Generate custom radio imaging audio, ads, and promo tracks with Llama & MusicGen!
""", unsafe_allow_html=True ) st.markdown("---") # --------------------------------------------------------------------- # Instructions Section in an Expander # --------------------------------------------------------------------- with st.expander("๐ How to Use This Web App"): st.markdown( """ 1. **Enter your prompt**: Describe the type of audio you need (e.g., an energetic 15-second jingle for a pop radio promo). 2. **Generate Description**: Let Llama 2 (or another open-source model) refine your prompt into a creative script. 3. **Generate Audio**: Pass that script to MusicGen to get a custom audio file. 4. **Playback & Download**: Listen to your new track and download it for further editing. **Tips**: - Keep descriptions short & specific for best results. - If the Llama model is too large, switch to a smaller open-source model or try a GPU-based environment. - If you see errors about model permissions, ensure youโve accepted the license on Hugging Face. """ ) # --------------------------------------------------------------------- # Sidebar: Model Selection & Options # --------------------------------------------------------------------- with st.sidebar: st.header("๐ง Model Config") # Llama 2 chat model from Hugging Face llama_model_id = st.text_input( "Llama 2 Model ID on Hugging Face", value="meta-llama/Llama-2-7b-chat-hf", help="For example: meta-llama/Llama-2-7b-chat-hf (requires license acceptance)." ) device_option = st.selectbox( "Hardware Device", ["auto", "cpu"], help="If running locally with a GPU, choose 'auto'. If you only have a CPU, pick 'cpu'." ) # --------------------------------------------------------------------- # Prompt Input # --------------------------------------------------------------------- st.markdown("## โ๐ป Write Your Brief / Concept") prompt = st.text_area( "Describe the radio imaging or jingle you want to create. Include style, mood, duration, etc.", placeholder="e.g. 'An energetic 15-second pop jingle for a morning radio show, upbeat and fun...'" ) # --------------------------------------------------------------------- # Text Generation with Llama # --------------------------------------------------------------------- @st.cache_resource def load_llama_pipeline(model_id: str, device: str): """ Load the Llama or other open-source model as a text-generation pipeline. The user must have accepted the license for certain models like Llama 2. """ tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16 if device == "auto" else torch.float32, device_map=device ) gen_pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, device_map=device ) return gen_pipeline def generate_description(user_prompt: str, pipeline_gen): """ Use the pipeline to create a refined description for MusicGen. """ # Instruction format for Llama 2 chat # or simpler prompt if it's not a chat model system_prompt = ( "You are a helpful assistant specialized in creative advertising scripts and radio imaging. " "Refine the user's short concept into a more detailed, creative script. " "Keep it concise, but highlight any relevant tone, instruments, or style to guide music generation." ) # We'll feed a combined prompt combined_prompt = f"{system_prompt}\nUser request: {user_prompt}\nYour refined script:" # Generate text result = pipeline_gen( combined_prompt, max_new_tokens=200, do_sample=True, temperature=0.7 ) # Extract generated text (some models output extra tokens or the entire prompt again) generated_text = result[0]["generated_text"] # Attempt to cut out the system prompt if it reappears # Just a heuristic: find the last occurrence of "script:" or any relevant marker if "script:" in generated_text.lower(): generated_text = generated_text.split("script:")[-1].strip() # Optional: add a sign-off or credit line generated_text += "\n\n(Generated by Radio Imaging Audio Generator - Llama Edition)" return generated_text # Button: Generate Description if st.button("๐ Refine Description with Llama"): if not prompt.strip(): st.error("Please provide a brief concept before generating a description.") else: with st.spinner("Generating a refined description..."): try: pipeline_llama = load_llama_pipeline(llama_model_id, device_option) refined_text = generate_description(prompt, pipeline_llama) st.session_state['refined_prompt'] = refined_text st.success("Description successfully refined!") st.write(refined_text) st.download_button( "๐ฅ Download Description", refined_text, file_name="refined_description.txt" ) except Exception as e: st.error(f"Error while generating with Llama: {e}") st.markdown("---") # --------------------------------------------------------------------- # MusicGen: Generate Audio # --------------------------------------------------------------------- @st.cache_resource def load_musicgen_model(): """Load and cache the MusicGen model and processor.""" mg_model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small") mg_processor = AutoProcessor.from_pretrained("facebook/musicgen-small") return mg_model, mg_processor if st.button("โถ Generate Audio with MusicGen"): if 'refined_prompt' not in st.session_state or not st.session_state['refined_prompt']: st.error("Please generate or have a refined description first.") else: descriptive_text = st.session_state['refined_prompt'] with st.spinner("Generating your audio... This can take a moment."): try: musicgen_model, processor = load_musicgen_model() # Use the refined prompt as input inputs = processor( text=[descriptive_text], padding=True, return_tensors="pt" ) audio_values = musicgen_model.generate(**inputs, max_new_tokens=512) sampling_rate = musicgen_model.config.audio_encoder.sampling_rate # Save & display the audio audio_filename = "radio_imaging_output.wav" scipy.io.wavfile.write( audio_filename, rate=sampling_rate, data=audio_values[0, 0].numpy() ) st.success("Audio successfully generated!") st.audio(audio_filename) except Exception as e: st.error(f"Error while generating audio: {e}") # --------------------------------------------------------------------- # Footer Section # --------------------------------------------------------------------- st.markdown("---") st.markdown( "", unsafe_allow_html=True ) # Hide Streamlit's default menu and footer if you wish st.markdown("", unsafe_allow_html=True)