Update app.py

app.py

@@ -1,29 +1,21 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import difflib
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
+# 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 respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
 
-    messages.append({"role": "user", "content": message})
+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 = ""
 
@@ -35,29 +27,65 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
-
         response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+
+    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(source="microphone", 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
 )
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(enable_queue=True, show_error=True)