app.py

from transformers import pipeline
import gradio as gr
import os
import torch
import srt
from datetime import timedelta



device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


auth_token = os.environ.get("hf_token")
if not auth_token:
    raise ValueError("Hugging Face token is missing! Add it as a secret.")
    
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

iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(type="filepath"),
    outputs=["text", "file"],
    title="Whisper Small Sindhi",
    description="Realtime demo for Sindhi speech recognition using a fine-tuned Whisper small model.",
)

iface.launch()