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"""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 PyannoteEmbedding:
def __init__(self):
model = Model.from_pretrained("pyannote/embedding")
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(self.device)
model.eval()
self.inference = Inference(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), dtype=torch.float32).to(self.device)
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.inference.model.specifications, __first_sample, outputs
)
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