# +
import argparse
import os

import torch
from api import TextToSpeech

from tortoise.utils.audio import get_voices, load_required_audio

"""
Dumps the conditioning latents for the specified voice to disk. These are expressive latents which can be used for
other ML models, or can be augmented manually and fed back into Tortoise to affect vocal qualities.
"""
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--voice",
        type=str,
        help="Selects the voice to convert to conditioning latents",
        default="pat2",
    )
    parser.add_argument(
        "--output_path",
        type=str,
        help="Where to store outputs.",
        default="../results/conditioning_latents",
    )
    parser.add_argument(
        "--latent_averaging_mode",
        type=int,
        help="How to average voice latents, 0 for standard, 1 for per-sample, 2 for per-minichunk",
        default=0,
    )

    args = parser.parse_args()
    os.makedirs(args.output_path, exist_ok=True)

    tts = TextToSpeech()
    voices = get_voices()
    print(list(voices.keys()))
    selected_voices = args.voice.split(",")
    for voice in selected_voices:
        cond_paths = voices[voice]
        conds = []
        for cond_path in cond_paths:
            c = load_required_audio(cond_path)
            conds.append(c)
        conditioning_latents = tts.get_conditioning_latents(
            conds, latent_averaging_mode=args.latent_averaging_mode
        )
        torch.save(conditioning_latents, os.path.join(args.output_path, f"{voice}.pth"))