app.py

import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import gradio as gr

device = "cpu"  # Free CPU only
torch_dtype = torch.float32

model_id = "KBLab/kb-whisper-large"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype
).to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    device=device,
    torch_dtype=torch_dtype,
)

def transcribe(audio):
    result = pipe(audio, chunk_length_s=30, generate_kwargs={"task": "transcribe", "language": "sv"})
    return result["text"]

gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(type="filepath", label="Upload Swedish Audio"),
    outputs=gr.Textbox(label="Transcribed Text"),
    title="KB-Whisper Transcriber (Swedish, Free CPU)",
    description="Transcribes Swedish audio using KBLab's Whisper Large model. Running on free CPU — may be slow."
).launch(share=True)