Create app.py

app.py

@@ -0,0 +1,62 @@
+import torch
+import torchaudio
+import json
+from transformers import Trainer, TrainingArguments, Wav2Vec2ForCTC, Wav2Vec2Processor
+
+# 1. Load Audio Data from JSON File
+def load_audio_from_json(json_file):
+    with open(json_file, 'r') as f:
+        data = json.load(f)
+    audio_samples = []
+    for item in data['audio_files']:
+        if item.get('url'):
+            # Downloading from URL (requires additional handling if desired)
+            continue
+        audio, sr = torchaudio.load(item['path'])
+        audio_samples.append((audio, sr))
+    return audio_samples
+
+audio_samples = load_audio_from_json('audio_data.json')
+
+# 2. Load Pre-trained Model and Processor
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+
+# 3. Preprocess Data
+def preprocess_audio(audio_sample):
+    audio, sr = audio_sample
+    inputs = processor(audio.numpy(), sampling_rate=sr, return_tensors="pt", padding=True)
+    return inputs.input_values[0], inputs.attention_mask[0]
+
+dataset = [(preprocess_audio(sample)) for sample in audio_samples]
+
+# 4. Training Arguments
+training_args = TrainingArguments(
+    output_dir="./rvc_checkpoints",
+    evaluation_strategy="epoch",
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=2,
+    gradient_accumulation_steps=2,
+    num_train_epochs=3,
+    save_strategy="epoch",
+    logging_dir="./logs",
+    logging_steps=10,
+    report_to="none",
+    fp16=torch.cuda.is_available(),
+)
+
+# 5. Trainer Setup
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset,
+)
+
+# 6. Train Model
+trainer.train()
+
+# 7. Save Model
+model.save_pretrained("./rvc_trained_model")
+processor.save_pretrained("./rvc_trained_model")
+
+print("Training Completed!")