140 lines
4.7 KiB
Python
140 lines
4.7 KiB
Python
import os
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import tqdm
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import csv
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import argparse
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import numpy as np
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import librosa
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from pathlib import Path
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import pandas as pd
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from paddle.io import Dataset
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from parakeet.data import batch_spec, batch_wav
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from parakeet.datasets import LJSpeechMetaData
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from parakeet.audio import AudioProcessor
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from parakeet.audio.spec_normalizer import UnitMagnitude
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from config import get_cfg_defaults
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class Transform(object):
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def __init__(self, sample_rate, n_fft, win_length, hop_length, n_mels):
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self.sample_rate = sample_rate
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self.n_fft = n_fft
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self.win_length = win_length
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self.hop_length = hop_length
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self.n_mels = n_mels
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self.spec_normalizer = UnitMagnitude(min=1e-5)
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def __call__(self, example):
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wav_path, _, _ = example
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sr = self.sample_rate
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n_fft = self.n_fft
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win_length = self.win_length
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hop_length = self.hop_length
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n_mels = self.n_mels
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wav, loaded_sr = librosa.load(wav_path, sr=None)
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assert loaded_sr == sr, "sample rate does not match, resampling applied"
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# Pad audio to the right size.
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frames = int(np.ceil(float(wav.size) / hop_length))
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fft_padding = (n_fft - hop_length) // 2 # sound
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desired_length = frames * hop_length + fft_padding * 2
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pad_amount = (desired_length - wav.size) // 2
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if wav.size % 2 == 0:
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wav = np.pad(wav, (pad_amount, pad_amount), mode='reflect')
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else:
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wav = np.pad(wav, (pad_amount, pad_amount + 1), mode='reflect')
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# Normalize audio.
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wav = wav / np.abs(wav).max() * 0.999
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# Compute mel-spectrogram.
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# Turn center to False to prevent internal padding.
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spectrogram = librosa.core.stft(
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wav,
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hop_length=hop_length,
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win_length=win_length,
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n_fft=n_fft,
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center=False)
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spectrogram_magnitude = np.abs(spectrogram)
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# Compute mel-spectrograms.
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mel_filter_bank = librosa.filters.mel(sr=sr,
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n_fft=n_fft,
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n_mels=n_mels)
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mel_spectrogram = np.dot(mel_filter_bank, spectrogram_magnitude)
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mel_spectrogram = mel_spectrogram
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# log scale mel_spectrogram.
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mel_spectrogram = self.spec_normalizer.transform(mel_spectrogram)
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# Extract the center of audio that corresponds to mel spectrograms.
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audio = wav[fft_padding:-fft_padding]
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assert mel_spectrogram.shape[1] * hop_length == audio.size
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# there is no clipping here
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return audio, mel_spectrogram
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def create_dataset(config, input_dir, output_dir, verbose=True):
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input_dir = Path(input_dir).expanduser()
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dataset = LJSpeechMetaData(input_dir)
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output_dir = Path(output_dir).expanduser()
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output_dir.mkdir(exist_ok=True)
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transform = Transform(
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config.sample_rate,
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config.n_fft,
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config.win_length,
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config.hop_length,
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config.n_mels)
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file_names = []
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for example in tqdm.tqdm(dataset):
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fname, _, _ = example
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base_name = os.path.splitext(os.path.basename(fname))[0]
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wav_dir = output_dir / "wav"
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mel_dir = output_dir / "mel"
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wav_dir.mkdir(exist_ok=True)
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mel_dir.mkdir(exist_ok=True)
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audio, mel = transform(example)
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np.save(str(wav_dir / base_name), audio)
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np.save(str(mel_dir / base_name), mel)
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file_names.append((base_name, mel.shape[-1], audio.shape[-1]))
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meta_data = pd.DataFrame.from_records(file_names)
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meta_data.to_csv(str(output_dir / "metadata.csv"), sep="\t", index=None, header=None)
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print("saved meta data in to {}".format(os.path.join(output_dir, "metadata.csv")))
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print("Done!")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="create dataset")
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parser.add_argument("--config", type=str, metavar="FILE", help="extra config to overwrite the default config")
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parser.add_argument("--input", type=str, help="path of the ljspeech dataset")
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parser.add_argument("--output", type=str, help="path to save output dataset")
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parser.add_argument("--opts", nargs=argparse.REMAINDER,
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help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
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)
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parser.add_argument("-v", "--verbose", action="store_true", help="print msg")
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config = get_cfg_defaults()
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args = parser.parse_args()
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if args.config:
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config.merge_from_file(args.config)
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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if args.verbose:
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print(config.data)
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print(args)
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create_dataset(config.data, args.input, args.output, args.verbose)
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