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import whisper # Ensure 'openai-whisper' is installed
import gradio as gr
import subprocess
# Load the Whisper model
try:
model = whisper.load_model("large") # Official Whisper model
except Exception as e:
print(f"Error loading Whisper model: {e}")
raise e
def transcribe_video(video_path):
try:
# Extract audio from the uploaded video
audio_path = "audio.wav"
subprocess.run(
["ffmpeg", "-i", video_path, "-ar", "16000", "-ac", "1", "-c:a", "pcm_s16le", audio_path],
check=True
)
# Transcribe the audio in Urdu
result = model.transcribe(audio_path, task="transcribe", language="ur")
return result["text"]
except FileNotFoundError:
return "Error: ffmpeg is not installed or not found in the environment."
except Exception as e:
return f"An error occurred: {e}"
# Create the Gradio interface
interface = gr.Interface(
fn=transcribe_video,
inputs=gr.Video(label="Upload your Urdu-speaking video"),
outputs=gr.Textbox(label="Transcribed Text"),
title="Urdu Video Transcription App",
description="Upload a video file in Urdu, and this app will transcribe the speech into text using Whisper.",
)
# Launch the app
if __name__ == "__main__":
interface.launch()
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