mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
317 lines
14 KiB
Python
Executable File
317 lines
14 KiB
Python
Executable File
from const import ERROR_NO_ONNX_SESSION
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import torch
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import math, os, traceback
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from scipy.io.wavfile import write, read
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import numpy as np
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from dataclasses import dataclass, asdict
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import utils
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import commons
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from models import SynthesizerTrn
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from text.symbols import symbols
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from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
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from mel_processing import spectrogram_torch
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from text import text_to_sequence, cleaned_text_to_sequence
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import onnxruntime
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providers = ['OpenVINOExecutionProvider',"CUDAExecutionProvider","DmlExecutionProvider","CPUExecutionProvider"]
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@dataclass
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class VocieChangerSettings():
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gpu:int = 0
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srcId:int = 107
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dstId:int = 100
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crossFadeOffsetRate:float = 0.1
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crossFadeEndRate:float = 0.9
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crossFadeOverlapRate:float = 0.9
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convertChunkNum:int = 32
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framework:str = "PyTorch" # PyTorch or ONNX
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pyTorchModelFile:str = ""
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onnxModelFile:str = ""
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configFile:str = ""
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# ↓mutableな物だけ列挙
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intData = ["gpu","srcId", "dstId", "convertChunkNum"]
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floatData = [ "crossFadeOffsetRate", "crossFadeEndRate", "crossFadeOverlapRate"]
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strData = ["framework"]
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class VoiceChanger():
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def __init__(self, config:str):
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# 初期化
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self.settings = VocieChangerSettings(configFile=config)
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self.unpackedData_length=0
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self.net_g = None
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self.onnx_session = None
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self.currentCrossFadeOffsetRate=0
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self.currentCrossFadeEndRate=0
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self.currentCrossFadeOverlapRate=0
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# 共通で使用する情報を収集
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self.hps = utils.get_hparams_from_file(config)
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self.gpu_num = torch.cuda.device_count()
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text_norm = text_to_sequence("a", self.hps.data.text_cleaners)
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text_norm = commons.intersperse(text_norm, 0)
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self.text_norm = torch.LongTensor(text_norm)
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self.audio_buffer = torch.zeros(1, 0)
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self.prev_audio = np.zeros(1)
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self.mps_enabled = getattr(torch.backends, "mps", None) is not None and torch.backends.mps.is_available()
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print(f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num}, mps_enabled:{self.mps_enabled})")
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def loadModel(self, config:str, pyTorch_model_file:str=None, onnx_model_file:str=None):
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self.settings.configFile = config
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if pyTorch_model_file != None:
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self.settings.pyTorchModelFile = pyTorch_model_file
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if onnx_model_file:
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self.settings.onnxModelFile = onnx_model_file
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# PyTorchモデル生成
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if pyTorch_model_file != None:
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self.net_g = SynthesizerTrn(
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len(symbols),
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model)
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self.net_g.eval()
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utils.load_checkpoint(pyTorch_model_file, self.net_g, None)
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# ONNXモデル生成
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if onnx_model_file != None:
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ort_options = onnxruntime.SessionOptions()
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ort_options.intra_op_num_threads = 8
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self.onnx_session = onnxruntime.InferenceSession(
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onnx_model_file,
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providers=providers
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)
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return self.get_info()
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def destroy(self):
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del self.net_g
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del self.onnx_session
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def get_info(self):
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data = asdict(self.settings)
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data["providers"] = self.onnx_session.get_providers() if self.onnx_session != None else []
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files = ["configFile", "pyTorchModelFile", "onnxModelFile"]
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for f in files:
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if data[f]!=None and os.path.exists(data[f]):
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data[f] = os.path.basename(data[f])
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else:
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data[f] = ""
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return data
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def update_setteings(self, key:str, val:any):
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if key == "onnxExecutionProvider" and self.onnx_session != None:
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if val == "CUDAExecutionProvider":
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if self.settings.gpu < 0 or self.settings.gpu >= self.gpu_num:
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self.settings.gpu = 0
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provider_options=[{'device_id': self.settings.gpu}]
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self.onnx_session.set_providers(providers=[val], provider_options=provider_options)
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else:
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self.onnx_session.set_providers(providers=[val])
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elif key in self.settings.intData:
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setattr(self.settings, key, int(val))
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if key == "gpu" and val >= 0 and val < self.gpu_num and self.onnx_session != None:
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providers = self.onnx_session.get_providers()
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print("Providers:", providers)
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if "CUDAExecutionProvider" in providers:
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provider_options=[{'device_id': self.settings.gpu}]
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self.onnx_session.set_providers(providers=["CUDAExecutionProvider"], provider_options=provider_options)
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if key == "crossFadeOffsetRate" or key == "crossFadeEndRate":
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self.unpackedData_length = 0
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elif key in self.settings.floatData:
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setattr(self.settings, key, float(val))
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elif key in self.settings.strData:
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setattr(self.settings, key, str(val))
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else:
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print(f"{key} is not mutalbe variable!")
