341 lines
14 KiB
Python
341 lines
14 KiB
Python
|
import os
|
|||
|
|
import re
|
|||
|
|
from glob import glob
|
|||
|
|
from tqdm.auto import tqdm
|
|||
|
|
import soundfile as sf
|
|||
|
|
import numpy as np
|
|||
|
|
import torch
|
|||
|
|
from typing import Optional, Union
|
|||
|
|
# melo
|
|||
|
|
from melo.api import TTS
|
|||
|
|
from melo.utils import get_text_for_tts_infer
|
|||
|
|
# openvoice
|
|||
|
|
from .openvoice import se_extractor
|
|||
|
|
from .openvoice.api import ToneColorConverter
|
|||
|
|
from .openvoice.mel_processing import spectrogram_torch
|
|||
|
|
# torchaudio
|
|||
|
|
import torchaudio.functional as F
|
|||
|
|
|
|||
|
|
# 存储 BASE SPEAKER 的 embedding(source_se) 的路径
|
|||
|
|
current_file_path = os.path.abspath(__file__)
|
|||
|
|
utils_dir = os.path.dirname(os.path.dirname(current_file_path))
|
|||
|
|
|
|||
|
|
SOURCE_SE_DIR = os.path.join(utils_dir,'assets','ses')
|
|||
|
|
|
|||
|
|
# 存储缓存文件的路径
|
|||
|
|
CACHE_PATH = r"/tmp/openvoice_cache"
|
|||
|
|
|
|||
|
|
OPENVOICE_BASE_TTS={
|
|||
|
|
"model_type": "open_voice_base_tts",
|
|||
|
|
# 转换的语言
|
|||
|
|
"language": "ZH",
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
converter_path = os.path.join(os.path.dirname(current_file_path),'openvoice_model')
|
|||
|
|
OPENVOICE_TONE_COLOR_CONVERTER={
|
|||
|
|
"model_type": "open_voice_converter",
|
|||
|
|
# 模型参数路径
|
|||
|
|
"converter_path": converter_path,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
class TextToSpeech:
|
|||
|
|
def __init__(self,
|
|||
|
|
use_tone_convert=True,
|
|||
|
|
device="cuda",
|
|||
|
|
debug:bool=False,
|
|||
|
|
):
|
|||
|
|
self.debug = debug
|
|||
|
|
self.device = device
|
|||
|
|
self.use_tone_convert = use_tone_convert
|
|||
|
|
# 默认的源说话人 se
|
|||
|
|
self.source_se = None
|
|||
|
|
# 默认的目标说话人 se
|
|||
|
|
self.target_se = None
|
|||
|
|
|
|||
|
|
self.initialize_base_tts(**OPENVOICE_BASE_TTS)
|
|||
|
|
if self.debug:
|
|||
|
|
print("use tone converter is", self.use_tone_convert)
|
|||
|
|
if self.use_tone_convert:
|
|||
|
|
self.initialize_tone_color_converter(**OPENVOICE_TONE_COLOR_CONVERTER)
|
|||
|
|
self.initialize_source_se()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def initialize_tone_color_converter(self, **kwargs):
|
|||
|
|
"""
|
|||
|
|
初始化 tone color converter
|
|||
|
|
"""
|
|||
|
|
model_type = kwargs.pop('model_type')
|
|||
|
|
self.tone_color_converter_model_type = model_type
|
|||
|
|
if model_type == 'open_voice_converter':
|
|||
|
|
# 加载模型
|
|||
|
|
converter_path = kwargs.pop('converter_path')
|
|||
|
|
self.tone_color_converter = ToneColorConverter(f'{converter_path}/config.json', self.device)
|
|||
|
|
self.tone_color_converter.load_ckpt(f'{converter_path}/checkpoint.pth')
|
|||
|
|
if self.debug:
|
|||
|
|
print("load tone color converter successfully!")
|
|||
|
|
else:
|
|||
|
|
raise NotImplementedError(f"only [open_voice_converter] model type expected, but get [{model_type}]. ")
|
|||
|
|
|
|||
|
|
def initialize_base_tts(self, **kwargs):
|
|||
|
|
"""
|
|||
|
|
初始化 base tts model
|
|||
|
|
"""
|
|||
|
|
model_type = kwargs.pop('model_type')
|
|||
|
|
self.base_tts_model_type = model_type
|
|||
|
|
if model_type == "open_voice_base_tts":
|
|||
|
|
language = kwargs.pop('language')
|
|||
|
|
self.base_tts_model = TTS(language=language, device=self.device)
|
|||
|
|
speaker_ids = self.base_tts_model.hps.data.spk2id
|
|||
|
|
flag = False
|
|||
|
|
for speaker_key in speaker_ids.keys():
|
|||
|
|
if flag:
|
|||
|
|
Warning(f'loaded model has more than one speaker, only the first speaker is used. The input speaker ids are {speaker_ids}')
|
|||
|
|
break
|
|||
|
|
self.speaker_id = speaker_ids[speaker_key]
|
|||
|
|
self.speaker_key = speaker_key.lower().replace('_', '-')
|
|||
|
|
flag=True
|
|||
|
|
if self.debug:
|
|||
|
|
print("load base tts model successfully!")
