampling_rate = load_wav_to_torch(filepath)
if sampling_rate != self.stft.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
sampling_rate, self.stft.sampling_rate))
audio_norm = audio / self.max_wav_value # max_wav_value must be set to 1 when wav is float32 format already
# I changed them to float32 during preprocessing so this normalization is unnecessary.
audio_norm = audio_norm.unsqueeze(0)
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
f0 = self.get_f0(audio.cpu().numpy(), self.sampling_rate,
self.filter_length, self.hop_length, self.f0_min,
self.f0_max, self.harm_thresh)
f0 = torch.from_numpy(f0)[None]
f0 = f0[:, :melspec.size(1)]
return melspec, f0
def get_text(self, text, lang_code):
text_norm = torch.IntTensor(
text_to_sequence(text, self.text_cleaners, lang_code, self.cmudict))
return text_norm
def __getitem__(self, index):
return self.get_data(self.audiopaths_and_text[index])
def __len__(self):
return len(self.audiopaths_and_text)
class TextMelCollate():
""" Zero-pads model inputs and targets based on number of frames per setep
"""
def __init__(self, n_frames_per_step):
self.n_frames_per_step = n_frames_per_step
def __call__(self, batch):
"""Collate's training batch from normalized text and mel-spectrogram
PARAMS
------
batch: [text_normalized, mel_normalized]
"""
# Right zero-pad all one-hot text sequences to max input length
input_lengths, ids_sorted_decreasing = torch.sort(
torch.LongTensor([len(x[0]) for x in batch]),
dim=0, descending=True)
max_input_len = input_lengths[0]
text_padded = torch.LongTensor(len(batch), max_input_len)
text_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
text = batch[ids_sorted_decreasing[i]][0]
text_padded[i, :text.size(0)] = text
# Right zero-pad mel-spec
num_mels = batch[0][1].size(0)
max_target_len = max([x[1].size(1) for x in batch])
if max_target_len % self.n_frames_per_step != 0:
max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
assert max_target_len % self.n_frames_per_step == 0
# include mel padded, gate padded and speaker ids
mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
mel_padded.zero_()
gate_padded = torch.FloatTensor(len(batch), max_target_len)
gate_padded.zero_()
output_lengths = torch.LongTensor(len(batch))
speaker_ids = torch.LongTensor(len(batch))
f0_padded = torch.FloatTensor(len(batch), 1, max_target_len)
f0_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
mel = batch[ids_sorted_decreasing[i]][1]
mel_padded[i, :, :mel.size(1)] = mel
gate_padded[i, mel.size(1)-1:] = 1
output_lengths[i] = mel.size(1)
speaker_ids[i] = batch[ids_sorted_decreasing[i]][2]
f0 = batch[ids_sorted_decreasing[i]][3]
f0_padded[i, :, :f0.size(1)] = f0
model_inputs = (text_padded, input_lengths, mel_padded, gate_padded,
output_lengths, speaker_ids, f0_padded)
return model_inputs