Update app.py

app.py

@@ -12,7 +12,6 @@ from transformers.pipelines.audio_utils import ffmpeg_read
 import tempfile
 import os
 import time
-os.environ["GRADIO_TEMP_DIR"] = "/home/yoach/spaces/tmp"
 
 
 MODEL_NAME = "openai/whisper-large-v3"
@@ -182,11 +181,11 @@ mf_transcribe = gr.Interface(
     ],
     outputs="text",
     theme="huggingface",
-    title="Whisper Large V3: Transcribe Audio",
+    title="Create your own TTS dataset using your own recordings",
     description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
+        "This demo allows use to create a text-to-speech dataset from an input audio snippet and push it to hub to keep track of it."
+        f"Demo uses the checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to automatically transcribe audio files"
+        " of arbitrary length. It then merge chunks of audio and push it to the hub."
     ),
     allow_flagging="never",
 )
@@ -200,11 +199,11 @@ yt_transcribe = gr.Interface(
     ],
     outputs=["html", "text"],
     theme="huggingface",
-    title="Whisper Large V3: Transcribe YouTube",
+    title="Create your own TTS dataset using Youtube",
     description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
-        " arbitrary length."
+        "This demo allows use to create a text-to-speech dataset from an input audio snippet and push it to hub to keep track of it."
+        f"Demo uses the checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to automatically transcribe audio files"
+        " of arbitrary length. It then merge chunks of audio and push it to the hub."
     ),
     allow_flagging="never",
 )