File size: 847 Bytes
c191eff 9c90712 e1df0d7 c191eff 6e0a649 c191eff f6f922b 6e0a649 |
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 |
import whisper
import gradio as gr
import time
model = whisper.load_model("base")
def transcribe(audio):
#time.sleep(3)
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")
# decode the audio
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)
return result.text
gr.Interface(
title="OpenAI-Whisper Audio to Text Web UI",
fn=transcribe,
inputs=[gr.components.Audio(type="filepath")],
outputs=["textbox"],
live=True
).launch()
|