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Update index.html

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  1. index.html +21 -20
index.html CHANGED
@@ -20,38 +20,39 @@
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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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- def process(input_image):
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- output_image = as_gray(input_image)
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- return output_image
 
 
 
 
 
 
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  demo = gr.Interface(
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- process,
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- "image",
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- "image",
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- examples=["lion.jpg", "logo.png"],
 
 
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  )
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  demo.launch()
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  </gradio-file>
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- <gradio-file name="filters.py">
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- from skimage.color import rgb2gray
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-
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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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-
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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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- # Same syntax as requirements.txt
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- scikit-image
 
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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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+
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+
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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>