Update app.py

app.py

@@ -1,19 +1,19 @@
 from transformers import WhisperTokenizer
 
-tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="marathi", task="transcribe")
+tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small") #, language="marathi", task="transcribe"
 
 from transformers import pipeline
 import gradio as gr
 import torch 
 
-pipe = pipeline(model="thak123/whisper-small-LDC-V1.1", #"thak123/whisper-small-gom", 
+pipe = pipeline(model="thak123/whisper-small-LDC-V1", #"thak123/whisper-small-gom", 
                 task="automatic-speech-recognition", tokenizer= tokenizer)  # change to "your-username/the-name-you-picked"
 
-pipe.model.config.forced_decoder_ids = (
-        pipe.tokenizer.get_decoder_prompt_ids(
-            language="marathi", task="transcribe"
-        )
-    )
+# pipe.model.config.forced_decoder_ids = (
+#         pipe.tokenizer.get_decoder_prompt_ids(
+#             language="marathi", task="transcribe"
+#         )
+#     )
 
 def transcribe(audio):
     text = pipe(audio)["text"]
@@ -23,6 +23,11 @@ iface = gr.Interface(
     fn=transcribe, 
     inputs=gr.Audio(source="microphone", type="filepath"), 
     outputs="text",
+    examples=[
+        [os.path.join(os.path.dirname("."),"audio/chalyaami.mp3")],
+        [os.path.join(os.path.dirname("."),"audio/ekdonteen.flac")],
+        [os.path.join(os.path.dirname("."),"audio/heyatachadjaale.mp3")],
+    ],
     title="Whisper Small Konkani",
     description="Realtime demo for Konkani speech recognition using a fine-tuned Whisper small model.",
 )