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index.html
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<body>
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<gradio-lite>
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<gradio-file name="app.py" entrypoint>
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import gradio as gr
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from filters import as_gray
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demo = gr.Interface(
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"
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examples=[
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)
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demo.launch()
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</gradio-file>
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<gradio-file name="
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from skimage.color import rgb2gray
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def as_gray(image):
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return rgb2gray(image)
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</gradio-file>
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<gradio-file name="lion.jpg" url="https://raw.githubusercontent.com/gradio-app/gradio/main/gradio/test_data/lion.jpg" />
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<gradio-file name="logo.png" url="https://raw.githubusercontent.com/gradio-app/gradio/main/guides/assets/logo.png" />
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<gradio-requirements>
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</gradio-requirements>
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</gradio-lite>
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</body>
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</html>
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<body>
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<gradio-lite>
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<gradio-file name="app.py" entrypoint>
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from transformers_js_py import import_transformers_js, read_audio
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import gradio as gr
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transformers = await import_transformers_js()
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pipeline = transformers.pipeline
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pipe = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en')
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async def asr(audio_path):
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audio = read_audio(audio_path, 16000)
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result = await pipe(audio)
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return result["text"]
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demo = gr.Interface(
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asr,
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gr.Audio(type="filepath"),
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gr.Text(),
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examples=[
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["jfk.wav"],
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]
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)
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demo.launch()
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</gradio-file>
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<gradio-file name="jfk.wav" url="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav" />
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<gradio-requirements>
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transformers_js_py
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numpy
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scipy
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</gradio-requirements>
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</gradio-lite>
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</body>
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</html>
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