↓ 2 callersFunctionspectrogram_torch(y: Tensor, n_fft: int, sampling_rate: int, hop_size: int, win_size: int, center: bool = False)
GPT_SoVITS/export_torch_script.py:112
↓ 2 callersFunctiontrain_and_evaluate(
rank,
epoch,
hps,
nets,
optims,
schedulers,
scaler,
loaders,
logger,
GPT_SoVITS/s2_train_v3.py:275
↓ 2 callersFunctiontrain_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers)
GPT_SoVITS/s2_train.py:318
↓ 1 callersMethod__init__(
self,
params,
lr=3e-02,
clipping_scale=None,
betas=(0.9, 0.98),
GPT_SoVITS/AR/modules/optim.py:156
↓ 1 callersMethod_absolute_position_to_relative_position x: [b, h, l, l] ret: [b, h, l, 2*l-1]
GPT_SoVITS/module/attentions.py:311
↓ 1 callersMethod_absolute_position_to_relative_position x: [b, h, l, l] ret: [b, h, l, 2*l-1]
GPT_SoVITS/module/attentions_onnx.py:274
↓ 1 callersFunction_compute_scale_factor(
x: Tensor,
channel_dim: int,
min_abs: float,
max_abs: float,
gain_factor: float,
max
GPT_SoVITS/AR/modules/scaling.py:133
↓ 1 callersMethod_matmul_with_relative_keys x: [b, h, l, d] y: [h or 1, m, d] ret: [b, h, l, m]
GPT_SoVITS/module/attentions.py:269
↓ 1 callersMethod_matmul_with_relative_keys x: [b, h, l, d] y: [h or 1, m, d] ret: [b, h, l, m]
GPT_SoVITS/module/attentions_onnx.py:232
↓ 1 callersMethod_matmul_with_relative_values x: [b, h, l, m] y: [h or 1, m, d] ret: [b, h, l, d]
GPT_SoVITS/module/attentions.py:260
↓ 1 callersMethod_matmul_with_relative_values x: [b, h, l, m] y: [h or 1, m, d] ret: [b, h, l, d]
GPT_SoVITS/module/attentions_onnx.py:223
↓ 1 callersMethod_relative_position_to_absolute_position x: [b, h, l, 2*l-1] ret: [b, h, l, l]
GPT_SoVITS/module/attentions.py:294
↓ 1 callersMethod_relative_position_to_absolute_position x: [b, h, l, 2*l-1] ret: [b, h, l, l]
GPT_SoVITS/module/attentions_onnx.py:257