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return self.get_info()
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def _generate_strength(self, unpackedData):
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if self.unpackedData_length != unpackedData.shape[0] or self.currentCrossFadeOffsetRate != self.settings.crossFadeOffsetRate or self.currentCrossFadeEndRate != self.settings.crossFadeEndRate or self.currentCrossFadeOverlapRate != self.settings.crossFadeOverlapRate:
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self.unpackedData_length = unpackedData.shape[0]
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self.currentCrossFadeOffsetRate = self.settings.crossFadeOffsetRate
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self.currentCrossFadeEndRate = self.settings.crossFadeEndRate
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self.currentCrossFadeOverlapRate = self.settings.crossFadeOverlapRate
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overlapSize = int(unpackedData.shape[0] * self.settings.crossFadeOverlapRate)
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cf_offset = int(overlapSize * self.settings.crossFadeOffsetRate)
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cf_end = int(overlapSize * self.settings.crossFadeEndRate)
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cf_range = cf_end - cf_offset
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percent = np.arange(cf_range) / cf_range
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np_prev_strength = np.cos(percent * 0.5 * np.pi) ** 2
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np_cur_strength = np.cos((1-percent) * 0.5 * np.pi) ** 2
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self.np_prev_strength = np.concatenate([np.ones(cf_offset), np_prev_strength, np.zeros(overlapSize - cf_offset - len(np_prev_strength))])
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self.np_cur_strength = np.concatenate([np.zeros(cf_offset), np_cur_strength, np.ones(overlapSize - cf_offset - len(np_cur_strength))])
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self.prev_strength = torch.FloatTensor(self.np_prev_strength)
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self.cur_strength = torch.FloatTensor(self.np_cur_strength)
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# torch.set_printoptions(edgeitems=2100)
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print("Generated Strengths")
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# print(f"cross fade: start:{cf_offset} end:{cf_end} range:{cf_range}")
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# print(f"target_len:{unpackedData.shape[0]}, prev_len:{len(self.prev_strength)} cur_len:{len(self.cur_strength)}")
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# print("Prev", self.prev_strength)
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# print("Cur", self.cur_strength)
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# ひとつ前の結果とサイズが変わるため、記録は消去する。
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if hasattr(self, 'prev_audio1') == True:
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delattr(self,"prev_audio1")
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def _generate_input(self, unpackedData:any, convertSize:int):
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# 今回変換するデータをテンソルとして整形する
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audio = torch.FloatTensor(unpackedData.astype(np.float32)) # float32でtensorfを作成
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audio_norm = audio / self.hps.data.max_wav_value # normalize
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audio_norm = audio_norm.unsqueeze(0) # unsqueeze
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self.audio_buffer = torch.cat([self.audio_buffer, audio_norm], axis=1) # 過去のデータに連結
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audio_norm = self.audio_buffer[:, -convertSize:] # 変換対象の部分だけ抽出
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self.audio_buffer = audio_norm
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spec = spectrogram_torch(audio_norm, self.hps.data.filter_length,
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self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
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center=False)
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spec = torch.squeeze(spec, 0)
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sid = torch.LongTensor([int(self.settings.srcId)])
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data = (self.text_norm, spec, audio_norm, sid)
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data = TextAudioSpeakerCollate()([data])
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return data
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def _onnx_inference(self, data, inputSize):
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if hasattr(self, "onnx_session") == False or self.onnx_session == None:
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print("[Voice Changer] No ONNX session.")
