Spaces:
Running
Running
File size: 1,416 Bytes
1f0f03f 0591d69 1f0f03f 0591d69 1f0f03f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
<!DOCTYPE html>
<html>
<head>
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto&display=swap" >
<style>
body {
font-family: 'Roboto', sans-serif;
font-size: 16px;
}
.logo {
height: 1em;
vertical-align: middle;
margin-bottom: 0.1em;
}
</style>
<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite@0.4.2/dist/lite.js"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite@0.4.2/dist/lite.css" />
<link rel="stylesheet" href="https://gradio-hello-world.hf.space/theme.css">
</head>
<body>
<gradio-lite>
<gradio-requirements>
transformers_js_py
</gradio-requirements>
<gradio-file name="app.py" entrypoint>
from transformers_js import import_transformers_js
import gradio as gr
import numpy as np
transformers = await import_transformers_js()
pipeline = transformers.pipeline
pipe = await pipeline("automatic-speech-recognition",'Xenova/whisper-small')
async def transcribe(audio):
sr, y = audio
y = y.astype(np.float32)
y /= np.max(np.abs(y))
return pipe({"sampling_rate": sr, "raw": y})["text"]
demo = gr.Interface(
transcribe,
gr.Audio(source="microphone"),
"text",
theme=gr.themes.Soft(),)
demo.launch()
</gradio-file>
</gradio-lite>
</body>
</html>
|