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import numpy as np
import soundfile as sf
import gradio as gr
from g2p_en import G2p
from tensorflow.keras.models import load_model
import os
# Load the pre-trained model from the local directory
model_path = './model.h5'
model = load_model(model_path)
# Initialize the g2p model for converting text to phonemes
g2p = G2p()
def text_to_phonemes(text):
"""Convert input text to phonemes."""
phonemes = g2p(text)
return phonemes
def generate_audio_features(phonemes, duration):
"""Generate audio features from phonemes using the trained model."""
# Convert phonemes into a format suitable for the model input
# This is an example; modify it to match your model's requirements
phoneme_features = np.array([ord(p) for p in phonemes]) # Convert phonemes to numerical features
phoneme_features = np.expand_dims(phoneme_features, axis=0) # Reshape for model input
# Generate audio features using the model
audio_features = model.predict(phoneme_features)
# Adjust the length of the features based on the selected duration
num_samples = int(duration * 22050) # Example calculation assuming 22050 samples per second
audio_features = np.resize(audio_features, (num_samples,))
return audio_features
def features_to_audio(features):
"""Convert generated features back to audio."""
# Normalize the audio to a suitable range (-1 to 1)
audio = np.interp(features, (features.min(), features.max()), (-1, 1))
return audio
def generate_audio(text, duration):
"""Main function to handle text-to-audio conversion."""
# Step 1: Convert text to phonemes
phonemes = text_to_phonemes(text)
# Step 2: Generate audio features using the pre-trained model and duration
audio_features = generate_audio_features(phonemes, duration)
# Step 3: Convert features to actual audio
audio_data = features_to_audio(audio_features)
# Step 4: Save the generated audio
audio_file = 'generated_audio.wav'
sample_rate = 22050 # Use the sample rate for audio generation
sf.write(audio_file, audio_data, sample_rate)
return audio_file
# Gradio interface
def text_to_audio_interface(text, duration):
"""Gradio interface function to generate and return audio."""
# Call the generate_audio function with the text and selected duration
audio_file = generate_audio(text, duration)
# Return the path to the generated audio file
return audio_file
# Create the Gradio interface with a note, labeled button, and a slider for duration
gr.Interface(
fn=text_to_audio_interface,
inputs=[
gr.inputs.Textbox(label="Enter a Word", placeholder="Write a Word To Convert it into Sfx Sound"),
gr.inputs.Slider(minimum=1, maximum=20, default=5, step=1, label="Audio Duration (seconds)")
],
outputs=gr.outputs.Audio(label="Generated Audio Preview"),
title="Text-to-Audio Generator",
description="Write a Word, set the duration, and press 'Generate' to convert the word into an audio effect!",
live=True
).launch()
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