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import time | |
import gradio as gr | |
from transformers import pipeline | |
# Initialize the transcription pipeline using a Hugging Face model | |
pipe = pipeline(model="Ussen/whisper-medium-swc-drc-kat-1") | |
def transcribe_with_timing(audio): | |
# Start timing | |
start_time = time.time() | |
# Perform transcription | |
text = pipe(audio)["text"] | |
# Calculate elapsed time | |
elapsed_time = time.time() - start_time | |
return text, f"Transcription time: {elapsed_time:.2f} seconds" | |
# Create Gradio interface | |
demo = gr.Interface( | |
fn=transcribe_with_timing, | |
inputs=gr.Audio(type="filepath", label="Bonyeza kitufe cha kurekodi na uliza swali lako"), | |
outputs=[ | |
gr.Textbox(label="Jibu (kwa njia ya maandishi)"), | |
gr.Textbox(label="Transcription Time") | |
], | |
description="Rekodi sauti yako na upate maandishi (Swahili ASR)", | |
live=True | |
) | |
demo.queue(api_open=True) | |
demo.launch() |