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train/mel_processing.py

@@ -51,9 +51,9 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
         :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
     """
     # Validation
-    if torch.min(y) < -1.0:
+    if torch.min(y) < -1.07:
         print("min value is ", torch.min(y))
-    if torch.max(y) > 1.0:
+    if torch.max(y) > 1.07:
         print("max value is ", torch.max(y))
 
     # Window - Cache if needed

train/process_ckpt.py

@@ -2,7 +2,7 @@ import torch, traceback, os, pdb
 from collections import OrderedDict
 
 
-def savee(ckpt, sr, if_f0, name, epoch):
+def savee(ckpt, sr, if_f0, name, epoch,version):
     try:
         opt = OrderedDict()
         opt["weight"] = {}
@@ -76,6 +76,7 @@ def savee(ckpt, sr, if_f0, name, epoch):
         opt["info"] = "%sepoch" % epoch
         opt["sr"] = sr
         opt["f0"] = if_f0
+        opt["version"] = version
         torch.save(opt, "weights/%s.pth" % name)
         return "Success."
     except:
@@ -85,16 +86,17 @@ def savee(ckpt, sr, if_f0, name, epoch):
 def show_info(path):
     try:
         a = torch.load(path, map_location="cpu")
-        return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s" % (
+        return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % (
             a.get("info", "None"),
             a.get("sr", "None"),
             a.get("f0", "None"),
+            a.get("version", "None"),
         )
     except:
         return traceback.format_exc()
 
 
-def extract_small_model(path, name, sr, if_f0, info):
+def extract_small_model(path, name, sr, if_f0, info,version):
     try:
         ckpt = torch.load(path, map_location="cpu")
         if "model" in ckpt:
@@ -171,6 +173,7 @@ def extract_small_model(path, name, sr, if_f0, info):
         if info == "":
             info = "Extracted model."
         opt["info"] = info
+        opt["version"] = version
         opt["sr"] = sr
         opt["f0"] = int(if_f0)
         torch.save(opt, "weights/%s.pth" % name)
@@ -191,7 +194,7 @@ def change_info(path, info, name):
         return traceback.format_exc()
 
 
-def merge(path1, path2, alpha1, sr, f0, info, name):
+def merge(path1, path2, alpha1, sr, f0, info, name,version):
     try:
 
         def extract(ckpt):
@@ -241,6 +244,7 @@ def merge(path1, path2, alpha1, sr, f0, info, name):
         """
         opt["sr"] = sr
         opt["f0"] = 1 if f0 == "是" else 0
+        opt["version"] = version
         opt["info"] = info
         torch.save(opt, "weights/%s.pth" % name)
         return "Success."

train/utils.py

@@ -284,8 +284,8 @@ def get_hparams(init=True):
         bs                                    done
         pretrainG、pretrainD                  done
         卡号：os.en["CUDA_VISIBLE_DEVICES"]   done
-        if_latest                             todo
-      模型：if_f0                             todo
+        if_latest                             done
+      模型：if_f0                             done
       采样率：自动选择config                  done
       是否缓存数据集进GPU:if_cache_data_in_gpu done
 
@@ -321,6 +321,12 @@ def get_hparams(init=True):
     parser.add_argument(
         "-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
     )
+    parser.add_argument(
+        "-sw", "--save_every_weights", type=str, default="0", help="save the extracted model in weights directory when saving checkpoints"
+    )
+    parser.add_argument(
+        "-v", "--version", type=str, required=True, help="model version"
+    )
     parser.add_argument(
         "-f0",
         "--if_f0",
@@ -369,11 +375,13 @@ def get_hparams(init=True):
     hparams.total_epoch = args.total_epoch
     hparams.pretrainG = args.pretrainG
     hparams.pretrainD = args.pretrainD
+    hparams.version = args.version
     hparams.gpus = args.gpus
     hparams.train.batch_size = args.batch_size
     hparams.sample_rate = args.sample_rate
     hparams.if_f0 = args.if_f0
     hparams.if_latest = args.if_latest
+    hparams.save_every_weights = args.save_every_weights
     hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
     hparams.data.training_files = "%s/filelist.txt" % experiment_dir
     return hparams