|
|||
|
|
# 第一次使用tts时会加载bert模型
|
|||
|
|
self._base_tts("初始化bert模型。")
|
|||
|
|
else:
|
|||
|
|
raise NotImplementedError(f"only [open_voice_base_tts] model type expected, but get [{model_type}]. ")
|
|||
|
|
|
|||
|
|
def initialize_source_se(self):
|
|||
|
|
"""
|
|||
|
|
初始化source se
|
|||
|
|
"""
|
|||
|
|
if self.source_se is not None:
|
|||
|
|
Warning("replace source speaker embedding with new source speaker embedding!")
|
|||
|
|
self.source_se = torch.load(os.path.join(SOURCE_SE_DIR, f"{self.speaker_key}.pth"), map_location=self.device)
|
|||
|
|
|
|||
|
|
def initialize_target_se(self, se: Union[np.ndarray, torch.Tensor]):
|
|||
|
|
"""
|
|||
|
|
设置 target se
|
|||
|
|
param:
|
|||
|
|
se: 输入的se，类型可以为np.ndarray或torch.Tensor
|
|||
|
|
"""
|
|||
|
|
if self.target_se is not None:
|
|||
|
|
Warning("replace target source speaker embedding with new target speaker embedding!")
|
|||
|
|
if isinstance(se, np.ndarray):
|
|||
|
|
self.target_se = torch.tensor(se.astype(np.float32)).to(self.device)
|
|||
|
|
elif isinstance(se, torch.Tensor):
|
|||
|
|
self.target_se = se.float().to(self.device)
|
|||
|
|
|
|||
|
|
#语音转numpy
|
|||
|
|
def audio2numpy(self, audio_data: Union[bytes, np.ndarray]):
|
|||
|
|
"""
|
|||
|
|
将字节流的audio转为numpy类型，也可以传入numpy类型
|
|||
|
|
return: np.float32
|
|||
|
|
"""
|
|||
|
|
# TODO 是否归一化判断
|
|||
|
|
if isinstance(audio_data, bytes):
|
|||
|
|
audio_data = np.frombuffer(audio_data, dtype=np.int16).flatten().astype(np.float32) / 32768.0
|
|||
|
|
elif isinstance(audio_data, np.ndarray):
|
|||
|
|
if audio_data.dtype != np.float32:
|
|||
|
|
audio_data = audio_data.astype(np.int16).flatten().astype(np.float32) / 32768.0
|
|||
|
|
else:
|
|||
|
|
raise TypeError(f"audio_data must be bytes or numpy array, but got {type(audio_data)}")
|
|||
|
|
return audio_data
|
|||
|
|
|
|||
|
|
def audio2emb(self, audio_data: Union[bytes, np.ndarray], rate=44100, vad=True):
|
|||
|
|
"""
|
|||
|
|
将输入的字节流/numpy类型的audio转为speaker embedding
|
|||
|
|
param:
|
|||
|
|
audio_data: 输入的音频字节
|
|||
|
|
rate: 输入音频的采样率
|
|||
|
|
vad: 是否使用vad模型
|
|||
|
|
return: np.ndarray
|
|||
|
|
"""
|
|||
|
|
audio_data = self.audio2numpy(audio_data)
|
|||
|
|
if not os.path.exists(CACHE_PATH):
|
|||
|
|
os.makedirs(CACHE_PATH)
|
|||
|
|
|
|||
|
|
from scipy.io import wavfile
|
|||
|
|
audio_path = os.path.join(CACHE_PATH, "tmp.wav")
|
|||
|
|
wavfile.write(audio_path, rate=rate, data=audio_data)
|
|||
|
|
|
|||
|
|
se, _ = se_extractor.get_se(audio_path, self.tone_color_converter, target_dir=CACHE_PATH, vad=False)
|
|||
|
|
return se.cpu().detach().numpy()
|
|||
|
|
|
|||
|
|
def tensor2numpy(self, audio_data: torch.Tensor):
|
|||
|
|
"""
|
|||
|
|
tensor类型转numpy
|
|||
|
|
"""
|
|||
|
|
return audio_data.cpu().detach().float().numpy()
|
|||
|
|
|
|||
|
|
def numpy2bytes(self, audio_data: np.ndarray):
|
|||
|
|
"""
|
|||
|
|
numpy类型转bytes
|
|||
|
|
"""
|
|||
|
|
return (audio_data*32768.0).astype(np.int32).tobytes()
|
|||
|
|
|
|||
|
|
def _base_tts(self,
|
|||
|
|
text: str,
|
|||
|
|
sdp_ratio=0.2,
|
|||
|
|
noise_scale=0.6,
|
|||
|
|
noise_scale_w=0.8,
|
|||
|
|
speed=1.0,
|
|||
|
|
quite=True):
|
|||
|
|
"""
|
|||
|
|
base语音合成
|
|||
|
|
param:
|
|||
|
|
text: 要合成的文本
|
|||
|
|
sdp_ratio: SDP在合成时的占比, 理论上此比率越高, 合成的语音语调方差越大.
