# 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 base64
import math
import os
import time
from typing import Optional
import numpy as np
import paddle
import yaml
from yacs.config import CfgNode
from paddlespeech.cli.log import logger
from paddlespeech.cli.tts.infer import TTSExecutor
from paddlespeech.resource import CommonTaskResource
from paddlespeech.server.engine.base_engine import BaseEngine
from paddlespeech.server.utils.audio_process import float2pcm
from paddlespeech.server.utils.util import denorm
from paddlespeech.server.utils.util import get_chunks
from paddlespeech.t2s.frontend import English
from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.t2s.modules.normalizer import ZScore
__all__ = ['TTSEngine', 'PaddleTTSConnectionHandler']
class TTSServerExecutor(TTSExecutor):
def __init__(self):
super().__init__()
self.task_resource = CommonTaskResource(
task='tts', model_format='dynamic', inference_mode='online')
def get_model_info(self,
field: str,
model_name: str,
ckpt: Optional[os.PathLike],
stat: Optional[os.PathLike]):
"""get model information
Args:
field (str): am or voc
model_name (str): model type, support fastspeech2, higigan, mb_melgan
ckpt (Optional[os.PathLike]): ckpt file
stat (Optional[os.PathLike]): stat file, including mean and standard deviation
Returns:
[module]: model module
[Tensor]: mean
[Tensor]: standard deviation
"""
model_class = self.task_resource.get_model_class(model_name)
if field == "am":
odim = self.am_config.n_mels
model = model_class(
idim=self.vocab_size, odim=odim, **self.am_config["model"])
model.set_state_dict(paddle.load(ckpt)["main_params"])
elif field == "voc":
model = model_class(**self.voc_config["generator_params"])
model.set_state_dict(paddle.load(ckpt)["generator_params"])
model.remove_weight_norm()
else:
logger.error("Please set correct field, am or voc")
model.eval()
model_mu, model_std = np.load(stat)
model_mu = paddle.to_tensor(model_mu)
model_std = paddle.to_tensor(model_std)
return model, model_mu, model_std
def _init_from_path(
self,
am: str='fastspeech2_csmsc',
am_config: Optional[os.PathLike]=None,
am_ckpt: Optional[os.PathLike]=None,
am_stat: Optional[os.PathLike]=None,
phones_dict: Optional[os.PathLike]=None,
tones_dict: Optional[os.PathLike]=None,
speaker_dict: Optional[os.PathLike]=None,
voc: str='mb_melgan_csmsc',
voc_config: Optional[os.PathLike]=None,
voc_ckpt: Optional[os.PathLike]=None,
voc_stat: Optional[os.PathLike]=None,
lang: str='zh', ):
"""
Init model and other resources from a specific path.
"""
if hasattr(self, 'am_inference') and hasattr(self, 'voc_inference'):
logger.info('Models had been initialized.')
return
# am model info
am_tag = am + '-' + lang
self.task_resource.set_task_model(
model_tag=am_tag,
model_type=0, # am
version=None, # default version
)
if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
self.am_res_path = self.task_resource.res_dir
self.am_config = os.path.join(self.am_res_path,
self.task_resource.res_dict['config'])
self.am_ckpt = os.path.join(self.am_res_path,
self.task_resource.res_dict['ckpt'])
self.am_stat = os.path.join(
self.am_res_path, self.task_resource.res_dict['speech_stats'])
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
self.am_res_path, self.task_resource.res_dict['phones_dict'])
print("self.phones_dict:", self.phones_dict)
logger.info(self.am_res_path)
logger.info(self.am_config)
logger.info(self.am_ckpt)
else:
self.am_config = os.path.abspath(am_config)
self.am_ckpt = os.path.abspath(am_ckpt)
self.am_stat = os.path.abspath(am_stat)
self.phones_dict = os.path.abspath(phones_dict)
self.am_res_path = os.path.dirname(os.path.abspath(self.am_config))
print("self.phones_dict:", self.phones_dict)
self.tones_dict = None
self.speaker_dict = None
# voc model info
voc_tag = voc + '-' + lang
self.task_resource.set_task_model(
model_tag=voc_tag,
model_type=1, # vocoder
version=None, # default version
)
if voc_ckpt is None or voc_config is None or voc_stat is None:
self.voc_res_path = self.task_resource.voc_res_dir
self.voc_config = os.path.join(
self.voc_res_path, self.task_resource.voc_res_dict['config'])
self.voc_ckpt = os.path.join(
self.voc_res_path, self.task_resource.voc_res_dict['ckpt'])
self.voc_stat = os.path.join(
self.voc_res_path,
self.task_resource.voc_res_dict['speech_stats'])
logger.info(self.voc_res_path)
logger.info(self.voc_config)
logger.info(self.voc_ckpt)
else:
self.voc_config = os.path.abspath(voc_config)
self.voc_ckpt = os.path.abspath(voc_ckpt)
self.voc_stat = os.path.abspath(voc_stat)
self.voc_res_path = os.path.dirname(
os.path.abspath(self.voc_config))
