Update app.py

app.py

@@ -5,7 +5,7 @@ import gradio as gr
 import pytube as pt
 from speechbox import ASRDiarizationPipeline
 
-MODEL_NAME = "openai/whisper-base"
+MODEL_NAME = "openai/whisper-small"
 
 device = 0 if torch.cuda.is_available() else "cpu"
 HF_TOKEN = os.environ.get("HF_TOKEN")
@@ -69,15 +69,15 @@ mf_transcribe = gr.Interface(
     title="Whisper Speaker Diarization: Transcribe Audio",
     description=(
         "Transcribe audio files with speaker diarization using [🤗 Speechbox](https://github.com/huggingface/speechbox/). "
-        "Demo uses the pre-trained checkpoint [Whisper Tiny](https://huggingface.co/openai/whisper-tiny) for the ASR "
+        "Demo uses the pre-trained checkpoint [Whisper Small](https://huggingface.co/openai/whisper-small) for the ASR "
         "transcriptions and [pyannote.audio](https://huggingface.co/pyannote/speaker-diarization) to label the speakers."
         "\n\n"
         "Check out the repo here: https://github.com/huggingface/speechbox/"
     ),
-    examples=[
-        ["./processed.wav", True],
-        ["./processed.wav", False],
-    ],
+    #examples=[
+    #    ["./processed.wav", True],
+    #    ["./processed.wav", False],
+    #],
     allow_flagging="never",
 )