Update app.py

app.py

@@ -1,64 +1,46 @@
 from transformers import pipeline
 import gradio as gr
+from unsloth import FastModel
+from transformers import WhisperForConditionalGeneration
+import torch
+
+
+model, tokenizer = FastModel.from_pretrained(
+    model_name = "jsbeaudry/creole-speech-to-text",
+    dtype = None, # Leave as None for auto detection
+    load_in_4bit = False, # Set to True to do 4bit quantization which reduces memory
+    auto_model = WhisperForConditionalGeneration,
+    whisper_language = "Haitian",
+    whisper_task = "transcribe",
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+# Reuse the previously created pipeline object
+# pipe = pipeline(model) # This line caused the error
+
+# Initialize the pipeline correctly
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=tokenizer.tokenizer,
+    feature_extractor=tokenizer.feature_extractor,
+    processor=tokenizer,
+    return_language=True,
+    torch_dtype=torch.float16
+)
 
-pipe = pipeline(model="jsbeaudry/creole-speech-to-text")  
 
 def transcribe(audio):
+    # Use the 'pipe' pipeline
     text = pipe(audio)["text"]
     return text
 
 
 iface = gr.Interface(
     fn=transcribe,
-    inputs=gr.Audio(type="filepath"), 
+    inputs=gr.Audio(type="filepath"),
     outputs="text",
     title="Whisper medium Creole",
     description="Realtime demo for Haitian Creole speech recognition using a fine-tuned medium small model.",
 )
 
-iface.launch()
-
-
-
-# from transformers import pipeline
-# import gradio as gr
-
-# 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=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
-#     text = pipe(audio)["text"]
-#     return text
-
-# iface = gr.Interface(
-#     fn=transcribe,
-#     inputs=gr.Audio(type="filepath"),
-#     outputs="text",
-#     title="Whisper medium Creole",
-#     description="Realtime demo for Haitian Creole speech recognition using a fine-tuned medium small model.",
-# )
-
-# iface.launch()
+iface.launch()