| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en", generate_kwargs={"temperature": 1.0}) | |
| def transcribe(audio): | |
| sr, y = audio | |
| # Convert to mono if stereo | |
| if y.ndim > 1: | |
| y = y.mean(axis=1) | |
| y = y.astype(np.float32) | |
| y /= np.max(np.abs(y)) | |
| return transcriber({"sampling_rate": sr, "raw": y})["text"] | |
| demo = gr.Interface( | |
| transcribe, | |
| gr.Audio(sources="microphone"), | |
| "text", | |
| ) | |
| demo.launch(share=True) | |