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https://github.com/w-okada/voice-changer.git
synced 2025-02-02 16:23:58 +03:00
WIP: supprt vrc
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wataru
parent
c9aaf751f0
commit
1fd0422b43
@ -1,6 +1,7 @@
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import sys
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import os
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# avoiding parse arg error in RVC
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sys.argv = ["MMVCServerSIO.py"]
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if sys.platform.startswith('darwin'):
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@ -13,7 +14,6 @@ if sys.platform.startswith('darwin'):
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else:
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sys.path.append("RVC")
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print("RVC 3")
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import io
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from dataclasses import dataclass, asdict, field
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from functools import reduce
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@ -74,30 +74,8 @@ class RVC:
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def loadModel(self, config: str, pyTorch_model_file: str = None, onnx_model_file: str = None, clusterTorchModel: str = None):
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self.device = torch.device("cuda", index=self.settings.gpu)
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self.settings.configFile = config
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# self.hps = utils.get_hparams_from_file(config)
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# self.settings.speakers = self.hps.spk
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# hubert model
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try:
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# hubert_path = self.params["hubert"]
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# useHubertOnnx = self.params["useHubertOnnx"]
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# self.useHubertOnnx = useHubertOnnx
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# if useHubertOnnx == True:
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# ort_options = onnxruntime.SessionOptions()
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# ort_options.intra_op_num_threads = 8
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# self.hubert_onnx = onnxruntime.InferenceSession(
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# HUBERT_ONNX_MODEL_PATH,
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# providers=providers
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# )
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# else:
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# models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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# [hubert_path],
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# suffix="",
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# )
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# model = models[0]
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# model.eval()
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# self.hubert_model = model.cpu()
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(["hubert_base.pt"], suffix="",)
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model = models[0]
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model.eval()
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@ -117,7 +95,6 @@ class RVC:
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if pyTorch_model_file != None:
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cpt = torch.load(pyTorch_model_file, map_location="cpu")
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self.tgt_sr = cpt["config"][-1]
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# n_spk = cpt["config"][-3]
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is_half = False
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
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net_g.eval()
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@ -126,14 +103,6 @@ class RVC:
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self.net_g = net_g
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self.net_g = self.net_g.to(self.device)
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# self.net_g = SynthesizerTrn(
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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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# **self.hps.model
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# )
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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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@ -201,20 +170,6 @@ class RVC:
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# return 24000
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def generate_input(self, newData: any, inputSize: int, crossfadeSize: int):
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# import wave
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# filename = "testc2.wav"
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# if os.path.exists(filename):
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# print("[IORecorder] delete old analyze file.", filename)
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# os.remove(filename)
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# fo = wave.open(filename, 'wb')
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# fo.setnchannels(1)
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# fo.setsampwidth(2)
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# # fo.setframerate(24000)
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# fo.setframerate(self.tgt_sr)
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# fo.writeframes(newData.astype(np.int16))
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# fo.close()
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# newData = newData.astype(np.float32) / self.hps.data.max_wav_value
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newData = newData.astype(np.float32) / 32768.0
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if hasattr(self, "audio_buffer"):
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@ -267,9 +222,6 @@ class RVC:
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audio = data[0]
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convertSize = data[1]
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vol = data[2]
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# from scipy.io import wavfile
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# # wavfile.write("testa.wav", self.tgt_sr, audio * 32768.0)
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# wavfile.write("testa.wav", 24000, audio * 32768.0)
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filename = "testc2.wav"
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audio = load_audio(filename, 16000)
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@ -309,57 +261,6 @@ class RVC:
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del self.onnx_session
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# def resize_f0(x, target_len):
