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143 lines
5.0 KiB
143 lines
5.0 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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import argparse
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import logging
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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 yacs.config import CfgNode
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from parakeet.datasets.data_table import DataTable
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from parakeet.models.transformer_tts import TransformerTTS
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from parakeet.models.transformer_tts import TransformerTTSInference
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from parakeet.models.waveflow import ConditionalWaveFlow
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from parakeet.modules.normalizer import ZScore
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from parakeet.utils import layer_tools
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def evaluate(args, acoustic_model_config, vocoder_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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with jsonlines.open(args.test_metadata, 'r') as reader:
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test_metadata = list(reader)
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test_dataset = DataTable(data=test_metadata, fields=["utt_id", "text"])
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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 = acoustic_model_config.n_mels
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model = TransformerTTS(
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idim=vocab_size, odim=odim, **acoustic_model_config["model"])
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model.set_state_dict(
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paddle.load(args.transformer_tts_checkpoint)["main_params"])
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model.eval()
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# remove ".pdparams" in waveflow_checkpoint
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vocoder_checkpoint_path = args.waveflow_checkpoint[:-9] if args.waveflow_checkpoint.endswith(
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".pdparams") else args.waveflow_checkpoint
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vocoder = ConditionalWaveFlow.from_pretrained(vocoder_config,
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vocoder_checkpoint_path)
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layer_tools.recursively_remove_weight_norm(vocoder)
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vocoder.eval()
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print("model done!")
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stat = np.load(args.transformer_tts_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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transformer_tts_normalizer = ZScore(mu, std)
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transformer_tts_inference = TransformerTTSInference(
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transformer_tts_normalizer, model)
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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 datum in test_dataset:
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utt_id = datum["utt_id"]
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text = paddle.to_tensor(datum["text"])
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with paddle.no_grad():
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mel = transformer_tts_inference(text)
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# mel shape is (T, feats) and waveflow's input shape is (batch, feats, T)
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mel = mel.unsqueeze(0).transpose([0, 2, 1])
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# wavflow's output shape is (B, T)
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wav = vocoder.infer(mel)[0]
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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=acoustic_model_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 transformer tts & waveflow.")
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parser.add_argument(
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"--transformer-tts-config",
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type=str,
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help="transformer tts config file.")
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parser.add_argument(
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"--transformer-tts-checkpoint",
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type=str,
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help="transformer tts checkpoint to load.")
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parser.add_argument(
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"--transformer-tts-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training transformer tts."
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)
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parser.add_argument(
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"--waveflow-config", type=str, help="waveflow config file.")
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# not normalize when training waveflow
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parser.add_argument(
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"--waveflow-checkpoint", type=str, help="waveflow checkpoint to load.")
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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("--test-metadata", type=str, help="test metadata.")
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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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"--device", type=str, default="gpu", help="device type to use.")
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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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paddle.set_device(args.device)
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with open(args.transformer_tts_config) as f:
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transformer_tts_config = CfgNode(yaml.safe_load(f))
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with open(args.waveflow_config) as f:
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waveflow_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(transformer_tts_config)
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print(waveflow_config)
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evaluate(args, transformer_tts_config, waveflow_config)
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if __name__ == "__main__":
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main()
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