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from vencoder.encoder import SpeechEncoder | |
import onnxruntime | |
import torch | |
class ContentVec768L12_Onnx(SpeechEncoder): | |
def __init__(self,vec_path = "pretrain/vec-768-layer-12.onnx",device=None): | |
print("load model(s) from {}".format(vec_path)) | |
self.hidden_dim = 768 | |
if device is None: | |
self.dev = torch.device("cpu") | |
else: | |
self.dev = torch.device(device) | |
if device == 'cpu' or device == torch.device("cpu") or device is None: | |
providers = ['CPUExecutionProvider'] | |
elif device == 'cuda' or device == torch.device("cuda"): | |
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] | |
self.model = onnxruntime.InferenceSession(vec_path, providers=providers) | |
def encoder(self, wav): | |
feats = wav | |
if feats.dim() == 2: # double channels | |
feats = feats.mean(-1) | |
assert feats.dim() == 1, feats.dim() | |
feats = feats.view(1, -1) | |
feats = feats.unsqueeze(0).cpu().detach().numpy() | |
onnx_input = {self.model.get_inputs()[0].name: feats} | |
logits = self.model.run(None, onnx_input) | |
return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) |