app.py

import gradio as gr
import tensorflow as tf
import numpy as np
import nltk
from nltk.corpus import cmudict
from scipy.io.wavfile import write

# Download required NLTK data
nltk.download('averaged_perceptron_tagger')
nltk.download('cmudict')

# Load your model
model = tf.keras.models.load_model('audio_model.h5')

# Preprocess input text
def preprocess_text(text):
    """
    Process the input text to prepare it for the model.
    """
    d = cmudict.dict()
    words = text.lower().split()
    phonemes = []

    for word in words:
        if word in d:
            phonemes.append(d[word][0])
        else:
            phonemes.append(['UNKNOWN'])
    
    # Flatten the list of phonemes
    flattened_phonemes = [p for sublist in phonemes for p in sublist]
    
    # Create dummy feature vectors (this should be replaced with actual feature extraction)
    num_features = 13
    sequence_length = len(flattened_phonemes)
    input_data = np.random.rand(sequence_length, num_features)  # Placeholder
    
    # Add batch dimension
    input_data = np.expand_dims(input_data, axis=0)
    
    return input_data

# Convert model output to an audio file
def convert_to_audio(model_output, sample_rate=22050):
    """
    Convert the model output into a .wav file.
    """
    # Check if model_output is empty
    if model_output is None or len(model_output) == 0:
        raise ValueError("Model output is empty.")

    # Normalize the audio output
    normalized_output = np.interp(model_output, (model_output.min(), model_output.max()), (-1, 1))
    
    # Return normalized output for further processing
    return normalized_output

# Generate sound effect with specified duration
def generate_sfx(text, duration=30):
    """
    Takes input text, preprocesses it, runs it through the model,
    and generates a downloadable audio file for a specified duration.
    """
    input_data = preprocess_text(text)
    
    # Initialize an empty list to hold audio segments
    audio_segments = []
    total_samples = duration * 22050  # Calculate total samples for 30 seconds
    generated_samples = 0

    while generated_samples < total_samples:
        # Generate prediction
        prediction = model.predict(input_data)

        # Ensure prediction shape is correct
        if prediction.ndim == 2 and prediction.shape[1] > 1:
            prediction = prediction.flatten()  # Flatten if necessary
        
        # Convert the prediction to audio data
        audio_segment = convert_to_audio(prediction)

        # Append the generated segment to the list
        audio_segments.append(audio_segment)

        # Increment the total samples generated
        generated_samples += len(audio_segment)

    # Concatenate all segments to form the final audio output
    final_audio = np.concatenate(audio_segments)[:total_samples]  # Ensure we cut to the correct length

    # Write the audio data to a file
    output_filename = "output.wav"
    write(output_filename, 22050, final_audio)

    return output_filename

# Define the Gradio interface
interface = gr.Interface(
    fn=generate_sfx,
    inputs=[
        gr.Textbox(label="Enter a Word", placeholder="Write a Word To Convert it into SFX Sound"),
        gr.Slider(label="Duration (seconds)", minimum=1, maximum=60, value=30)  # Added duration slider
    ],
    outputs=gr.Audio(label="Generated SFX", type="filepath"),
    live=False,
    title="SFX Generator from Text",
    description="Enter a word or sentence, and the model will generate an SFX sound for the specified duration.",
)

# Run the interface
if __name__ == "__main__":
    interface.launch()