Create app.py

app.py

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+import subprocess
+
+subprocess.run(["pip", "install", "datasets"])
+subprocess.run(["pip", "install", "transformers"])
+subprocess.run(["pip", "install", "torch", "torchvision", "torchaudio", "-f", "https://download.pytorch.org/whl/torch_stable.html"])
+
+import gradio as gr
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+# Load model and processor
+processor = WhisperProcessor.from_pretrained("openai/whisper-large")
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
+forced_decoder_ids = processor.get_decoder_prompt_ids(language="italian", task="transcribe")
+
+
+# Custom preprocessing function
+def preprocess_audio(audio_data):
+    # Apply any custom preprocessing to the audio data here if needed
+    return processor(audio_data, return_tensors="pt").input_features
+
+# Function to perform ASR on audio data
+def transcribe_audio(input_features):
+    # Generate token ids
+    predicted_ids = model.generate(input_features)
+    
+    # Decode token ids to text
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+    
+    return transcription[0]
+
+# Create Gradio interface
+audio_input = gr.Audio(preprocess=preprocess_audio)
+gr.Interface(fn=transcribe_audio, inputs=audio_input, outputs="text").launch()