Update app.py

app.py

@@ -1,13 +1,22 @@
 import subprocess
+import gradio as gr  # Add this import statement
 
 subprocess.run(["python", "-m", "pip", "install", "--upgrade", "pip"])
 subprocess.run(["pip", "install", "gradio", "--upgrade"])
 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"])
+
 from transformers import WhisperProcessor, WhisperForConditionalGeneration
 from datasets import load_dataset
 
+# Define the transcribe_audio function
+def transcribe_audio(audio):
+    input_features = processor(audio, return_tensors="pt").input_features 
+    predicted_ids = model.generate(input_features)
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+    return transcription[0]
+
 # load model and processor
 processor = WhisperProcessor.from_pretrained("openai/whisper-small")
 model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
@@ -16,14 +25,6 @@ forced_decoder_ids = processor.get_decoder_prompt_ids(language="italian", task="
 # load dummy dataset and read audio files
 ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 sample = ds[0]["audio"]
-input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").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=False)
-
-transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
 
 # Create Gradio interface
 audio_input = gr.Audio()