Create app.py

app.py

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+from transformers import AutoModelForCTC, Wav2Vec2Processor
+import torch
+import gradio as gr
+
+# Load model and processor
+model_name = "nada15/wav2vec2-large-xls-r-300m-dm32"
+processor = AutoModelForCTC.from_pretrained(model_name)
+model = AutoModelForCTC.from_pretrained(model_name)
+
+def transcribe(audio):
+    inputs = processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
+    logits = model(inputs.input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)
+    return transcription[0]
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs="text",
+    live=True
+)
+
+interface.launch()