|
|||
|
|
noise_scale: 样本噪声张量的噪声标度。
|
|||
|
|
noise_scale_w: 推理中随机持续时间预测器的噪声标度
|
|||
|
|
speed: 说话语速
|
|||
|
|
quite: 是否显示进度条
|
|||
|
|
return:
|
|||
|
|
audio: tensor
|
|||
|
|
sr: 生成音频的采样速率
|
|||
|
|
"""
|
|||
|
|
speaker_id = self.speaker_id
|
|||
|
|
if self.base_tts_model_type != "open_voice_base_tts":
|
|||
|
|
raise NotImplementedError("only [open_voice_base_tts] model type expected.")
|
|||
|
|
language = self.base_tts_model.language
|
|||
|
|
texts = self.base_tts_model.split_sentences_into_pieces(text, language, quite)
|
|||
|
|
audio_list = []
|
|||
|
|
if quite:
|
|||
|
|
tx = texts
|
|||
|
|
else:
|
|||
|
|
tx = tqdm(texts)
|
|||
|
|
for t in tx:
|
|||
|
|
if language in ['EN', 'ZH_MIX_EN']:
|
|||
|
|
t = re.sub(r'([a-z])([A-Z])', r'\1 \2', t)
|
|||
|
|
device = self.base_tts_model.device
|
|||
|
|
bert, ja_bert, phones, tones, lang_ids = get_text_for_tts_infer(t, language, self.base_tts_model.hps, device, self.base_tts_model.symbol_to_id)
|
|||
|
|
with torch.no_grad():
|
|||
|
|
x_tst = phones.to(device).unsqueeze(0)
|
|||
|
|
tones = tones.to(device).unsqueeze(0)
|
|||
|
|
lang_ids = lang_ids.to(device).unsqueeze(0)
|
|||
|
|
bert = bert.to(device).unsqueeze(0)
|
|||
|
|
ja_bert = ja_bert.to(device).unsqueeze(0)
|
|||
|
|
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
|
|||
|
|
del phones
|
|||
|
|
speakers = torch.LongTensor([speaker_id]).to(device)
|
|||
|
|
audio = self.base_tts_model.model.infer(
|
|||
|
|
x_tst,
|
|||
|
|
x_tst_lengths,
|
|||
|
|
speakers,
|
|||
|
|
tones,
|
|||
|
|
lang_ids,
|
|||
|
|
bert,
|
|||
|
|
ja_bert,
|
|||
|
|
sdp_ratio=sdp_ratio,
|
|||
|
|
noise_scale=noise_scale,
|
|||
|
|
noise_scale_w = noise_scale_w,
|
|||
|
|
length_scale = 1. / speed,
|
|||
|
|
)[0][0, 0].data
|
|||
|
|
del x_tst, tones, lang_ids, bert, ja_bert, x_tst_lengths, speakers
|
|||
|
|
audio_list.append(audio)
|
|||
|
|
torch.cuda.empty_cache()
|
|||
|
|
audio_segments = []
|
|||
|
|
sr = self.base_tts_model.hps.data.sampling_rate
|
|||
|
|
for segment_data in audio_list:
|
|||
|
|
audio_segments.append(segment_data.reshape(-1).contiguous())
|
|||
|
|
audio_segments.append(torch.tensor([0]*int((sr * 0.05) / speed), dtype=segment_data.dtype, device=segment_data.device))
|
|||
|
|
audio_segments = torch.cat(audio_segments, dim=-1)
|
|||
|
|
if self.debug:
|
|||
|
|
print("generate base speech!")
