Spaces:
Running
Running
| import gradio as gr | |
| from transformers import pipeline | |
| # Load the audio classification model | |
| pipe = pipeline("audio-classification", model="dima806/english_accents_classification") | |
| # Define the inference function with styled, color-coded output | |
| def classify_accent(audio): | |
| try: | |
| result = pipe(audio) | |
| if not result: | |
| return "<p style='color: red; font-weight: bold;'>⚠️ No prediction returned. Please try a different audio file.</p>" | |
| # Start HTML table with styling | |
| table = """ | |
| <table style=" | |
| width: 100%; | |
| border-collapse: collapse; | |
| font-family: Arial, sans-serif; | |
| margin-top: 1em; | |
| "> | |
| <thead> | |
| <tr style="border-bottom: 2px solid #4CAF50; background-color: #f2f2f2;"> | |
| <th style="text-align:left; padding: 8px; font-size: 1.1em; color: #333;">Accent</th> | |
| <th style="text-align:left; padding: 8px; font-size: 1.1em; color: #333;">Confidence</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| """ | |
| for i, r in enumerate(result): | |
| label = r['label'].capitalize() | |
| score = f"{r['score'] * 100:.2f}%" | |
| if i == 0: | |
| # Highlight top accent with green background and bold text | |
| row = f""" | |
| <tr style="background-color:#d4edda; font-weight: bold; color: #155724;"> | |
| <td style="padding: 8px; border-bottom: 1px solid #c3e6cb;">{label}</td> | |
| <td style="padding: 8px; border-bottom: 1px solid #c3e6cb;">{score}</td> | |
| </tr> | |
| """ | |
| else: | |
| row = f""" | |
| <tr style="color: #333;"> | |
| <td style="padding: 8px; border-bottom: 1px solid #ddd;">{label}</td> | |
| <td style="padding: 8px; border-bottom: 1px solid #ddd;">{score}</td> | |
| </tr> | |
| """ | |
| table += row | |
| table += "</tbody></table>" | |
| top_result = result[0] | |
| return f""" | |
| <h3 style='color: #2E7D32; font-family: Arial, sans-serif;'> | |
| 🎤 Predicted Accent: <span style='font-weight:bold'>{top_result['label'].capitalize()}</span> | |
| </h3> | |
| {table} | |
| """ | |
| except Exception as e: | |
| error_message = str(e) | |
| if "numpy ndarray" in error_message.lower(): | |
| return "<p style='color: red; font-weight: bold;'>⚠️ Error: Invalid input.<br> Please end the recording then press submit.</p>" | |
| else: | |
| return f"<p style='color: red; font-weight: bold;'>⚠️ Unexpected Error: {error_message}<br>Please try again with a different audio file.</p>" | |
| # Create and launch the Gradio app | |
| gr.Interface( | |
| fn=classify_accent, | |
| inputs=gr.Audio(type="filepath", label="🎙 Record or Upload English Audio"), | |
| outputs=gr.HTML(), # Use HTML to render styled output | |
| title="🌍 English Accent Classifier", | |
| description=( | |
| "Upload or record an English audio sample to detect the speaker's accent.\n\n" | |
| "**Supported accents:** American, British, Indian, African, Australian.\n" | |
| "Audio Classification Model:\n" | |
| "[dima806/english_accents_classification](https://huggingface.co/dima806/english_accents_classification)\n" | |
| "Dataset: https://www.kaggle.com/code/dima806/common-voice-accent-classification\n" | |
| ), | |
| flagging_mode="never", | |
| theme="default" | |
| ).launch(share=True) | |