from concurrent.futures import ProcessPoolExecutor
from functools import partial
import numpy as np
import os
from hparams import hparams
from util import audio
_max_out_length = 2500
def build_from_path(in_dir, out_dir, num_workers=1, tqdm=lambda x: x):
'''Preprocesses the LJ Speech dataset from a given input path into a given output directory.
Args:
in_dir: The directory where you have downloaded the LJ Speech dataset
out_dir: The directory to write the output into
num_workers: Optional number of worker processes to parallelize across
tqdm: You can optionally pass tqdm to get a nice progress bar
Returns:
A list of tuples describing the training examples. This should be written to train.txt
'''
# We use ProcessPoolExecutor to parallize across processes. This is just an optimization and you
# can omit it and just call _process_utterance on each input if you want.
executor = ProcessPoolExecutor(max_workers=num_workers)
futures = []
index = 1
with open(os.path.join(in_dir, 'metadata.train'), encoding='utf-8') as f:
for line in f:
parts = line.strip().split('|')
wav_path = os.path.join(in_dir, 'wavs', '%s.wav' % parts[0])
text = parts[1]
futures.append(executor.submit(partial(_process_utterance, out_dir, index, wav_path, text)))
index += 1
results = [future.result() for future in tqdm(futures)]
return [r for r in results if r is not None]
def _process_utterance(out_dir, index, wav_path, text):
'''Preprocesses a single utterance audio/text pair.
This writes the mel and linear scale spectrograms to disk and returns a tuple to write
to the train.txt file.
Args:
out_dir: The directory to write the spectrograms into
index: The numeric index to use in the spectrogram filenames.
wav_path: Path to the audio file containing the speech input
text: The text spoken in the input audio file
Returns:
A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
'''
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
max_samples = _max_out_length * hparams.frame_shift_ms / 1000 * hparams.sample_rate
if len(wav) > max_samples and _max_out_length is not None:
return None
# Compute the linear-scale spectrogram from the wav:
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
# Compute a mel-scale spectrogram from the wav:
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
# Write the spectrograms to disk:
spectrogram_filename = 'blizzard2013-spec-%05d.npy' % index
mel_filename = 'blizzard2013-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename), spectrogram.T, allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.T, allow_pickle=False)
# Return a tuple describing this training example:
return (spectrogram_filename, mel_filename, n_frames, text)
