(base) C:\Users\Administrator>conda activate tts
(tts) C:\Users\Administrator>d:
(tts) D:\>cd vits
(tts) D:\vits>python train_ms.py -c config.json -m i
INFO:i:{'train': {'log_interval': 200, 'eval_interval': 1000, 'seed': 1234, 'epochs': 5000, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 16, '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/i_cleaned.txt', 'validation_files': 'filelists/i.txt', 'text_cleaners': ['korean_cleaners'], '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': 3, '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}, 'speakers': ['i', 'h', 'm'], 'symbols': ['_', ',', '.', '!', '?', '…', '~', 'ㄱ', 'ㄴ', 'ㄷ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅅ', 'ㅇ', 'ㅈ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ', 'ㄲ', 'ㄸ', 'ㅃ', 'ㅆ', 'ㅉ', 'ㅏ', 'ㅓ', 'ㅗ', 'ㅜ', 'ㅡ', 'ㅣ', 'ㅐ', 'ㅔ', ' '], 'model_dir': 'checkpoints\\i'}
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.
checkpoints\i\g_0.pth
INFO:i:Loaded checkpoint 'checkpoints\i\g_0.pth' (iteration 1)
checkpoints\i\D_0.pth
INFO:i:Loaded checkpoint 'checkpoints\i\D_0.pth' (iteration 1)
0%| | 0/5 [00:00<?, ?it/s]D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\functional.py:633: 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 C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\SpectralOps.cpp:804.)
normalized, onesided, return_complex)
C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\functional.py:633: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\EmptyTensor.cpp:32.)
normalized, onesided, return_complex)
INFO:torch.nn.parallel.distributed:Reducer buckets have been rebuilt in this iteration.
C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\autograd\__init__.py:199: 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() = [32736, 96, 1]
bucket_view.sizes() = [1, 9, 96], strides() = [864, 96, 1] (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\distributed\c10d\reducer.cpp:339.)
allow_unreachable=True, accumulate_grad=True) # Calls into the C++ engine to run the backward pass
INFO:i:Train Epoch: 1 [0%]
INFO:i:[6.1022419929504395, 6.101133346557617, 0.28196045756340027, 26.16242790222168, 1.875349760055542, 4.486478805541992, 0, 0.00019882837179971516]
DEBUG:matplotlib:(private) matplotlib data path: C:\ProgramData\anaconda3\envs\tts\lib\site-packages\matplotlib\mpl-data
DEBUG:matplotlib:matplotlib data path: C:\ProgramData\anaconda3\envs\tts\lib\site-packages\matplotlib\mpl-data
DEBUG:matplotlib:CONFIGDIR=C:\Users\Administrator\.matplotlib
DEBUG:matplotlib:matplotlib version 3.3.1
DEBUG:matplotlib:interactive is False
DEBUG:matplotlib:platform is win32
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'numba.core.datamodel', 'numba.core.datamodel.manager', 'numba.core.datamodel.packer', 'numba.core.datamodel.registry', 'numba.core.datamodel.models', 'numba.core.typing.asnumbatype', 'numba.core.decorators', 'numba.stencils', 'numba.stencils.stencil', 'numba.core.ir_utils', 'numba.core.extending', 'numba.core.imputils', 'numba.core.pythonapi', 'numba._helperlib', 'numba.core.lowering', 'numba.core.funcdesc', 'numba.core.itanium_mangler', 'numba.core.generators', 'numba.core.removerefctpass', 'numba.core.environment', 'numba._dynfunc', 'numba.core.analysis', 'numba.core.controlflow', 'numba.misc.firstlinefinder', 'numba.core.serialize', 'numba.cloudpickle', 'numba.cloudpickle.cloudpickle', 'numba.cloudpickle.compat', 'numba.cloudpickle.cloudpickle_fast', 'numba.core.postproc', 'numba.core.transforms', 'numba.core.rewrites', 'numba.core.rewrites.registry', 'numba.core.rewrites.static_getitem', 'numba.core.rewrites.static_raise', 'numba.core.rewrites.static_binop', 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'numba.core.runtime', 'numba.core.runtime.nrt', 'numba.core.runtime.nrtdynmod', 'numba.core.runtime._nrt_python', 'numba.core.runtime.nrtopt', 