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329 lines
12 KiB
329 lines
12 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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from pathlib import Path
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from typing import Union
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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 paddlespeech.t2s.frontend.zh_frontend import Frontend
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Inference
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from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
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from paddlespeech.t2s.models.parallel_wavegan import PWGInference
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from paddlespeech.t2s.modules.normalizer import ZScore
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class StyleFastSpeech2Inference(FastSpeech2Inference):
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def __init__(self, normalizer, model, pitch_stats_path, energy_stats_path):
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super().__init__(normalizer, model)
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pitch_mean, pitch_std = np.load(pitch_stats_path)
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self.pitch_mean = paddle.to_tensor(pitch_mean)
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self.pitch_std = paddle.to_tensor(pitch_std)
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energy_mean, energy_std = np.load(energy_stats_path)
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self.energy_mean = paddle.to_tensor(energy_mean)
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self.energy_std = paddle.to_tensor(energy_std)
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def denorm(self, data, mean, std):
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return data * std + mean
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def norm(self, data, mean, std):
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return (data - mean) / std
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def forward(self,
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text: paddle.Tensor,
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durations: Union[paddle.Tensor, np.ndarray]=None,
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durations_scale: Union[int, float]=None,
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durations_bias: Union[int, float]=None,
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pitch: Union[paddle.Tensor, np.ndarray]=None,
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pitch_scale: Union[int, float]=None,
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pitch_bias: Union[int, float]=None,
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energy: Union[paddle.Tensor, np.ndarray]=None,
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energy_scale: Union[int, float]=None,
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energy_bias: Union[int, float]=None,
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robot: bool=False):
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"""
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Parameters
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----------
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text : Tensor(int64)
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Input sequence of characters (T,).
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speech : Tensor, optional
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Feature sequence to extract style (N, idim).
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durations : paddle.Tensor/np.ndarray, optional (int64)
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Groundtruth of duration (T,), this will overwrite the set of durations_scale and durations_bias
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durations_scale: int/float, optional
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durations_bias: int/float, optional
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pitch : paddle.Tensor/np.ndarray, optional
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Groundtruth of token-averaged pitch (T, 1), this will overwrite the set of pitch_scale and pitch_bias
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pitch_scale: int/float, optional
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In denormed HZ domain.
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pitch_bias: int/float, optional
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In denormed HZ domain.
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energy : paddle.Tensor/np.ndarray, optional
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Groundtruth of token-averaged energy (T, 1), this will overwrite the set of energy_scale and energy_bias
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energy_scale: int/float, optional
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In denormed domain.
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energy_bias: int/float, optional
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In denormed domain.
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robot : bool, optional
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Weather output robot style
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Returns
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----------
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Tensor
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Output sequence of features (L, odim).
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"""
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normalized_mel, d_outs, p_outs, e_outs = self.acoustic_model.inference(
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text, durations=None, pitch=None, energy=None)
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# priority: groundtruth > scale/bias > previous output
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# set durations
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if isinstance(durations, np.ndarray):
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durations = paddle.to_tensor(durations)
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elif isinstance(durations, paddle.Tensor):
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durations = durations
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elif durations_scale or durations_bias:
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durations_scale = durations_scale if durations_scale is not None else 1
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durations_bias = durations_bias if durations_bias is not None else 0
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durations = durations_scale * d_outs + durations_bias
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else:
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durations = d_outs
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if robot:
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# set normed pitch to zeros have the same effect with set denormd ones to mean
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pitch = paddle.zeros(p_outs.shape)
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# set pitch, can overwrite robot set
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if isinstance(pitch, np.ndarray):
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pitch = paddle.to_tensor(pitch)
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elif isinstance(pitch, paddle.Tensor):
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pitch = pitch
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elif pitch_scale or pitch_bias:
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pitch_scale = pitch_scale if pitch_scale is not None else 1
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pitch_bias = pitch_bias if pitch_bias is not None else 0
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p_Hz = paddle.exp(
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self.denorm(p_outs, self.pitch_mean, self.pitch_std))
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p_HZ = pitch_scale * p_Hz + pitch_bias
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pitch = self.norm(paddle.log(p_HZ), self.pitch_mean, self.pitch_std)
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else:
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pitch = p_outs
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# set energy
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if isinstance(energy, np.ndarray):
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energy = paddle.to_tensor(energy)
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elif isinstance(energy, paddle.Tensor):
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energy = energy
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elif energy_scale or energy_bias:
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energy_scale = energy_scale if energy_scale is not None else 1
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energy_bias = energy_bias if energy_bias is not None else 0
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e_dnorm = self.denorm(e_outs, self.energy_mean, self.energy_std)
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e_dnorm = energy_scale * e_dnorm + energy_bias
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energy = self.norm(e_dnorm, self.energy_mean, self.energy_std)
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else:
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energy = e_outs
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normalized_mel, d_outs, p_outs, e_outs = self.acoustic_model.inference(
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text,
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durations=durations,
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pitch=pitch,
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energy=energy,
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use_teacher_forcing=True)
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logmel = self.normalizer.inverse(normalized_mel)
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return logmel
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def evaluate(args, fastspeech2_config, pwg_config):
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# construct dataset for evaluation
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sentences = []
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with open(args.text, 'rt') as f:
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for line in f:
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utt_id, sentence = line.strip().split()
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sentences.append((utt_id, sentence))
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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 = fastspeech2_config.n_mels
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model = FastSpeech2(
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idim=vocab_size, odim=odim, **fastspeech2_config["model"])
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model.set_state_dict(
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paddle.load(args.fastspeech2_checkpoint)["main_params"])
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model.eval()
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vocoder = PWGGenerator(**pwg_config["generator_params"])
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vocoder.set_state_dict(paddle.load(args.pwg_checkpoint)["generator_params"])
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vocoder.remove_weight_norm()
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vocoder.eval()
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print("model done!")
