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| from huggingface_hub import login | |
| import os | |
| token = os.environ.get("HUGGINGFACE_TOKEN") | |
| login(token) | |
| import gradio as gr | |
| import spaces | |
| from peft import PeftModel | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| import torch | |
| from f5py import generate_tts | |
| from stitch import create_music_speech_mix | |
| import traceback | |
| import warnings | |
| # Suppress NVML initialization warning | |
| warnings.filterwarnings("ignore", message="Can't initialize NVML") | |
| def generate_quote(temperature, top_p, max_length): | |
| try: | |
| def initialize_model(): | |
| adapter_path = "./checkpoints/epoch-11" | |
| base_model = "mistralai/Mistral-7B-Instruct-v0.3" | |
| # Check CUDA availability more thoroughly | |
| device = "cuda" if torch.cuda.is_available() and torch.cuda.device_count() > 0 else "cpu" | |
| print(f"Using device: {device}") | |
| tokenizer = AutoTokenizer.from_pretrained(base_model) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| base_model, | |
| torch_dtype=torch.float16 if device == "cuda" else torch.float32, | |
| device_map="auto" if device == "cuda" else None | |
| ) | |
| model = PeftModel.from_pretrained(model, adapter_path) | |
| model.eval() | |
| return pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| generator = initialize_model() | |
| prompt = "Generate a Masood Boomgaard style quote:" | |
| output = generator( | |
| prompt, | |
| max_new_tokens=max_length, | |
| do_sample=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| top_k=50, | |
| num_return_sequences=1 | |
| ) | |
| text = output[0]["generated_text"].replace(prompt, "") | |
| output_path = generate_tts(input_text=text) | |
| final_audio_path = create_music_speech_mix(speech_path=output_path) | |
| return text, final_audio_path, None | |
| except Exception as e: | |
| error_msg = f"Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}" | |
| return None, None, error_msg | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# MasoodishWisdom") | |
| with gr.Row(): | |
| with gr.Column(): | |
| temperature = gr.Slider( | |
| minimum=0.1, maximum=1.0, step=0.1, value=0.9, | |
| label="Temperature" | |
| ) | |
| top_p = gr.Slider( | |
| minimum=0.1, maximum=1.0, step=0.05, value=0.95, | |
| label="Top-p" | |
| ) | |
| max_length = gr.Slider( | |
| minimum=50, maximum=200, step=10, value=100, | |
| label="Max Length" | |
| ) | |
| generate_btn = gr.Button("Generate Quote") | |
| gr.Markdown("---") | |
| gr.Markdown("## Examples") | |
| for file in sorted(os.listdir("examples")): | |
| if file.startswith("output") and file.endswith(".wav"): | |
| gr.Audio(os.path.join("examples", file), label=file, type="filepath") | |
| with gr.Column(): | |
| text_output = gr.Textbox(label="Generated Quote") | |
| audio_output = gr.Audio(label="Generated Audio") | |
| error_output = gr.Textbox(label="Error Log", visible=True) | |
| def handle_generation(*args): | |
| text, audio, error = generate_quote(*args) | |
| if error: | |
| return [None, None, error] | |
| return [text, audio, None] | |
| generate_btn.click( | |
| handle_generation, | |
| inputs=[temperature, top_p, max_length], | |
| outputs=[text_output, audio_output, error_output] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| show_error=True, | |
| share=False | |
| ) |