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import torch |
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import gradio as gr |
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from transformers import pipeline |
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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 and file_upload: |
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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 not (microphone or file_upload): |
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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 else file_upload |
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text = pipe(file)["text"] |
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return warn_output + text |
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examples = [ |
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['Martin Luther king - FREE AT LAST.mp3'], |
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['Winston Churchul - ARCH OF VICTOR.mp3'], |
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['Voice of Neil Armstrong.mp3'], |
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['Speeh by George Washington.mp3'], |
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['Speech by John Kennedy.mp3'], |
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['Al Gore on Inventing the Internet.mp3'], |
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['Alan Greenspan.mp3'], |
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['Neil Armstrong - ONE SMALL STEP.mp3'], |
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['General Eisenhower announcing D-Day landing.mp3'], |
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['Hey Siri.wav'] |
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] |
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css = """ |
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footer {display:none !important} |
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.output-markdown{display:none !important} |
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button.primary { |
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z-index: 14; |
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left: 0px; |
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top: 0px; |
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cursor: pointer !important; |
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background: none rgb(17, 20, 45) !important; |
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border: none !important; |
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color: rgb(255, 255, 255) !important; |
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line-height: 1 !important; |
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border-radius: 6px !important; |
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transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; |
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box-shadow: none !important; |
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} |
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button.primary:hover{ |
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z-index: 14; |
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left: 0px; |
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top: 0px; |
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cursor: pointer !important; |
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background: none rgb(66, 133, 244) !important; |
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border: none !important; |
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color: rgb(255, 255, 255) !important; |
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line-height: 1 !important; |
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border-radius: 6px !important; |
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transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; |
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box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important; |
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} |
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button.gallery-item:hover { |
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border-color: rgb(37 56 133) !important; |
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background-color: rgb(229,225,255) !important; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Row(): |
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gr.Markdown("## Speech Recognition Demo") |
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with gr.Row(): |
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mic_input = gr.Audio(label="Microphone Input", interactive=True, type="filepath") |
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file_upload = gr.Audio(label="File Upload", interactive=True, type="filepath") |
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with gr.Row(): |
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output = gr.Textbox(label="Transcription Output") |
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with gr.Row(): |
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gr.Examples(examples=examples, inputs=[file_upload], label="Examples") |
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transcribe_button = gr.Button("Transcribe") |
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transcribe_button.click(transcribe, inputs=[mic_input, file_upload], outputs=[output]) |
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demo.launch() |
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