mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-24 05:55:01 +03:00
315 lines
15 KiB
Python
315 lines
15 KiB
Python
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import sys
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sys.path.append("MMVC_Client/python")
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import os
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from dataclasses import dataclass, asdict
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import numpy as np
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import torch
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import onnxruntime
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import pyworld as pw
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from voice_changer.client_modules import convert_continuos_f0, spectrogram_torch, TextAudioSpeakerCollate, get_hparams_from_file, load_checkpoint
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from models import SynthesizerTrn
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from const import ERROR_NO_ONNX_SESSION, TMP_DIR
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providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
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@dataclass
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class MMVCv15Settings():
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gpu: int = 0
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srcId: int = 0
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dstId: int = 101
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inputSampleRate: int = 24000 # 48000 or 24000
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crossFadeOffsetRate: float = 0.1
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crossFadeEndRate: float = 0.9
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crossFadeOverlapSize: int = 4096
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f0Factor: float = 1.0
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f0Detector: str = "dio" # dio or harvest
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recordIO: int = 0 # 0:off, 1:on
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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", "inputSampleRate", "crossFadeOverlapSize", "recordIO"]
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floatData = ["crossFadeOffsetRate", "crossFadeEndRate", "f0Factor"]
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strData = ["framework", "f0Detector"]
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class MMVCv15:
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def __init__(self):
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# 初期化
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self.settings = MMVCv15Settings()
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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 = torch.zeros(1, 0)
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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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self.hps = get_hparams_from_file(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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spec_channels=self.hps.data.filter_length // 2 + 1,
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segment_size=self.hps.train.segment_size // self.hps.data.hop_length,
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inter_channels=self.hps.model.inter_channels,
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hidden_channels=self.hps.model.hidden_channels,
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upsample_rates=self.hps.model.upsample_rates,
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upsample_initial_channel=self.hps.model.upsample_initial_channel,
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upsample_kernel_sizes=self.hps.model.upsample_kernel_sizes,
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n_flow=self.hps.model.n_flow,
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dec_out_channels=1,
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dec_kernel_size=7,
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n_speakers=self.hps.data.n_speakers,
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gin_channels=self.hps.model.gin_channels,
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requires_grad_pe=self.hps.requires_grad.pe,
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requires_grad_flow=self.hps.requires_grad.flow,
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requires_grad_text_enc=self.hps.requires_grad.text_enc,
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requires_grad_dec=self.hps.requires_grad.dec
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)
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self.net_g.eval()
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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["onnxExecutionProviders"] = 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_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 + 1280 * 2):] # 変換対象の部分だけ抽出
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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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# TBD: numpy <--> pytorch変換が行ったり来たりしているが、まずは動かすことを最優先。
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audio_norm_np = audio_norm.squeeze().numpy().astype(np.float64)
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if self.settings.f0Detector == "dio":
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_f0, _time = pw.dio(audio_norm_np, self.hps.data.sampling_rate, frame_period=5.5)
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f0 = pw.stonemask(audio_norm_np, _f0, _time, self.hps.data.sampling_rate)
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else:
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f0, t = pw.harvest(audio_norm_np, self.hps.data.sampling_rate, frame_period=5.5, f0_floor=71.0, f0_ceil=1000.0)
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f0 = convert_continuos_f0(f0, int(audio_norm_np.shape[0] / self.hps.data.hop_length))
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f0 = torch.from_numpy(f0.astype(np.float32))
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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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# dispose_stft_specs = 2
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# spec = spec[:, dispose_stft_specs:-dispose_stft_specs]
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# f0 = f0[dispose_stft_specs:-dispose_stft_specs]
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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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data = TextAudioSpeakerCollate(
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sample_rate=self.hps.data.sampling_rate,
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hop_size=self.hps.data.hop_length,
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f0_factor=self.settings.f0Factor
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)([(spec, sid, f0)])
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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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spec, spec_lengths, sid_src, sin, d = 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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"sin": sin.numpy(),
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"d0": d[0][:1].numpy(),
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"d1": d[1][:1].numpy(),
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"d2": d[2][:1].numpy(),
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"d3": d[3][:1].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 = min(self.settings.crossFadeOverlapSize, inputSize)
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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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spec, spec_lengths, sid_src, sin, d = data
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spec = spec.cpu()
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spec_lengths = spec_lengths.cpu()
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sid_src = sid_src.cpu()
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sin = sin.cpu()
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d = tuple([d[:1].cpu() for d in d])
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sid_target = torch.LongTensor([self.settings.dstId]).cpu()
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audio1 = self.net_g.cpu().voice_conversion(spec, spec_lengths, sin, d, sid_src, sid_target)[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'): # prev_audio1が所望のデバイスに無い場合は一回休み。
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overlapSize = min(self.settings.crossFadeOverlapSize, inputSize)
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prev_overlap = self.prev_audio1[-1 * overlapSize:]
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cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
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powered_prev = prev_overlap * self.prev_strength
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powered_cur = cur_overlap * self.cur_strength
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powered_result = powered_prev + powered_cur
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cur = audio1[-1 * inputSize:-1 * overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
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result = torch.cat([powered_result, cur], axis=0) # Crossfadeと今回のインプットの生部分を結合
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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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spec, spec_lengths, sid_src, sin, d = data
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spec = spec.cuda(self.settings.gpu)
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spec_lengths = spec_lengths.cuda(self.settings.gpu)
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sid_src = sid_src.cuda(self.settings.gpu)
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sin = sin.cuda(self.settings.gpu)
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d = tuple([d[:1].cuda(self.settings.gpu) for d in d])
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sid_target = 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,
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# sid_tgt=sid_tgt1)[0, 0].data * self.hps.data.max_wav_value
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audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sin, d,
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sid_src, sid_target)[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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overlapSize = min(self.settings.crossFadeOverlapSize, inputSize)
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prev_overlap = self.prev_audio1[-1 * overlapSize:]
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cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize]
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powered_prev = prev_overlap * self.prev_strength
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powered_cur = cur_overlap * self.cur_strength
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powered_result = powered_prev + powered_cur
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# print(overlapSize, prev_overlap.shape, cur_overlap.shape, self.prev_strength.shape, self.cur_strength.shape)
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# print(self.prev_audio1.shape, audio1.shape, inputSize, overlapSize)
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cur = audio1[-1 * inputSize:-1 * overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。
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result = torch.cat([powered_result, cur], axis=0) # Crossfadeと今回のインプットの生部分を結合
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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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return result
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