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import gradio as gr
from huggingface_hub import InferenceClient
import difflib
# Load Hugging Face Inference client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# Load the speech-to-text model from Hugging Face
s2t = gr.Interface.load('huggingface/facebook/s2t-medium-librispeech-asr')
def generate_text_with_huggingface(system_message, max_tokens, temperature, top_p):
"""
Function to generate text using Hugging Face Inference API
based on the system message, max tokens, temperature, and top-p.
"""
messages = [{"role": "system", "content": system_message}]
message = ""
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
return response.strip() # Return the generated text
def pronunciation_feedback(transcription, reference_text):
"""
Function to provide feedback on pronunciation based on differences
between the transcription and the reference (expected) text.
"""
diff = difflib.ndiff(reference_text.split(), transcription.split())
# Identify words that are incorrect or missing in the transcription
errors = [word for word in diff if word.startswith('- ')]
if errors:
feedback = "Mispronounced words: " + ', '.join([error[2:] for error in errors])
else:
feedback = "Great job! Your pronunciation is spot on."
return feedback
def transcribe_and_feedback(audio, system_message, max_tokens, temperature, top_p):
"""
Transcribe the audio and provide pronunciation feedback using the generated text.
"""
# Generate the reference text using Hugging Face Inference API
reference_text = generate_text_with_huggingface(system_message, max_tokens, temperature, top_p)
# Transcribe the audio using the speech-to-text model
transcription = s2t(audio)
# Provide pronunciation feedback based on the transcription and the generated text
feedback = pronunciation_feedback(transcription, reference_text)
return transcription, feedback, reference_text
# Gradio interface
demo = gr.Interface(
fn=transcribe_and_feedback, # The function that transcribes audio and provides feedback
inputs=[
gr.Audio(type="filepath", label="Record Audio"), # Microphone input for recording
gr.Textbox(value="Please read a simple sentence.", label="System message"), # Message used to generate text
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), # Controls max token length for the generated text
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), # Temperature control for text generation
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)") # Top-p control for text generation
],
outputs=[
gr.Textbox(label="Transcription"), # Display transcription of the audio
gr.Textbox(label="Pronunciation Feedback"), # Feedback on pronunciation
gr.Textbox(label="Generated Text (What You Were Supposed to Read)") # Display the text generated by the API
],
title="Speech-to-Text with Pronunciation Feedback",
description="Record an audio sample and the system will transcribe it, "
"compare your transcription to the generated text, and give pronunciation feedback.",
live=True # Real-time interaction
)
# Enable queuing and launch the app
demo.queue().launch(show_error=True)
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