switch to first training step 900

app.py

@@ -13,11 +13,12 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Define the model checkpoint and language
 model_checkpoint = "oza75/whisper-bambara-asr-002"
+revision = "7f81e01332600196152f043f132c7ddf936b600a"
 language = "bambara"
 
 # Load the custom tokenizer designed for Bambara and the ASR model
 tokenizer = BambaraWhisperTokenizer.from_pretrained(model_checkpoint, language=language, device=device)
-pipe = pipeline(model=model_checkpoint, tokenizer=tokenizer, device=device)
+pipe = pipeline(model=model_checkpoint, tokenizer=tokenizer, device=device, revision=revision)
 
 
 @spaces.GPU()