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193 lines
6.5 KiB
193 lines
6.5 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 logging
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import os
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from pathlib import Path
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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 jit
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from paddle.static import InputSpec
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from yacs.config import CfgNode
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Inference
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from paddlespeech.t2s.models.melgan import MelGANGenerator
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from paddlespeech.t2s.models.melgan import MelGANInference
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from paddlespeech.t2s.modules.normalizer import ZScore
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def evaluate(args, fastspeech2_config, melgan_config):
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# dataloader has been too verbose
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logging.getLogger("DataLoader").disabled = True
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# construct dataset for evaluation
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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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with open(args.phones_dict, "r") as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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print("vocab_size:", vocab_size)
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odim = fastspeech2_config.n_mels
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model = FastSpeech2(
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idim=vocab_size, odim=odim, **fastspeech2_config["model"])
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model.set_state_dict(
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paddle.load(args.fastspeech2_checkpoint)["main_params"])
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model.eval()
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vocoder = MelGANGenerator(**melgan_config["generator_params"])
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vocoder.set_state_dict(
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paddle.load(args.melgan_checkpoint)["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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frontend = Frontend(phone_vocab_path=args.phones_dict)
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print("frontend done!")
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stat = np.load(args.fastspeech2_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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fastspeech2_normalizer = ZScore(mu, std)
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stat = np.load(args.melgan_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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pwg_normalizer = ZScore(mu, std)
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fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model)
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fastspeech2_inference.eval()
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fastspeech2_inference = jit.to_static(
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fastspeech2_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
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paddle.jit.save(fastspeech2_inference,
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os.path.join(args.inference_dir, "fastspeech2"))
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fastspeech2_inference = paddle.jit.load(
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os.path.join(args.inference_dir, "fastspeech2"))
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mb_melgan_inference = MelGANInference(pwg_normalizer, vocoder)
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mb_melgan_inference.eval()
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mb_melgan_inference = jit.to_static(
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mb_melgan_inference,
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input_spec=[
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InputSpec([-1, 80], dtype=paddle.float32),
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])
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paddle.jit.save(mb_melgan_inference,
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os.path.join(args.inference_dir, "mb_melgan"))
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mb_melgan_inference = paddle.jit.load(
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os.path.join(args.inference_dir, "mb_melgan"))
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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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for utt_id, sentence in sentences:
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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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flags = 0
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for part_phone_ids in phone_ids:
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with paddle.no_grad():
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mel = fastspeech2_inference(part_phone_ids)
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temp_wav = mb_melgan_inference(mel)
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if flags == 0:
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wav = temp_wav
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flags = 1
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else:
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wav = paddle.concat([wav, temp_wav])
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sf.write(
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str(output_dir / (utt_id + ".wav")),
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wav.numpy(),
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samplerate=fastspeech2_config.fs)
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print(f"{utt_id} 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 fastspeech2 & parallel wavegan.")
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parser.add_argument(
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"--fastspeech2-config", type=str, help="fastspeech2 config file.")
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parser.add_argument(
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"--fastspeech2-checkpoint",
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type=str,
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help="fastspeech2 checkpoint to load.")
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parser.add_argument(
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"--fastspeech2-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
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)
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parser.add_argument(
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"--melgan-config", type=str, help="parallel wavegan config file.")
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parser.add_argument(
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"--melgan-checkpoint",
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type=str,
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help="parallel wavegan generator parameters to load.")
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parser.add_argument(
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"--melgan-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(
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"--phones-dict",
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type=str,
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default="phone_id_map.txt",
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help="phone vocabulary file.")
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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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"--inference-dir", type=str, help="dir to save inference models")
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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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parser.add_argument("--verbose", type=int, default=1, help="verbose.")
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args = parser.parse_args()
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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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with open(args.fastspeech2_config) as f:
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fastspeech2_config = CfgNode(yaml.safe_load(f))
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with open(args.melgan_config) as f:
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melgan_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(fastspeech2_config)
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print(melgan_config)
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evaluate(args, fastspeech2_config, melgan_config)
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if __name__ == "__main__":
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main()
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