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Running
on
Zero
| 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 | |
| from TTS.api import TTS | |
| # ------------------------------- | |
| # Configuration | |
| # ------------------------------- | |
| load_dotenv() | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| MODEL_CONFIG = { | |
| "llama_models": { | |
| "Meta-Llama-3-8B": "meta-llama/Meta-Llama-3-8B-Instruct", | |
| "Mistral-7B": "mistralai/Mistral-7B-Instruct-v0.2", | |
| }, | |
| "tts_models": { | |
| "Standard English": "tts_models/en/ljspeech/tacotron2-DDC", | |
| "High Quality": "tts_models/en/ljspeech/vits", | |
| } | |
| } | |
| # ------------------------------- | |
| # Model Manager | |
| # ------------------------------- | |
| class ModelManager: | |
| def __init__(self): | |
| self.llama_pipelines = {} | |
| self.musicgen_models = {} | |
| self.tts_models = {} | |
| def get_llama_pipeline(self, model_id, token): | |
| if model_id not in self.llama_pipelines: | |
| 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" | |
| ) | |
| self.llama_pipelines[model_id] = pipeline( | |
| "text-generation", | |
| model=model, | |
| tokenizer=tokenizer | |
| ) | |
| return self.llama_pipelines[model_id] | |
| def get_musicgen_model(self, model_key="facebook/musicgen-large"): | |
| if model_key not in self.musicgen_models: | |
| model = MusicgenForConditionalGeneration.from_pretrained(model_key) | |
| processor = AutoProcessor.from_pretrained(model_key) | |
| self.musicgen_models[model_key] = (model, processor) | |
| return self.musicgen_models[model_key] | |
| def get_tts_model(self, model_name): | |
| if model_name not in self.tts_models: | |
| self.tts_models[model_name] = TTS(model_name) | |
| return self.tts_models[model_name] | |
| model_manager = ModelManager() | |
| # ------------------------------- | |
| # Core Functions | |
| # ------------------------------- | |
| def generate_script(user_prompt, model_id, duration): | |
| try: | |
| text_pipeline = model_manager.get_llama_pipeline(model_id, HF_TOKEN) | |
| system_prompt = f"""Create a {duration}-second audio promo with these elements: | |
| 1. Voice Script: [Clear narration] | |
| 2. Sound Design: [3-5 effects] | |
| 3. Music: [Genre/tempo] | |
| Concept: {user_prompt}""" | |
| result = text_pipeline( | |
| system_prompt, | |
| max_new_tokens=300, | |
| temperature=0.7, | |
| do_sample=True | |
| ) | |
| generated_text = result[0]["generated_text"] | |
| return parse_generated_content(generated_text) | |
| except Exception as e: | |
| return f"Error: {str(e)}", "", "" | |
| def parse_generated_content(text): | |
| sections = { | |
| "Voice Script": "", | |
| "Sound Design": "", | |
| "Music": "" | |
| } | |
| current_section = None | |
| for line in text.split('\n'): | |
| line = line.strip() | |
| if "Voice Script:" in line: | |
| current_section = "Voice Script" | |
| line = line.replace("Voice Script:", "").strip() | |
| elif "Sound Design:" in line: | |
| current_section = "Sound Design" | |
| line = line.replace("Sound Design:", "").strip() | |
| elif "Music:" in line: | |
| current_section = "Music" | |
| line = line.replace("Music:", "").strip() | |
| if current_section and line: | |
| sections[current_section] += line + "\n" | |
| return sections["Voice Script"].strip(), sections["Sound Design"].strip(), sections["Music"].strip() | |
| def generate_voice(script, tts_model): | |
| try: | |
| if not script.strip(): | |
| return "Error: No script provided" | |
| tts = model_manager.get_tts_model(tts_model) | |
| output_path = os.path.join(tempfile.gettempdir(), "voice.wav") | |
| tts.tts_to_file(text=script, file_path=output_path) | |
| return output_path | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def generate_music(prompt, duration_sec=30): | |
| try: | |
| model, processor = model_manager.get_musicgen_model() | |
| inputs = processor(text=[prompt], padding=True, return_tensors="pt") | |
| audio_values = model.generate(**inputs, max_new_tokens=int(duration_sec * 50)) | |
| output_path = os.path.join(tempfile.gettempdir(), "music.wav") | |
| write(output_path, 44100, audio_values[0, 0].cpu().numpy()) | |
| return output_path | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def blend_audio(voice_path, music_path, ducking=True, duck_level=10): | |
| try: | |
| voice = AudioSegment.from_wav(voice_path) | |
| music = AudioSegment.from_wav(music_path) | |
| # Align durations | |
| if len(music) < len(voice): | |
| music = music * (len(voice) // len(music) + 1) | |
| music = music[:len(voice)] | |
| # Apply ducking | |
| if ducking: | |
| music = music - duck_level | |
| mixed = music.overlay(voice) | |
| output_path = os.path.join(tempfile.gettempdir(), "final_mix.wav") | |
| mixed.export(output_path, format="wav") | |
| return output_path | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # ------------------------------- | |
| # Gradio Interface (Second UI Version) | |
| # ------------------------------- | |
| with gr.Blocks(title="AI Radio Studio", css=""" | |
| .gradio-container {max-width: 800px; margin: auto;} | |
| .tab-item {padding: 20px; border-radius: 10px;} | |
| """) as demo: | |
| gr.Markdown(""" | |
| # 🎙️ AI Radio Studio | |
| *Professional Audio Production Made Simple* | |
| """) | |
| with gr.Tabs(): | |
| # Concept Tab | |
| with gr.Tab("🎯 Concept"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| concept_input = gr.Textbox( | |
| label="Your Idea", | |
| placeholder="Describe your audio project...", | |
| lines=3 | |
| ) | |
| model_select = gr.Dropdown( | |
| choices=list(MODEL_CONFIG["llama_models"].values()), | |
| label="AI Model", | |
| value="meta-llama/Meta-Llama-3-8B-Instruct" | |
| ) | |
| duration_select = gr.Slider(15, 60, 30, step=15, label="Duration (seconds)") | |
| generate_btn = gr.Button("Generate Script", variant="primary") | |
| with gr.Column(): | |
| script_output = gr.Textbox(label="Voice Script", interactive=True) | |
| sound_output = gr.Textbox(label="Sound Design", interactive=True) | |
| music_output = gr.Textbox(label="Music Suggestions", interactive=True) | |
| # Voice Tab | |
| with gr.Tab("🗣️ Voice"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| tts_select = gr.Dropdown( | |
| choices=list(MODEL_CONFIG["tts_models"].values()), | |
| label="Voice Model", | |
| value="tts_models/en/ljspeech/tacotron2-DDC" | |
| ) | |
| voice_btn = gr.Button("Generate Voiceover", variant="primary") | |
| with gr.Column(): | |
| voice_preview = gr.Audio(label="Preview", type="filepath") | |
| # Music Tab | |
| with gr.Tab("🎵 Music"): | |
| music_btn = gr.Button("Generate Music Track", variant="primary") | |
| music_preview = gr.Audio(label="Preview", type="filepath") | |
| # Mix Tab | |
| with gr.Tab("🔊 Mix"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| ducking_toggle = gr.Checkbox(True, label="Enable Voice Ducking") | |
| duck_level = gr.Slider(0, 20, 10, label="Ducking Level (dB)") | |
| mix_btn = gr.Button("Create Final Mix", variant="primary") | |
| with gr.Column(): | |
| final_mix = gr.Audio(label="Final Output", type="filepath") | |
| # Footer Section | |
| gr.Markdown(""" | |
| <div style="text-align: center; margin-top: 30px; padding: 15px; border-top: 1px solid #e0e0e0;"> | |
| <p style="font-size: 0.9em; color: #666;"> | |
| Created with ❤️ by <a href="https://bilsimaging.com" target="_blank">bilsimaging.com</a> | |
| </p> | |
| <a href="https://visitorbadge.io/status?path=https://huggingface.co/spaces/Bils/radiogold"> | |
| <img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2Fradiogold&countColor=%23263759"/> | |
| </a> | |
| </div> | |
| """) | |
| # Event Handlers | |
| generate_btn.click( | |
| generate_script, | |
| inputs=[concept_input, model_select, duration_select], | |
| outputs=[script_output, sound_output, music_output] | |
| ) | |
| voice_btn.click( | |
| generate_voice, | |
| inputs=[script_output, tts_select], | |
| outputs=voice_preview | |
| ) | |
| music_btn.click( | |
| generate_music, | |
| inputs=[music_output], | |
| outputs=music_preview | |
| ) | |
| mix_btn.click( | |
| blend_audio, | |
| inputs=[voice_preview, music_preview, ducking_toggle, duck_level], | |
| outputs=final_mix | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860) |