Create app.py

app.py

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+import torchaudio
+import streamlit as st
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
+
+
+# Load the ASR model and processor
+processor = AutoProcessor.from_pretrained("mohammed/whisper-large-arabic-cv-11")
+model = AutoModelForSpeechSeq2Seq.from_pretrained("mohammed/whisper-large-arabic-cv-11")
+
+audio_file = st.file_uploader("Upload Audio", type=["wav", "mp3", "m4a"])
+st.title("Arabic ASR model")
+
+if st.sidebar.button("Transcribe Audio"):
+    if audio_file is not None:
+        st.sidebar.success("Transcribing Audio >>>>")
+
+        # Load the audio file
+        audio_tensor, sample_rate = torchaudio.load(audio_file)
+        
+        # Resample if necessary
+        if sample_rate != 16000:
+            resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
+            audio_tensor = resampler(audio_tensor)
+
+        # Convert the audio tensor to a numpy array
+        audio_np = audio_tensor.squeeze().numpy()
+
+        # Process the audio
+        inputs = processor(audio_np, sampling_rate=16000, return_tensors="pt")
+        
+        # Generate transcription
+        generated_ids = model.generate(inputs["input_features"])
+        transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
+        
+        # Display transcription
+        st.sidebar.success("Transcription Complete!")
+        st.text(transcription[0])
+    else:
+        st.sidebar.error("Please upload a valid audio file")