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Running
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Zero
| import os | |
| import re | |
| import torch | |
| import tempfile | |
| import logging | |
| 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 | |
| # --------------------------------------------------------------------- | |
| # Setup Logging and Environment Variables | |
| # --------------------------------------------------------------------- | |
| logging.basicConfig(level=logging.INFO) | |
| load_dotenv() | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| # --------------------------------------------------------------------- | |
| # Global Model Caches | |
| # --------------------------------------------------------------------- | |
| LLAMA_PIPELINES = {} | |
| MUSICGEN_MODELS = {} | |
| TTS_MODELS = {} | |
| # --------------------------------------------------------------------- | |
| # Utility Function | |
| # --------------------------------------------------------------------- | |
| 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) | |
| # --------------------------------------------------------------------- | |
| # Model Helper Functions | |
| # --------------------------------------------------------------------- | |
| def get_llama_pipeline(model_id: str, token: str): | |
| """ | |
| Returns a cached LLaMA text-generation pipeline if available; otherwise, loads and caches 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 and processor if available; otherwise, loads and caches them. | |
| 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 and caches 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 voice-over 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:\n" | |
| "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() | |
| pattern = r"Voice-Over Script:\s*(.*?)\s*Sound Design Suggestions:\s*(.*?)\s*Music Suggestions:\s*(.*)" | |
| match = re.search(pattern, generated_text, re.DOTALL) | |
| if match: | |
| voice_script, sound_design, music_suggestions = (grp.strip() for grp in match.groups()) | |
| else: | |
| voice_script = "No voice-over script found." | |
| sound_design = "No sound design suggestions found." | |
| music_suggestions = "No music suggestions found." | |
| return voice_script, sound_design, music_suggestions | |
| except Exception as e: | |
| logging.exception("Error generating script") | |
| 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 audio file 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: | |
| logging.exception("Error generating voice") | |
| return f"Error generating voice: {e}" | |
| # --------------------------------------------------------------------- | |
| # Music Generation Function | |
| # --------------------------------------------------------------------- | |
| def generate_music(prompt: str, audio_length: int): | |
| """ | |
| Generates a music track 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").to(device) | |
| 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: | |
| logging.exception("Error generating music") | |
| return f"Error generating music: {e}" | |
| # --------------------------------------------------------------------- | |
| # Audio Blending with Duration Sync & Ducking | |
| # --------------------------------------------------------------------- | |
| def blend_audio(voice_path: str, music_path: str, ducking: bool, duck_level: int = 10): | |
| """ | |
| Blends two audio files (voice and music). | |
| - Loops music if shorter than voice. | |
| - Trims music if longer than voice. | |
| - Applies ducking to lower music volume during voice segments if enabled. | |
| 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) | |
| if len(music) < voice_len: | |
| looped_music = AudioSegment.empty() | |
| while len(looped_music) < voice_len: | |
| looped_music += music | |
| music = looped_music | |
| music = music[:voice_len] | |
| if ducking: | |
| ducked_music = music - duck_level | |
| final_audio = ducked_music.overlay(voice) | |
| else: | |
| final_audio = 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: | |
| logging.exception("Error blending audio") | |
| return f"Error blending audio: {e}" | |
| # --------------------------------------------------------------------- | |
| # Gradio Interface | |
| # --------------------------------------------------------------------- | |
| with gr.Blocks(css=""" | |
| /* Global Styles */ | |
| 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 Ads Promo</h1> | |
| <p>Your all-in-one AI solution for creating professional audio ads.</p> | |
| """) | |
| gr.Markdown(""" | |
| **Welcome to Ai Ads Promo!** | |
| This simple, easy-to-use app helps you create amazing audio ads in just a few steps. Here’s how it works: | |
| 1. **Script Generation:** | |
| - Share your idea and let our AI craft a clear and engaging voice-over script. | |
| 2. **Voice Synthesis:** | |
| - Convert your script into a natural-sounding voice-over using advanced text-to-speech technology. | |
| 3. **Music Production:** | |
| - Generate a custom music track that perfectly fits your ad. | |
| 4. **Audio Blending:** | |
| - Combine your voice-over and music seamlessly. You can even adjust the music volume (ducking) when the voice plays. | |
| **Benefits:** | |
| - **Easy to Use:** Designed for everyone – no technical skills required. | |
| - **Fast Results:** Quickly produce professional-sounding audio ads. | |
| - **All-In-One:** Everything you need in one convenient app. | |
| Get started now and create your perfect audio ad with Ai Ads Promo! | |
| """) | |
| with gr.Tabs(): | |
| # Step 1: Script Generation | |
| with gr.Tab("📝 Script Generation"): | |
| with gr.Row(): | |
| user_prompt = gr.Textbox( | |
| label="Promo Idea", | |
| placeholder="E.g., A 30-second ad 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="Desired Ad Duration (seconds)", | |
| minimum=15, | |
| maximum=60, | |
| step=15, | |
| value=30 | |
| ) | |
| generate_script_button = gr.Button("Generate Script", variant="primary") | |
| script_output = gr.Textbox(label="Generated 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_id, dur: generate_script(prompt, model_id, HF_TOKEN, dur), | |
| inputs=[user_prompt, llama_model_id, duration], | |
| outputs=[script_output, sound_design_output, music_suggestion_output], | |
| ) | |
| # Step 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_model: generate_voice(script, tts_model), | |
| inputs=[script_output, selected_tts_model], | |
| outputs=voice_audio_output, | |
| ) | |
| # Step 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 music_prompt, length: generate_music(music_prompt, length), | |
| inputs=[music_suggestion_output, audio_length], | |
| outputs=[music_output], | |
| ) | |
| # Step 4: Audio Blending | |
| with gr.Tab("🎚️ Audio Blending"): | |
| gr.Markdown("Blend your voice-over and music track. Music will be looped or trimmed to match your voice duration. Enable ducking to lower the music while the voice plays.") | |
| 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 | |
| ) | |
| # Footer | |
| 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 Ads Promo © 2025</small> | |
| </div> | |
| """) | |
| # Visitor Badge | |
| 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) | |