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data.py
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data.py
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###############################################################################
#
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
###############################################################################
import re
import os
import sys
import argparse
import json
import random
import numpy as np
import torch
import torch.utils.data
from scipy.io.wavfile import read
from audio_processing import TacotronSTFT
from text import text_to_sequence, cmudict, _clean_text, get_arpabet
def load_filepaths_and_text(filename, split="|"):
with open(filename, encoding='utf-8') as f:
filepaths_and_text = [line.strip().split(split) for line in f]
return filepaths_and_text
def load_wav_to_torch(full_path):
""" Loads wavdata into torch array """
sampling_rate, data = read(full_path)
return torch.from_numpy(data).float(), sampling_rate
class Data(torch.utils.data.Dataset):
def __init__(self, filelist_path, filter_length, hop_length, win_length,
sampling_rate, mel_fmin, mel_fmax, max_wav_value, p_arpabet,
cmudict_path, text_cleaners, speaker_ids=None, randomize=True,
seed=1234):
self.max_wav_value = max_wav_value
self.audiopaths_and_text = load_filepaths_and_text(filelist_path)
self.stft = TacotronSTFT(filter_length=filter_length,
hop_length=hop_length,
win_length=win_length,
sampling_rate=sampling_rate,
mel_fmin=mel_fmin, mel_fmax=mel_fmax)
self.sampling_rate = sampling_rate
self.text_cleaners = text_cleaners
self.p_arpabet = p_arpabet
self.cmudict = cmudict.CMUDict(cmudict_path, keep_ambiguous=True)
if speaker_ids is None:
self.speaker_ids = self.create_speaker_lookup_table(self.audiopaths_and_text)
else:
self.speaker_ids = speaker_ids
random.seed(seed)
if randomize:
random.shuffle(self.audiopaths_and_text)
def create_speaker_lookup_table(self, audiopaths_and_text):
speaker_ids = np.sort(np.unique([x[2] for x in audiopaths_and_text]))
d = {int(speaker_ids[i]): i for i in range(len(speaker_ids))}
print("Number of speakers :", len(d))
return d
def get_mel(self, audio):
audio_norm = audio / self.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = torch.squeeze(melspec, 0)
return melspec
def get_speaker_id(self, speaker_id):
return torch.LongTensor([self.speaker_ids[int(speaker_id)]])
def get_text(self, text):
text = _clean_text(text, self.text_cleaners)
words = re.findall(r'\S*\{.*?\}\S*|\S+', text)
text = ' '.join([get_arpabet(word, self.cmudict)
if random.random() < self.p_arpabet else word
for word in words])
text_norm = torch.LongTensor(text_to_sequence(text))
return text_norm
def __getitem__(self, index):
# Read audio and text
audiopath, text, speaker_id = self.audiopaths_and_text[index]
audio, sampling_rate = load_wav_to_torch(audiopath)
if sampling_rate != self.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate))
mel = self.get_mel(audio)
text_encoded = self.get_text(text)
speaker_id = self.get_speaker_id(speaker_id)
return (mel, speaker_id, text_encoded)
def __len__(self):
return len(self.audiopaths_and_text)
class DataCollate():
""" Zero-pads model inputs and targets based on number of frames per step """
def __init__(self, n_frames_per_step=1):
self.n_frames_per_step = n_frames_per_step
def __call__(self, batch):
"""Collate's training batch from normalized text and mel-spectrogram """
# Right zero-pad all one-hot text sequences to max input length
input_lengths, ids_sorted_decreasing = torch.sort(
torch.LongTensor([len(x[2]) 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]][2]
text_padded[i, :text.size(0)] = text
# Right zero-pad mel-spec
num_mel_channels = batch[0][0].size(0)
max_target_len = max([x[0].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_mel_channels, 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))
for i in range(len(ids_sorted_decreasing)):
mel = batch[ids_sorted_decreasing[i]][0]
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]][1]
return mel_padded, speaker_ids, text_padded, input_lengths, output_lengths, gate_padded
# ===================================================================
# Takes directory of clean audio and makes directory of spectrograms
# Useful for making test sets
# ===================================================================
if __name__ == "__main__":
# Get defaults so it can work with no Sacred
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str,
help='JSON file for configuration')
parser.add_argument('-f', '--filelist', type=str,
help='List of files to generate mels')
parser.add_argument('-o', '--output_dir', type=str,
help='Output directory')
args = parser.parse_args()
with open(args.config) as f:
data = f.read()
data_config = json.loads(data)["data_config"]
mel2samp = Data(**data_config)
# Make directory if it doesn't exist
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
os.chmod(args.output_dir, 0o775)
filepaths_and_text = load_filepaths_and_text(args.filelist)
for (filepath, text, speaker_id) in filepaths_and_text:
print("speaker id", speaker_id)
print("text", text)
print("text encoded", mel2samp.get_text(text))
audio, sr = load_wav_to_torch(filepath)
melspectrogram = mel2samp.get_mel(audio)
filename = os.path.basename(filepath)
new_filepath = args.output_dir + '/' + filename + '.pt'
print(new_filepath)
torch.save(melspectrogram, new_filepath)