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from transformers import pipeline |
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import gradio as gr |
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import time |
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import unicodedata |
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p = pipeline("automatic-speech-recognition",model="Yehor/wav2vec2-xls-r-base-uk-with-small-lm") |
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def transcribe(audio, state=""): |
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time.sleep(2) |
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text = p(audio)["text"] |
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state += unicodedata.normalize("NFC",text) + " " |
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return state, state |
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title = "Real-Time Urdu ASR" |
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description = """ |
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<p> |
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<center> |
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This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. |
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</center> |
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</p> |
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<center> |
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<img src="https://huggingface.co/spaces/kingabzpro/real-time-Urdu-ASR/resolve/main/Images/cover.jpg" alt="logo" width="550"/> |
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</center> |
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""" |
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article = "<p style='text-align: center'><a href='https://dagshub.com/kingabzpro/Urdu-ASR-SOTA' target='_blank'>Source Code on DagsHub</a></p><p style='text-align: center'><a href='https://huggingface.co/blog/fine-tune-xlsr-wav2vec2' target='_blank'>Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers</a></p></center><center><img src='https://visitor-badge.glitch.me/badge?page_id=kingabzpro/real-time-Urdu-ASR' alt='visitor badge'></center></p>" |
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gr.Interface( |
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fn=transcribe, |
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inputs=[ |
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gr.Audio(source="microphone", type="filepath", streaming=True), |
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"state" |
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], |
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outputs=[ |
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"textbox", |
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"state" |
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], |
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title=title, |
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description=description, |
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article=article, |
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theme='EveryPizza/Cartoony-Gradio-Theme', |
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live=True).launch() |
