Create createfile.py

createfile.py

@@ -0,0 +1,133 @@
+import os
+import numpy as np
+import faiss
+from sklearn.cluster import MiniBatchKMeans
+import traceback
+
+# Set the working directory
+os.chdir('/content/RVC')
+
+# Parameters
+model_name = 'My-Voice'
+dataset_folder = '/content/dataset'
+
+def calculate_audio_duration(file_path):
+    # Placeholder function - replace with actual implementation
+    return 0
+
+# Check cache status based on audio duration
+try:
+    duration = calculate_audio_duration(dataset_folder)
+    cache = duration < 600
+except:
+    cache = False
+
+# Ensure dataset folder is not empty
+while len(os.listdir(dataset_folder)) < 1:
+    input("Your dataset folder is empty.")
+
+os.makedirs(f'./logs/{model_name}', exist_ok=True)
+
+# Run the preprocessing script
+os.system(f'python infer/modules/train/preprocess.py {dataset_folder} 32000 2 ./logs/{model_name} False 3.0 > /dev/null 2>&1')
+
+with open(f'./logs/{model_name}/preprocess.log', 'r') as f:
+    if 'end preprocess' in f.read():
+        print("✔ Success")
+    else:
+        print("Error preprocessing data... Make sure your dataset folder is correct.")
+
+f0method = "rmvpe_gpu"
+
+# Run the feature extraction scripts
+if f0method != "rmvpe_gpu":
+    os.system(f'python infer/modules/train/extract/extract_f0_print.py ./logs/{model_name} 2 {f0method}')
+else:
+    os.system(f'python infer/modules/train/extract/extract_f0_rmvpe.py 1 0 0 ./logs/{model_name} True')
+
+os.system(f'python infer/modules/train/extract_feature_print.py cuda:0 1 0 ./logs/{model_name} v2 True')
+
+with open(f'./logs/{model_name}/extract_f0_feature.log', 'r') as f:
+    if 'all-feature-done' in f.read():
+        print("✔ Success")
+    else:
+        print("Error preprocessing data... Make sure your data was preprocessed.")
+
+def train_index(exp_dir1, version19):
+    exp_dir = f"logs/{exp_dir1}"
+    os.makedirs(exp_dir, exist_ok=True)
+    feature_dir = f"{exp_dir}/3_feature256" if version19 == "v1" else f"{exp_dir}/3_feature768"
+    
+    if not os.path.exists(feature_dir):
+        return "请先进行特征提取!"
+    
+    listdir_res = list(os.listdir(feature_dir))
+    if len(listdir_res) == 0:
+        return "请先进行特征提取！"
+    
+    infos = []
+    npys = []
+    
+    for name in sorted(listdir_res):
+        phone = np.load(f"{feature_dir}/{name}")
+        npys.append(phone)
+    
+    big_npy = np.concatenate(npys, 0)
+    big_npy_idx = np.arange(big_npy.shape[0])
+    np.random.shuffle(big_npy_idx)
+    big_npy = big_npy[big_npy_idx]
+    
+    if big_npy.shape[0] > 2e5:
+        infos.append(f"Trying doing kmeans {big_npy.shape[0]} shape to 10k centers.")
+        yield "\n".join(infos)
+        
+        try:
+            big_npy = MiniBatchKMeans(
+                n_clusters=10000,
+                verbose=True,
+                batch_size=256,
+                compute_labels=False,
+                init="random"
+            ).fit(big_npy).cluster_centers_
+        except:
+            info = traceback.format_exc()
+            infos.append(info)
+            yield "\n".join(infos)
+    
+    np.save(f"{exp_dir}/total_fea.npy", big_npy)
+    n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
+    infos.append(f"{big_npy.shape},{n_ivf}")
+    yield "\n".join(infos)
+    
+    index = faiss.index_factory(256 if version19 == "v1" else 768, f"IVF{n_ivf},Flat")
+    infos.append("training")
+    yield "\n".join(infos)
+    
+    index_ivf = faiss.extract_index_ivf(index)
+    index_ivf.nprobe = 1
+    index.train(big_npy)
+    faiss.write_index(
+        index,
+        f"{exp_dir}/trained_IVF{n_ivf}_Flat_nprobe_{index_ivf.nprobe}_{exp_dir1}_{version19}.index"
+    )
+    
+    infos.append("adding")
+    yield "\n".join(infos)
+    
+    batch_size_add = 8192
+    for i in range(0, big_npy.shape[0], batch_size_add):
+        index.add(big_npy[i: i + batch_size_add])
+    
+    faiss.write_index(
+        index,
+        f"{exp_dir}/added_IVF{n_ivf}_Flat_nprobe_{index_ivf.nprobe}_{exp_dir1}_{version19}.index"
+    )
+    
+    infos.append(f"成功构建索引，added_IVF{n_ivf}_Flat_nprobe_{index_ivf.nprobe}_{exp_dir1}_{version19}.index")
+
+training_log = train_index(model_name, 'v2')
+
+for line in training_log:
+    print(line)
+    if 'adding' in line:
+        print("✔ Success")