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108 lines
3.4 KiB
108 lines
3.4 KiB
# 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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from pathlib import Path
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import jsonlines
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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 paddle import distributed as dist
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from timer import timer
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from yacs.config import CfgNode
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from paddlespeech.t2s.datasets.data_table import DataTable
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from paddlespeech.t2s.models.wavernn import WaveRNN
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def main():
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parser = argparse.ArgumentParser(description="Synthesize with WaveRNN.")
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parser.add_argument("--config", type=str, help="Vocoder config file.")
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parser.add_argument("--checkpoint", type=str, help="snapshot to load.")
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parser.add_argument("--test-metadata", type=str, help="dev data.")
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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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"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
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args = parser.parse_args()
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with open(args.config) as f:
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config = CfgNode(yaml.safe_load(f))
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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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print(
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f"master see the word size: {dist.get_world_size()}, from pid: {os.getpid()}"
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)
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if args.ngpu == 0:
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paddle.set_device("cpu")
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elif args.ngpu > 0:
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paddle.set_device("gpu")
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else:
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print("ngpu should >= 0 !")
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model = WaveRNN(**config["model"])
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state_dict = paddle.load(args.checkpoint)
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model.set_state_dict(state_dict["main_params"])
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model.eval()
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with jsonlines.open(args.test_metadata, 'r') as reader:
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metadata = list(reader)
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test_dataset = DataTable(
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metadata,
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fields=['utt_id', 'feats'],
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converters={
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'utt_id': None,
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'feats': np.load,
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})
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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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N = 0
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T = 0
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for example in test_dataset:
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utt_id = example['utt_id']
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mel = example['feats']
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mel = paddle.to_tensor(mel) # (T, C)
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with timer() as t:
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with paddle.no_grad():
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wav = model.generate(
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c=mel,
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batched=config.inference.gen_batched,
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target=config.inference.target,
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overlap=config.inference.overlap,
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mu_law=config.mu_law,
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gen_display=False)
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wav = wav.numpy()
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N += wav.size
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T += t.elapse
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speed = wav.size / t.elapse
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rtf = config.fs / speed
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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sf.write(str(output_dir / (utt_id + ".wav")), wav, samplerate=config.fs)
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print(f"generation speed: {N / T}Hz, RTF: {config.fs / (N / T) }")
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if __name__ == "__main__":
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main()
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