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306 lines
11 KiB
306 lines
11 KiB
3 years ago
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# 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 base64
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import io
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import time
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import librosa
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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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from scipy.io import wavfile
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from paddlespeech.cli.log import logger
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from paddlespeech.cli.tts.infer import TTSExecutor
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from paddlespeech.server.engine.base_engine import BaseEngine
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from paddlespeech.server.utils.audio_process import change_speed
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from paddlespeech.server.utils.errors import ErrorCode
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from paddlespeech.server.utils.exception import ServerBaseException
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from paddlespeech.server.utils.audio_process import float2pcm
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from paddlespeech.server.utils.config import get_config
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from paddlespeech.server.utils.util import denorm
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from paddlespeech.server.utils.util import get_chunks
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import math
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__all__ = ['TTSEngine']
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class TTSServerExecutor(TTSExecutor):
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def __init__(self):
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super().__init__()
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pass
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@paddle.no_grad()
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def infer(self,
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text: str,
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lang: str='zh',
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am: str='fastspeech2_csmsc',
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spk_id: int=0,
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am_block: int=42,
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am_pad: int=12,
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voc_block: int=14,
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voc_pad: int=14,):
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"""
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Model inference and result stored in self.output.
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"""
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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get_tone_ids = False
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merge_sentences = False
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frontend_st = time.time()
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if am_name == 'speedyspeech':
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get_tone_ids = True
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if lang == 'zh':
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input_ids = self.frontend.get_input_ids(
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text,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids)
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phone_ids = input_ids["phone_ids"]
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if get_tone_ids:
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tone_ids = input_ids["tone_ids"]
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elif lang == 'en':
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input_ids = self.frontend.get_input_ids(
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text, merge_sentences=merge_sentences)
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phone_ids = input_ids["phone_ids"]
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else:
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print("lang should in {'zh', 'en'}!")
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self.frontend_time = time.time() - frontend_st
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for i in range(len(phone_ids)):
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am_st = time.time()
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part_phone_ids = phone_ids[i]
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# am
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if am_name == 'speedyspeech':
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part_tone_ids = tone_ids[i]
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mel = self.am_inference(part_phone_ids, part_tone_ids)
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# fastspeech2
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else:
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# multi speaker
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if am_dataset in {"aishell3", "vctk"}:
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mel = self.am_inference(
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part_phone_ids, spk_id=paddle.to_tensor(spk_id))
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else:
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mel = self.am_inference(part_phone_ids)
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am_et = time.time()
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# voc streaming
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voc_upsample = self.voc_config.n_shift
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mel_chunks = get_chunks(mel, voc_block, voc_pad, "voc")
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chunk_num = len(mel_chunks)
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voc_st = time.time()
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for i, mel_chunk in enumerate(mel_chunks):
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sub_wav = self.voc_inference(mel_chunk)
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front_pad = min(i*voc_block, voc_pad)
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if i == 0:
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sub_wav = sub_wav[: voc_block * voc_upsample]
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elif i == chunk_num - 1:
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sub_wav = sub_wav[front_pad * voc_upsample : ]
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else:
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sub_wav = sub_wav[front_pad * voc_upsample: (front_pad + voc_block) * voc_upsample]
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yield sub_wav
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class TTSEngine(BaseEngine):
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"""TTS server engine
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Args:
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metaclass: Defaults to Singleton.
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"""
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def __init__(self, name=None):
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"""Initialize TTS server engine
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"""
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super(TTSEngine, self).__init__()
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def init(self, config: dict) -> bool:
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self.executor = TTSServerExecutor()
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try:
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self.config = config
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if self.config.device:
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self.device = self.config.device
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else:
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self.device = paddle.get_device()
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paddle.set_device(self.device)
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except Exception as e:
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logger.error(
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"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
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)
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logger.error("Initialize TTS server engine Failed on device: %s." %
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(self.device))
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return False
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try:
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self.executor._init_from_path(
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am=self.config.am,
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am_config=self.config.am_config,
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am_ckpt=self.config.am_ckpt,
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am_stat=self.config.am_stat,
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phones_dict=self.config.phones_dict,
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tones_dict=self.config.tones_dict,
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speaker_dict=self.config.speaker_dict,
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voc=self.config.voc,
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voc_config=self.config.voc_config,
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voc_ckpt=self.config.voc_ckpt,
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voc_stat=self.config.voc_stat,
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lang=self.config.lang)
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except Exception as e:
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logger.error("Failed to get model related files.")
