PaddleSpeech/demos/style_fs2/style_syn.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
from pathlib import Path

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

from paddlespeech.t2s.frontend.zh_frontend import Frontend
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


class StyleFastSpeech2Inference(FastSpeech2Inference):
    def __init__(self, normalizer, model, pitch_stats_path, energy_stats_path):
        super().__init__(normalizer, model)
        self.pitch_mean, self.pitch_std = np.load(pitch_stats_path)
        self.pitch_mean = paddle.to_tensor(self.pitch_mean)
        self.pitch_std = paddle.to_tensor(self.pitch_std)
        self.energy_mean, self.energy_std = np.load(energy_stats_path)
        self.energy_mean = paddle.to_tensor(self.energy_mean)
        self.energy_std = paddle.to_tensor(self.energy_std)

    def denorm(self, data, mean, std):
        return data * std + mean

    def norm(self, data, mean, std):
        return (data - mean) / std

    def forward(self,
                text,
                durations=None,
                pitch=None,
                energy=None,
                robot=False):
        """
        Parameters
        ----------
        text : Tensor(int64)
            Input sequence of characters (T,).
        speech : Tensor, optional
            Feature sequence to extract style (N, idim).
        durations : Tensor, optional (int64)
            Groundtruth of duration (T,) or 
            float/int (represents ratio)
        pitch : Tensor, optional
            Groundtruth of token-averaged pitch (T, 1) or
            float/int (represents ratio)
        energy : Tensor, optional
            Groundtruth of token-averaged energy (T, 1) or 
            float (represents ratio)
        robot : bool, optional
            Weather output robot style
        Returns
        ----------
        Tensor
            Output sequence of features (L, odim).
        """
        normalized_mel, d_outs, p_outs, e_outs = self.acoustic_model.inference(
            text, durations=None, pitch=None, energy=None)

        # set duration
        if isinstance(durations, float):
            durations = durations * d_outs
        elif isinstance(durations, paddle.Tensor):
            durations = durations
        else:
            durations = d_outs

        if robot:
            # set normed pitch to zeros have the same effect with set denormd ones to mean
            pitch = paddle.zeros(p_outs.shape)

        # set pitch, can overwrite robot set    
        if isinstance(pitch, (int, float)):
            p_Hz = paddle.exp(
                self.denorm(p_outs, self.pitch_mean, self.pitch_std))
            p_HZ = pitch * p_Hz
            pitch = self.norm(paddle.log(p_HZ), self.pitch_mean, self.pitch_std)
        elif isinstance(pitch, paddle.Tensor):
            pitch = pitch
        else:
            pitch = p_outs

        # set energy
        if isinstance(energy, (int, float)):
            e_dnorm = self.denorm(e_outs, self.energy_mean, self.energy_std)
            e_dnorm = energy * e_dnorm
            energy = self.norm(e_dnorm, self.energy_mean, self.energy_std)
        elif isinstance(energy, paddle.Tensor):
            energy = energy
        else:
            energy = e_outs

        normalized_mel, d_outs, p_outs, e_outs = self.acoustic_model.inference(
            text,
            durations=durations,
            pitch=pitch,
            energy=energy,
            use_teacher_forcing=True)

        logmel = self.normalizer.inverse(normalized_mel)
        return logmel


def evaluate(args, fastspeech2_config, pwg_config):

    # construct dataset for evaluation
    sentences = []
    with open(args.text, 'rt') as f:
        for line in f:
            utt_id, sentence = line.strip().split()
            sentences.append((utt_id, sentence))

    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 = fastspeech2_config.n_mels
    model = FastSpeech2(
        idim=vocab_size, odim=odim, **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!")

    frontend = Frontend(phone_vocab_path=args.phones_dict)
    print("frontend 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 = StyleFastSpeech2Inference(
        fastspeech2_normalizer, model, args.fastspeech2_pitch_stat,
        args.fastspeech2_energy_stat)
    fastspeech2_inference.eval()

    pwg_inference = PWGInference(pwg_normalizer, vocoder)
    pwg_inference.eval()

    output_dir = Path(args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    styles = ["normal", "robot", "1.2xspeed", "0.8xspeed", "child_voice"]
    for style in styles:
        robot = False
        durations = None
        pitch = None
        energy = None

        if style == "robot":
            # all tones in phones be `1`
            # all pitch should be the same, we use mean here
            robot = True
        if style == "1.2xspeed":
            durations = 1 / 1.2
        if style == "0.8xspeed":
            durations = 1 / 0.8
        if style == "child_voice":
            pitch = 1.3
        sub_output_dir = output_dir / style
        sub_output_dir.mkdir(parents=True, exist_ok=True)
        for utt_id, sentence in sentences:
            input_ids = frontend.get_input_ids(
                sentence, merge_sentences=True, robot=robot)
            phone_ids = input_ids["phone_ids"][0]

            with paddle.no_grad():
                mel = fastspeech2_inference(
                    phone_ids,
                    durations=durations,
                    pitch=pitch,
                    energy=energy,
                    robot=robot)
                wav = pwg_inference(mel)

            sf.write(
                str(sub_output_dir / (utt_id + ".wav")),
                wav.numpy(),
                samplerate=fastspeech2_config.fs)
            print(f"{style}_{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(
        "--fastspeech2-pitch-stat",
        type=str,
        help="mean and standard deviation used to normalize pitch when training fastspeech2"
    )
    parser.add_argument(
        "--fastspeech2-energy-stat",
        type=str,
        help="mean and standard deviation used to normalize energy 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="phone_id_map.txt",
        help="phone vocabulary file.")
    parser.add_argument(
        "--text",
        type=str,
        help="text to synthesize, a 'utt_id sentence' pair per line.")
    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()