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154 lines
4.7 KiB
154 lines
4.7 KiB
# Copyright (c) 2022 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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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 timer import timer
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from yacs.config import CfgNode
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from paddlespeech.t2s.datasets.data_table import DataTable
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from paddlespeech.t2s.models.vits import VITS
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from paddlespeech.t2s.utils import str2bool
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def evaluate(args):
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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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# Init body.
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with open(args.config) as f:
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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(config)
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fields = ["utt_id", "text"]
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converters = {}
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spk_num = None
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if args.speaker_dict is not None:
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print("multiple speaker vits!")
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with open(args.speaker_dict, 'rt') as f:
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spk_id = [line.strip().split() for line in f.readlines()]
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spk_num = len(spk_id)
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fields += ["spk_id"]
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elif args.voice_cloning:
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print("Evaluating voice cloning!")
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fields += ["spk_emb"]
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else:
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print("single speaker vits!")
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print("spk_num:", spk_num)
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test_dataset = DataTable(
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data=test_metadata,
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fields=fields,
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converters=converters, )
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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 = config.n_fft // 2 + 1
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config["model"]["generator_params"]["spks"] = spk_num
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vits = VITS(idim=vocab_size, odim=odim, **config["model"])
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vits.set_state_dict(paddle.load(args.ckpt)["main_params"])
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vits.eval()
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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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N = 0
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T = 0
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for datum in test_dataset:
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utt_id = datum["utt_id"]
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phone_ids = paddle.to_tensor(datum["text"])
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with timer() as t:
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with paddle.no_grad():
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spk_emb = None
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spk_id = None
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# multi speaker
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if args.voice_cloning and "spk_emb" in datum:
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spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
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elif "spk_id" in datum:
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spk_id = paddle.to_tensor(datum["spk_id"])
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out = vits.inference(
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text=phone_ids, sids=spk_id, spembs=spk_emb)
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wav = out["wav"]
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wav = wav.numpy()
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N += wav.size
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T += t.elapse
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speed = wav.size / t.elapse
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rtf = config.fs / speed
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print(
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f"{utt_id}, wave: {wav.size}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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sf.write(str(output_dir / (utt_id + ".wav")), wav, samplerate=config.fs)
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print(f"{utt_id} done!")
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print(f"generation speed: {N / T}Hz, RTF: {config.fs / (N / T) }")
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def parse_args():
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# parse args and config
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parser = argparse.ArgumentParser(description="Synthesize with VITS")
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# model
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parser.add_argument(
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'--config', type=str, default=None, help='Config of VITS.')
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parser.add_argument(
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'--ckpt', type=str, default=None, help='Checkpoint file of VITS.')
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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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"--speaker_dict", type=str, default=None, help="speaker id map file.")
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parser.add_argument(
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"--voice-cloning",
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type=str2bool,
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default=False,
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help="whether training voice cloning model.")
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# other
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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("--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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args = parser.parse_args()
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return args
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def main():
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args = 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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evaluate(args)
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if __name__ == "__main__":
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main()
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