import locale
from pathlib import Path
from whisper import Whisper
from pyannote.audio import Pipeline
import torch
import static_ffmpeg
import gdown


def prep() -> None:
    locale.getpreferredencoding = lambda: "UTF-8"
    # download and add ffmpeg to env
    static_ffmpeg.add_paths()

def audiofile(drive_url: str, path: str) -> Path | None:
    if not drive_url:
        return None
    fn = Path.joinpath(Path(path), "interview")
    gdown.download(drive_url, str(fn))
    return fn

def diarization(access_token: str | None) -> Pipeline:
    return Pipeline.from_pretrained(
        "pyannote/speaker-diarization", use_auth_token=access_token
    )


def whisper() -> Whisper:
    # LOAD MODEL INTO VRAM
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    return whisper.load_model("large", device=device)