Update app.py

app.py

@@ -23,16 +23,30 @@
 from transformers import pipeline
 import gradio as gr
 
-# Reuse the previously created pipeline object
-# pipe = pipeline(model="jsbeaudry/creole-speech-to-text")  
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from datasets import load_dataset
+
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "jsbeaudry/creole-speech-to-text""
+
+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)
+
 pipe = pipeline(
     "automatic-speech-recognition",
-    model="jsbeaudry/creole-speech-to-text",
-    # tokenizer=tokenizer.tokenizer,
-    # feature_extractor=tokenizer.feature_extractor,
-    # processor=tokenizer,
-    # return_language=True,
-    # torch_dtype=torch.float16  # Remove the device parameter
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
 )
 def transcribe(audio):
     # Use the 'whisper' pipeline defined in the previous cell