mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
302 lines
10 KiB
Python
302 lines
10 KiB
Python
import sys
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import os
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from data.ModelSlot import MMVCv13ModelSlot
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from voice_changer.VoiceChangerParamsManager import VoiceChangerParamsManager
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from voice_changer.utils.VoiceChangerModel import AudioInOut, VoiceChangerModel
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if sys.platform.startswith("darwin"):
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baseDir = [x for x in sys.path if x.endswith("Contents/MacOS")]
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if len(baseDir) != 1:
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print("baseDir should be only one ", baseDir)
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sys.exit()
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modulePath = os.path.join(baseDir[0], "MMVC_Client_v13", "python")
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sys.path.append(modulePath)
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else:
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modulePath = os.path.join("MMVC_Client_v13", "python")
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sys.path.append(modulePath)
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from dataclasses import dataclass, asdict, field
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import numpy as np
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import torch
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import onnxruntime
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# from symbols import symbols # type:ignore
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# from models import SynthesizerTrn # type:ignore
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from voice_changer.MMVCv13.models.models import SynthesizerTrn
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from voice_changer.MMVCv13.models.symbols import symbols
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from voice_changer.MMVCv13.TrainerFunctions import (
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TextAudioSpeakerCollate,
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spectrogram_torch,
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load_checkpoint,
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get_hparams_from_file,
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)
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from Exceptions import NoModeLoadedException
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@dataclass
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class MMVCv13Settings:
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gpu: int = -9999
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srcId: int = 0
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dstId: int = 101
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# ↓mutableな物だけ列挙
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intData = ["gpu", "srcId", "dstId"]
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floatData: list[str] = field(default_factory=lambda: [])
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strData: list[str] = field(default_factory=lambda: [])
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class MMVCv13(VoiceChangerModel):
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def __init__(self, slotInfo: MMVCv13ModelSlot):
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print("[Voice Changer] [MMVCv13] Creating instance ")
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self.voiceChangerType = "MMVCv13"
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self.settings = MMVCv13Settings()
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self.net_g = None
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self.onnx_session = None
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self.gpu_num = torch.cuda.device_count()
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self.text_norm = torch.LongTensor([0, 6, 0])
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self.audio_buffer: AudioInOut | None = None
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self.slotInfo = slotInfo
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self.initialize()
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def initialize(self):
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print("[Voice Changer] [MMVCv13] Initializing... ")
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vcparams = VoiceChangerParamsManager.get_instance().params
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configPath = os.path.join(
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vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.configFile
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)
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modelPath = os.path.join(
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vcparams.model_dir, str(self.slotInfo.slotIndex), self.slotInfo.modelFile
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)
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self.hps = get_hparams_from_file(configPath)
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if self.slotInfo.isONNX:
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providers, options = self.getOnnxExecutionProvider()
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self.onnx_session = onnxruntime.InferenceSession(
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modelPath,
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providers=providers,
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provider_options=options,
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)
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else:
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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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)
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self.net_g.eval()
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load_checkpoint(modelPath, self.net_g, None)
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# その他の設定
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self.settings.srcId = self.slotInfo.srcId
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self.settings.dstId = self.slotInfo.dstId
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print("[Voice Changer] [MMVCv13] Initializing... done")
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def getOnnxExecutionProvider(self):
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availableProviders = onnxruntime.get_available_providers()
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devNum = torch.cuda.device_count()
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if (
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self.settings.gpu >= 0
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and "CUDAExecutionProvider" in availableProviders
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and devNum > 0
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):
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return ["CUDAExecutionProvider"], [{"device_id": self.settings.gpu}]
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elif self.settings.gpu >= 0 and "DmlExecutionProvider" in availableProviders:
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return ["DmlExecutionProvider"], [{}]
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else:
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return ["CPUExecutionProvider"], [
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{
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"intra_op_num_threads": 8,
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"execution_mode": onnxruntime.ExecutionMode.ORT_PARALLEL,
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"inter_op_num_threads": 8,
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}
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]
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def update_settings(self, key: str, val: int | float | str):
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if key in self.settings.intData:
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val = int(val)
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setattr(self.settings, key, val)
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if key == "gpu" and self.slotInfo.isONNX:
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providers, options = self.getOnnxExecutionProvider()
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vcparams = VoiceChangerParamsManager.get_instance().params
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modelPath = os.path.join(
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vcparams.model_dir,
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str(self.slotInfo.slotIndex),
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self.slotInfo.modelFile,
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)
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self.onnx_session = onnxruntime.InferenceSession(
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modelPath,
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providers=providers,
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provider_options=options,