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# source = np.array(x)
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# source[source < 0.001] = np.nan
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# target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)), source)
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# res = np.nan_to_num(target)
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# return res
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# def compute_f0_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
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# if p_len is None:
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# p_len = wav_numpy.shape[0] // hop_length
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# f0, t = pw.dio(
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# wav_numpy.astype(np.double),
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# fs=sampling_rate,
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# f0_ceil=800,
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# frame_period=1000 * hop_length / sampling_rate,
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# )
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# f0 = pw.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
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# for index, pitch in enumerate(f0):
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# f0[index] = round(pitch, 1)
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# return resize_f0(f0, p_len)
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# def compute_f0_harvest(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
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# if p_len is None:
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# p_len = wav_numpy.shape[0] // hop_length
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# f0, t = pw.harvest(wav_numpy.astype(np.double), fs=sampling_rate, frame_period=5.5, f0_floor=71.0, f0_ceil=1000.0)
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# for index, pitch in enumerate(f0):
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# f0[index] = round(pitch, 1)
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# return resize_f0(f0, p_len)
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# def get_hubert_content_layer9(hmodel, wav_16k_tensor):
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# feats = wav_16k_tensor
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# if feats.dim() == 2: # double channels
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# feats = feats.mean(-1)
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# assert feats.dim() == 1, feats.dim()
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# feats = feats.view(1, -1)
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# padding_mask = torch.BoolTensor(feats.shape).fill_(False)
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# inputs = {
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# "source": feats.to(wav_16k_tensor.device),
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# "padding_mask": padding_mask.to(wav_16k_tensor.device),
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# "output_layer": 9, # layer 9
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# }
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# with torch.no_grad():
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# logits = hmodel.extract_features(**inputs)
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# return logits[0].transpose(1, 2)
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import ffmpeg
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@ -49,35 +49,25 @@ class VoiceChanger():
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self.modelType = getModelType()
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print("[VoiceChanger] activate model type:", self.modelType)
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print("RVC!!! 1")
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if self.modelType == "MMVCv15":
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print("RVC!!! 2")
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from voice_changer.MMVCv15.MMVCv15 import MMVCv15
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self.voiceChanger = MMVCv15()
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elif self.modelType == "MMVCv13":
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print("RVC!!! 2")
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from voice_changer.MMVCv13.MMVCv13 import MMVCv13
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self.voiceChanger = MMVCv13()
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elif self.modelType == "so-vits-svc-40v2":
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print("RVC!!! 2")
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from voice_changer.SoVitsSvc40v2.SoVitsSvc40v2 import SoVitsSvc40v2
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self.voiceChanger = SoVitsSvc40v2(params)
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elif self.modelType == "so-vits-svc-40" or self.modelType == "so-vits-svc-40_c":
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print("RVC!!! 2")
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from voice_changer.SoVitsSvc40.SoVitsSvc40 import SoVitsSvc40
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self.voiceChanger = SoVitsSvc40(params)
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elif self.modelType == "DDSP-SVC":
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print("RVC!!! 2")
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from voice_changer.DDSP_SVC.DDSP_SVC import DDSP_SVC
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self.voiceChanger = DDSP_SVC(params)
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elif self.modelType == "RVC":
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print("RVC!!! 22222222222")
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from voice_changer.RVC.RVC import RVC
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print("RVC!!! 2")
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self.voiceChanger = RVC(params)
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else:
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print("RVC!!! 3")
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from voice_changer.MMVCv13.MMVCv13 import MMVCv13
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self.voiceChanger = MMVCv13()
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@ -220,10 +210,6 @@ class VoiceChanger():
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f" audio:{audio.shape}, cur_overlap:{cur_overlap.shape}, self.np_cur_strength:{self.np_cur_strength.shape}")
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print_convert_processing(f" cur_overlap_strt:{cur_overlap_start}, cur_overlap_end{cur_overlap_end}")
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print(
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f" audio:{audio.shape}, cur_overlap:{cur_overlap.shape}, self.np_cur_strength:{self.np_cur_strength.shape}")
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print(f" cur_overlap_strt:{cur_overlap_start}, cur_overlap_end{cur_overlap_end}")
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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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