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import onnxruntime
import torch
from vencoder.encoder import SpeechEncoder
class ContentVec256L9_Onnx(SpeechEncoder):
def __init__(self, vec_path="pretrain/vec-256-layer-9.onnx", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
self.hidden_dim = 256
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)
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