Update app.py

app.py

@@ -10,13 +10,13 @@ lang = "ta"
 
 device = 0 if torch.cuda.is_available() else "cpu"
 pipe = pipeline(
-    task="automatic-speech-recognition",
+    task="translate",
     model=MODEL_NAME,
     chunk_length_s=30,
     device=device,
 )
 
-pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
+pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="translate")
 
 def transcribe(microphone, file_upload):
     warn_output = ""
@@ -67,7 +67,7 @@ mf_transcribe = gr.Interface(
     outputs="text",
     layout="horizontal",
     theme="huggingface",
-    title="Whisper Small Tamil Demo: Transcribe Audio",
+    title="Whisper Small Tamil Demo: Translate Audio",
     description=(
         "Translate 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"
@@ -82,9 +82,9 @@ yt_transcribe = gr.Interface(
     outputs=["html", "text"],
     layout="horizontal",
     theme="huggingface",
-    title="Whisper Small Tamil Demo: transcribe YouTube",
+    title="Whisper Small Tamil Demo: translate YouTube",
     description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
+        "Translate 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."
     ),
@@ -92,6 +92,6 @@ yt_transcribe = gr.Interface(
 )
 
 with demo:
-    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["transcribe Audio", "transcribe YouTube"])
+    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["translate Audio", "translate YouTube"])
 
 demo.launch(enable_queue=True)