| import torch | |
| from transformers import pipeline | |
| import numpy as np | |
| import gradio as gr | |
| pipe = pipeline( | |
| "automatic-speech-recognition", model="openai/whisper-base" | |
| ) | |
| def transcribe(audio): | |
| sr, y = audio | |
| # Pasamos el array de muestras a tipo NumPy de 32 bits | |
| 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(sources=["microphone"]), | |
| "text" | |
| ) | |
| demo.launch() |