TianYuan
3 years ago
parent
fb0acd40a2
commit
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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from multiprocessing import cpu_count
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from multiprocessing import Pool
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from pathlib import Path
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import librosa
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import numpy as np
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import pandas as pd
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import tqdm
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import yaml
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from yacs.config import CfgNode
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from paddlespeech.t2s.data.get_feats import LogMelFBank
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from paddlespeech.t2s.datasets import CSMSCMetaData
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from paddlespeech.t2s.datasets import LJSpeechMetaData
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from paddlespeech.t2s.datasets.vocoder_batch_fn import encode_mu_law
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from paddlespeech.t2s.datasets.vocoder_batch_fn import float_2_label
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class Transform(object):
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def __init__(self, output_dir: Path, config):
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self.fs = config.fs
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self.peak_norm = config.peak_norm
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self.bits = config.model.bits
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self.mode = config.model.mode
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self.mu_law = config.mu_law
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self.wav_dir = output_dir / "wav"
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self.mel_dir = output_dir / "mel"
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self.wav_dir.mkdir(exist_ok=True)
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self.mel_dir.mkdir(exist_ok=True)
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self.mel_extractor = LogMelFBank(
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sr=config.fs,
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n_fft=config.n_fft,
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hop_length=config.n_shift,
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win_length=config.win_length,
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window=config.window,
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n_mels=config.n_mels,
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fmin=config.fmin,
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fmax=config.fmax)
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if self.mode != 'RAW' and self.mode != 'MOL':
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raise RuntimeError('Unknown mode value - ', self.mode)
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def __call__(self, example):
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wav_path, _, _ = example
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base_name = os.path.splitext(os.path.basename(wav_path))[0]
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# print("self.sample_rate:",self.sample_rate)
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wav, _ = librosa.load(wav_path, sr=self.fs)
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peak = np.abs(wav).max()
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if self.peak_norm or peak > 1.0:
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wav /= peak
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mel = self.mel_extractor.get_log_mel_fbank(wav).T
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if self.mode == 'RAW':
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if self.mu_law:
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quant = encode_mu_law(wav, mu=2**self.bits)
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else:
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quant = float_2_label(wav, bits=self.bits)
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elif self.mode == 'MOL':
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quant = float_2_label(wav, bits=16)
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mel = mel.astype(np.float32)
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audio = quant.astype(np.int64)
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np.save(str(self.wav_dir / base_name), audio)
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np.save(str(self.mel_dir / base_name), mel)
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return base_name, mel.shape[-1], audio.shape[-1]
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def create_dataset(config,
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input_dir,
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output_dir,
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nprocs: int=1,
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dataset_type: str="ljspeech"):
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input_dir = Path(input_dir).expanduser()
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'''
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LJSpeechMetaData.records: [filename, normalized text, speaker name(ljspeech)]
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CSMSCMetaData.records: [filename, normalized text, pinyin]
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'''
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if dataset_type == 'ljspeech':
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dataset = LJSpeechMetaData(input_dir)
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else:
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dataset = CSMSCMetaData(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(output_dir, config)
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file_names = []
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pool = Pool(processes=nprocs)
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for info in tqdm.tqdm(pool.imap(transform, dataset), total=len(dataset)):
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base_name, mel_len, audio_len = info
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file_names.append((base_name, mel_len, audio_len))
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meta_data = pd.DataFrame.from_records(file_names)
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meta_data.to_csv(
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str(output_dir / "metadata.csv"), sep="\t", index=None, header=None)
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print("saved meta data in to {}".format(
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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(
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"--config", type=str, help="config file to overwrite default config.")
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parser.add_argument(
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"--input", type=str, help="path of the ljspeech dataset")
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parser.add_argument(
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"--output", type=str, help="path to save output dataset")
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parser.add_argument(
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"--num-cpu",
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type=int,
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default=cpu_count() // 2,
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help="number of process.")
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parser.add_argument(
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"--dataset",
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type=str,
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default="ljspeech",
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help="The dataset to preprocess, ljspeech or csmsc")
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args = parser.parse_args()
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with open(args.config, 'rt') as f:
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config = CfgNode(yaml.safe_load(f))
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if args.dataset != "ljspeech" and args.dataset != "csmsc":
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raise RuntimeError('Unknown dataset - ', args.dataset)
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create_dataset(
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config,
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input_dir=args.input,
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output_dir=args.output,
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nprocs=args.num_cpu,
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dataset_type=args.dataset)
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