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# Script used to convert from WeSpeaker to pyannote.audio

import sys
from pathlib import Path

import lightning
import torch

import pyannote.audio.models.embedding.wespeaker as wespeaker
from pyannote.audio import Model
from pyannote.audio.core.task import Problem, Resolution, Specifications

wespeaker_checkpoint_dir = sys.argv[1]  # /path/to/wespeaker_cnceleb-resnet34-LM

wespeaker_checkpoint = Path(wespeaker_checkpoint_dir) / "wespeaker.pt"

depth = Path(wespeaker_checkpoint_dir).parts[-1].split("-")[-2][6:]  # '34'
Klass = getattr(wespeaker, f"WeSpeakerResNet{depth}")  # WeSpeakerResNet34

duration = 5.0  # whatever
specifications = Specifications(
    problem=Problem.REPRESENTATION, resolution=Resolution.CHUNK, duration=duration
)

state_dict = torch.load(wespeaker_checkpoint, map_location=torch.device("cpu"))
state_dict.pop("projection.weight")

model = Klass()
model.resnet.load_state_dict(state_dict, strict=True)
model.specifications = specifications

checkpoint = {"state_dict": model.state_dict()}
model.on_save_checkpoint(checkpoint)
checkpoint["pytorch-lightning_version"] = lightning.__version__

pyannote_checkpoint = Path(wespeaker_checkpoint_dir) / "pytorch_model.bin"
torch.save(checkpoint, pyannote_checkpoint)

model = Model.from_pretrained(pyannote_checkpoint)
print(model)