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Update app.py

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	import subprocess
	subprocess.run(["pip", "install", "gradio=2.7.5.2"])
	subprocess.run(["pip", "install", "transformers"])
	subprocess.run(["pip", "install", "torchaudio", "--upgrade"])

	import gradio as gr
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
	import torchaudio
	import torch

	# Load model and processor
	processor = Wav2Vec2Processor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-italian")
	model = Wav2Vec2ForCTC.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-italian")

	# Function to perform ASR on audio data
	def transcribe_audio(audio_data):
	print("Received audio data:", audio_data) # Debug print

	# Check if audio_data is None or not a tuple of length 2
	if audio_data is None or not isinstance(audio_data, tuple) or len(audio_data) != 2:
	return "Invalid audio data format."

	sample_rate, waveform = audio_data

	# Check if waveform is None or not a NumPy array
	if waveform is None or not isinstance(waveform, torch.Tensor):
	return "Invalid audio data format."

	try:
	# Convert audio data to mono and normalize
	audio_data = torchaudio.transforms.Resample(sample_rate, 100000)(waveform)
	audio_data = torchaudio.functional.gain(audio_data, gain_db=5.0)

	# Apply custom preprocessing to the audio data if needed
	input_values = processor(audio_data[0], return_tensors="pt").input_values

	# Perform ASR
	with torch.no_grad():
	logits = model(input_values).logits

	# Decode the output
	predicted_ids = torch.argmax(logits, dim=-1)
	transcription = processor.batch_decode(predicted_ids)

	return transcription[0]

	except Exception as e:
	return f"An error occurred: {str(e)}"

	# Create Gradio interface
	audio_input = gr.Audio(sources=["microphone"])
	gr.Interface(fn=transcribe_audio, inputs=audio_input, outputs="text").launch()