|
|||
|
|
print("**********************,tts sr",sr)
|
|||
|
|
print(f"audio segment length is [{audio_segments.shape}]")
|
|||
|
|
return audio_segments, sr
|
|||
|
|
|
|||
|
|
def _convert_tone(self,
|
|||
|
|
audio_data: torch.Tensor,
|
|||
|
|
source_se: Optional[np.ndarray]=None,
|
|||
|
|
target_se: Optional[np.ndarray]=None,
|
|||
|
|
tau :float=0.3,
|
|||
|
|
message :str="default"):
|
|||
|
|
"""
|
|||
|
|
音色转换
|
|||
|
|
param:
|
|||
|
|
audio_data: _base_tts输出的音频数据
|
|||
|
|
source_se: 如果为None, 则使用self.source_se
|
|||
|
|
target_se: 如果为None, 则使用self.target_se
|
|||
|
|
tau:
|
|||
|
|
message: 水印信息 TODO
|
|||
|
|
return:
|
|||
|
|
audio: tensor
|
|||
|
|
sr: 生成音频的采样速率
|
|||
|
|
"""
|
|||
|
|
if source_se is None:
|
|||
|
|
source_se = self.source_se
|
|||
|
|
if target_se is None:
|
|||
|
|
target_se = self.target_se
|
|||
|
|
|
|||
|
|
hps = self.tone_color_converter.hps
|
|||
|
|
sr = hps.data.sampling_rate
|
|||
|
|
if self.debug:
|
|||
|
|
print("**********************************, convert sr", sr)
|
|||
|
|
audio_data = audio_data.float()
|
|||
|
|
|
|||
|
|
with torch.no_grad():
|
|||
|
|
y = audio_data.to(self.tone_color_converter.device)
|
|||
|
|
y = y.unsqueeze(0)
|
|||
|
|
spec = spectrogram_torch(y, hps.data.filter_length,
|
|||
|
|
sr, hps.data.hop_length, hps.data.win_length,
|
|||
|
|
center=False).to(self.tone_color_converter.device)
|
|||
|
|
spec_lengths = torch.LongTensor([spec.size(-1)]).to(self.tone_color_converter.device)
|
|||
|
|
audio = self.tone_color_converter.model.voice_conversion(spec, spec_lengths, sid_src=source_se, sid_tgt=target_se, tau=tau)[0][
|
|||
|
|
0, 0].data
|
|||
|
|
# audio = self.tone_color_converter.add_watermark(audio, message)
|
|||
|
|
if self.debug:
|
|||
|
|
print("tone color has been converted!")
|
|||
|
|
return audio, sr
|
|||
|
|
|
|||
|
|
def synthesize(self,
|
|||
|
|
text: str,
|
|||
|
|
sdp_ratio=0.2,
|
|||
|
|
noise_scale=0.6,
|
|||
|
|
noise_scale_w=0.8,
|
|||
|
|
speed=1.0,
|
|||
|
|
quite=True,
|
|||
|
|
|
|||
|
|
source_se: Optional[np.ndarray]=None,
|
|||
|
|
target_se: Optional[np.ndarray]=None,
|
|||
|
|
tau :float=0.3,
|
|||
|
|
message :str="default"):
|
|||
|
|
"""
|
|||
|
|
整体pipeline
|
|||
|
|
_base_tts()
|
|||
|
|
_convert_tone()
|
|||
|
|
tensor2numpy()
|
|||
|
|
numpy2bytes()
|
|||
|
|
param:
|
|||
|
|
见_base_tts和_convert_tone
|
|||
|
|
return:
|
|||
|
|
audio: 字节流音频数据
|
|||
|
|
sr: 音频数据的采样率
|
|||
|
|
"""
|
|||
|
|
audio, sr = self._base_tts(text,
|
|||
|
|
sdp_ratio=sdp_ratio,
|
|||
|
|
noise_scale=noise_scale,
|
|||
|
|
noise_scale_w=noise_scale_w,
|
|||
|
|
speed=speed,
|
|||
|
|
quite=quite)
|
|||
|
|
if self.use_tone_convert:
|
|||
|
|
tts_sr = self.base_tts_model.hps.data.sampling_rate
|
|||
|
|
converter_sr = self.tone_color_converter.hps.data.sampling_rate
|
|||
|
|
audio = F.resample(audio, tts_sr, converter_sr)
|
|||
|
|
print(audio.dtype)
|
|||
|
|
audio, sr = self._convert_tone(audio,
|
|||
|
|
source_se=source_se,
|
|||
|
|
target_se=target_se,
|
|||
|
|
tau=tau,
|
|||
|
|
message=message)
|
|||
|
|
audio = self.tensor2numpy(audio)
|
|||
|
|
audio = self.numpy2bytes(audio)
|
|||
|
|
return audio, sr
|
|||
|
|
|
|||
|
|
def save_audio(self, audio, sample_rate, save_path):
|
|||
|
|
"""
|
|||
|
|
将numpy类型的音频数据保存至本地
|
|||
|
|
param:
|
|||
|
|
audio: numpy类型的音频数据
|
|||
|
|
sample_rate: 数据采样率
|
|||
|
|
save_path: 保存路径
|
|||
|
|
"""
|
|||
|
|
sf.write(save_path, audio, sample_rate)
|
|||
|
|
print(f"Audio saved to {save_path}")
|