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116 lines
4.0 KiB
116 lines
4.0 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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# remain for chains
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import argparse
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from pathlib import Path
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import soundfile as sf
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from paddle import inference
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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def main():
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parser = argparse.ArgumentParser(
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description="Paddle Infernce with speedyspeech & parallel wavegan.")
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parser.add_argument(
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"--inference-dir", type=str, help="dir to save inference models")
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parser.add_argument(
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"--text",
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type=str,
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help="text to synthesize, a 'utt_id sentence' pair per line")
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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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"--phones-dict",
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type=str,
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default="phones.txt",
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help="phone vocabulary file.")
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parser.add_argument(
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"--tones-dict",
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type=str,
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default="tones.txt",
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help="tone vocabulary file.")
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args, _ = parser.parse_known_args()
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frontend = Frontend(
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phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict)
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print("frontend done!")
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speedyspeech_config = inference.Config(
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str(Path(args.inference_dir) / "speedyspeech.pdmodel"),
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str(Path(args.inference_dir) / "speedyspeech.pdiparams"))
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speedyspeech_config.enable_use_gpu(100, 0)
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speedyspeech_config.enable_memory_optim()
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speedyspeech_predictor = inference.create_predictor(speedyspeech_config)
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pwg_config = inference.Config(
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str(Path(args.inference_dir) / "pwg.pdmodel"),
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str(Path(args.inference_dir) / "pwg.pdiparams"))
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pwg_config.enable_use_gpu(100, 0)
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pwg_config.enable_memory_optim()
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pwg_predictor = inference.create_predictor(pwg_config)
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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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sentences = []
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with open(args.text, 'rt') as f:
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for line in f:
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items = line.strip().split()
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utt_id = items[0]
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sentence = "".join(items[1:])
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sentences.append((utt_id, sentence))
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for utt_id, sentence in sentences:
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input_ids = frontend.get_input_ids(
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sentence, merge_sentences=True, get_tone_ids=True)
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phone_ids = input_ids["phone_ids"]
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tone_ids = input_ids["tone_ids"]
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phones = phone_ids[0].numpy()
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tones = tone_ids[0].numpy()
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input_names = speedyspeech_predictor.get_input_names()
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phones_handle = speedyspeech_predictor.get_input_handle(input_names[0])
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tones_handle = speedyspeech_predictor.get_input_handle(input_names[1])
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phones_handle.reshape(phones.shape)
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phones_handle.copy_from_cpu(phones)
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tones_handle.reshape(tones.shape)
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tones_handle.copy_from_cpu(tones)
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speedyspeech_predictor.run()
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output_names = speedyspeech_predictor.get_output_names()
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output_handle = speedyspeech_predictor.get_output_handle(
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output_names[0])
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output_data = output_handle.copy_to_cpu()
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input_names = pwg_predictor.get_input_names()
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mel_handle = pwg_predictor.get_input_handle(input_names[0])
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mel_handle.reshape(output_data.shape)
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mel_handle.copy_from_cpu(output_data)
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pwg_predictor.run()
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output_names = pwg_predictor.get_output_names()
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output_handle = pwg_predictor.get_output_handle(output_names[0])
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wav = output_data = output_handle.copy_to_cpu()
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sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000)
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print(f"{utt_id} done!")
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if __name__ == "__main__":
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main()
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