Update app.py

app.py

@@ -5,35 +5,17 @@ import tempfile
 import spaces
 import torch
 from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from huggingface_hub import hf_hub_download
 from datasets import load_dataset
+from whisper import load_model, transcribe
 
 
 @spaces.GPU
 def transcribe_audio(zip_file):
-    device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-    
-    model_id = "distil-whisper/distil-large-v3"
-    
-    model = AutoModelForSpeechSeq2Seq.from_pretrained(
-        model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-    )
-    model.to(device)
-    
-    processor = AutoProcessor.from_pretrained(model_id)
-    
-    transcribe = pipeline(
-        "automatic-speech-recognition",
-        model=model,
-        tokenizer=processor.tokenizer,
-        feature_extractor=processor.feature_extractor,
-        max_new_tokens=128,
-        torch_dtype=torch_dtype,
-        device=device,
-    )
-    
-    dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
+    model_path = hf_hub_download(repo_id="distil-whisper/distil-large-v3-openai", filename="model.bin")
+    model = load_model(model_path)
     
+    dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")    
     
     # Create a temporary directory to extract the ZIP file
     with tempfile.TemporaryDirectory() as temp_dir: