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Running
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Zero
| import os | |
| import re | |
| import torch | |
| import tempfile | |
| from scipy.io.wavfile import write | |
| from pydub import AudioSegment | |
| from dotenv import load_dotenv | |
| import spaces | |
| import gradio as gr | |
| # Transformers & Models | |
| from transformers import ( | |
| AutoTokenizer, | |
| AutoModelForCausalLM, | |
| pipeline, | |
| AutoProcessor, | |
| MusicgenForConditionalGeneration, | |
| ) | |
| # Coqui TTS | |
| from TTS.api import TTS | |
| # --------------------------------------------------------------------- | |
| # Load Environment Variables | |
| # --------------------------------------------------------------------- | |
| load_dotenv() | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| # --------------------------------------------------------------------- | |
| # Global Model Caches | |
| # --------------------------------------------------------------------- | |
| LLAMA_PIPELINES = {} | |
| MUSICGEN_MODELS = {} | |
| TTS_MODELS = {} | |
| # --------------------------------------------------------------------- | |
| # Utility Function: Clean Text | |
| # --------------------------------------------------------------------- | |
| def clean_text(text: str) -> str: | |
| """ | |
| Removes undesired characters (e.g., asterisks) that might not be recognized by the model's vocabulary. | |
| """ | |
| return re.sub(r'\*', '', text) | |
| # --------------------------------------------------------------------- | |
| # Helper Functions | |
| # --------------------------------------------------------------------- | |
| def get_llama_pipeline(model_id: str, token: str): | |
| """ | |
| Returns a cached LLaMA pipeline if available; otherwise, loads it. | |
| """ | |
| if model_id in LLAMA_PIPELINES: | |
| return LLAMA_PIPELINES[model_id] | |
| 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, | |
| ) | |
| text_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| LLAMA_PIPELINES[model_id] = text_pipeline | |
| return text_pipeline | |
| def get_musicgen_model(model_key: str = "facebook/musicgen-large"): | |
| """ | |
| Returns a cached MusicGen model if available; otherwise, loads it. | |
| Uses the 'large' variant for higher quality outputs. | |
| """ | |
| if model_key in MUSICGEN_MODELS: | |
| return MUSICGEN_MODELS[model_key] | |
| model = MusicgenForConditionalGeneration.from_pretrained(model_key) | |
| processor = AutoProcessor.from_pretrained(model_key) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| MUSICGEN_MODELS[model_key] = (model, processor) | |
| return model, processor | |
| def get_tts_model(model_name: str = "tts_models/en/ljspeech/tacotron2-DDC"): | |
| """ | |
| Returns a cached TTS model if available; otherwise, loads it. | |
| """ | |
| if model_name in TTS_MODELS: | |
| return TTS_MODELS[model_name] | |
| tts_model = TTS(model_name) | |
| TTS_MODELS[model_name] = tts_model | |
| return tts_model | |
| # --------------------------------------------------------------------- | |
| # Script Generation Function | |
| # --------------------------------------------------------------------- | |
| def generate_script(user_prompt: str, model_id: str, token: str, duration: int): | |
| """ | |
| Generates a script, sound design suggestions, and music ideas from a user prompt. | |
| Returns a tuple: (voice_script, sound_design, music_suggestions). | |
| """ | |
| try: | |
| text_pipeline = get_llama_pipeline(model_id, token) | |
| system_prompt = ( | |
| "You are an expert radio imaging producer specializing in sound design and music. " | |
| f"Based on the user's concept and the selected duration of {duration} seconds, produce the following: " | |
| "1. A concise voice-over script. Prefix this section with 'Voice-Over Script:'.\n" | |
| "2. Suggestions for sound design. Prefix this section with 'Sound Design Suggestions:'.\n" | |
| "3. Music styles or track recommendations. Prefix this section with 'Music Suggestions:'." | |
| ) | |
| combined_prompt = f"{system_prompt}\nUser concept: {user_prompt}\nOutput:" | |
| with torch.inference_mode(): | |
| result = text_pipeline( | |
| combined_prompt, | |
| max_new_tokens=300, | |
| do_sample=True, | |
| temperature=0.8 | |
| ) | |
| generated_text = result[0]["generated_text"] | |
| if "Output:" in generated_text: | |
| generated_text = generated_text.split("Output:")[-1].strip() | |
| voice_script = "No voice-over script found." | |
| sound_design = "No sound design suggestions found." | |
| music_suggestions = "No music suggestions found." | |
| if "Voice-Over Script:" in generated_text: | |
| parts = generated_text.split("Voice-Over Script:") | |
| voice_script_part = parts[1] | |
| if "Sound Design Suggestions:" in voice_script_part: | |
| voice_script = voice_script_part.split("Sound Design Suggestions:")[0].strip() | |
| else: | |
| voice_script = voice_script_part.strip() | |
| if "Sound Design Suggestions:" in generated_text: | |
| parts = generated_text.split("Sound Design Suggestions:") | |
| sound_design_part = parts[1] | |
| if "Music Suggestions:" in sound_design_part: | |
| sound_design = sound_design_part.split("Music Suggestions:")[0].strip() | |
| else: | |
| sound_design = sound_design_part.strip() | |
| if "Music Suggestions:" in generated_text: | |
| parts = generated_text.split("Music Suggestions:") | |
| music_suggestions = parts[1].strip() | |
| return voice_script, sound_design, music_suggestions | |
| except Exception as e: | |
| return f"Error generating script: {e}", "", "" | |
| # --------------------------------------------------------------------- | |
| # Voice-Over Generation Function | |
| # --------------------------------------------------------------------- | |
| def generate_voice(script: str, tts_model_name: str = "tts_models/en/ljspeech/tacotron2-DDC"): | |
| """ | |
| Generates a voice-over from the provided script using Coqui TTS. | |
| Returns the file path to the generated .wav file. | |
| """ | |
| try: | |
| if not script.strip(): | |
| return "Error: No script provided." | |
| cleaned_script = clean_text(script) | |
| tts_model = get_tts_model(tts_model_name) | |
| output_path = os.path.join(tempfile.gettempdir(), "voice_over.wav") | |
| tts_model.tts_to_file(text=cleaned_script, file_path=output_path) | |
| return output_path | |
| except Exception as e: | |
| return f"Error generating voice: {e}" | |
| # --------------------------------------------------------------------- | |
| # Music Generation Function | |
| # --------------------------------------------------------------------- | |
| def generate_music(prompt: str, audio_length: int): | |
| """ | |
| Generates music from the 'facebook/musicgen-large' model based on the prompt. | |
| Returns the file path to the generated .wav file. | |
| """ | |
| try: | |
| if not prompt.strip(): | |
| return "Error: No music suggestion provided." | |
| model_key = "facebook/musicgen-large" | |
| musicgen_model, musicgen_processor = get_musicgen_model(model_key) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| inputs = musicgen_processor(text=[prompt], padding=True, return_tensors="pt") | |
| inputs = {k: v.to(device) for k, v in inputs.items()} | |
| with torch.inference_mode(): | |
| 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 = os.path.join(tempfile.gettempdir(), "musicgen_large_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 | |
| # --------------------------------------------------------------------- | |
| def blend_audio(voice_path: str, music_path: str, ducking: bool, duck_level: int = 10): | |
| """ | |
| Blends two audio files (voice and music). | |
| Returns the file path to the blended .wav file. | |
| """ | |
| try: | |
| if not os.path.isfile(voice_path) or not os.path.isfile(music_path): | |
| return "Error: Missing audio files for blending." | |
| voice = AudioSegment.from_wav(voice_path) | |
| music = AudioSegment.from_wav(music_path) | |
| voice_len = len(voice) | |
| music_len = len(music) | |
| if music_len < voice_len: | |
| looped_music = AudioSegment.empty() | |
| while len(looped_music) < voice_len: | |
| looped_music += music | |
| music = looped_music | |
| if len(music) > voice_len: | |
| music = music[:voice_len] | |
| final_audio = music.overlay(voice, gain_during_overlay=-duck_level) if ducking else music.overlay(voice) | |
| output_path = os.path.join(tempfile.gettempdir(), "blended_output.wav") | |
| final_audio.export(output_path, format="wav") | |
| return output_path | |
| except Exception as e: | |
| return f"Error blending audio: {e}" | |
| # --------------------------------------------------------------------- | |
| # Agent Function: Orchestrate the Full Workflow | |
| # --------------------------------------------------------------------- | |
| def run_agent(user_prompt: str, llama_model_id: str, duration: int, tts_model_name: str, music_length: int, ducking: bool, duck_level: int): | |
| """ | |
| Runs the full workflow as an agent: | |
| 1. Generates a script (voice-over, sound design, and music suggestions). | |
| 2. Synthesizes a voice-over. | |
| 3. Generates a music track. | |
| 4. Blends the voice and music. | |
| Returns all generated components. | |
| """ | |
| voice_script, sound_design, music_suggestions = generate_script(user_prompt, llama_model_id, HF_TOKEN, duration) | |
| voice_file = generate_voice(voice_script, tts_model_name) | |
| music_file = generate_music(music_suggestions, music_length) | |
| blended_file = blend_audio(voice_file, music_file, ducking, duck_level) | |
| return voice_script, sound_design, music_suggestions, voice_file, music_file, blended_file | |
| # --------------------------------------------------------------------- | |
| # Gradio Interface with Enhanced UI | |
| # --------------------------------------------------------------------- | |
| with gr.Blocks(css=""" | |
| body { | |
| background: linear-gradient(135deg, #1d1f21, #3a3d41); | |
| color: #f0f0f0; | |
| font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; | |
| } | |
| .header { | |
| text-align: center; | |
| padding: 2rem 1rem; | |
| background: linear-gradient(90deg, #6a11cb, #2575fc); | |
| border-radius: 0 0 20px 20px; | |
| margin-bottom: 2rem; | |
| } | |
| .header h1 { | |
| margin: 0; | |
| font-size: 2.5rem; | |
| } | |
| .header p { | |
| font-size: 1.2rem; | |
| } | |
| .gradio-container { | |
| background: #2e2e2e; | |
| border-radius: 10px; | |
| padding: 1rem; | |
| } | |
| .tab-title { | |
| font-size: 1.1rem; | |
| font-weight: bold; | |
| } | |
| .footer { | |
| text-align: center; | |
| font-size: 0.9em; | |
| margin-top: 2rem; | |
| padding: 1rem; | |
| color: #cccccc; | |
| } | |
| """) as demo: | |
| # Custom Header | |
| with gr.Row(elem_classes="header"): | |
| gr.Markdown(""" | |
| <h1>🎧 AI Promo Studio</h1> | |
| <p>Your all-in-one AI solution for crafting engaging audio promos.</p> | |
| """) | |
| gr.Markdown(""" | |
| Welcome to **AI Promo Studio**! This platform leverages state-of-the-art AI models to help you generate: | |
| - A compelling voice-over script (with sound design and music suggestions), | |
| - A natural-sounding voice-over, | |
| - Custom music tracks, | |
| - And a fully blended audio promo. | |
| """) | |
| with gr.Tabs(): | |
| # Tab 1: Script Generation | |
| with gr.Tab("📝 Script Generation"): | |
| with gr.Row(): | |
| user_prompt = gr.Textbox(label="Promo Idea", placeholder="E.g., A 30-second promo for a morning show...", lines=2) | |
| with gr.Row(): | |
| llama_model_id = gr.Textbox(label="LLaMA Model ID", value="meta-llama/Meta-Llama-3-8B-Instruct", placeholder="Enter a valid Hugging Face model ID") | |
| duration = gr.Slider(label="Promo Duration (seconds)", minimum=15, maximum=60, step=15, value=30) | |
| generate_script_button = gr.Button("Generate Script", variant="primary") | |
| script_output = gr.Textbox(label="Voice-Over Script", lines=5, interactive=False) | |
| sound_design_output = gr.Textbox(label="Sound Design Suggestions", lines=3, interactive=False) | |
| music_suggestion_output = gr.Textbox(label="Music Suggestions", lines=3, interactive=False) | |
| generate_script_button.click(fn=lambda prompt, model, dur: generate_script(prompt, model, HF_TOKEN, dur), | |
| inputs=[user_prompt, llama_model_id, duration], | |
| outputs=[script_output, sound_design_output, music_suggestion_output]) | |
| # Tab 2: Voice Synthesis | |
| with gr.Tab("🎤 Voice Synthesis"): | |
| gr.Markdown("Generate a natural-sounding voice-over using Coqui TTS.") | |
| selected_tts_model = gr.Dropdown(label="TTS Model", | |
| choices=["tts_models/en/ljspeech/tacotron2-DDC", "tts_models/en/ljspeech/vits", "tts_models/en/sam/tacotron-DDC"], | |
| value="tts_models/en/ljspeech/tacotron2-DDC", multiselect=False) | |
| generate_voice_button = gr.Button("Generate Voice-Over", variant="primary") | |
| voice_audio_output = gr.Audio(label="Voice-Over (WAV)", type="filepath") | |
| generate_voice_button.click(fn=lambda script, tts: generate_voice(script, tts), | |
| inputs=[script_output, selected_tts_model], | |
| outputs=voice_audio_output) | |
| # Tab 3: Music Production | |
| with gr.Tab("🎶 Music Production"): | |
| gr.Markdown("Generate a custom music track using the MusicGen Large model.") | |
| audio_length = gr.Slider(label="Music Length (tokens)", minimum=128, maximum=1024, step=64, value=512, info="Increase tokens for longer audio (inference time may vary).") | |
| generate_music_button = gr.Button("Generate Music", variant="primary") | |
| music_output = gr.Audio(label="Generated Music (WAV)", type="filepath") | |
| generate_music_button.click(fn=lambda sugg, length: generate_music(sugg, length), | |
| inputs=[music_suggestion_output, audio_length], | |
| outputs=[music_output]) | |
| # Tab 4: Audio Blending | |
| with gr.Tab("🎚️ Audio Blending"): | |
| gr.Markdown("Blend your voice-over and music track. Enable ducking to lower the music during voice segments.") | |
| ducking_checkbox = gr.Checkbox(label="Enable Ducking?", value=True) | |
| duck_level_slider = gr.Slider(label="Ducking Level (dB attenuation)", minimum=0, maximum=20, step=1, value=10) | |
| blend_button = gr.Button("Blend Voice + Music", variant="primary") | |
| blended_output = gr.Audio(label="Final Blended Output (WAV)", type="filepath") | |
| blend_button.click(fn=blend_audio, | |
| inputs=[voice_audio_output, music_output, ducking_checkbox, duck_level_slider], | |
| outputs=blended_output) | |
| # Tab 5: Agent – Full Workflow | |
| with gr.Tab("🤖 Agent"): | |
| gr.Markdown("Let the agent handle everything in one go: generate script, synthesize voice, produce music, and blend the final ad.") | |
| with gr.Row(): | |
| agent_prompt = gr.Textbox(label="Ad Promo Idea", placeholder="Enter your ad promo concept...", lines=2) | |
| with gr.Row(): | |
| agent_llama_model_id = gr.Textbox(label="LLaMA Model ID", value="meta-llama/Meta-Llama-3-8B-Instruct", placeholder="Enter a valid Hugging Face model ID") | |
| agent_duration = gr.Slider(label="Promo Duration (seconds)", minimum=15, maximum=60, step=15, value=30) | |
| with gr.Row(): | |
| agent_tts_model = gr.Dropdown(label="TTS Model", | |
| choices=["tts_models/en/ljspeech/tacotron2-DDC", "tts_models/en/ljspeech/vits", "tts_models/en/sam/tacotron-DDC"], | |
| value="tts_models/en/ljspeech/tacotron2-DDC", multiselect=False) | |
| agent_music_length = gr.Slider(label="Music Length (tokens)", minimum=128, maximum=1024, step=64, value=512) | |
| with gr.Row(): | |
| agent_ducking = gr.Checkbox(label="Enable Ducking?", value=True) | |
| agent_duck_level = gr.Slider(label="Ducking Level (dB attenuation)", minimum=0, maximum=20, step=1, value=10) | |
| agent_run_button = gr.Button("Run Agent", variant="primary") | |
| agent_script_output = gr.Textbox(label="Voice-Over Script", lines=5, interactive=False) | |
| agent_sound_output = gr.Textbox(label="Sound Design Suggestions", lines=3, interactive=False) | |
| agent_music_suggestions_output = gr.Textbox(label="Music Suggestions", lines=3, interactive=False) | |
| agent_voice_audio = gr.Audio(label="Voice-Over (WAV)", type="filepath") | |
| agent_music_audio = gr.Audio(label="Generated Music (WAV)", type="filepath") | |
| agent_blended_audio = gr.Audio(label="Final Blended Output (WAV)", type="filepath") | |
| agent_run_button.click(fn=run_agent, | |
| inputs=[agent_prompt, agent_llama_model_id, agent_duration, agent_tts_model, agent_music_length, agent_ducking, agent_duck_level], | |
| outputs=[agent_script_output, agent_sound_output, agent_music_suggestions_output, agent_voice_audio, agent_music_audio, agent_blended_audio]) | |
| gr.Markdown(""" | |
| <div class="footer"> | |
| <hr> | |
| Created with ❤️ by <a href="https://bilsimaging.com" target="_blank" style="color: #88aaff;">bilsimaging.com</a> | |
| <br> | |
| <small>AI Promo Studio © 2025</small> | |
| </div> | |
| """) | |
| gr.HTML(""" | |
| <div style="text-align: center; margin-top: 1rem;"> | |
| <a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2Fradiogold"> | |
| <img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2Fradiogold&countColor=%23263759" alt="visitor badge"/> | |
| </a> | |
| </div> | |
| """) | |
| demo.launch(debug=True) | |