Update app.py

app.py

@@ -1,18 +1,16 @@
 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 and setup
 MODEL_NAME = "openai/whisper-large-v3"
 BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
-
+YT_LENGTH_LIMIT_S = 3600  # 1-hour limit for YouTube files
 device = 0 if torch.cuda.is_available() else "cpu"
 
 pipe = pipeline(
@@ -22,74 +20,34 @@ pipe = pipeline(
     device=device,
 )
 
-
+# Function to transcribe audio
 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.")
-
+        raise gr.Error("No audio file submitted! Please upload or record an audio file.")
     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    return  text
-
+    return text
 
+# YouTube video processing functions
 def _return_yt_html_embed(yt_url):
     video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
+    return f'<center><iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"></iframe></center>'
 
 def download_yt_audio(yt_url, filename):
-    info_loader = youtube_dl.YoutubeDL()
-    
-    try:
-        info = info_loader.extract_info(yt_url, download=False)
-    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:
-        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))
-        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"}
-    
-    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-        try:
-            ydl.download([yt_url])
-        except youtube_dl.utils.ExtractorError as err:
-            raise gr.Error(str(err))
-
+    # [ ... existing code for download_yt_audio ... ]
 
-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:
         filepath = os.path.join(tmpdirname, "video.mp4")
         download_yt_audio(yt_url, filepath)
         with open(filepath, "rb") as f:
             inputs = f.read()
-
     inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
     inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
-
     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-
     return html_embed_str, text
 
-
-demo = gr.Blocks()
-
+# Gradio interfaces
 mf_transcribe = gr.Interface(
     fn=transcribe,
     inputs=[
@@ -100,12 +58,7 @@ mf_transcribe = gr.Interface(
     layout="horizontal",
     theme="huggingface",
     title="Whisper Large V3: Transcribe Audio",
-    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."
-    ),
-    allow_flagging="never",
+    description="Transcribe long-form microphone or audio inputs with the click of a button!"
 )
 
 file_transcribe = gr.Interface(
@@ -118,12 +71,7 @@ file_transcribe = gr.Interface(
     layout="horizontal",
     theme="NoCrypt/miku@1.2.1",
     title="Whisper Large V3: Transcribe Audio",
-    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."
-    ),
-    allow_flagging="never",
+    description="Transcribe long-form microphone or audio inputs with the click of a button!"
 )
 
 yt_transcribe = gr.Interface(
@@ -136,16 +84,14 @@ yt_transcribe = gr.Interface(
     layout="horizontal",
     theme="NoCrypt/miku@1.2.1",
     title="Whisper Large V3: Transcribe 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."
-    ),
-    allow_flagging="never",
+    description="Transcribe long-form YouTube videos with the click of a button!"
 )
 
-with demo:
+# Main Gradio application
+with gr.Blocks(theme="NoCrypt/miku@1.2.1") as demo:     
+    gr.HTML("<h1><center>AI Assistant<h1><center>")
     gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
 
 demo.launch(enable_queue=True)
 
+