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| import gradio as gr | |
| import torch | |
| from outetts.v0_1.interface import InterfaceHF | |
| import soundfile as sf | |
| import tempfile | |
| import os | |
| def initialize_model(): | |
| """Initialize the OuteTTS model""" | |
| interface = InterfaceHF("OuteAI/OuteTTS-0.1-350M") | |
| return interface | |
| def process_audio_file(audio_path, reference_text, text_to_speak, temperature=0.1, repetition_penalty=1.1): | |
| """Process the audio file and generate speech with the cloned voice""" | |
| try: | |
| # Initialize model | |
| interface = initialize_model() | |
| # Create speaker from reference audio | |
| speaker = interface.create_speaker( | |
| audio_path, | |
| reference_text | |
| ) | |
| # Generate speech with cloned voice | |
| output = interface.generate( | |
| text=text_to_speak, | |
| speaker=speaker, | |
| temperature=temperature, | |
| repetition_penalty=repetition_penalty, | |
| max_lenght=4096 | |
| ) | |
| # Save to temporary file and return path | |
| temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav") | |
| output.save(temp_file.name) | |
| return temp_file.name, "Voice cloning successful!" | |
| except Exception as e: | |
| return None, f"Error: {str(e)}" | |
| # Create Gradio interface | |
| with gr.Blocks(title="Voice Cloning with OuteTTS") as demo: | |
| gr.Markdown("# 🎙️ Voice Cloning with OuteTTS") | |
| gr.Markdown(""" | |
| This app uses OuteTTS to clone voices. Upload a reference audio file, provide the text being spoken in that audio, | |
| and enter the new text you want to be spoken in the cloned voice. | |
| Note: For best results, use clear audio with minimal background noise. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| # Input components | |
| audio_input = gr.Audio(label="Upload Reference Audio", type="filepath") | |
| reference_text = gr.Textbox(label="Reference Text (what is being said in the audio)") | |
| text_to_speak = gr.Textbox(label="Text to Speak (what you want the cloned voice to say)") | |
| with gr.Row(): | |
| temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.1, step=0.1, | |
| label="Temperature (higher = more variation)") | |
| repetition_penalty = gr.Slider(minimum=1.0, maximum=2.0, value=1.1, step=0.1, | |
| label="Repetition Penalty") | |
| # Submit button | |
| submit_btn = gr.Button("Generate Voice", variant="primary") | |
| with gr.Column(): | |
| # Output components | |
| output_audio = gr.Audio(label="Generated Speech") | |
| output_message = gr.Textbox(label="Status") | |
| # Handle submission | |
| submit_btn.click( | |
| fn=process_audio_file, | |
| inputs=[audio_input, reference_text, text_to_speak, temperature, repetition_penalty], | |
| outputs=[output_audio, output_message] | |
| ) | |
| gr.Markdown(""" | |
| ### Tips for best results: | |
| 1. Use high-quality reference audio (clear speech, minimal background noise) | |
| 2. Ensure reference text matches the audio exactly | |
| 3. Keep generated text relatively short for better quality | |
| 4. Adjust temperature and repetition penalty if needed: | |
| - Lower temperature (0.1-0.3) for more consistent output | |
| - Higher repetition penalty (1.1-1.3) to avoid repetition | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch() |