doc: removed comments

app.py

@@ -1,17 +1,17 @@
 import gradio as gr
 from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, AutoTokenizer, BartForConditionalGeneration
 import torch
-import torchaudio  # Replace librosa for faster audio processing
+import torchaudio  
 
-# Load BART tokenizer and model for summarization
+# Load BART 
 tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
 summarizer = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
 
-# Load Wav2Vec2 processor and model for transcription
+# Load Wav2Vec2 
 processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
 model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
 
-# Check if CUDA is available
+# Check for CUDA
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 summarizer.to(device)
@@ -21,30 +21,25 @@ summarizer = torch.quantization.quantize_dynamic(summarizer, {torch.nn.Linear},
 
 
 def transcribe_and_summarize(audioFile):
-    # Load audio using torchaudio
     audio, sampling_rate = torchaudio.load(audioFile)
     
-    # Resample audio to 16kHz if necessary
     if sampling_rate != 16000:
         resample_transform = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)
         audio = resample_transform(audio)
     audio = audio.squeeze()
 
-    # Process audio in chunks for large files
-    chunk_size = int(16000 * 30)  # 10-second chunks
+    chunk_size = int(16000 * 30)  
     transcription = ""
 
     for i in range(0, len(audio), chunk_size):
         chunk = audio[i:i+chunk_size].numpy()
         inputs = processor(chunk, sampling_rate=16000, return_tensors="pt").input_values.to(device)
 
-        # Transcription
         with torch.no_grad():
             logits = model(inputs).logits
             predicted_ids = torch.argmax(logits, dim=-1)
             transcription += processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] + " "
 
-    # Summarization
     inputs = tokenizer(transcription, return_tensors="pt", truncation=True, max_length=1024).to(device)
     
     result = summarizer.generate(
@@ -54,20 +49,19 @@ def transcribe_and_summarize(audioFile):
         no_repeat_ngram_size=2,
         encoder_no_repeat_ngram_size=2,
         repetition_penalty=2.0,
-        num_beams=2,  # Reduced beams for faster inference
+        num_beams=2, 
         early_stopping=True,
     )
     summary = tokenizer.decode(result[0], skip_special_tokens=True)
 
     return transcription.strip(), summary.strip()
 
-# Gradio interface
 iface = gr.Interface(
     fn=transcribe_and_summarize,
     inputs=gr.Audio(type="filepath", label="Upload Audio"),
     outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Summary")], 
     title="Audio Transcription and Summarization",
-    description="Transcribe and summarize audio using Wav2Vec2 and BART.",
+    description="Transcribe and summarize audio using Audio Summarizer.",
 )
 
 iface.launch()