Format code (#330)
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@ -2,6 +2,7 @@ import argparse
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import torch
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from multiprocessing import cpu_count
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def config_file_change_fp32():
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for config_file in ["32k.json", "40k.json", "48k.json"]:
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with open(f"configs/{config_file}", "r") as f:
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@ -13,6 +14,7 @@ def config_file_change_fp32():
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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class Config:
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def __init__(self):
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self.device = "cuda:0"
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@ -52,4 +52,3 @@ if __name__ == "__main__":
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input_names=input_names,
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output_names=output_names,
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)
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44
gui.py
44
gui.py
@ -1,4 +1,4 @@
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'''
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"""
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0416后的更新：
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引入config中half
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重建npy而不用填写
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@ -9,12 +9,14 @@
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int16：
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增加无索引支持
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f0算法改harvest(怎么看就只有这个会影响CPU占用)，但是不这么改效果不好
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'''
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"""
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import os, sys, traceback
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from config import Config
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is_half=Config().is_half
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is_half = Config().is_half
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import PySimpleGUI as sg
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import sounddevice as sd
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import noisereduce as nr
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@ -26,7 +28,12 @@ import torchaudio.transforms as tat
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import scipy.signal as signal
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# import matplotlib.pyplot as plt
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono,SynthesizerTrnMs768NSFsid,SynthesizerTrnMs768NSFsid_nono
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from infer_pack.models import (
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SynthesizerTrnMs256NSFsid,
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SynthesizerTrnMs256NSFsid_nono,
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SynthesizerTrnMs768NSFsid,
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SynthesizerTrnMs768NSFsid_nono,
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)
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from i18n import I18nAuto
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i18n = I18nAuto()
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@ -63,7 +70,7 @@ class RVC:
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)
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self.model = models[0]
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self.model = self.model.to(device)
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if(is_half==True):
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if is_half == True:
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self.model = self.model.half()
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else:
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self.model = self.model.float()
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@ -75,21 +82,25 @@ class RVC:
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self.version = cpt.get("version", "v1")
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if version == "v1":
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if if_f0 == 1:
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self.net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
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self.net_g = SynthesizerTrnMs256NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif version == "v2":
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if if_f0 == 1:
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self.net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
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self.net_g = SynthesizerTrnMs768NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del self.net_g.enc_q
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print(self.net_g.load_state_dict(cpt["weight"], strict=False))
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self.net_g.eval().to(device)
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if(is_half==True):
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self.net_g=self.net_g.half()
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if is_half == True:
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self.net_g = self.net_g.half()
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else:
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self.net_g=self.net_g.float()
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self.net_g = self.net_g.float()
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except:
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print(traceback.format_exc())
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@ -151,15 +162,18 @@ class RVC:
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####索引优化
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try:
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if hasattr(self, "index") and hasattr(self, "big_npy") and self.index_rate != 0:
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if (
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hasattr(self, "index")
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and hasattr(self, "big_npy")
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and self.index_rate != 0
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):
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npy = feats[0].cpu().numpy().astype("float32")
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score, ix = self.index.search(npy, k=8)
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weight = np.square(1 / score)
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weight /= weight.sum(axis=1, keepdims=True)
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npy = np.sum(
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self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1
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)
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if(is_half==True):npy=npy.astype("float16")
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npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
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if is_half == True:
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npy = npy.astype("float16")
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feats = (
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torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
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+ (1 - self.index_rate) * feats
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4
i18n.py
4
i18n.py
@ -18,11 +18,11 @@ class I18nAuto:
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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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#print("Use Language:", language)
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# print("Use Language:", language)
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self.language_map = load_language_list(language)
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def __call__(self, key):
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return self.language_map.get(key, key)
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def print(self):
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print("Use Language:", self.language)
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@ -1069,6 +1069,8 @@ def change_info_(ckpt_path):
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from infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
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def export_onnx(ModelPath, ExportedPath, MoeVS=True):
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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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@ -633,14 +633,14 @@ class SynthesizerTrnMsNSFsidM(nn.Module):
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self.speaker_map = self.speaker_map.unsqueeze(0)
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def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
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if self.speaker_map is not None: # [N, S] * [S, B, 1, H]
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if self.speaker_map is not None: # [N, S] * [S, B, 1, H]
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g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1]
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g = g * self.speaker_map # [N, S, B, 1, H]
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g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
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g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N]
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g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
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g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N]
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else:
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g = g.unsqueeze(0)
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g = self.emb_g(g).transpose(1,2)
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g = self.emb_g(g).transpose(1, 2)
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m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
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z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
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