| import sys | |
| import os | |
| import torch | |
| sys.path.append(f"{os.getcwd()}/eres2net") | |
| sv_path = "pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt" | |
| from ERes2NetV2 import ERes2NetV2 | |
| import kaldi as Kaldi | |
| class SV: | |
| def __init__(self, device, is_half): | |
| pretrained_state = torch.load(sv_path, map_location="cpu", weights_only=False) | |
| embedding_model = ERes2NetV2(baseWidth=24, scale=4, expansion=4) | |
| embedding_model.load_state_dict(pretrained_state) | |
| embedding_model.eval() | |
| self.embedding_model = embedding_model | |
| if is_half == False: | |
| self.embedding_model = self.embedding_model.to(device) | |
| else: | |
| self.embedding_model = self.embedding_model.half().to(device) | |
| self.is_half = is_half | |
| def compute_embedding3(self, wav): | |
| with torch.no_grad(): | |
| if self.is_half == True: | |
| wav = wav.half() | |
| feat = torch.stack( | |
| [Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav] | |
| ) | |
| sv_emb = self.embedding_model.forward3(feat) | |
| return sv_emb | |