# Init body.
with open(self.am_config) as f:
self.am_config = CfgNode(yaml.safe_load(f))
with open(self.voc_config) as f:
self.voc_config = CfgNode(yaml.safe_load(f))
with open(self.phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
self.vocab_size = len(phn_id)
print("vocab_size:", self.vocab_size)
# frontend
if lang == 'zh':
self.frontend = Frontend(
phone_vocab_path=self.phones_dict,
tone_vocab_path=self.tones_dict)
elif lang == 'en':
self.frontend = English(phone_vocab_path=self.phones_dict)
print("frontend done!")
# am infer info
self.am_name = am[:am.rindex('_')]
if self.am_name == "fastspeech2_cnndecoder":
self.am_inference, self.am_mu, self.am_std = self.get_model_info(
"am", "fastspeech2", self.am_ckpt, self.am_stat)
else:
am, am_mu, am_std = self.get_model_info("am", self.am_name,
self.am_ckpt, self.am_stat)
am_normalizer = ZScore(am_mu, am_std)
am_inference_class = self.task_resource.get_model_class(
self.am_name + '_inference')
self.am_inference = am_inference_class(am_normalizer, am)
self.am_inference.eval()
print("acoustic model done!")
# voc infer info
self.voc_name = voc[:voc.rindex('_')]
voc, voc_mu, voc_std = self.get_model_info("voc", self.voc_name,
self.voc_ckpt, self.voc_stat)
voc_normalizer = ZScore(voc_mu, voc_std)
voc_inference_class = self.task_resource.get_model_class(self.voc_name +
'_inference')
self.voc_inference = voc_inference_class(voc_normalizer, voc)
self.voc_inference.eval()
print("voc done!")
class TTSEngine(BaseEngine):
"""TTS server engine
Args:
metaclass: Defaults to Singleton.
"""
def __init__(self, name=None):
"""Initialize TTS server engine
"""
super().__init__()
def init(self, config: dict) -> bool:
self.executor = TTSServerExecutor()
self.config = config
self.lang = self.config.lang
self.engine_type = "online"
assert (
config.am == "fastspeech2_csmsc" or
config.am == "fastspeech2_cnndecoder_csmsc"
) and (
config.voc == "hifigan_csmsc" or config.voc == "mb_melgan_csmsc"
), 'Please check config, am support: fastspeech2, voc support: hifigan_csmsc-zh or mb_melgan_csmsc.'
assert (
config.voc_block > 0 and config.voc_pad > 0
), "Please set correct voc_block and voc_pad, they should be more than 0."
try:
if self.config.device is not None:
self.device = self.config.device
else:
self.device = paddle.get_device()
paddle.set_device(self.device)
except Exception as e:
logger.error(
"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
)
logger.error("Initialize TTS server engine Failed on device: %s." %
(self.device))
logger.error(e)
return False
try:
self.executor._init_from_path(
am=self.config.am,
am_config=self.config.am_config,
am_ckpt=self.config.am_ckpt,
am_stat=self.config.am_stat,
phones_dict=self.config.phones_dict,
tones_dict=self.config.tones_dict,
speaker_dict=self.config.speaker_dict,
voc=self.config.voc,
voc_config=self.config.voc_config,
voc_ckpt=self.config.voc_ckpt,
voc_stat=self.config.voc_stat,
lang=self.config.lang)
except Exception as e:
logger.error("Failed to get model related files.")
