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module.py
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# 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 importlib
import os
from typing import List
import numpy as np
import paddle
import paddle.nn as nn
from paddlehub.env import MODULE_HOME
from paddlehub.module.module import moduleinfo
from paddlehub.utils.log import logger
from paddlenlp.data import Pad
from parakeet.models import ConditionalWaveFlow, Tacotron2
from parakeet.models.lstm_speaker_encoder import LSTMSpeakerEncoder
import soundfile as sf
from .audio_processor import SpeakerVerificationPreprocessor
from .chinese_g2p import convert_sentence
from .preprocess_transcription import voc_phones, voc_tones, phone_pad_token, tone_pad_token
@moduleinfo(
name="lstm_tacotron2",
version="1.0.0",
summary="",
author="paddlepaddle",
author_email="",
type="audio/voice_cloning",
)
class VoiceCloner(nn.Layer):
def __init__(self, speaker_audio: str = None, output_dir: str = './'):
super(VoiceCloner, self).__init__()
self.sample_rate = 22050 # Hyper params for the following model ckpts.
speaker_encoder_ckpt = os.path.join(MODULE_HOME, 'lstm_tacotron2', 'assets',
'ge2e_ckpt_0.3/step-3000000.pdparams')
synthesizer_ckpt = os.path.join(MODULE_HOME, 'lstm_tacotron2', 'assets',
'tacotron2_aishell3_ckpt_0.3/step-450000.pdparams')
vocoder_ckpt = os.path.join(MODULE_HOME, 'lstm_tacotron2', 'assets',
'waveflow_ljspeech_ckpt_0.3/step-2000000.pdparams')
# Speaker encoder
self.speaker_processor = SpeakerVerificationPreprocessor(
sampling_rate=16000,
audio_norm_target_dBFS=-30,
vad_window_length=30,
vad_moving_average_width=8,
vad_max_silence_length=6,
mel_window_length=25,
mel_window_step=10,
n_mels=40,
partial_n_frames=160,
min_pad_coverage=0.75,
partial_overlap_ratio=0.5)
self.speaker_encoder = LSTMSpeakerEncoder(n_mels=40, num_layers=3, hidden_size=256, output_size=256)
self.speaker_encoder.set_state_dict(paddle.load(speaker_encoder_ckpt))
self.speaker_encoder.eval()
# Voice synthesizer
self.synthesizer = Tacotron2(
vocab_size=68,
n_tones=10,
d_mels=80,
d_encoder=512,
encoder_conv_layers=3,
encoder_kernel_size=5,
d_prenet=256,
d_attention_rnn=1024,
d_decoder_rnn=1024,
attention_filters=32,
attention_kernel_size=31,
d_attention=128,
d_postnet=512,
postnet_kernel_size=5,
postnet_conv_layers=5,
reduction_factor=1,
p_encoder_dropout=0.5,
p_prenet_dropout=0.5,
p_attention_dropout=0.1,
p_decoder_dropout=0.1,
p_postnet_dropout=0.5,
d_global_condition=256,
use_stop_token=False)
self.synthesizer.set_state_dict(paddle.load(synthesizer_ckpt))
self.synthesizer.eval()
# Vocoder
self.vocoder = ConditionalWaveFlow(
upsample_factors=[16, 16], n_flows=8, n_layers=8, n_group=16, channels=128, n_mels=80, kernel_size=[3, 3])
self.vocoder.set_state_dict(paddle.load(vocoder_ckpt))
self.vocoder.eval()
# Speaking embedding
self._speaker_embedding = None
if speaker_audio is None or not os.path.isfile(speaker_audio):
speaker_audio = os.path.join(MODULE_HOME, 'lstm_tacotron2', 'assets', 'voice_cloning.wav')
logger.warning(f'Due to no speaker audio is specified, speaker encoder will use defult '
f'waveform({speaker_audio}) to extract speaker embedding. You can use '
'"set_speaker_embedding()" method to reset a speaker audio for voice cloning.')
self.set_speaker_embedding(speaker_audio)
self.output_dir = os.path.abspath(output_dir)
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
def get_speaker_embedding(self):
return self._speaker_embedding.numpy()
def set_speaker_embedding(self, speaker_audio: str):
assert os.path.exists(speaker_audio), f'Speaker audio file: {speaker_audio} does not exists.'
mel_sequences = self.speaker_processor.extract_mel_partials(
self.speaker_processor.preprocess_wav(speaker_audio))
self._speaker_embedding = self.speaker_encoder.embed_utterance(paddle.to_tensor(mel_sequences))
logger.info(f'Speaker embedding has been set from file: {speaker_audio}')
def forward(self, phones: paddle.Tensor, tones: paddle.Tensor, speaker_embeddings: paddle.Tensor):
outputs = self.synthesizer.infer(phones, tones=tones, global_condition=speaker_embeddings)
mel_input = paddle.transpose(outputs["mel_outputs_postnet"], [0, 2, 1])
waveforms = self.vocoder.infer(mel_input)
return waveforms
def _convert_text_to_input(self, text: str):
"""
Convert input string to phones and tones.
"""
phones, tones = convert_sentence(text)
phones = np.array([voc_phones.lookup(item) for item in phones], dtype=np.int64)
tones = np.array([voc_tones.lookup(item) for item in tones], dtype=np.int64)
return phones, tones
def _batchify(self, data: List[str], batch_size: int):
"""
Generate input batches.
"""
phone_pad_func = Pad(voc_phones.lookup(phone_pad_token))
tone_pad_func = Pad(voc_tones.lookup(tone_pad_token))
def _parse_batch(batch_data):
phones, tones = zip(*batch_data)
speaker_embeddings = paddle.expand(self._speaker_embedding, shape=(len(batch_data), -1))
return phone_pad_func(phones), tone_pad_func(tones), speaker_embeddings
examples = [] # [(phones, tones), ...]
for text in data:
examples.append(self._convert_text_to_input(text))
# Seperates data into some batches.
one_batch = []
for example in examples:
one_batch.append(example)
if len(one_batch) == batch_size:
yield _parse_batch(one_batch)
one_batch = []
if one_batch:
yield _parse_batch(one_batch)
def generate(self, data: List[str], batch_size: int = 1, use_gpu: bool = False):
assert self._speaker_embedding is not None, f'Set speaker embedding before voice cloning.'
paddle.set_device('gpu') if use_gpu else paddle.set_device('cpu')
batches = self._batchify(data, batch_size)
results = []
for batch in batches:
phones, tones, speaker_embeddings = map(paddle.to_tensor, batch)
waveforms = self(phones, tones, speaker_embeddings).numpy()
results.extend(list(waveforms))
files = []
for idx, waveform in enumerate(results):
output_wav = os.path.join(self.output_dir, f'{idx+1}.wav')
sf.write(output_wav, waveform, samplerate=self.sample_rate)
files.append(output_wav)
return files