support sovits v2Pro v2ProPlus

support sovits v2Pro v2ProPlus

GPT_SoVITS/prepare_datasets/2-get-sv.py

@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+
+import sys
+import os
+
+inp_text = os.environ.get("inp_text")
+inp_wav_dir = os.environ.get("inp_wav_dir")
+exp_name = os.environ.get("exp_name")
+i_part = os.environ.get("i_part")
+all_parts = os.environ.get("all_parts")
+if "_CUDA_VISIBLE_DEVICES" in os.environ:
+    os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
+from feature_extractor import cnhubert
+
+opt_dir = os.environ.get("opt_dir")
+sv_path = os.environ.get("sv_path")
+import torch
+
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
+
+import traceback
+import numpy as np
+from scipy.io import wavfile
+import torchaudio
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+sys.path.append(f"{now_dir}/GPT_SoVITS/eres2net")
+from tools.my_utils import load_audio, clean_path
+from time import time as ttime
+import shutil
+from ERes2NetV2 import ERes2NetV2
+import kaldi as Kaldi
+
+def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
+    dir = os.path.dirname(path)
+    name = os.path.basename(path)
+    # tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
+    tmp_path = "%s%s.pth" % (ttime(), i_part)
+    torch.save(fea, tmp_path)
+    shutil.move(tmp_path, "%s/%s" % (dir, name))
+
+
+sv_cn_dir = "%s/7-sv_cn" % (opt_dir)
+wav32dir = "%s/5-wav32k" % (opt_dir)
+os.makedirs(opt_dir, exist_ok=True)
+os.makedirs(sv_cn_dir, exist_ok=True)
+os.makedirs(wav32dir, exist_ok=True)
+
+maxx = 0.95
+alpha = 0.5
+if torch.cuda.is_available():
+    device = "cuda:0"
+# elif torch.backends.mps.is_available():
+#     device = "mps"
+else:
+    device = "cpu"
+
+class SV:
+    def __init__(self,device,is_half):
+        pretrained_state = torch.load(sv_path, map_location='cpu')
+        embedding_model = ERes2NetV2(baseWidth=24,scale=4,expansion=4)
+        embedding_model.load_state_dict(pretrained_state)
+        embedding_model.eval()
+        self.embedding_model=embedding_model
+        self.res=torchaudio.transforms.Resample(32000, 16000).to(device)
+        if is_half == False:
+            self.embedding_model=self.embedding_model.to(device)
+        else:
+            self.embedding_model=self.embedding_model.half().to(device)
+        self.is_half=is_half
+
+    def compute_embedding3(self,wav):#(1,x)#-1~1
+        with torch.no_grad():
+            wav=self.res(wav)
+            if self.is_half==True:wav=wav.half()
+            feat = torch.stack([Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav])
+            sv_emb = self.embedding_model.forward3(feat)
+        return sv_emb
+
+sv=SV(device,is_half)
+def name2go(wav_name, wav_path):
+    sv_cn_path = "%s/%s.pt" % (sv_cn_dir, wav_name)
+    if os.path.exists(sv_cn_path):return
+    wav_path="%s/%s" % (wav32dir, wav_name)
+    wav32k,sr0 = torchaudio.load(wav_path)
+    assert sr0==32000
+    wav32k = wav32k.to(device)
+    emb=sv.compute_embedding3(wav32k).cpu()  # torch.Size([1, 20480])
+    my_save(emb, sv_cn_path)
+
+
+with open(inp_text, "r", encoding="utf8") as f:
+    lines = f.read().strip("\n").split("\n")
+
+for line in lines[int(i_part) :: int(all_parts)]:
+    try:
+        wav_name, spk_name, language, text = line.split("|")
+        wav_name = clean_path(wav_name)
+        if inp_wav_dir != "" and inp_wav_dir != None:
+            wav_name = os.path.basename(wav_name)
+            wav_path = "%s/%s" % (inp_wav_dir, wav_name)
+
+        else:
+            wav_path = wav_name
+            wav_name = os.path.basename(wav_name)
+        name2go(wav_name, wav_path)
+    except:
+        print(line, traceback.format_exc())