Create app.py

app.py

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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()