Upload app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from transformers import pipeline, Wav2Vec2ProcessorWithLM
+from transformers import pipeline, AutoProcessor
 import os
 
 def transcribe(audio, language):
@@ -24,15 +24,10 @@ def transcribe(audio, language):
         "bambara": "asr-africa/mms-bambara-50-hours-mixed-bambara-dataset",
     }
 
-    if language in ["hausa", "igbo", "yoruba"]:
-        revision = "lm"
-    else:
-        revision = "main"
-
     # load processor
-    p = Wav2Vec2ProcessorWithLM.from_pretrained(model_map[language], revision=revision)
+    p = AutoProcessor.from_pretrained(model_map[language])
     # load eval pipeline
-    asr = pipeline("automatic-speech-recognition", model=model_map[language], tokenizer=p.tokenizer, feature_extractor=p.feature_extractor, decoder=p.decoder, token=os.getenv('HF_TOKEN'), device=0)
+    asr = pipeline("automatic-speech-recognition", model=model_map[language], tokenizer=p.tokenizer, feature_extractor=p.feature_extractor, decoder=p.decoder, device=0, token=os.getenv('HF_TOKEN'))
 
     text = asr(audio)["text"]
     return text