initial refactor of rvc studio

lib/model_utils.py

@@ -0,0 +1,53 @@
+
+import hashlib
+import os
+import torch.nn.functional as F
+import librosa
+import torch
+
+def get_hash(model_path):
+    try:
+        with open(model_path, 'rb') as f:
+            f.seek(- 10000 * 1024, 2)
+            model_hash = hashlib.md5(f.read()).hexdigest()
+    except:
+        model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
+
+    return model_hash
+
+def load_hubert(model_path,config):
+    try:
+        from fairseq import checkpoint_utils
+        models, _, _ = checkpoint_utils.load_model_ensemble_and_task([model_path],suffix="",)
+        hubert_model = models[0]
+        hubert_model = hubert_model.to(config.device)
+        if config.is_half:
+            hubert_model = hubert_model.half()
+        else:
+            hubert_model = hubert_model.float()
+        hubert_model.eval()
+        return hubert_model
+    except Exception as e:
+        print(e)
+        return None
+    
+def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
+    # print(data1.max(),data2.max())
+    rms1 = librosa.feature.rms(
+        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
+    )  # 每半秒一个点
+    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
+    rms1 = torch.from_numpy(rms1)
+    rms1 = F.interpolate(
+        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
+    ).squeeze()
+    rms2 = torch.from_numpy(rms2)
+    rms2 = F.interpolate(
+        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
+    ).squeeze()
+    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
+    data2 *= (
+        torch.pow(rms1, torch.tensor(1 - rate))
+        * torch.pow(rms2, torch.tensor(rate - 1))
+    ).numpy()
+    return data2