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return np.zeros(1).astype(np.int16)
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data]
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sid_tgt1 = torch.LongTensor([self.settings.dstId])
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# if spec.size()[2] >= 8:
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audio1 = self.onnx_session.run(
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["audio"],
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{
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"specs": spec.numpy(),
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"lengths": spec_lengths.numpy(),
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"sid_src": sid_src.numpy(),
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"sid_tgt": sid_tgt1.numpy()
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})[0][0,0] * self.hps.data.max_wav_value
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if hasattr(self, 'np_prev_audio1') == True:
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overlapSize = int(inputSize * self.settings.crossFadeOverlapRate)
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prev_overlap = self.np_prev_audio1[-1*overlapSize:]
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cur_overlap = audio1[-1*(inputSize + overlapSize) :-1*inputSize]
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# print(prev_overlap.shape, self.np_prev_strength.shape, cur_overlap.shape, self.np_cur_strength.shape)
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# print(">>>>>>>>>>>", -1*(inputSize + overlapSize) , -1*inputSize)
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powered_prev = prev_overlap * self.np_prev_strength
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powered_cur = cur_overlap * self.np_cur_strength
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powered_result = powered_prev + powered_cur
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cur = audio1[-1*inputSize:-1*overlapSize]
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result = np.concatenate([powered_result, cur],axis=0)
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else:
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result = np.zeros(1).astype(np.int16)
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self.np_prev_audio1 = audio1
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return result
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def _pyTorch_inference(self, data, inputSize):
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if hasattr(self, "net_g") == False or self.net_g ==None:
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print("[Voice Changer] No pyTorch session.")
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return np.zeros(1).astype(np.int16)
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if self.settings.gpu < 0 or self.gpu_num == 0:
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cpu() for x in data]
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sid_tgt1 = torch.LongTensor([self.settings.dstId]).cpu()
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audio1 = (self.net_g.cpu().voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0, 0].data * self.hps.data.max_wav_value)
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if self.prev_strength.device != torch.device('cpu'):
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print(f"prev_strength move from {self.prev_strength.device} to cpu")
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self.prev_strength = self.prev_strength.cpu()
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if self.cur_strength.device != torch.device('cpu'):
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print(f"cur_strength move from {self.cur_strength.device} to cpu")
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self.cur_strength = self.cur_strength.cpu()
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if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cpu'):
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prev = self.prev_audio1[-1*inputSize:]
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cur = audio1[-2*inputSize:-1*inputSize]
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result = prev * self.prev_strength + cur * self.cur_strength
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else:
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cur = audio1[-2*inputSize:-1*inputSize]
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result = cur
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self.prev_audio1 = audio1
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result = result.cpu().float().numpy()
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else:
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda(self.settings.gpu) for x in data]
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sid_tgt1 = torch.LongTensor([self.settings.dstId]).cuda(self.settings.gpu)
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audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0, 0].data * self.hps.data.max_wav_value
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if self.prev_strength.device != torch.device('cuda', self.settings.gpu):
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print(f"prev_strength move from {self.prev_strength.device} to gpu{self.settings.gpu}")
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self.prev_strength = self.prev_strength.cuda(self.settings.gpu)
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if self.cur_strength.device != torch.device('cuda', self.settings.gpu):
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print(f"cur_strength move from {self.cur_strength.device} to gpu{self.settings.gpu}")
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self.cur_strength = self.cur_strength.cuda(self.settings.gpu)
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if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cuda', self.settings.gpu):
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prev = self.prev_audio1[-1*inputSize:]
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cur = audio1[-2*inputSize:-1*inputSize]
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result = prev * self.prev_strength + cur * self.cur_strength
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# print("merging...", prev.shape, cur.shape)
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else:
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cur = audio1[-2*inputSize:-1*inputSize]
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result = cur
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# print("no merging...", cur.shape)
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self.prev_audio1 = audio1
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#print(result)
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result = result.cpu().float().numpy()
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return result
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def on_request(self, unpackedData:any):
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convertSize = self.settings.convertChunkNum * 128 # 128sample/1chunk
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if unpackedData.shape[0]*(1 + self.settings.crossFadeOverlapRate) + 1024 > convertSize:
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convertSize = int(unpackedData.shape[0]*(1 + self.settings.crossFadeOverlapRate)) + 1024
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# print("convert Size", unpackedData.shape[0], unpackedData.shape[0]*(1 + self.settings.crossFadeOverlapRate), convertSize)
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self._generate_strength(unpackedData)
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data = self._generate_input(unpackedData, convertSize)
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try:
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if self.settings.framework == "ONNX":
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result = self._onnx_inference(data, unpackedData.shape[0])
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else:
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result = self._pyTorch_inference(data, unpackedData.shape[0])
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except Exception as e:
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print("VC PROCESSING!!!! EXCEPTION!!!", e)
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print(traceback.format_exc())
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if hasattr(self, "np_prev_audio1"):
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del self.np_prev_audio1
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if hasattr(self, "prev_audio1"):
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del self.prev_audio1
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return np.zeros(1).astype(np.int16)
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result = result.astype(np.int16)
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# print("on_request result size:",result.shape)
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return result
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