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
from TTS.utils.synthesizer import Synthesizer
# Load environment variables
load_dotenv()
hf_token = os.getenv("HF_TOKEN")
# ---------------------------------------------------------------------
# Script Generation Function
# ---------------------------------------------------------------------
@spaces.GPU(duration=300)
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 with clear structure instructions
system_prompt = (
f"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: "
f"1. A concise voice-over script. Prefix this section with 'Voice-Over Script:'.\n"
f"2. Suggestions for sound design. Prefix this section with 'Sound Design Suggestions:'.\n"
f"3. Music styles or track recommendations. Prefix this section with 'Music Suggestions:'."
)
combined_prompt = f"{system_prompt}\nUser concept: {user_prompt}\nOutput:"
result = llama_pipeline(combined_prompt, max_new_tokens=300, do_sample=True, temperature=0.8)
# Parsing output
generated_text = result[0]["generated_text"].split("Output:")[-1].strip()
# Extract sections based on prefixes
voice_script = generated_text.split("Voice-Over Script:")[1].split("Sound Design Suggestions:")[0].strip() if "Voice-Over Script:" in generated_text else "No voice-over script found."
sound_design = generated_text.split("Sound Design Suggestions:")[1].split("Music Suggestions:")[0].strip() if "Sound Design Suggestions:" in generated_text else "No sound design suggestions found."
music_suggestions = generated_text.split("Music Suggestions:")[1].strip() if "Music Suggestions:" in generated_text else "No music suggestions found."
return voice_script, sound_design, music_suggestions
except Exception as e:
return f"Error generating script: {e}", "", ""
# ---------------------------------------------------------------------
# Voice-Over Generation Function (Inactive)
# ---------------------------------------------------------------------
@spaces.GPU(duration=300)
def generate_voice(script: str, speaker: str = "default"):
try:
# Placeholder for inactive state
return "Voice-over generation is currently inactive."
except Exception as e:
return f"Error: {e}"
# ---------------------------------------------------------------------
# Music Generation Function (facebook/musicgen-medium)
# ---------------------------------------------------------------------
@spaces.GPU(duration=300)
def generate_music(prompt: str, audio_length: int):
try:
# Load facebook/musicgen-medium model
musicgen_model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-medium")
musicgen_processor = AutoProcessor.from_pretrained("facebook/musicgen-medium")
# Move the model to the appropriate device (CUDA or CPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
musicgen_model.to(device)
# Prepare inputs
inputs = musicgen_processor(text=[prompt], padding=True, return_tensors="pt").to(device)
# Generate music
outputs = musicgen_model.generate(**inputs, max_new_tokens=audio_length)
# Process audio data
audio_data = outputs[0, 0].cpu().numpy()
normalized_audio = (audio_data / max(abs(audio_data)) * 32767).astype("int16")
# Save generated music to a file
output_path = f"{tempfile.gettempdir()}/musicgen_medium_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 (Inactive)
# ---------------------------------------------------------------------
def blend_audio(voice_path: str, music_path: str, ducking: bool):
try:
# Placeholder for inactive state
return "Audio blending functionality is currently inactive."
except Exception as e:
return f"Error: {e}"
# ---------------------------------------------------------------------
# Gradio Interface
# ---------------------------------------------------------------------
with gr.Blocks() as demo:
gr.Markdown("""
# 🎧 AI Promo Studio 🚀
Welcome to **AI Promo Studio**, your one-stop solution for creating stunning and professional radio promos with ease!
Whether you're a sound designer, radio producer, or content creator, our AI-driven tools, powered by advanced LLM Llama models, empower you to bring your vision to life in just a few steps.
""")
with gr.Tabs():
# Step 1: Generate Script
with gr.Tab("Step 1: Generate Script"):
with gr.Row():
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 Voice-Over Script", lines=5)
sound_design_output = gr.Textbox(label="Sound Design Suggestions", lines=3)
music_suggestion_output = gr.Textbox(label="Music Suggestions", lines=3)
generate_script_button.click(
fn=lambda user_prompt, model_id, duration: generate_script(user_prompt, model_id, hf_token, duration),
inputs=[user_prompt, llama_model_id, duration],
outputs=[script_output, sound_design_output, music_suggestion_output],
)
# Step 2: Generate Voice
with gr.Tab("Step 2: Generate Voice"):
gr.Markdown("""
**Note:** Voice-over generation is currently inactive.
This feature will be available in future updates!
""")
with gr.Row():
speaker = gr.Textbox(label="Voice Style (optional)", placeholder="E.g., male, female, or neutral.")
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_output, speaker],
outputs=[voice_output],
)
# Step 3: Generate Music
with gr.Tab("Step 3: Generate Music"):
with gr.Row():
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=lambda music_suggestion, audio_length: generate_music(music_suggestion, audio_length),
inputs=[music_suggestion_output, audio_length],
outputs=[music_output],
)
# Step 4: Blend Audio
with gr.Tab("Step 4: Blend Audio"):
gr.Markdown("""
**Note:** Audio blending functionality is currently inactive.
This feature will be available in future updates!
""")
with gr.Row():
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_output, music_output, ducking],
outputs=[final_output],
)
gr.Markdown("""
Created with ❤️ by bilsimaging.com
""")
# Add visitor badge HTML
gr.HTML("""
""")
demo.launch(debug=True)