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204 lines
6.9 KiB
204 lines
6.9 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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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.parallel_wavegan import PWGGenerator
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from paddlespeech.t2s.models.parallel_wavegan import PWGInference
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from paddlespeech.t2s.models.speedyspeech import SpeedySpeech
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from paddlespeech.t2s.models.speedyspeech import SpeedySpeechInference
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from paddlespeech.t2s.modules.normalizer import ZScore
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def evaluate(args, speedyspeech_config, pwg_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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with open(args.tones_dict, "r") as f:
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tone_id = [line.strip().split() for line in f.readlines()]
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tone_size = len(tone_id)
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print("tone_size:", tone_size)
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model = SpeedySpeech(
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vocab_size=vocab_size,
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tone_size=tone_size,
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**speedyspeech_config["model"])
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model.set_state_dict(
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paddle.load(args.speedyspeech_checkpoint)["main_params"])
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model.eval()
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vocoder = PWGGenerator(**pwg_config["generator_params"])
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vocoder.set_state_dict(paddle.load(args.pwg_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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stat = np.load(args.speedyspeech_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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speedyspeech_normalizer = ZScore(mu, std)
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stat = np.load(args.pwg_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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speedyspeech_inference = SpeedySpeechInference(speedyspeech_normalizer,
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model)
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speedyspeech_inference.eval()
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speedyspeech_inference = jit.to_static(
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speedyspeech_inference,
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input_spec=[
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InputSpec([-1], dtype=paddle.int64), InputSpec(
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[-1], dtype=paddle.int64)
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])
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paddle.jit.save(speedyspeech_inference,
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os.path.join(args.inference_dir, "speedyspeech"))
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speedyspeech_inference = paddle.jit.load(
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os.path.join(args.inference_dir, "speedyspeech"))
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pwg_inference = PWGInference(pwg_normalizer, vocoder)
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pwg_inference.eval()
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pwg_inference = jit.to_static(
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pwg_inference, input_spec=[
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InputSpec([-1, 80], dtype=paddle.float32),
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])
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paddle.jit.save(pwg_inference, os.path.join(args.inference_dir, "pwg"))
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pwg_inference = paddle.jit.load(os.path.join(args.inference_dir, "pwg"))
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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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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(
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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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flags = 0
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for i in range(len(phone_ids)):
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part_phone_ids = phone_ids[i]
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part_tone_ids = tone_ids[i]
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with paddle.no_grad():
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mel = speedyspeech_inference(part_phone_ids, part_tone_ids)
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temp_wav = pwg_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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output_dir / (utt_id + ".wav"),
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wav.numpy(),
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samplerate=speedyspeech_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 speedyspeech & parallel wavegan.")
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parser.add_argument(
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"--speedyspeech-config", type=str, help="config file for speedyspeech.")
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parser.add_argument(
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"--speedyspeech-checkpoint",
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type=str,
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help="speedyspeech checkpoint to load.")
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parser.add_argument(
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"--speedyspeech-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
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)
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parser.add_argument(
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"--pwg-config", type=str, help="config file for parallelwavegan.")
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parser.add_argument(
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"--pwg-checkpoint",
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type=str,
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help="parallel wavegan checkpoint to load.")
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parser.add_argument(
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"--pwg-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training speedyspeech."
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)
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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(
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"--phones-dict", type=str, default=None, help="phone vocabulary file.")
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parser.add_argument(
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"--tones-dict", type=str, default=None, help="tone vocabulary file.")
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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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args, _ = parser.parse_known_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.speedyspeech_config) as f:
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speedyspeech_config = CfgNode(yaml.safe_load(f))
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with open(args.pwg_config) as f:
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pwg_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(speedyspeech_config)
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print(pwg_config)
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evaluate(args, speedyspeech_config, pwg_config)
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if __name__ == "__main__":
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main()
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