Create app.py

app.py

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+import subprocess
+subprocess.run(["pip", "install", "gradio", "--upgrade"])
+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()