initial commit

app.py

@@ -5,7 +5,7 @@ import pytube as pt
 from transformers import pipeline
 from huggingface_hub import model_info
 
-MODEL_NAME = "openai/whisper-small" #this always needs to stay in line 8 :D sorry for the hackiness
+MODEL_NAME = "openai/whisper-small" 
 lang = "en"
 
 device = 0 if torch.cuda.is_available() else "cpu"
@@ -67,12 +67,7 @@ mf_transcribe = gr.Interface(
     outputs="text",
     layout="horizontal",
     theme="huggingface",
-    title="Automatic Voice recognition: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
+    title="AI VORA(VOice Recognition Analysis: AI 보이스 인식 분석",
     allow_flagging="never",
 )
 
@@ -82,16 +77,11 @@ yt_transcribe = gr.Interface(
     outputs=["html", "text"],
     layout="horizontal",
     theme="huggingface",
-    title="Automatic Voice recognition: Transcribe Audio",
-    description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
-        " arbitrary length."
-    ),
+    title="AI VORA(VOice Recognition Analysis: AI 보이스 인식 분석",
     allow_flagging="never",
 )
 
 with demo:
-    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
+    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["레코딩 및 오디오 파일 분석", "유투브 링크 분석"])
 
 demo.launch(enable_queue=True)