"""Pyannote speaker embedding model.
- pip install pyannote.audio
- feature dimension: 512
- source: https://huggingface.co/pyannote/embedding
"""
from typing import Optional, Union, Tuple
import torch
import numpy as np
from pyannote.audio import Model
from pyannote.audio import Inference
from pyannote.audio.core.inference import fix_reproducibility, map_with_specifications


class PyannoteSE:

    def __init__(self):
        self.model = Model.from_pretrained("pyannote/embedding")
        self.inference = Inference(self.model, window="whole")

    def get_speaker_embedding(self, wav: np.ndarray, sampling_rate: Optional[int] = None) -> np.ndarray:
        wav = torch.as_tensor(wav.reshape(1, -1))
        fix_reproducibility(self.inference.device)
        if self.inference.window == "sliding":
            return self.inference.slide(wav, sampling_rate, hook=None)

        outputs: Union[np.ndarray, Tuple[np.ndarray]] = self.inference.infer(wav[None])

        def __first_sample(outputs: np.ndarray, **kwargs) -> np.ndarray:
            return outputs[0]

        return map_with_specifications(
            self.model.specifications, __first_sample, outputs
        )