Format code (#142)
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@ -2,27 +2,29 @@ from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
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from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
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import torch
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if __name__ == '__main__':
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MoeVS = True #模型是否为MoeVoiceStudio（原MoeSS）使用
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if __name__ == "__main__":
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MoeVS = True # 模型是否为MoeVoiceStudio（原MoeSS）使用
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ModelPath = "Shiroha/shiroha.pth" #模型路径
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ExportedPath = "model.onnx" #输出路径
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hidden_channels = 256 # hidden_channels，为768Vec做准备
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cpt = torch.load(ModelPath, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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ModelPath = "Shiroha/shiroha.pth" # 模型路径
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ExportedPath = "model.onnx" # 输出路径
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hidden_channels = 256 # hidden_channels，为768Vec做准备
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cpt = torch.load(ModelPath, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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print(*cpt["config"])
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test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
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test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
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test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
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test_pitchf = torch.rand(1, 200) # nsf基频
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test_ds = torch.LongTensor([0]) # 说话人ID
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test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
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test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
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test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
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test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
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test_pitchf = torch.rand(1, 200) # nsf基频
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test_ds = torch.LongTensor([0]) # 说话人ID
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test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
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device = "cpu" #导出时设备（不影响使用模型）
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device = "cpu" # 导出时设备（不影响使用模型）
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if MoeVS:
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net_g = SynthesizerTrnMs256NSFsidM(*cpt["config"], is_half=False) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
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net_g = SynthesizerTrnMs256NSFsidM(
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*cpt["config"], is_half=False
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) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
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output_names = [
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@ -52,7 +54,9 @@ if __name__ == '__main__':
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output_names=output_names,
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)
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else:
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net_g = SynthesizerTrnMs256NSFsidO(*cpt["config"], is_half=False) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
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net_g = SynthesizerTrnMs256NSFsidO(
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*cpt["config"], is_half=False
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) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds"]
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output_names = [
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@ -78,4 +82,4 @@ if __name__ == '__main__':
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verbose=False,
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input_names=input_names,
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output_names=output_names,
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)
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)
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@ -35,7 +35,7 @@ class FeatureInput(object):
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def compute_f0(self, path, f0_method):
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# default resample type of librosa.resample is "soxr_hq".
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# Quality: soxr_vhq > soxr_hq
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x, sr = librosa.load(path, self.fs)#, res_type='soxr_vhq'
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x, sr = librosa.load(path, self.fs) # , res_type='soxr_vhq'
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p_len = x.shape[0] // self.hop
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assert sr == self.fs
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if f0_method == "pm":
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17
gui.py
17
gui.py
@ -67,7 +67,7 @@ class RVC:
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print(e)
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def get_f0(self, x, f0_up_key, inp_f0=None):
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x_pad=1
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x_pad = 1
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f0_min = 50
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f0_max = 1100
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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@ -137,7 +137,7 @@ class RVC:
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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torch.cuda.synchronize()
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print(feats.shape)
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if(self.if_f0==1):
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if self.if_f0 == 1:
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pitch, pitchf = self.get_f0(audio, self.f0_up_key)
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p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存
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else:
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@ -146,7 +146,7 @@ class RVC:
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torch.cuda.synchronize()
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# print(feats.shape,pitch.shape)
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feats = feats[:, :p_len, :]
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if(self.if_f0==1):
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if self.if_f0 == 1:
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pitch = pitch[:p_len]
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pitchf = pitchf[:p_len]
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pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
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@ -155,17 +155,15 @@ class RVC:
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ii = 0 # sid
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sid = torch.LongTensor([ii]).to(device)
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with torch.no_grad():
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if(self.if_f0==1):
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if self.if_f0 == 1:
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infered_audio = (
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self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
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.data.cpu()
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.float()
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)
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else:
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infered_audio = (
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self.net_g.infer(feats, p_len, sid)[0][0, 0]
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.data.cpu()
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.float()
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infered_audio = (
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self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
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)
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torch.cuda.synchronize()
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return infered_audio
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@ -387,7 +385,7 @@ class GUI:
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self.config.pth_path,
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self.config.index_path,
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self.config.npy_path,
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self.config.index_rate
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self.config.index_rate,
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)
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self.input_wav: np.ndarray = np.zeros(
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self.extra_frame
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@ -511,7 +509,6 @@ class GUI:
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total_time = time.perf_counter() - start_time
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self.window["infer_time"].update(int(total_time * 1000))
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print("infer time:" + str(total_time))
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def get_devices(self, update: bool = True):
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"""获取设备列表"""
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6
i18n.py
6
i18n.py
@ -11,8 +11,10 @@ def load_language_list(language):
