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| import gradio as gr | |
| import torch | |
| import tempfile | |
| import os | |
| from TTS.api import TTS | |
| # Initialize the XTTS model | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| print(f"Using device: {device}") | |
| # Initialize XTTS model | |
| tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device) | |
| # Get list of supported languages | |
| supported_languages = [ | |
| "en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", | |
| "cs", "ar", "zh-cn", "ja", "hu", "ko" | |
| ] | |
| def generate_speech( | |
| text, | |
| language, | |
| speaker_wav=None, | |
| voice_preset=None, | |
| speed=1.0, | |
| temperature=0.7 | |
| ): | |
| """ | |
| Generate speech from text using XTTS model | |
| """ | |
| # Create a temporary file for output | |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_file: | |
| output_path = tmp_file.name | |
| try: | |
| # If speaker wav is provided, use it for voice cloning | |
| if speaker_wav is not None: | |
| tts.tts_to_file( | |
| text=text, | |
| file_path=output_path, | |
| speaker_wav=speaker_wav, | |
| language=language, | |
| speed=speed, | |
| temperature=temperature | |
| ) | |
| else: | |
| # Use default voice if no speaker wav is provided | |
| tts.tts_to_file( | |
| text=text, | |
| file_path=output_path, | |
| language=language, | |
| speed=speed, | |
| temperature=temperature | |
| ) | |
| return output_path | |
| except Exception as e: | |
| # Clean up temporary file if error occurs | |
| if os.path.exists(output_path): | |
| os.unlink(output_path) | |
| raise gr.Error(f"Error generating speech: {str(e)}") | |
| # Create Gradio interface | |
| with gr.Blocks(title="XTTS Text-to-Speech") as demo: | |
| gr.Markdown("# XTTS Text-to-Speech Generator") | |
| gr.Markdown("Generate speech from text with voice cloning capabilities using XTTS v2") | |
| with gr.Row(): | |
| with gr.Column(): | |
| text_input = gr.Textbox( | |
| label="Input Text", | |
| placeholder="Enter text to convert to speech...", | |
| lines=3 | |
| ) | |
| language_input = gr.Dropdown( | |
| label="Language", | |
| choices=[(lang, lang) for lang in supported_languages], | |
| value="en", | |
| info="Select the language for synthesis" | |
| ) | |
| speaker_wav_input = gr.Audio( | |
| label="Reference Voice (Optional)", | |
| type="filepath", | |
| info="Upload a 3-10 second audio sample for voice cloning" | |
| ) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| speed_input = gr.Slider( | |
| label="Speed", | |
| minimum=0.5, | |
| maximum=2.0, | |
| value=1.0, | |
| step=0.1, | |
| info="Speech speed (0.5 = slow, 2.0 = fast)" | |
| ) | |
| temperature_input = gr.Slider( | |
| label="Temperature", | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.7, | |
| step=0.1, | |
| info="Voice variability (lower = more deterministic)" | |
| ) | |
| generate_btn = gr.Button("Generate Speech", variant="primary") | |
| with gr.Column(): | |
| audio_output = gr.Audio( | |
| label="Generated Speech", | |
| type="filepath" | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| ["Hello, world! This is a sample text to speech generation.", "en"], | |
| ["Bonjour, comment allez-vous aujourd'hui?", "fr"], | |
| ["Hola, ¿cómo estás?", "es"], | |
| ], | |
| inputs=[text_input, language_input], | |
| outputs=audio_output, | |
| fn=generate_speech, | |
| cache_examples=True | |
| ) | |
| generate_btn.click( | |
| fn=generate_speech, | |
| inputs=[ | |
| text_input, | |
| language_input, | |
| speaker_wav_input, | |
| speed_input, | |
| temperature_input | |
| ], | |
| outputs=audio_output | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860) |