112 lines
3.4 KiB
Python
112 lines
3.4 KiB
Python
# Copyright (c) 2021 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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import logging
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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 paddle
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import soundfile as sf
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import yaml
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from parakeet.data.get_feats import LogMelFBank
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from parakeet.models.parallel_wavegan import PWGGenerator, PWGInference
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from parakeet.modules.normalizer import ZScore
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from config import get_cfg_default
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def evaluate(args, config):
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# dataloader has been too verbose
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logging.getLogger("DataLoader").disabled = True
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vocoder = PWGGenerator(**config["generator_params"])
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state_dict = paddle.load(args.checkpoint)
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vocoder.set_state_dict(state_dict["generator_params"])
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vocoder.remove_weight_norm()
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vocoder.eval()
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print("model done!")
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stat = np.load(args.stat)
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mu, std = stat
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mu = paddle.to_tensor(mu)
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std = paddle.to_tensor(std)
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normalizer = ZScore(mu, std)
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pwg_inference = PWGInference(normalizer, vocoder)
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input_dir = Path(args.input_dir)
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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mel_extractor = LogMelFBank(
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sr=config.sr,
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n_fft=config.n_fft,
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hop_length=config.hop_length,
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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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for utt_name in os.listdir(input_dir):
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wav, _ = librosa.load(str(input_dir / utt_name), sr=config.sr)
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# extract mel feats
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mel = mel_extractor.get_log_mel_fbank(wav)
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mel = paddle.to_tensor(mel)
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gen_wav = pwg_inference(mel)
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sf.write(
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str(output_dir / ("gen_" + utt_name)),
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gen_wav.numpy(),
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samplerate=config.sr)
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print(f"{utt_name} done!")
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def main():
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# parse args and config and redirect to train_sp
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parser = argparse.ArgumentParser(
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description="Synthesize with parallel wavegan.")
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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("--checkpoint", type=str, help="snapshot to load.")
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parser.add_argument(
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"--stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training parallel wavegan."
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)
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parser.add_argument("--input-dir", type=str, help="input dir of wavs.")
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parser.add_argument("--output-dir", type=str, help="output dir.")
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parser.add_argument(
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"--device", type=str, default="gpu", help="device to run.")
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parser.add_argument("--verbose", type=int, default=1, help="verbose.")
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args = parser.parse_args()
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config = get_cfg_default()
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if args.config:
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config.merge_from_file(args.config)
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print("========Args========")
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print(yaml.safe_dump(vars(args)))
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print("========Config========")
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print(config)
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evaluate(args, config)
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if __name__ == "__main__":
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main()
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