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| import gradio as gr | |
| import whisper | |
| from pybraille import convertText | |
| # Translate speech to braille | |
| model = whisper.load_model('base') | |
| def speech_to_text(audio): | |
| """ | |
| :param audio: Speech data | |
| :return: Transcript of speech data | |
| """ | |
| return model.transcribe(audio) | |
| def transcribe_audio(audio, store=None): | |
| """ | |
| :param audio: Speech data from gradio interface | |
| :param store: Container for user data | |
| :return: Detected language, transcript text, braille translation, session state | |
| """ | |
| if store is None: | |
| store = {} | |
| result = speech_to_text(audio) | |
| store['transcription'] = result['text'] | |
| store['braille'] = convertText(store['transcription']) | |
| return f"Detected language: {result['language']}", store[ | |
| 'transcription'], store['braille'], store | |
| # Setup gradio interface | |
| title = "Speech to Braille Translator" | |
| transcription_tb = gr.Textbox(label="Transcription", lines=10, max_lines=20) | |
| detected_lang = gr.outputs.HTML(label="Detected Language") | |
| transcription_braille = gr.Textbox(label="Braille", lines=10, max_lines=20) | |
| state = gr.State({"transcription": ""}) | |
| demo = gr.Interface(fn=transcribe_audio, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath", | |
| streaming=False), | |
| state | |
| ], | |
| outputs=[ | |
| detected_lang, | |
| transcription_tb, | |
| transcription_braille, | |
| state, | |
| ], | |
| # live=True, | |
| allow_flagging='never', | |
| title=title, | |
| ) | |
| # Launch gradio app | |
| demo.launch() | |