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| #@markdown Output: Accuracy Score | |
| import gradio as gr | |
| import speech_recognition as sr | |
| from Levenshtein import ratio | |
| import tempfile | |
| import numpy as np | |
| import soundfile as sf | |
| import pandas as pd | |
| # Sample dataframe with sentences ordered from easy to hard | |
| data = { | |
| "Sentences": [ | |
| "A stitch in time saves nine.", | |
| "To be or not to be, that is the question.", | |
| "Five cats were living in safe caves.", | |
| "Hives give shelter to bees in large caves.", | |
| "His decision to plant a rose was amazing.", | |
| "She sells sea shells by the sea shore.", | |
| "The colorful parrot likes rolling berries.", | |
| "Time flies like an arrow; fruit flies like a banana.", | |
| "Good things come to those who wait.", | |
| "All human beings are born free and equal in dignity and rights." | |
| ] | |
| } | |
| df = pd.DataFrame(data) | |
| def transcribe_audio(file_info): | |
| r = sr.Recognizer() | |
| with tempfile.NamedTemporaryFile(delete=True, suffix=".wav") as tmpfile: | |
| sf.write(tmpfile.name, data=file_info[1], samplerate=44100, format='WAV') | |
| tmpfile.seek(0) | |
| with sr.AudioFile(tmpfile.name) as source: | |
| audio_data = r.record(source) | |
| try: | |
| text = r.recognize_google(audio_data) | |
| return text | |
| except sr.UnknownValueError: | |
| return "Could not understand audio" | |
| except sr.RequestError as e: | |
| return f"Could not request results; {e}" | |
| def pronunciation_correction(expected_text, file_info): | |
| user_spoken_text = transcribe_audio(file_info) | |
| similarity = ratio(expected_text.lower(), user_spoken_text.lower()) | |
| description = f"{similarity:.2f}" | |
| if similarity >= 0.9: | |
| feedback = "Excellent pronunciation!" | |
| elif similarity >= 0.7: | |
| feedback = "Good pronunciation!" | |
| elif similarity >= 0.5: | |
| feedback = "Needs improvement." | |
| else: | |
| feedback = "Poor pronunciation, try to focus more on clarity." | |
| return feedback, description | |
| with gr.Blocks() as app: | |
| with gr.Row(): | |
| sentence_dropdown = gr.Dropdown(choices=df['Sentences'].tolist(), label="Select a Sentence") | |
| selected_sentence_output = gr.Textbox(label="Selected Text", interactive=False) | |
| audio_input = gr.Audio(label="Upload Audio File", type="numpy") | |
| check_pronunciation_button = gr.Button("Check Pronunciation") | |
| pronunciation_feedback = gr.Textbox(label="Pronunciation Feedback") | |
| pronunciation_score = gr.Number(label="Pronunciation Accuracy Score: 0 (No Match) ~ 1 (Perfect)") | |
| sentence_dropdown.change(lambda x: x, inputs=sentence_dropdown, outputs=selected_sentence_output) | |
| check_pronunciation_button.click( | |
| pronunciation_correction, | |
| inputs=[sentence_dropdown, audio_input], | |
| outputs=[pronunciation_feedback, pronunciation_score] | |
| ) | |
| app.launch(debug=True) |