Update app.py

app.py

@@ -1,12 +1,11 @@
 import torch
-
 import gradio as gr
 import yt_dlp as youtube_dl
 from transformers import pipeline
 from transformers.pipelines.audio_utils import ffmpeg_read
-
 import tempfile
 import os
+import time
 
 MODEL_NAME = "openai/whisper-large-v3"
 BATCH_SIZE = 8
@@ -22,14 +21,12 @@ pipe = pipeline(
     device=device,
 )
 
-
 def transcribe(inputs, task):
     if inputs is None:
         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
 
     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    return  text
-
+    return text
 
 def _return_yt_html_embed(yt_url):
     video_id = yt_url.split("?v=")[-1]
@@ -47,19 +44,10 @@ def download_yt_audio(yt_url, filename):
     except youtube_dl.utils.DownloadError as err:
         raise gr.Error(str(err))
     
-    file_length = info["duration_string"]
-    file_h_m_s = file_length.split(":")
-    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
-    
-    if len(file_h_m_s) == 1:
-        file_h_m_s.insert(0, 0)
-    if len(file_h_m_s) == 2:
-        file_h_m_s.insert(0, 0)
-    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
-    
-    if file_length_s > YT_LENGTH_LIMIT_S:
+    file_length = info["duration"]
+    if file_length > YT_LENGTH_LIMIT_S:
         yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
+        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length))
         raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
     
     ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
@@ -70,8 +58,7 @@ def download_yt_audio(yt_url, filename):
         except youtube_dl.utils.ExtractorError as err:
             raise gr.Error(str(err))
 
-
-def yt_transcribe(yt_url, task, max_filesize=75.0):
+def yt_transcribe(yt_url, task):
     html_embed_str = _return_yt_html_embed(yt_url)
 
     with tempfile.TemporaryDirectory() as tmpdirname:
@@ -87,8 +74,7 @@ def yt_transcribe(yt_url, task, max_filesize=75.0):
 
     return html_embed_str, text
 
-
-demo = gr.Blocks(theme="TogetherAI/Alex2_Theme")  
+demo = gr.Blocks(theme="ParityError/Interstellar")  
 
 mf_transcribe = gr.Interface(
     fn=transcribe,
@@ -98,7 +84,7 @@ mf_transcribe = gr.Interface(
     ],
     outputs="text",
     layout="horizontal",
-    theme="TogetherAI/Alex2_Theme",
+    theme="ParityError/Interstellar",
     title="Whisper Large V3: Transcribe Audio",
     description=(
         "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
@@ -116,7 +102,7 @@ file_transcribe = gr.Interface(
     ],
     outputs="text",
     layout="horizontal",
-    theme="TogetherAI/Alex2_Theme",
+    theme="ParityError/Interstellar",
     title="Whisper Large V3: Transcribe Audio",
     description=(
         "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
@@ -134,7 +120,7 @@ yt_transcribe = gr.Interface(
     ],
     outputs=["html", "text"],
     layout="horizontal",
-    theme="TogetherAI/Alex2_Theme",
+    theme="ParityError/Interstellar",
     title="Whisper Large V3: Transcribe YouTube",
     description=(
         "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
@@ -147,4 +133,4 @@ yt_transcribe = gr.Interface(
 with demo:
     gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
 
-demo.launch(enable_queue=True)
+demo.launch(enable_queue=True)