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import torch |
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import gradio as gr |
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import pytube as pt |
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from transformers import pipeline |
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from huggingface_hub import model_info |
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import time |
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import unicodedata |
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from gradio.themes.utils.theme_dropdown import create_theme_dropdown |
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MODEL_NAME = "SakshiRathi77/Fine-tune-Whisper-Kagglex" |
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lang = "hi" |
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device = 0 if torch.cuda.is_available() else "cpu" |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=MODEL_NAME, |
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device=device, |
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) |
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def transcribe(microphone, file_upload): |
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warn_output = "" |
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if (microphone is not None) and (file_upload is not None): |
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warn_output = ( |
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"WARNING: You've uploaded an audio file and used the microphone. " |
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"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" |
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) |
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elif (microphone is None) and (file_upload is None): |
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return "ERROR: You have to either use the microphone or upload an audio file" |
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file = microphone if microphone is not None else file_upload |
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text = pipe(file)["text"] |
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return warn_output + text |
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def rt_transcribe(audio, state=""): |
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time.sleep(2) |
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text = pipe(audio)["text"] |
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state += unicodedata.normalize("NFC",text) + " " |
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return state, state |
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demo = gr.Blocks() |
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examples=[["examples/example1.mp3"], ["examples/example2.mp3"],["examples/example3.mp3"]] |
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title =""" |
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HindiSpeechPro: Whisper-Powered ASR Interface |
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""" |
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description = """ |
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<p> |
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<center> |
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Welcome to the HindiSpeechPro, a cutting-edge interface powered by a fine-tuned version of OpenAi/Whisper on the common_voice dataset. |
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<img src="https://huggingface.co/spaces/SakshiRathi77/SakshiRathi77-Wishper-Hi-Kagglex/resolve/main/Images/main_image.png" alt="logo" ;> |
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</center> |
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</p> |
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""" |
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mf_transcribe = gr.Interface( |
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fn=transcribe, |
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inputs=[ |
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gr.inputs.Audio(source="microphone", type="filepath"), |
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gr.inputs.Audio(source="upload", type="filepath"), |
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], |
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outputs="text", |
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title=title, |
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description= description , |
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allow_flagging="never", |
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examples=examples, |
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) |
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rt_transcribe = gr.Interface( |
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fn=rt_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=[ "textbox", |
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"state"], |
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title=title, |
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description= description , |
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allow_flagging="never", |
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live=True, |
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) |
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with demo: |
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gr.TabbedInterface([mf_transcribe, rt_transcribe], ["Transcribe Audio", "Transcribe Realtime Voice"]) |
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demo.launch(share=True) |
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