Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import TTSConfig, TTSForConditionalGeneration
+import numpy as np
+import soundfile as sf
+
+# Load your model and configuration
+def load_model(model_path, config_path):
+    config = TTSConfig.from_json_file(config_path)
+    model = TTSForConditionalGeneration.from_pretrained(model_path, config=config)
+    model.eval()
+    return model
+
+# Define the path to your model and config
+MODEL_PATH = 'path/to/best_model.pth'
+CONFIG_PATH = 'path/to/config.json'
+
+model = load_model(MODEL_PATH, CONFIG_PATH)
+
+# Define the function to generate speech
+def generate_speech(text):
+    # Convert text to input format
+    inputs = tokenizer(text, return_tensors="pt")
+    
+    with torch.no_grad():
+        # Generate speech
+        outputs = model.generate(inputs['input_ids'])
+    
+    # Convert outputs to numpy array (audio waveform)
+    # This conversion depends on your model
+    audio_waveform = outputs.squeeze().numpy()
+
+    # Save the waveform to a temporary file
+    temp_file = 'temp.wav'
+    sf.write(temp_file, audio_waveform, 22050)  # Adjust sample rate if necessary
+    
+    return temp_file
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=generate_speech,
+    inputs="text",
+    outputs="audio",
+    title="Text-to-Speech Model",
+    description="Generate speech from text using your TTS model."
+)
+
+# Launch the Gradio interface
+if __name__ == "__main__":
+    interface.launch()