Update app.py

app.py

@@ -2,8 +2,6 @@ from transformers import pipeline
 import gradio as gr
 import os
 import torch
-import srt
-from datetime import timedelta
 
 
 
@@ -17,33 +15,13 @@ if not auth_token:
 pipe = pipeline(model="fahadqazi/whisper-small-sindhi", device=device, token=auth_token)  # change to "your-username/the-name-you-picked"
 
 def transcribe(audio):
-    # Perform transcription
-    result = pipe(audio)
-    transcription = result["text"]
-    
-    # Generate timestamps for transcription (You might need to tweak this to match your desired chunks)
-    segments = result.get("chunks", [])  # Assuming the model returns chunks (this depends on model and pipeline)
-    
-    # Create an SRT object
-    subtitle_generator = []
-    start_time = timedelta(seconds=0)
-    
-    for i, segment in enumerate(segments):
-        end_time = start_time + timedelta(seconds=segment["end"])  # Using segment['end'] to create time intervals
-        subtitle_generator.append(srt.Subtitle(index=i+1, start=start_time, end=end_time, content=segment["text"]))
-        start_time = end_time  # Update start_time for next subtitle
-    
-    # Write subtitles to .srt file
-    srt_file = "output.srt"
-    with open(srt_file, "w") as f:
-        f.write(srt.compose(subtitle_generator))
-    
-    return transcription, srt_file
+    text = pipe(audio)["text"]
+    return text
 
 iface = gr.Interface(
     fn=transcribe,
     inputs=gr.Audio(type="filepath"),
-    outputs=["text", "file"],
+    outputs="text",
     title="Whisper Small Sindhi",
     description="Realtime demo for Sindhi speech recognition using a fine-tuned Whisper small model.",
 )