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| <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> | |