PaddleSpeech/paddlespeech/t2s/exps/vits/synthesize.py

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from pathlib import Path

import jsonlines
import paddle
import soundfile as sf
import yaml
from timer import timer
from yacs.config import CfgNode

from paddlespeech.t2s.datasets.data_table import DataTable
from paddlespeech.t2s.models.vits import VITS


def evaluate(args):

    # construct dataset for evaluation
    with jsonlines.open(args.test_metadata, 'r') as reader:
        test_metadata = list(reader)
    # Init body.
    with open(args.config) as f:
        config = CfgNode(yaml.safe_load(f))

    print("========Args========")
    print(yaml.safe_dump(vars(args)))
    print("========Config========")
    print(config)

    fields = ["utt_id", "text"]

    test_dataset = DataTable(data=test_metadata, fields=fields)

    with open(args.phones_dict, "r") as f:
        phn_id = [line.strip().split() for line in f.readlines()]
    vocab_size = len(phn_id)
    print("vocab_size:", vocab_size)

    odim = config.n_fft // 2 + 1

    vits = VITS(idim=vocab_size, odim=odim, **config["model"])
    vits.set_state_dict(paddle.load(args.ckpt)["main_params"])
    vits.eval()

    output_dir = Path(args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    N = 0
    T = 0

    for datum in test_dataset:
        utt_id = datum["utt_id"]
        phone_ids = paddle.to_tensor(datum["text"])
        with timer() as t:
            with paddle.no_grad():
                out = vits.inference(text=phone_ids)
            wav = out["wav"]
            wav = wav.numpy()
            N += wav.size
            T += t.elapse
            speed = wav.size / t.elapse
            rtf = config.fs / speed
        print(
            f"{utt_id}, wave: {wav.size}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
        )
        sf.write(str(output_dir / (utt_id + ".wav")), wav, samplerate=config.fs)
        print(f"{utt_id} done!")
    print(f"generation speed: {N / T}Hz, RTF: {config.fs / (N / T) }")


def parse_args():
    # parse args and config
    parser = argparse.ArgumentParser(description="Synthesize with VITS")
    # model
    parser.add_argument(
        '--config', type=str, default=None, help='Config of VITS.')
    parser.add_argument(
        '--ckpt', type=str, default=None, help='Checkpoint file of VITS.')
    parser.add_argument(
        "--phones_dict", type=str, default=None, help="phone vocabulary file.")
    # other
    parser.add_argument(
        "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
    parser.add_argument("--test_metadata", type=str, help="test metadata.")
    parser.add_argument("--output_dir", type=str, help="output dir.")

    args = parser.parse_args()
    return args


def main():
    args = parse_args()

    if args.ngpu == 0:
        paddle.set_device("cpu")
    elif args.ngpu > 0:
        paddle.set_device("gpu")
    else:
        print("ngpu should >= 0 !")

    evaluate(args)


if __name__ == "__main__":
    main()