Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import os | |
| import torch | |
| from transformers import ( | |
| AutoTokenizer, | |
| AutoModelForCausalLM, | |
| pipeline, | |
| AutoProcessor, | |
| MusicgenForConditionalGeneration, | |
| ) | |
| from scipy.io.wavfile import write | |
| from pydub import AudioSegment | |
| from dotenv import load_dotenv | |
| import tempfile | |
| import spaces | |
| # Load environment variables | |
| load_dotenv() | |
| hf_token = os.getenv("HF_TOKEN") | |
| # --------------------------------------------------------------------- | |
| # Script Generation Function | |
| # --------------------------------------------------------------------- | |
| def generate_script(user_prompt: str, model_id: str, token: str, duration: int): | |
| try: | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=token) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| use_auth_token=token, | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| trust_remote_code=True, | |
| ) | |
| llama_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| system_prompt = ( | |
| "You are an expert radio imaging producer specializing in sound design and music.\n" | |
| "---\n" | |
| f"Based on the user's concept and the selected duration of {duration} seconds, craft a concise, engaging promo script.\n" | |
| "---\n" | |
| "Ensure the script fits within the time limit and suggest a matching music style that complements the theme." | |
| ) | |
| combined_prompt = f"{system_prompt}\nUser concept: {user_prompt}\nRefined script and music suggestion:" | |
| result = llama_pipeline(combined_prompt, max_new_tokens=500, do_sample=True, temperature=0.9) | |
| generated_text = result[0]["generated_text"] | |
| if "Refined script and music suggestion:" in generated_text: | |
| parts = generated_text.split("Refined script and music suggestion:", 1)[-1].strip() | |
| if "Music Style:" in parts: | |
| script, music_suggestion = parts.split("Music Style:", 1) | |
| return script.strip(), music_suggestion.strip() | |
| else: | |
| return parts.strip(), "No specific music suggestion found." | |
| return "Error: Could not parse the script.", None | |
| except Exception as e: | |
| return f"Error generating script: {e}", None | |
| # --------------------------------------------------------------------- | |
| # Voice-Over Generation Function | |
| # --------------------------------------------------------------------- | |
| def generate_voice(script: str): | |
| try: | |
| tts_model = "coqui/xtts-en-ljspeech-v2" | |
| processor = AutoProcessor.from_pretrained(tts_model) | |
| model = AutoModelForCausalLM.from_pretrained(tts_model) | |
| inputs = processor(script, return_tensors="pt") | |
| speech = model.generate(**inputs) | |
| output_path = f"{tempfile.gettempdir()}/generated_voice.wav" | |
| write(output_path, 22050, speech.cpu().numpy()) | |
| return output_path | |
| except Exception as e: | |
| return f"Error generating voice-over: {e}" | |
| # --------------------------------------------------------------------- | |
| # Music Generation Function | |
| # --------------------------------------------------------------------- | |
| def generate_music(prompt: str, audio_length: int): | |
| try: | |
| musicgen_model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small") | |
| musicgen_processor = AutoProcessor.from_pretrained("facebook/musicgen-small") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| musicgen_model.to(device) | |
| inputs = musicgen_processor(text=[prompt], padding=True, return_tensors="pt").to(device) | |
| outputs = musicgen_model.generate(**inputs, max_new_tokens=audio_length) | |
| audio_data = outputs[0, 0].cpu().numpy() | |
| normalized_audio = (audio_data / max(abs(audio_data)) * 32767).astype("int16") | |
| output_path = f"{tempfile.gettempdir()}/generated_music.wav" | |
| write(output_path, 44100, normalized_audio) | |
| return output_path | |
| except Exception as e: | |
| return f"Error generating music: {e}" | |
| # --------------------------------------------------------------------- | |
| # Audio Blending Function with Ducking | |
| # --------------------------------------------------------------------- | |
| def blend_audio(voice_path: str, music_path: str, ducking: bool): | |
| try: | |
| voice = AudioSegment.from_file(voice_path) | |
| music = AudioSegment.from_file(music_path) | |
| if ducking: | |
| music = music - 10 # Lower music volume for ducking | |
| combined = music.overlay(voice) | |
| output_path = f"{tempfile.gettempdir()}/final_promo.wav" | |
| combined.export(output_path, format="wav") | |
| return output_path | |
| except Exception as e: | |
| return f"Error blending audio: {e}" | |
| # --------------------------------------------------------------------- | |
| # Gradio Interface | |
| # --------------------------------------------------------------------- | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| # 🎧 AI Promo Studio with Pages 🚀 | |
| Follow a step-by-step process to create amazing promos with AI. | |
| """) | |
| with gr.Tabs(): | |
| # Step 1: Script Generation | |
| with gr.Tab("Step 1: Generate Script"): | |
| user_prompt = gr.Textbox(label="Promo Idea", placeholder="E.g., A 30-second promo for a morning show.") | |
| llama_model_id = gr.Textbox(label="Llama Model ID", value="meta-llama/Meta-Llama-3-8B-Instruct") | |
| duration = gr.Slider(label="Duration (seconds)", minimum=15, maximum=60, step=15, value=30) | |
| generate_script_button = gr.Button("Generate Script") | |
| script_output = gr.Textbox(label="Generated Script") | |
| music_suggestion_output = gr.Textbox(label="Music Suggestion") | |
| generate_script_button.click( | |
| fn=generate_script, | |
| inputs=[user_prompt, llama_model_id, hf_token, duration], | |
| outputs=[script_output, music_suggestion_output], | |
| ) | |
| # Step 2: Voice Generation | |
| with gr.Tab("Step 2: Generate Voice"): | |
| script_input = gr.Textbox(label="Script for Voice", interactive=False) | |
| generate_voice_button = gr.Button("Generate Voice") | |
| voice_output = gr.Audio(label="Generated Voice", type="filepath") | |
| generate_voice_button.click( | |
| fn=generate_voice, | |
| inputs=[script_input], | |
| outputs=[voice_output], | |
| ) | |
| # Step 3: Music Generation | |
| with gr.Tab("Step 3: Generate Music"): | |
| music_prompt_input = gr.Textbox(label="Music Suggestion Prompt", interactive=False) | |
| audio_length = gr.Slider(label="Music Length (tokens)", minimum=128, maximum=1024, step=64, value=512) | |
| generate_music_button = gr.Button("Generate Music") | |
| music_output = gr.Audio(label="Generated Music", type="filepath") | |
| generate_music_button.click( | |
| fn=generate_music, | |
| inputs=[music_prompt_input, audio_length], | |
| outputs=[music_output], | |
| ) | |
| # Step 4: Blend Audio | |
| with gr.Tab("Step 4: Blend Audio"): | |
| voice_path = gr.Audio(label="Voice File", type="filepath") | |
| music_path = gr.Audio(label="Music File", type="filepath") | |
| ducking = gr.Checkbox(label="Enable Ducking", value=True) | |
| blend_button = gr.Button("Blend Audio") | |
| final_output = gr.Audio(label="Final Promo Audio", type="filepath") | |
| blend_button.click( | |
| fn=blend_audio, | |
| inputs=[voice_path, music_path, ducking], | |
| outputs=[final_output], | |
| ) | |
| gr.Markdown(""" | |
| <hr> | |
| <p style="text-align: center; font-size: 0.9em;"> | |
| Created with ❤️ by <a href="https://bilsimaging.com" target="_blank">bilsimaging.com</a> | |
| </p> | |
| """) | |
| demo.launch(debug=True) | |