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import whisper
# You can choose your model from - see it on readme file and update the modelname
modelname = "base"
model = whisper.load_model(modelname)
import gradio as gr
import time
def SpeechToText(audio):
if audio == None : return ""
time.sleep(1)
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 Max probability of language ?
_, probs = model.detect_language(mel)
language = max(probs, key=probs.get)
# Decode audio to Text
options = whisper.DecodingOptions(fp16 = False)
result = whisper.decode(model, mel, options)
return (language , result.text)
print("Starting the Gradio Web UI")
gr.Interface(
title = 'Voice Transcriber',
fn=SpeechToText,
inputs=[
gr.Audio(source="microphone", type="filepath")
],
outputs=[
"label",
"textbox",
],
live=True
).launch(
debug=False,
) |