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logger.error("Initialize TTS server engine Failed on device: %s." %
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(self.device))
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return False
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self.am_block = self.config.am_block
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self.am_pad = self.config.am_pad
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self.voc_block = self.config.voc_block
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self.voc_pad = self.config.voc_pad
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logger.info("Initialize TTS server engine successfully on device: %s." %
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(self.device))
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return True
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def preprocess(self, text_bese64: str=None, text_bytes: bytes=None):
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# Convert byte to text
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if text_bese64:
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text_bytes = base64.b64decode(text_bese64) # base64 to bytes
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text = text_bytes.decode('utf-8') # bytes to text
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return text
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def postprocess(self,
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wav,
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original_fs: int,
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target_fs: int=0,
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volume: float=1.0,
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speed: float=1.0,
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audio_path: str=None):
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"""Post-processing operations, including speech, volume, sample rate, save audio file
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Args:
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wav (numpy(float)): Synthesized audio sample points
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original_fs (int): original audio sample rate
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target_fs (int): target audio sample rate
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volume (float): target volume
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speed (float): target speed
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Raises:
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ServerBaseException: Throws an exception if the change speed unsuccessfully.
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Returns:
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target_fs: target sample rate for synthesized audio.
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wav_base64: The base64 format of the synthesized audio.
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"""
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# transform sample_rate
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if target_fs == 0 or target_fs > original_fs:
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target_fs = original_fs
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wav_tar_fs = wav
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logger.info(
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"The sample rate of synthesized audio is the same as model, which is {}Hz".
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format(original_fs))
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else:
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wav_tar_fs = librosa.resample(
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np.squeeze(wav), original_fs, target_fs)
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logger.info(
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"The sample rate of model is {}Hz and the target sample rate is {}Hz. Converting the sample rate of the synthesized audio successfully.".
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format(original_fs, target_fs))
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# transform volume
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wav_vol = wav_tar_fs * volume
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logger.info("Transform the volume of the audio successfully.")
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# transform speed
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try: # windows not support soxbindings
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wav_speed = change_speed(wav_vol, speed, target_fs)
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logger.info("Transform the speed of the audio successfully.")
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except ServerBaseException:
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raise ServerBaseException(
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ErrorCode.SERVER_INTERNAL_ERR,
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"Failed to transform speed. Can not install soxbindings on your system. \
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You need to set speed value 1.0.")
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except BaseException:
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logger.error("Failed to transform speed.")
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# wav to base64
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buf = io.BytesIO()
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wavfile.write(buf, target_fs, wav_speed)
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base64_bytes = base64.b64encode(buf.read())
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wav_base64 = base64_bytes.decode('utf-8')
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logger.info("Audio to string successfully.")
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# save audio
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if audio_path is not None:
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if audio_path.endswith(".wav"):
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sf.write(audio_path, wav_speed, target_fs)
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elif audio_path.endswith(".pcm"):
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wav_norm = wav_speed * (32767 / max(0.001,
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np.max(np.abs(wav_speed))))
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with open(audio_path, "wb") as f:
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f.write(wav_norm.astype(np.int16))
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logger.info("Save audio to {} successfully.".format(audio_path))
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else:
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logger.info("There is no need to save audio.")
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return target_fs, wav_base64
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def run(self,
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sentence: str,
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spk_id: int=0,
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speed: float=1.0,
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volume: float=1.0,
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sample_rate: int=0,
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save_path: str=None):
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""" run include inference and postprocess.
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Args:
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sentence (str): text to be synthesized
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spk_id (int, optional): speaker id for multi-speaker speech synthesis. Defaults to 0.
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speed (float, optional): speed. Defaults to 1.0.
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volume (float, optional): volume. Defaults to 1.0.
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sample_rate (int, optional): target sample rate for synthesized audio,
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0 means the same as the model sampling rate. Defaults to 0.
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save_path (str, optional): The save path of the synthesized audio.
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None means do not save audio. Defaults to None.
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Raises:
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ServerBaseException: Throws an exception if tts inference unsuccessfully.
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ServerBaseException: Throws an exception if postprocess unsuccessfully.
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Returns:
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lang: model language
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target_sample_rate: target sample rate for synthesized audio.
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wav_base64: The base64 format of the synthesized audio.
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"""
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lang = self.config.lang
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wav_list = []
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for wav in self.executor.infer(text=sentence, lang=lang, am=self.config.am, spk_id=spk_id, am_block=self.am_block, am_pad=self.am_pad, voc_block=self.voc_block, voc_pad=self.voc_pad):
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# wav type: <class 'numpy.ndarray'> float32, convert to pcm (base64)
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wav = float2pcm(wav) # float32 to int16
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wav_bytes = wav.tobytes() # to bytes
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wav_base64 = base64.b64encode(wav_bytes).decode('utf8') # to base64
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wav_list.append(wav)
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yield wav_base64
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wav_all = np.concatenate(wav_list, axis=0)
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logger.info("The durations of audio is: {} s".format(len(wav_all)/self.executor.am_config.fs))
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