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import os
import numpy as np
import gradio as gr
from transformers import pipeline
transcriber = pipeline(task="automatic-speech-recognition", model="geokanaan/Whisper_Base_Lebanese_Arabizi")
HF_TOKEN = os.getenv('WRITE')
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "flagged_Audio_Lebanese")
def transcribe(stream, new_chunk):
sr, y = new_chunk
# Convert to mono if stereo
if y.ndim > 1:
y = y.mean(axis=1)
y = y.astype(np.float32)
y /= np.max(np.abs(y))
if stream is not None:
stream = np.concatenate([stream, y])
else:
stream = y
return stream, transcriber({"sampling_rate": sr, "raw": stream})["text"]
demo = gr.Interface(
transcribe,
["state", gr.Audio(sources=["microphone"], streaming=True)],
["state", "text"],
live=True,
title="UNDER MAINTENANCE",
description="Realtime demo for Lebanese Arabizi speech recognition",
allow_flagging='manual', # Enable manual flagging
)
demo.launch()
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