mirror of https://github.com/MycroftAI/mimic2.git
70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
from concurrent.futures import ProcessPoolExecutor
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from functools import partial
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import numpy as np
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import os
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from util import audio
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def build_from_path(in_dir, out_dir, num_workers=1, tqdm=lambda x: x):
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'''Preprocesses the LJ Speech dataset from a given input path into a given output directory.
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Args:
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in_dir: The directory where you have downloaded the LJ Speech dataset
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out_dir: The directory to write the output into
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num_workers: Optional number of worker processes to parallelize across
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tqdm: You can optionally pass tqdm to get a nice progress bar
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Returns:
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A list of tuples describing the training examples. This should be written to train.txt
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'''
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# We use ProcessPoolExecutor to parallize across processes. This is just an optimization and you
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# can omit it and just call _process_utterance on each input if you want.
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executor = ProcessPoolExecutor(max_workers=num_workers)
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futures = []
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index = 1
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with open(os.path.join(in_dir, 'metadata.csv'), encoding='utf-8') as f:
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for line in f:
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parts = line.strip().split('|')
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wav_path = os.path.join(in_dir, 'wavs', '%s.wav' % parts[0])
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text = parts[2]
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futures.append(executor.submit(partial(_process_utterance, out_dir, index, wav_path, text)))
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index += 1
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return [future.result() for future in tqdm(futures)]
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def _process_utterance(out_dir, index, wav_path, text):
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'''Preprocesses a single utterance audio/text pair.
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This writes the mel and linear scale spectrograms to disk and returns a tuple to write
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to the train.txt file.
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Args:
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out_dir: The directory to write the spectrograms into
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index: The numeric index to use in the spectrogram filenames.
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wav_path: Path to the audio file containing the speech input
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text: The text spoken in the input audio file
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Returns:
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A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
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'''
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# Load the audio to a numpy array:
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wav = audio.load_wav(wav_path)
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# Compute the linear-scale spectrogram from the wav:
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spectrogram = audio.spectrogram(wav).astype(np.float32)
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n_frames = spectrogram.shape[1]
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# Compute a mel-scale spectrogram from the wav:
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mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
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# Write the spectrograms to disk:
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spectrogram_filename = 'ljspeech-spec-%05d.npy' % index
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mel_filename = 'ljspeech-mel-%05d.npy' % index
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np.save(os.path.join(out_dir, spectrogram_filename), spectrogram.T, allow_pickle=False)
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np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.T, allow_pickle=False)
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# Return a tuple describing this training example:
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return (spectrogram_filename, mel_filename, n_frames, text)
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