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import whisper
import gradio as gr
model = whisper.load_model('base')
def transcribe(inputs , timestamp):
if inputs is None:
raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
output = ""
result = model.transcribe(inputs)
if timestamp == "Yes":
for indx, segment in enumerate(result['segments']):
output += str(datetime.timedelta (seconds=segment['start'])) +" "+ str(datetime.timedelta (seconds=segment['end'])) + "\n"
output += segment['text'].strip() + '\n'
else:
output = result["text"]
print(result)
return output
interface = gr.Interface(
fn=transcribe,
inputs=[gr.Audio(sources=["upload"],type="filepath"),
gr.Radio(["Yes", "No"], label="Timestamp", info="Displays with timestamp if needed."),],
outputs="text",
title="Whisper Large V3: Transcribe Audio",
description=(
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper API"
)
)
interface.launch() |