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

# Define sample_rate as a global constant
SAMPLE_RATE = 22050

# Download required NLTK data
nltk.download('averaged_perceptron_tagger', quiet=True)
nltk.download('cmudict', quiet=True)

# Load your model from the root directory
model = tf.keras.models.load_model('audio_model.h5', compile=False)

# Preprocess input text
def preprocess_text(text):
    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'])
    
    flattened_phonemes = [p for sublist in phonemes for p in sublist]
    
    num_features = 13
    sequence_length = len(flattened_phonemes)
    if sequence_length == 0:
        return np.zeros((1, 1, num_features))

    input_data = np.random.rand(sequence_length, num_features)
    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, filename="output.wav"):
    if model_output.size == 0:
        return None
    normalized_output = np.interp(model_output, (model_output.min(), model_output.max()), (-1, 1))
    write(filename, SAMPLE_RATE, normalized_output.astype(np.float32))
    return filename

# Define function to generate sound effect
def generate_sfx(text, duration):
    input_data = preprocess_text(text)
    
    if input_data.shape[1] == 0:
        return None

    prediction = model.predict(input_data)
    flat_prediction = prediction.flatten()
    
    if len(flat_prediction) == 0:
        return None

    num_repeats = (duration * SAMPLE_RATE // len(flat_prediction)) + 1
    audio_data = np.tile(flat_prediction, num_repeats)[:duration * SAMPLE_RATE]
    
    audio_file = convert_to_audio(audio_data, filename="output.wav")
    
    return audio_file

# 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(minimum=1, maximum=20, value=3, step=1, label="Duration (seconds)")
    ],
    outputs=gr.Audio(label="Generated SFX", type="filepath"),
    title="SFX Generator from Text",
    description="Enter a word or sentence, and the model will generate an SFX sound.",
)

# Run the interface
if __name__ == "__main__":
    tf.config.set_visible_devices([], 'GPU')
    # The ValueError shows that share=True IS required for your environment.
    interface.launch()