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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) | |