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class I18nAuto:
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def __init__(self, language=None):
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if language in ['auto', None]:
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language = locale.getdefaultlocale()[0]#getlocale can't identify the system's language ((None, None))
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if language in ["auto", None]:
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language = locale.getdefaultlocale()[
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0
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] # getlocale can't identify the system's language ((None, None))
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if not os.path.exists(f"./i18n/{language}.json"):
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language = "en_US"
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self.language = language
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36
infer-web.py
36
infer-web.py
@ -119,7 +119,6 @@ for name in os.listdir(weight_uvr5_root):
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uvr5_names.append(name.replace(".pth", ""))
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def vc_single(
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sid,
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input_audio,
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@ -888,23 +887,27 @@ def change_info_(ckpt_path):
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from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
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from infer_pack.models_onnx import SynthesizerTrnMs256NSFsidO
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def export_onnx(ModelPath, ExportedPath, MoeVS=True):
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hidden_channels = 256 # hidden_channels，为768Vec做准备
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cpt = torch.load(ModelPath, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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hidden_channels = 256 # hidden_channels，为768Vec做准备
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cpt = torch.load(ModelPath, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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print(*cpt["config"])
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test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
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test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
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test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
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test_pitchf = torch.rand(1, 200) # nsf基频
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test_ds = torch.LongTensor([0]) # 说话人ID
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test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
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test_phone = torch.rand(1, 200, hidden_channels) # hidden unit
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test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
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test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
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test_pitchf = torch.rand(1, 200) # nsf基频
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test_ds = torch.LongTensor([0]) # 说话人ID
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test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
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device = "cpu" #导出时设备（不影响使用模型）
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device = "cpu" # 导出时设备（不影响使用模型）
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if MoeVS:
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net_g = SynthesizerTrnMs256NSFsidM(*cpt["config"], is_half=False) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
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net_g = SynthesizerTrnMs256NSFsidM(
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*cpt["config"], is_half=False
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) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
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output_names = [
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@ -934,7 +937,9 @@ def export_onnx(ModelPath, ExportedPath, MoeVS=True):
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output_names=output_names,
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)
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else:
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net_g = SynthesizerTrnMs256NSFsidO(*cpt["config"], is_half=False) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
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net_g = SynthesizerTrnMs256NSFsidO(
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*cpt["config"], is_half=False
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) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds"]
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output_names = [
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@ -963,6 +968,7 @@ def export_onnx(ModelPath, ExportedPath, MoeVS=True):
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)
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return "Finished"
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with gr.Blocks() as app:
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gr.Markdown(
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value=i18n(
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@ -1443,7 +1449,9 @@ with gr.Blocks() as app:
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with gr.Row():
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ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True)
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with gr.Row():
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onnx_dir = gr.Textbox(label=i18n("Onnx输出路径"), value="", interactive=True)
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onnx_dir = gr.Textbox(
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label=i18n("Onnx输出路径"), value="", interactive=True
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)
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with gr.Row():
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moevs = gr.Checkbox(label=i18n("MoeVS模型"), value=True)
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infoOnnx = gr.Label(label="Null")
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@ -18,4 +18,4 @@ def load_audio(file, sr):
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except Exception as e:
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raise RuntimeError(f"Failed to load audio: {e}")
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return np.frombuffer(out, np.float32).flatten()
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return np.frombuffer(out, np.float32).flatten()
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@ -99,8 +99,8 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
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)
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)
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audio_norm = audio
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# audio_norm = audio / self.max_wav_value
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# audio_norm = audio / np.abs(audio).max()
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# audio_norm = audio / self.max_wav_value
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# audio_norm = audio / np.abs(audio).max()
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audio_norm = audio_norm.unsqueeze(0)
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spec_filename = filename.replace(".wav", ".spec.pt")
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@ -291,8 +291,8 @@ class TextAudioLoader(torch.utils.data.Dataset):
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)
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)
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audio_norm = audio
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# audio_norm = audio / self.max_wav_value
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# audio_norm = audio / np.abs(audio).max()
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# audio_norm = audio / self.max_wav_value
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# audio_norm = audio / np.abs(audio).max()
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audio_norm = audio_norm.unsqueeze(0)
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spec_filename = filename.replace(".wav", ".spec.pt")
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@ -61,7 +61,9 @@ class PreProcess:
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self.sr,
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tmp_audio.astype(np.float32),
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)
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tmp_audio = librosa.resample(tmp_audio, orig_sr=self.sr, target_sr=16000)#, res_type="soxr_vhq"
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tmp_audio = librosa.resample(
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tmp_audio, orig_sr=self.sr, target_sr=16000
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) # , res_type="soxr_vhq"
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wavfile.write(
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"%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
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16000,
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@ -72,7 +74,7 @@ class PreProcess:
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try:
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audio = load_audio(path, self.sr)
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# zero phased digital filter cause pre-ringing noise...
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# audio = signal.filtfilt(self.bh, self.ah, audio)
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# audio = signal.filtfilt(self.bh, self.ah, audio)
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audio = signal.lfilter(self.bh, self.ah, audio)
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idx1 = 0
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