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PaddleSpeech/parakeet/exps/waveflow/preprocess.py

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# Copyright (c) 2020 PaddlePaddle Authors. 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 argparse
import os
from pathlib import Path
import librosa
import numpy as np
import pandas as pd
import tqdm
from parakeet.audio import LogMagnitude
from parakeet.datasets import LJSpeechMetaData
from parakeet.exps.waveflow.config import get_cfg_defaults
class Transform(object):
def __init__(self, sample_rate, n_fft, win_length, hop_length, n_mels, fmin,
fmax):
self.sample_rate = sample_rate
self.n_fft = n_fft
self.win_length = win_length
self.hop_length = hop_length
self.n_mels = n_mels
self.fmin = fmin
self.fmax = fmax
self.spec_normalizer = LogMagnitude(min=1e-5)
def __call__(self, example):
wav_path, _, _ = example
sr = self.sample_rate
n_fft = self.n_fft
win_length = self.win_length
hop_length = self.hop_length
n_mels = self.n_mels
fmin = self.fmin
fmax = self.fmax
wav, loaded_sr = librosa.load(wav_path, sr=None)
assert loaded_sr == sr, "sample rate does not match, resampling applied"
# Pad audio to the right size.
frames = int(np.ceil(float(wav.size) / hop_length))
fft_padding = (n_fft - hop_length) // 2 # sound
desired_length = frames * hop_length + fft_padding * 2
pad_amount = (desired_length - wav.size) // 2
if wav.size % 2 == 0:
wav = np.pad(wav, (pad_amount, pad_amount), mode='reflect')
else:
wav = np.pad(wav, (pad_amount, pad_amount + 1), mode='reflect')
# Normalize audio.
wav = wav / np.abs(wav).max() * 0.999
# Compute mel-spectrogram.
# Turn center to False to prevent internal padding.
spectrogram = librosa.core.stft(
wav,
hop_length=hop_length,
win_length=win_length,
n_fft=n_fft,
center=False)
spectrogram_magnitude = np.abs(spectrogram)
# Compute mel-spectrograms.
mel_filter_bank = librosa.filters.mel(
sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
mel_spectrogram = np.dot(mel_filter_bank, spectrogram_magnitude)
# log scale mel_spectrogram.
mel_spectrogram = self.spec_normalizer.transform(mel_spectrogram)
# Extract the center of audio that corresponds to mel spectrograms.
audio = wav[fft_padding:-fft_padding]
assert mel_spectrogram.shape[1] * hop_length == audio.size
# there is no clipping here
return audio, mel_spectrogram
def create_dataset(config, input_dir, output_dir):
input_dir = Path(input_dir).expanduser()
dataset = LJSpeechMetaData(input_dir)
output_dir = Path(output_dir).expanduser()
output_dir.mkdir(exist_ok=True)
transform = Transform(config.sample_rate, config.n_fft, config.win_length,
config.hop_length, config.n_mels, config.fmin,
config.fmax)
file_names = []
for example in tqdm.tqdm(dataset):
fname, _, _ = example
base_name = os.path.splitext(os.path.basename(fname))[0]
wav_dir = output_dir / "wav"
mel_dir = output_dir / "mel"
wav_dir.mkdir(exist_ok=True)
mel_dir.mkdir(exist_ok=True)
audio, mel = transform(example)
np.save(str(wav_dir / base_name), audio)
np.save(str(mel_dir / base_name), mel)
file_names.append((base_name, mel.shape[-1], audio.shape[-1]))
meta_data = pd.DataFrame.from_records(file_names)
meta_data.to_csv(
str(output_dir / "metadata.csv"), sep="\t", index=None, header=None)
print("saved meta data in to {}".format(
os.path.join(output_dir, "metadata.csv")))
print("Done!")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="create dataset")
parser.add_argument(
"--config",
type=str,
metavar="FILE",
help="extra config to overwrite the default config")
parser.add_argument(
"--input", type=str, help="path of the ljspeech dataset")
parser.add_argument(
"--output", type=str, help="path to save output dataset")
parser.add_argument(
"--opts",
nargs=argparse.REMAINDER,
help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
)
parser.add_argument(
"-v", "--verbose", action="store_true", help="print msg")
config = get_cfg_defaults()
args = parser.parse_args()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
if args.verbose:
print(config.data)
print(args)
create_dataset(config.data, args.input, args.output)