im currently training a model with vctk from scratch on colab but everytime it loads a checkpoint, the training just restarts from 0 anyways
INFO:vctk_base:{'train': {'log_interval': 250, 'eval_interval': 1000, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 20, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'training_files': 'filelists/vctk_audio_sid_text_train_filelist.txt.cleaned', 'validation_files': 'filelists/vctk_audio_sid_text_val_filelist.txt.cleaned', 'text_cleaners': ['english_cleaners2'], 'max_wav_value': 32768.0, 'sampling_rate': 22050, 'filter_length': 1024, 'hop_length': 256, 'win_length': 1024, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 109, 'cleaned_text': True}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 256}, 'model_dir': '/content/drive/MyDrive/vits-finetune-en/vctk_base'}
2023-09-18 13:51:52.958848: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
DEBUG:tensorflow:Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.
2023-09-18 13:51:54.360757: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
DEBUG:h5py._conv:Creating converter from 7 to 5
DEBUG:h5py._conv:Creating converter from 5 to 7
DEBUG:h5py._conv:Creating converter from 7 to 5
DEBUG:h5py._conv:Creating converter from 5 to 7
DEBUG:jaxlib.mlir._mlir_libs:Initializing MLIR with module: _site_initialize_0
DEBUG:jaxlib.mlir._mlir_libs:Registering dialects from initializer <module 'jaxlib.mlir._mlir_libs._site_initialize_0' from '/usr/local/lib/python3.10/dist-packages/jaxlib/mlir/_mlir_libs/_site_initialize_0.so'>
DEBUG:jax._src.xla_bridge:No jax_plugins namespace packages available
DEBUG:jax._src.path:etils.epath found. Using etils.epath for file I/O.
INFO:numexpr.utils:NumExpr defaulting to 2 threads.
INFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:1 to store for rank: 0
INFO:torch.distributed.distributed_c10d:Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 1 nodes.
/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
warnings.warn(_create_warning_msg(
/content/drive/MyDrive/vits-finetune-en/vctk_base/G_2000.pth
INFO:vctk_base:Loaded checkpoint '/content/drive/MyDrive/vits-finetune-en/vctk_base/G_2000.pth' (iteration 1)
/content/drive/MyDrive/vits-finetune-en/vctk_base/D_2000.pth
INFO:vctk_base:Loaded checkpoint '/content/drive/MyDrive/vits-finetune-en/vctk_base/D_2000.pth' (iteration 1)
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/content/vits/mel_processing.py:78: FutureWarning: Pass sr=22050, n_fft=1024, n_mels=80, fmin=0.0, fmax=None as keyword args. From version 0.10 passing these as positional arguments will result in an error
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
/content/vits/mel_processing.py:96: FutureWarning: Pass sr=22050, n_fft=1024, n_mels=80, fmin=0.0, fmax=None as keyword args. From version 0.10 passing these as positional arguments will result in an error
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at ../aten/src/ATen/EmptyTensor.cpp:31.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
INFO:torch.nn.parallel.distributed:Reducer buckets have been rebuilt in this iteration.
/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py:197: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
grad.sizes() = [1, 9, 96], strides() = [32352, 96, 1]
bucket_view.sizes() = [1, 9, 96], strides() = [864, 96, 1] (Triggered internally at ../torch/csrc/distributed/c10d/reducer.cpp:325.)
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
INFO:vctk_base:Train Epoch: 1 [0%]
INFO:vctk_base:[2.420557975769043, 2.281930446624756, 3.198911666870117, 22.855037689208984, 1.96134352684021, 1.0768342018127441, 0, 0.0002]
DEBUG:matplotlib:matplotlib data path: /usr/local/lib/python3.10/dist-packages/matplotlib/mpl-data
DEBUG:matplotlib:CONFIGDIR=/root/.config/matplotlib
DEBUG:matplotlib:interactive is False
DEBUG:matplotlib:platform is linux
/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
warnings.warn(_create_warning_msg(
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:632: UserWarning: stft will soon require the return_complex parameter be given for real inputs, and will further require that return_complex=True in a future PyTorch release. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:801.)
return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
INFO:vctk_base:Saving model and optimizer state at iteration 1 to /content/drive/MyDrive/vits-finetune-en/vctk_base/G_0.pth
INFO:vctk_base:Saving model and optimizer state at iteration 1 to /content/drive/MyDrive/vits-finetune-en/vctk_base/D_0.pth
INFO:torch.nn.parallel.distributed:Reducer buckets have been rebuilt in this iteration.
INFO:vctk_base:Train Epoch: 1 [12%]
INFO:vctk_base:[2.675227165222168, 1.9731173515319824, 2.627897262573242, 22.075305938720703, 2.0091865062713623, 1.1244663000106812, 250, 0.0002]
as you can see, it should have started from 2000 but for some reason the latest log was made at 250.
im currently training a model with vctk from scratch on colab but everytime it loads a checkpoint, the training just restarts from 0 anyways
as you can see, it should have started from 2000 but for some reason the latest log was made at 250.