app.py

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

# 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 = {}

# ---------------------------------------------------------------------
# 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
# ---------------------------------------------------------------------
@spaces.GPU(duration=100)
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 of strings: (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()

        # Default placeholders
        voice_script = "No voice-over script found."
        sound_design = "No sound design suggestions found."
        music_suggestions = "No music suggestions found."

        # Voice-Over Script
        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()

        # Sound Design
        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()

        # Music Suggestions
        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
# ---------------------------------------------------------------------
@spaces.GPU(duration=100)
def generate_voice(script: str, tts_model_name: str = "tts_models/en/ljspeech/tacotron2-DDC"):
    """
    Generates a voice-over from the provided script using the Coqui TTS model.
    Returns the file path to the generated .wav file.
    """
    try:
        if not script.strip():
            return "Error: No script provided."

        tts_model = get_tts_model(tts_model_name)

        # Generate and save voice
        output_path = os.path.join(tempfile.gettempdir(), "voice_over.wav")
        tts_model.tts_to_file(text=script, file_path=output_path)
        return output_path

    except Exception as e:
        return f"Error generating voice: {e}"


# ---------------------------------------------------------------------
# Music Generation Function
# ---------------------------------------------------------------------
@spaces.GPU(duration=100)
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").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 = f"{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 with Duration Sync & Ducking
# ---------------------------------------------------------------------
@spaces.GPU(duration=100)
def blend_audio(voice_path: str, music_path: str, ducking: bool, duck_level: int = 10):
    """
    Blends two audio files (voice and music).
    1. If music < voice, loops the music until it meets/exceeds the voice duration.
    2. If music > voice, trims music to the voice duration.
    3. If ducking=True, the music is attenuated by 'duck_level' dB while the voice is playing.
    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)  # in milliseconds
        music_len = len(music)  # in milliseconds

        # 1) If the music is shorter than the voice, loop it:
        if music_len < voice_len:
            looped_music = AudioSegment.empty()
            # Keep appending until we exceed voice length
            while len(looped_music) < voice_len:
                looped_music += music
            music = looped_music

        # 2) If the music is longer than the voice, truncate it:
        if len(music) > voice_len:
            music = music[:voice_len]

        # Now music and voice are the same length
        if ducking:
            # Step 1: Reduce music dB while voice is playing
            ducked_music = music - duck_level
            # Step 2: Overlay voice on top of ducked music
            final_audio = ducked_music.overlay(voice)
        else:
            # No ducking, just overlay
            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:
        return f"Error blending audio: {e}"


# ---------------------------------------------------------------------
# Gradio Interface
# ---------------------------------------------------------------------
with gr.Blocks() as demo:
    gr.Markdown("""
# 🎧 AI Promo Studio
Welcome to **AI Promo Studio**, your all-in-one solution for creating professional, engaging audio promos with minimal effort!  

This next-generation platform uses powerful AI models to handle:
- **Script Generation**: Craft concise and impactful copy with LLaMA.
- **Voice Synthesis**: Convert text into natural-sounding voice-overs using Coqui TTS.
- **Music Production**: Generate custom music tracks with MusicGen Large for sound bed.
- **Seamless Blending**: Easily combine voice and music—loop or trim tracks to match your desired promo length, with optional ducking to keep the voice front and center.

Whether you’re a radio producer, podcaster, or content creator, **AI Promo Studio** streamlines your entire production pipeline—cutting hours of manual editing down to a few clicks.
""")


    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...",
                    lines=2
                )
                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 Promo 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, 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 user_prompt, model_id, dur: generate_script(user_prompt, model_id, HF_TOKEN, dur),
                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("Generate the voice-over using a Coqui TTS model.")
            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")
            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: Generate Music (MusicGen Large)
        with gr.Tab("Step 3: Generate Music"):
            gr.Markdown("Generate a music track with 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, but be mindful of inference time."
            )
            generate_music_button = gr.Button("Generate Music")
            music_output = gr.Audio(label="Generated Music (WAV)", type="filepath")

            generate_music_button.click(
                fn=lambda music_suggestion, length: generate_music(music_suggestion, length),
                inputs=[music_suggestion_output, audio_length],
                outputs=[music_output],
            )

        # Step 4: Blend Audio (Loop/Trim + Ducking)
        with gr.Tab("Step 4: Blend Audio"):
            gr.Markdown("**Music** will be looped or trimmed to match **Voice** duration, then optionally ducked.")
            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")
            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("""
    <hr>
    <p style="text-align: center; font-size: 0.9em;">
        Created with ❤️ by <a href="https://bilsimaging.com" target="_blank">bilsimaging.com</a>
    </p>
    """)
    
    # Visitor Badge
    gr.HTML("""
    <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" />
    </a>
    """)

demo.launch(debug=True)