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| import os | |
| import gradio as gr | |
| import tempfile | |
| import soundfile as sf | |
| from models import Tokenizer, Kokoro | |
| # Function to fetch available style vectors dynamically | |
| def get_style_vector_choices(directory="voices"): | |
| return [file for file in os.listdir(directory) if file.endswith(".pt")] | |
| def get_onnx_models(directory="weights"): | |
| return [file for file in os.listdir(directory) if file.endswith(".onnx")] | |
| # Function to perform TTS using your local model | |
| def local_tts( | |
| text: str, | |
| model_path: str, | |
| style_vector: str, | |
| output_file_format: str = "wav", | |
| speed: float = 1.0 | |
| ): | |
| if len(text) > 0: | |
| try: | |
| tokenizer = Tokenizer() | |
| style_vector_path = os.path.join("voices", style_vector) | |
| model_path = os.path.join("weights", model_path) | |
| inference = Kokoro(model_path, style_vector_path, tokenizer=tokenizer, lang='en-us') | |
| audio, sample_rate = inference.generate_audio(text, speed=speed) | |
| with tempfile.NamedTemporaryFile(suffix=f".{output_file_format}", delete=False) as temp_file: | |
| sf.write(temp_file.name, audio, sample_rate) | |
| temp_file_path = temp_file.name | |
| return temp_file_path | |
| except Exception as e: | |
| raise gr.Error(f"An error occurred during TTS inference: {str(e)}") | |
| else: | |
| raise gr.Error("Input text cannot be empty.") | |
| # Get the list of available style vectors | |
| style_vector_choices = get_style_vector_choices() | |
| onnx_models_choices = get_onnx_models() | |
| # sample texts and their corresponding audio | |
| sample_outputs = [ | |
| ("Educational Note", "Machine learning models rely on large datasets and complex algorithms to identify patterns and make predictions.", "assets/edu_note.wav"), | |
| ("Fun Fact", "Did you know that honey never spoils? Archaeologists have found pots of honey in ancient Egyptian tombs that are over 3,000 years old and still edible!", "assets/fun_fact.wav"), | |
| ("Thanks", "Thank you for listening to this audio. It was generated by the Kokoro TTS model.", "assets/thanks.wav") | |
| ] | |
| example_texts = [ | |
| ["Machine learning models rely on large datasets and complex algorithms to identify patterns and make predictions."], | |
| ["Did you know that honey never spoils? Archaeologists have found pots of honey in ancient Egyptian tombs that are over 3,000 years old and still edible!"], | |
| ["Thank you for listening to this audio. It was generated by the Kokoro TTS model."] | |
| ] | |
| # Gradio Interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## <center> Kokoro TTS ONNX Inference | [GitHub Link](https://github.com/yakhyo/kokoro-onnx) </center>") | |
| # Model-specific inputs | |
| with gr.Row(variant="panel"): | |
| model_path = gr.Dropdown(choices=onnx_models_choices, label="ONNX Model Path", value=onnx_models_choices[0]) | |
| style_vector = gr.Dropdown(choices=style_vector_choices, label="Style Vector", value=style_vector_choices[0]) | |
| output_file_format = gr.Dropdown(choices=["wav", "mp3"], label="Output Format", value="wav") | |
| speed = gr.Slider(minimum=0.5, maximum=2.0, value=1.0, step=0.1, label="Speed") | |
| # Text input and output | |
| text = gr.Textbox( | |
| label="Input Text", | |
| placeholder="Enter text to convert to speech." | |
| ) | |
| btn = gr.Button("Generate Speech") | |
| output_audio = gr.Audio(label="Generated Audio", type="filepath") | |
| # Link inputs and outputs | |
| btn.click( | |
| fn=local_tts, | |
| inputs=[text, model_path, style_vector, output_file_format, speed], | |
| outputs=output_audio | |
| ) | |
| # Add example texts | |
| gr.Examples( | |
| examples=example_texts, | |
| inputs=[text], | |
| label="Click an example to populate the input text" | |
| ) | |
| # Add example texts and audios | |
| gr.Markdown("### Sample Texts and Audio") | |
| for topic, sample_text, sample_audio in sample_outputs: | |
| with gr.Row(): | |
| gr.Textbox(value=sample_text, label=topic, interactive=False) | |
| gr.Audio(value=sample_audio, label="Example Audio", type="filepath", interactive=False) | |
| demo.launch(server_name="0.0.0.0") | |