Hui Zhang
3 years ago
committed by
GitHub
commit
04d84a87ae
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#!/bin/bash
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train_output_path=$1
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python3 ${BIN_DIR}/inference.py \
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--inference-dir=${train_output_path}/inference \
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--text=${BIN_DIR}/../sentences.txt \
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--output-dir=${train_output_path}/pd_infer_out \
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--phones-dict=dump/phone_id_map.txt
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@ -0,0 +1,133 @@
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# 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 soundfile as sf
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from paddle import inference
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from parakeet.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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"--enable-auto-log", action="store_true", help="use auto log")
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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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args, _ = parser.parse_known_args()
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frontend = Frontend(phone_vocab_path=args.phones_dict)
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print("frontend done!")
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fastspeech2_config = inference.Config(
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str(Path(args.inference_dir) / "fastspeech2.pdmodel"),
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str(Path(args.inference_dir) / "fastspeech2.pdiparams"))
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fastspeech2_config.enable_use_gpu(50, 0)
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# This line must be commented, if not, it will OOM
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# fastspeech2_config.enable_memory_optim()
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fastspeech2_predictor = inference.create_predictor(fastspeech2_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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if args.enable_auto_log:
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import auto_log
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os.makedirs("output", exist_ok=True)
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pid = os.getpid()
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logger = auto_log.AutoLogger(
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model_name="fastspeech2",
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model_precision='float32',
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batch_size=1,
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data_shape="dynamic",
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save_path="./output/auto_log.log",
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inference_config=fastspeech2_config,
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pids=pid,
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process_name=None,
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gpu_ids=0,
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time_keys=['preprocess_time', 'inference_time', 'postprocess_time'],
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warmup=0)
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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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utt_id, sentence = line.strip().split()
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sentences.append((utt_id, sentence))
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for utt_id, sentence in sentences:
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if args.enable_auto_log:
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logger.times.start()
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input_ids = frontend.get_input_ids(sentence, merge_sentences=True)
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phone_ids = input_ids["phone_ids"]
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phones = phone_ids[0].numpy()
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if args.enable_auto_log:
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logger.times.stamp()
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input_names = fastspeech2_predictor.get_input_names()
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phones_handle = fastspeech2_predictor.get_input_handle(input_names[0])
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phones_handle.reshape(phones.shape)
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phones_handle.copy_from_cpu(phones)
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fastspeech2_predictor.run()
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output_names = fastspeech2_predictor.get_output_names()
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output_handle = fastspeech2_predictor.get_output_handle(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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if args.enable_auto_log:
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logger.times.stamp()
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sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000)
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if args.enable_auto_log:
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logger.times.end(stamp=True)
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print(f"{utt_id} done!")
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if args.enable_auto_log:
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logger.report()
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if __name__ == "__main__":
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main()
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