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from transformers import pipeline
# Load model directly
from transformers import AutoProcessor, AutoModelForTextToWaveform
import gradio as gr
processor = AutoProcessor.from_pretrained("suno/bark-small")
# Load audio classification model
audio_classifier = pipeline(
"audio-classification", model="HareemFatima/distilhubert-finetuned-stutterdetection"
)
# Load text-to-speech model
tts_processor = AutoProcessor.from_pretrained("suno/bark-small")
tts_model = AutoModelForTextToWaveform.from_pretrained("suno/bark-small")
# Define therapy text for different stutter types (replace with your specific therapy content)
therapy_text = {
"Normal Speech": "Your speech sounds great! Keep practicing!",
"Blocking": "Take a deep breath and try speaking slowly. You can do it!",
"Prolongation": "Focus on relaxing your mouth muscles and speaking smoothly.",
# Add more stutter types and therapy text here
}
def predict_and_synthesize(audio):
"""Predicts stutter type and synthesizes speech with therapy text.
Args:
audio (bytes): Audio data from the user.
Returns:
tuple: A tuple containing the predicted stutter type (string) and synthesized speech (bytes).
"""
# Classify stuttering type using audio classification model
prediction = audio_classifier(audio)
stutter_type = prediction[0]["label"]
# Retrieve therapy text based on predicted stutter type
therapy = therapy_text.get(stutter_type, "General therapy tip: Practice slow, relaxed speech.")
# Generate synthesized speech with the therapy text
synthesized_speech = tts_model.generate(
tts_processor(therapy, return_tensors="pt").input_ids
)[0].squeeze().cpu().numpy()
return stutter_type, synthesized_speech
# Create Gradio interface
interface = gr.Interface(
fn=predict_and_synthesize,
inputs="microphone",
outputs=["text", "audio"],
title="Stuttering Therapy Assistant",
description="This app helps you identify stuttering types and provides personalized therapy suggestions. Upload an audio clip, and it will analyze the speech and generate audio with relevant therapy tips.",
)
interface.launch(debug=False)
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