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frontend = Frontend(phone_vocab_path=args.phones_dict)
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print("frontend done!")
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stat = np.load(args.fastspeech2_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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fastspeech2_normalizer = ZScore(mu, std)
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stat = np.load(args.pwg_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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pwg_normalizer = ZScore(mu, std)
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fastspeech2_inference = StyleFastSpeech2Inference(
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fastspeech2_normalizer, model, args.fastspeech2_pitch_stat,
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args.fastspeech2_energy_stat)
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fastspeech2_inference.eval()
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pwg_inference = PWGInference(pwg_normalizer, vocoder)
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pwg_inference.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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styles = ["normal", "robot", "1.2xspeed", "0.8xspeed", "child_voice"]
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for style in styles:
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robot = False
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durations = None
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durations_scale = None
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durations_bias = None
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pitch = None
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pitch_scale = None
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pitch_bias = None
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energy = None
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energy_scale = None
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energy_bias = None
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if style == "robot":
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# all tones in phones be `1`
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# all pitch should be the same, we use mean here
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robot = True
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if style == "1.2xspeed":
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durations_scale = 1 / 1.2
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if style == "0.8xspeed":
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durations_scale = 1 / 0.8
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if style == "child_voice":
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pitch_scale = 1.3
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sub_output_dir = output_dir / style
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sub_output_dir.mkdir(parents=True, exist_ok=True)
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for utt_id, sentence in sentences:
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input_ids = frontend.get_input_ids(
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sentence, merge_sentences=True, robot=robot)
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phone_ids = input_ids["phone_ids"][0]
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with paddle.no_grad():
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mel = fastspeech2_inference(
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phone_ids,
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durations=durations,
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durations_scale=durations_scale,
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durations_bias=durations_bias,
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pitch=pitch,
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pitch_scale=pitch_scale,
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pitch_bias=pitch_bias,
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energy=energy,
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energy_scale=energy_scale,
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energy_bias=energy_bias,
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robot=robot)
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wav = pwg_inference(mel)
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sf.write(
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str(sub_output_dir / (utt_id + ".wav")),
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wav.numpy(),
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samplerate=fastspeech2_config.fs)
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print(f"{style}_{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 fastspeech2 & parallel wavegan.")
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parser.add_argument(
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"--fastspeech2-config", type=str, help="fastspeech2 config file.")
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parser.add_argument(
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"--fastspeech2-checkpoint",
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type=str,
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help="fastspeech2 checkpoint to load.")
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parser.add_argument(
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"--fastspeech2-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training fastspeech2."
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)
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parser.add_argument(
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"--fastspeech2-pitch-stat",
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type=str,
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help="mean and standard deviation used to normalize pitch when training fastspeech2"
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)
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parser.add_argument(
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"--fastspeech2-energy-stat",
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type=str,
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help="mean and standard deviation used to normalize energy when training fastspeech2."
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)
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parser.add_argument(
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"--pwg-config", type=str, help="parallel wavegan config file.")
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parser.add_argument(
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"--pwg-checkpoint",
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type=str,
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help="parallel wavegan generator parameters to load.")
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parser.add_argument(
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"--pwg-stat",
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type=str,
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help="mean and standard deviation used to normalize spectrogram when training parallel wavegan."
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)
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parser.add_argument(
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"--phones-dict",
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type=str,
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default="phone_id_map.txt",
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help="phone vocabulary file.")
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parser.add_argument(
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"--text",
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type=str,
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help="text to synthesize, a 'utt_id sentence' pair per line.")
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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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"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
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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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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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with open(args.fastspeech2_config) as f:
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fastspeech2_config = CfgNode(yaml.safe_load(f))
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with open(args.pwg_config) as f:
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pwg_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(fastspeech2_config)
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print(pwg_config)
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evaluate(args, fastspeech2_config, pwg_config)
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if __name__ == "__main__":
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main()
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