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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()
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