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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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MODEL_NAME = "openai/whisper-small" |
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lang = "en" |
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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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chunk_length_s=30, |
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device=device, |
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) |
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pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") |
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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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demo = gr.Blocks() |
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examples = [ |
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['TestAudio1.mp3'], ['TestAudio2.wav'], ['TestAudio3.wav'], ['TestAudio4.wav'], ['TestAudio5.wav'], ['TestAudio6.wav'], ['TestAudio7.wav'], ['TestAudio8.wav'], ['TestAudio9.wav'], ['TestAudio10.wav'] |
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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", optional=True), |
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gr.inputs.Audio(source="upload", type="filepath", optional=True) |
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], |
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outputs="text", |
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layout="horizontal", |
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theme="huggingface", |
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allow_flagging="never", |
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examples = examples |
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).launch(enable_queue=True) |
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