PaddleSpeech/paddlespeech/t2s/exps/fastspeech2/synthesize.py

# Copyright (c) 2021 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
import logging
from pathlib import Path

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

from paddlespeech.t2s.datasets.data_table import DataTable
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Inference
from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
from paddlespeech.t2s.models.parallel_wavegan import PWGInference
from paddlespeech.t2s.modules.normalizer import ZScore


def evaluate(args, fastspeech2_config, pwg_config):
    # dataloader has been too verbose
    logging.getLogger("DataLoader").disabled = True

    # construct dataset for evaluation
    with jsonlines.open(args.test_metadata, 'r') as reader:
        test_metadata = list(reader)

    fields = ["utt_id", "text"]

    spk_num = None
    if args.speaker_dict is not None:
        print("multiple speaker fastspeech2!")
        with open(args.speaker_dict, 'rt') as f:
            spk_id = [line.strip().split() for line in f.readlines()]
        spk_num = len(spk_id)
        fields += ["spk_id"]
    elif args.voice_cloning:
        print("voice cloning!")
        fields += ["spk_emb"]
    else:
        print("single speaker fastspeech2!")
    print("spk_num:", spk_num)

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

    odim = fastspeech2_config.n_mels
    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)

    model = FastSpeech2(
        idim=vocab_size,
        odim=odim,
        spk_num=spk_num,
        **fastspeech2_config["model"])

    model.set_state_dict(
        paddle.load(args.fastspeech2_checkpoint)["main_params"])
    model.eval()

    vocoder = PWGGenerator(**pwg_config["generator_params"])
    vocoder.set_state_dict(paddle.load(args.pwg_checkpoint)["generator_params"])
    vocoder.remove_weight_norm()
    vocoder.eval()
    print("model done!")

    stat = np.load(args.fastspeech2_stat)
    mu, std = stat
    mu = paddle.to_tensor(mu)
    std = paddle.to_tensor(std)
    fastspeech2_normalizer = ZScore(mu, std)

    stat = np.load(args.pwg_stat)
    mu, std = stat
    mu = paddle.to_tensor(mu)
    std = paddle.to_tensor(std)
    pwg_normalizer = ZScore(mu, std)

    fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model)
    pwg_inference = PWGInference(pwg_normalizer, vocoder)

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

    for datum in test_dataset:
        utt_id = datum["utt_id"]
        text = paddle.to_tensor(datum["text"])
        spk_emb = None
        spk_id = None
        if args.voice_cloning and "spk_emb" in datum:
            spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
        elif "spk_id" in datum:
            spk_id = paddle.to_tensor(datum["spk_id"])
        with paddle.no_grad():
            wav = pwg_inference(
                fastspeech2_inference(text, spk_id=spk_id, spk_emb=spk_emb))
        sf.write(
            str(output_dir / (utt_id + ".wav")),
            wav.numpy(),
            samplerate=fastspeech2_config.fs)
        print(f"{utt_id} done!")


def main():
    # parse args and config and redirect to train_sp
    parser = argparse.ArgumentParser(
        description="Synthesize with fastspeech2 & parallel wavegan.")
    parser.add_argument(
        "--fastspeech2-config", type=str, help="fastspeech2 config file.")
    parser.add_argument(
        "--fastspeech2-checkpoint",
        type=str,
        help="fastspeech2 checkpoint to load.")
    parser.add_argument(
        "--fastspeech2-stat",
        type=str,
        help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
    )
    parser.add_argument(
        "--pwg-config", type=str, help="parallel wavegan config file.")
    parser.add_argument(
        "--pwg-checkpoint",
        type=str,
        help="parallel wavegan generator parameters to load.")
    parser.add_argument(
        "--pwg-stat",
        type=str,
        help="mean and standard deviation used to normalize spectrogram when training parallel wavegan."
    )
    parser.add_argument(
        "--phones-dict", type=str, default=None, help="phone vocabulary file.")
    parser.add_argument(
        "--speaker-dict",
        type=str,
        default=None,
        help="speaker id map file for multiple speaker model.")

    def str2bool(str):
        return True if str.lower() == 'true' else False

    parser.add_argument(
        "--voice-cloning",
        type=str2bool,
        default=False,
        help="whether training voice cloning model.")
    parser.add_argument("--test-metadata", type=str, help="test metadata.")
    parser.add_argument("--output-dir", type=str, help="output dir.")
    parser.add_argument(
        "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
    parser.add_argument("--verbose", type=int, default=1, help="verbose.")

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

    with open(args.fastspeech2_config) as f:
        fastspeech2_config = CfgNode(yaml.safe_load(f))
    with open(args.pwg_config) as f:
        pwg_config = CfgNode(yaml.safe_load(f))

    print("========Args========")
    print(yaml.safe_dump(vars(args)))
    print("========Config========")
    print(fastspeech2_config)
    print(pwg_config)

    evaluate(args, fastspeech2_config, pwg_config)


if __name__ == "__main__":
    main()