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)
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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(
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# providers=["CUDAExecutionProvider"],
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# provider_options=provider_options,
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# )
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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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return False
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return True
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def get_info(self):
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data = asdict(self.settings)
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data["onnxExecutionProviders"] = (
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self.onnx_session.get_providers() if self.onnx_session is not None else []
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)
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return data
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def get_processing_sampling_rate(self):
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if hasattr(self, "hps") is False:
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raise NoModeLoadedException("config")
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return self.hps.data.sampling_rate
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def _get_spec(self, audio: AudioInOut):
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spec = spectrogram_torch(
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audio,
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self.hps.data.filter_length,
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self.hps.data.sampling_rate,
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self.hps.data.hop_length,
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self.hps.data.win_length,
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center=False,
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)
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spec = torch.squeeze(spec, 0)
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return spec
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def generate_input(
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self,
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newData: AudioInOut,
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inputSize: int,
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crossfadeSize: int,
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solaSearchFrame: int = 0,
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):
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newData = newData.astype(np.float32) / self.hps.data.max_wav_value
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if self.audio_buffer is not None:
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self.audio_buffer = np.concatenate(
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[self.audio_buffer, newData], 0
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) # 過去のデータに連結
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else:
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self.audio_buffer = newData
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convertSize = inputSize + crossfadeSize + solaSearchFrame
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# if convertSize < 8192:
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# convertSize = 8192
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if convertSize % self.hps.data.hop_length != 0: # モデルの出力のホップサイズで切り捨てが発生するので補う。
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convertSize = convertSize + (
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self.hps.data.hop_length - (convertSize % self.hps.data.hop_length)
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)
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convertOffset = -1 * convertSize
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self.audio_buffer = self.audio_buffer[convertOffset:] # 変換対象の部分だけ抽出
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audio = torch.FloatTensor(self.audio_buffer)
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audio_norm = audio.unsqueeze(0) # unsqueeze
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spec = self._get_spec(audio_norm)
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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):
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if self.onnx_session is None:
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print("[Voice Changer] No ONNX session.")
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raise NoModeLoadedException("ONNX")
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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 = (
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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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},
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)[0][0, 0]
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* self.hps.data.max_wav_value
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)
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return audio1
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def _pyTorch_inference(self, data):
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if hasattr(self, "net_g") is False or self.net_g is None:
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print("[Voice Changer] No pyTorch session.")
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raise NoModeLoadedException("pytorch")
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if self.settings.gpu < 0 or self.gpu_num == 0:
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dev = torch.device("cpu")
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else:
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dev = torch.device("cuda", index=self.settings.gpu)
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
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x.to(dev) for x in data
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]
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sid_target = torch.LongTensor([self.settings.dstId]).to(dev)
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audio1 = (
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self.net_g.to(dev)
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.voice_conversion(
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spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_target
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)[0, 0]
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.data
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* self.hps.data.max_wav_value
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)
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result = audio1.float().cpu().numpy()
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return result
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def inference(self, data):
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if self.slotInfo.isONNX:
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audio = self._onnx_inference(data)
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else:
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audio = self._pyTorch_inference(data)
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return audio
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def __del__(self):
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del self.net_g
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del self.onnx_session
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remove_path = os.path.join("MMVC_Client_v13", "python")
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sys.path = [x for x in sys.path if x.endswith(remove_path) is False]
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for key in list(sys.modules):
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val = sys.modules.get(key)
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try:
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file_path = val.__file__
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if file_path.find(remove_path + os.path.sep) >= 0:
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# print("remove", key, file_path)
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sys.modules.pop(key)
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except: # NOQA
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pass
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def get_model_current(self):
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return [
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{
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"key": "srcId",
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"val": self.settings.srcId,
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},
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{
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"key": "dstId",
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"val": self.settings.dstId,
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},
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]
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