Loading audiolm_pytorch/encodec.py +4 −1 Original line number Diff line number Diff line from functools import reduce from einops import rearrange import torch from torch import nn from encodec import EncodecModel from encodec.utils import convert_audio, _linear_overlap_add Loading Loading @@ -36,7 +39,7 @@ class EncodecWrapper(nn.Module): @property def seq_len_multiple_of(self): return functools.reduce(lambda x, y: x * y, self.strides) return reduce(lambda x, y: x * y, self.strides) def forward(self, x, x_sampling_rate=24000, **kwargs): # kwargs for stuff like return_encoded=True, which SoundStream uses but Encodec doesn't Loading audiolm_pytorch/trainer.py +1 −1 Original line number Diff line number Diff line Loading @@ -968,7 +968,7 @@ class CoarseTransformerTrainer(nn.Module): # save model every so often if self.is_main and not (steps % self.save_model_every): model_path = str(self.results_folder / f'fine.transformer.{steps}.pt') model_path = str(self.results_folder / f'coarse.transformer.{steps}.pt') self.save(model_path) self.print(f'{steps}: saving model to {str(self.results_folder)}') Loading Loading
audiolm_pytorch/encodec.py +4 −1 Original line number Diff line number Diff line from functools import reduce from einops import rearrange import torch from torch import nn from encodec import EncodecModel from encodec.utils import convert_audio, _linear_overlap_add Loading Loading @@ -36,7 +39,7 @@ class EncodecWrapper(nn.Module): @property def seq_len_multiple_of(self): return functools.reduce(lambda x, y: x * y, self.strides) return reduce(lambda x, y: x * y, self.strides) def forward(self, x, x_sampling_rate=24000, **kwargs): # kwargs for stuff like return_encoded=True, which SoundStream uses but Encodec doesn't Loading
audiolm_pytorch/trainer.py +1 −1 Original line number Diff line number Diff line Loading @@ -968,7 +968,7 @@ class CoarseTransformerTrainer(nn.Module): # save model every so often if self.is_main and not (steps % self.save_model_every): model_path = str(self.results_folder / f'fine.transformer.{steps}.pt') model_path = str(self.results_folder / f'coarse.transformer.{steps}.pt') self.save(model_path) self.print(f'{steps}: saving model to {str(self.results_folder)}') Loading