| <!DOCTYPE html> |
| <html> |
| <head> |
| <meta charset="utf-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1"> |
| <title>Gradio-Lite: Serverless Gradio Running Entirely in Your Browser</title> |
| <meta name="description" content="Gradio-Lite: Serverless Gradio Running Entirely in Your Browser"> |
|
|
| <script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script> |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css" /> |
|
|
| <style> |
| html, body { |
| margin: 0; |
| padding: 0; |
| height: 100%; |
| } |
| </style> |
| </head> |
| <body> |
| <gradio-lite> |
| <gradio-file name="app.py" entrypoint> |
| from transformers_js_py import import_transformers_js, read_audio |
| import gradio as gr |
|
|
|
|
| transformers = await import_transformers_js() |
| pipeline = transformers.pipeline |
| pipe = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en') |
|
|
|
|
| async def asr(audio_path): |
| audio = read_audio(audio_path, 16000) |
| result = await pipe(audio) |
| return result["text"] |
|
|
| demo = gr.Interface( |
| asr, |
| gr.Audio(type="filepath"), |
| gr.Text(), |
| examples=[ |
| ["jfk.wav"], |
| ] |
| ) |
|
|
| demo.launch() |
| </gradio-file> |
|
|
| <gradio-file name="jfk.wav" url="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav" /> |
|
|
| <gradio-requirements> |
| transformers_js_py |
| numpy |
| scipy |
| </gradio-requirements> |
| </gradio-lite> |
| </body> |
| </html> |
|
|