logger.error("Initialize TTS server engine Failed on device: %s." %
(self.device))
logger.error(e)
return False
self.am_block = self.config.am_block
self.am_pad = self.config.am_pad
self.voc_block = self.config.voc_block
self.voc_pad = self.config.voc_pad
self.am_upsample = 1
self.voc_upsample = self.executor.voc_config.n_shift
logger.info("Initialize TTS server engine successfully on device: %s." %
(self.device))
return True
class PaddleTTSConnectionHandler:
def __init__(self, tts_engine):
"""The PaddleSpeech TTS Server Connection Handler
This connection process every tts server request
Args:
tts_engine (TTSEngine): The TTS engine
"""
super().__init__()
logger.info(
"Create PaddleTTSConnectionHandler to process the tts request")
self.tts_engine = tts_engine
self.executor = self.tts_engine.executor
self.config = self.tts_engine.config
self.am_block = self.tts_engine.am_block
self.am_pad = self.tts_engine.am_pad
self.voc_block = self.tts_engine.voc_block
self.voc_pad = self.tts_engine.voc_pad
self.am_upsample = self.tts_engine.am_upsample
self.voc_upsample = self.tts_engine.voc_upsample
def depadding(self, data, chunk_num, chunk_id, block, pad, upsample):
"""
Streaming inference removes the result of pad inference
"""
front_pad = min(chunk_id * block, pad)
# first chunk
if chunk_id == 0:
data = data[:block * upsample]
# last chunk
elif chunk_id == chunk_num - 1:
data = data[front_pad * upsample:]
# middle chunk
else:
data = data[front_pad * upsample:(front_pad + block) * upsample]
return data
@paddle.no_grad()
def infer(
self,
text: str,
lang: str='zh',
am: str='fastspeech2_csmsc',
spk_id: int=0, ):
"""
Model inference and result stored in self.output.
"""
# first_flag 用于标记首包
first_flag = 1
get_tone_ids = False
merge_sentences = False
frontend_st = time.time()
if lang == 'zh':
input_ids = self.executor.frontend.get_input_ids(
text,
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids)
phone_ids = input_ids["phone_ids"]
if get_tone_ids:
tone_ids = input_ids["tone_ids"]
elif lang == 'en':
input_ids = self.executor.frontend.get_input_ids(
text, merge_sentences=merge_sentences)
phone_ids = input_ids["phone_ids"]
else:
print("lang should in {'zh', 'en'}!")
frontend_et = time.time()
self.frontend_time = frontend_et - frontend_st
for i in range(len(phone_ids)):
part_phone_ids = phone_ids[i]
voc_chunk_id = 0
# fastspeech2_csmsc
if am == "fastspeech2_csmsc":
# am
mel = self.executor.am_inference(part_phone_ids)
if first_flag == 1:
first_am_et = time.time()
self.first_am_infer = first_am_et - frontend_et
# voc streaming
mel_chunks = get_chunks(mel, self.voc_block, self.voc_pad,
"voc")
voc_chunk_num = len(mel_chunks)
voc_st = time.time()
for i, mel_chunk in enumerate(mel_chunks):
sub_wav = self.executor.voc_inference(mel_chunk)
sub_wav = self.depadding(sub_wav, voc_chunk_num, i,
self.voc_block, self.voc_pad,
self.voc_upsample)
if first_flag == 1:
first_voc_et = time.time()
self.first_voc_infer = first_voc_et - first_am_et
self.first_response_time = first_voc_et - frontend_st
first_flag = 0
yield sub_wav
# fastspeech2_cnndecoder_csmsc
elif am == "fastspeech2_cnndecoder_csmsc":
# am
orig_hs = self.executor.am_inference.encoder_infer(
part_phone_ids)
# streaming voc chunk info
mel_len = orig_hs.shape[1]
voc_chunk_num = math.ceil(mel_len / self.voc_block)
start = 0
end = min(self.voc_block + self.voc_pad, mel_len)
