vrem ton working

utils/tts.py

@@ -0,0 +1,57 @@
+import os
+
+import torch
+import torchaudio
+
+
+def load_data(audio_folder):
+    audios = []
+    texts = []
+    for audio_file in os.listdir(audio_folder):
+        if audio_file.endswith('.wav'):
+            audio_path = os.path.join(audio_folder, audio_file)
+            waveform, sample_rate = torchaudio.load(audio_path)
+            text_path = audio_path.replace('.wav', '.txt')
+            with open(text_path) as f:
+                text = f.read().strip()
+            audios.append((waveform, sample_rate))
+            texts.append(text)
+    return audios, texts
+
+
+def train(model, audios, texts, epochs=3, learning_rate=1e-4):
+    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+    criterion = torch.nn.MSELoss()  # Вам нужно будет настроить эту функцию под вашу задачу
+
+    model.train()
+    for epoch in range(epochs):
+        total_loss = 0
+        for waveform, text in zip(audios, texts):
+            optimizer.zero_grad()
+            # Предполагается, что модель принимает текст и возвращает аудио
+            predicted_waveform = model(text)
+            loss = criterion(predicted_waveform, waveform)
+            loss.backward()
+            optimizer.step()
+            total_loss += loss.item()
+        average_loss = total_loss / len(audios)
+        print(f'Epoch {epoch + 1}: Average Loss = {average_loss}')
+
+
+def main():
+    model_path = 'data/v4_ru.pt'
+    model = torch.load(model_path)
+    model.eval()
+    audio_folder = 'wav_files'
+    audios, texts = load_data(audio_folder)
+    train(model, audios, texts)
+    torch.save(model.state_dict(), 'fine_tuned_model.pth')
+    model.eval()
+    sample_text = "Пример текста для синтеза."
+    with torch.no_grad():
+        generated_waveform = model(sample_text)
+        torchaudio.save('output_audio.wav', generated_waveform, 16000)
+
+
+if __name__ == '__main__':
+    main()