# streaming am
hss = get_chunks(orig_hs, self.am_block, self.am_pad, "am")
am_chunk_num = len(hss)
for i, hs in enumerate(hss):
before_outs = self.executor.am_inference.decoder(hs)
after_outs = before_outs + self.executor.am_inference.postnet(
before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
normalized_mel = after_outs[0]
sub_mel = denorm(normalized_mel, self.executor.am_mu,
self.executor.am_std)
sub_mel = self.depadding(sub_mel, am_chunk_num, i,
self.am_block, self.am_pad,
self.am_upsample)
if i == 0:
mel_streaming = sub_mel
else:
mel_streaming = np.concatenate(
(mel_streaming, sub_mel), axis=0)
# streaming voc
# 当流式AM推理的mel帧数大于流式voc推理的chunk size，开始进行流式voc 推理
while (mel_streaming.shape[0] >= end and
voc_chunk_id < voc_chunk_num):
if first_flag == 1:
first_am_et = time.time()
self.first_am_infer = first_am_et - frontend_et
voc_chunk = mel_streaming[start:end, :]
voc_chunk = paddle.to_tensor(voc_chunk)
sub_wav = self.executor.voc_inference(voc_chunk)
sub_wav = self.depadding(
sub_wav, voc_chunk_num, voc_chunk_id,
self.voc_block, self.voc_pad, self.voc_upsample)
if first_flag == 1:
first_voc_et = time.time()
self.first_voc_infer = first_voc_et - first_am_et
self.first_response_time = first_voc_et - frontend_st
first_flag = 0
yield sub_wav
voc_chunk_id += 1
start = max(
0, voc_chunk_id * self.voc_block - self.voc_pad)
end = min(
(voc_chunk_id + 1) * self.voc_block + self.voc_pad,
mel_len)
else:
logger.error(
"Only support fastspeech2_csmsc or fastspeech2_cnndecoder_csmsc on streaming tts."
)
self.final_response_time = time.time() - frontend_st
def preprocess(self, text_bese64: str=None, text_bytes: bytes=None):
# Convert byte to text
if text_bese64:
text_bytes = base64.b64decode(text_bese64) # base64 to bytes
text = text_bytes.decode('utf-8') # bytes to text
return text
def run(self,
sentence: str,
spk_id: int=0,
speed: float=1.0,
volume: float=1.0,
sample_rate: int=0,
save_path: str=None):
""" run include inference and postprocess.
Args:
sentence (str): text to be synthesized
spk_id (int, optional): speaker id for multi-speaker speech synthesis. Defaults to 0.
speed (float, optional): speed. Defaults to 1.0.
volume (float, optional): volume. Defaults to 1.0.
sample_rate (int, optional): target sample rate for synthesized audio,
0 means the same as the model sampling rate. Defaults to 0.
save_path (str, optional): The save path of the synthesized audio.
None means do not save audio. Defaults to None.
Returns:
wav_base64: The base64 format of the synthesized audio.
"""
wav_list = []
for wav in self.infer(
text=sentence,
lang=self.config.lang,
am=self.config.am,
spk_id=spk_id, ):
# wav type: <class 'numpy.ndarray'> float32, convert to pcm (base64)
wav = float2pcm(wav) # float32 to int16
wav_bytes = wav.tobytes() # to bytes
wav_base64 = base64.b64encode(wav_bytes).decode('utf8') # to base64
wav_list.append(wav)
yield wav_base64
wav_all = np.concatenate(wav_list, axis=0)
duration = len(wav_all) / self.executor.am_config.fs
logger.info(f"sentence: {sentence}")
logger.info(f"The durations of audio is: {duration} s")
logger.info(f"first response time: {self.first_response_time} s")
logger.info(f"final response time: {self.final_response_time} s")
logger.info(f"RTF: {self.final_response_time / duration}")
logger.info(
f"Other info: front time: {self.frontend_time} s, first am infer time: {self.first_am_infer} s, first voc infer time: {self.first_voc_infer} s,"
)