'numba.misc.inspection', 'numba.misc.llvm_pass_timings', 'numba.core.externals', 'numba.core.intrinsics', 'numba.core.fastmathpass', 'numba.core.options', 'numba.core.entrypoints', 'numba.np.ufunc_db', 'numba.core.compiler_machinery', 'numba.core.untyped_passes', 'numba.core.ssa', 'numba.core.typed_passes', 'numba.core.annotations', 'numba.core.annotations.type_annotations', 'numba.core.object_mode_passes', 'numba.core.pylowering', 'numba.core.caching', 'numba.misc.appdirs', 'numba.core.retarget', 'numba.np.ufunc', 'numba.np.ufunc.decorators', 'numba.np.ufunc._internal', 'numba.np.ufunc.parallel', 'numba.np.ufunc.wrappers', 'numba.np.ufunc.ufuncbuilder', 'numba.core.target_extension', 'numba.np.ufunc.sigparse', 'numba.np.ufunc.dufunc', 'numba.np.ufunc.gufunc', 'numba.np.ufunc.array_exprs', 'numba.experimental', 'numba.experimental.jitclass', 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'scipy.integrate._quadrature', 'scipy.integrate.odepack', 'scipy.integrate._odepack', 'scipy.integrate.quadpack', 'scipy.integrate._quadpack', 'scipy.integrate._ode', 'scipy.integrate.vode', 'scipy.integrate._dop', 'scipy.integrate.lsoda', 'scipy.integrate._bvp', 'scipy.integrate._ivp', 'scipy.integrate._ivp.ivp', 'scipy.integrate._ivp.bdf', 'scipy.integrate._ivp.common', 'scipy.integrate._ivp.base', 'scipy.integrate._ivp.radau', 'scipy.integrate._ivp.rk', 'scipy.integrate._ivp.dop853_coefficients', 'scipy.integrate._ivp.lsoda', 'scipy.integrate._quad_vec', 'scipy.interpolate', 'scipy.interpolate.interpolate', 'scipy.interpolate.fitpack', 'scipy.interpolate._fitpack_impl', 'scipy.interpolate._fitpack', 'scipy.interpolate.dfitpack', 'scipy.interpolate._bsplines', 'scipy.interpolate._bspl', 'scipy.interpolate.polyint', 'scipy.interpolate._ppoly', 'scipy.interpolate.fitpack2', 'scipy.interpolate.interpnd', 'scipy.interpolate.rbf', 'scipy.interpolate._cubic', 'scipy.interpolate.ndgriddata', 'scipy.interpolate._pade', 'scipy.signal.lti_conversion', 'scipy.signal.signaltools', 'scipy.signal._arraytools', 'scipy.signal._sosfilt', 'scipy.signal._savitzky_golay', 'scipy.signal.spectral', 'scipy.signal._spectral', 'scipy.signal.wavelets', 'scipy.signal._peak_finding', 'scipy.stats', 'scipy.stats.stats', 'scipy.stats.distributions', 'scipy.stats._distn_infrastructure', 'scipy.stats._distr_params', 'scipy.misc', 'scipy.misc.doccer', 'scipy.misc.common', 'scipy.stats._constants', 'scipy.stats._continuous_distns', 'scipy.stats._stats', 'scipy.special.cython_special', 'scipy.stats._rvs_sampling', 'scipy.stats._tukeylambda_stats', 'scipy.stats._ksstats', 'scipy.stats._discrete_distns', 'scipy.stats.mstats_basic', 'scipy.stats._stats_mstats_common', 'scipy.stats._hypotests', 'scipy.stats._wilcoxon_data', 'scipy.stats.morestats', 'scipy.stats.statlib', 'scipy.stats.contingency', 'scipy.stats._binned_statistic', 'scipy.stats.kde', 'scipy.stats.mvn', 'scipy.stats.mstats', 'scipy.stats.mstats_extras', 'scipy.stats._multivariate', 'scipy.signal._peak_finding_utils', 'resampy', 'resampy.version', 'resampy.filters', 'resampy.core', 'resampy.interpn', 'librosa.core.fft', 'encodings.cp437', 'librosa.core.spectrum', 'librosa.filters', 'librosa.core.pitch', 'librosa.sequence', 'librosa.core.constantq', 'librosa.core.harmonic', 'librosa.beat', 'librosa.onset', 'librosa.feature', 'librosa.feature.utils', 'librosa.feature.spectral', 'scipy.fftpack', 'scipy.fftpack.basic', 'scipy.fftpack.helper', 'scipy.fftpack.pseudo_diffs', 'scipy.fftpack.convolve', 'scipy.fftpack.realtransforms', 'librosa.feature.rhythm', 'librosa.feature.inverse', 'librosa.decompose', 'sklearn', 'sklearn._config', 'sklearn._distributor_init', 'sklearn.__check_build', 'sklearn.__check_build._check_build', 'sklearn.base', 'sklearn.utils', 'sklearn.utils.murmurhash', 'sklearn.utils.class_weight', 'sklearn.utils._joblib', 'sklearn.exceptions', 'sklearn.utils.deprecation', 'sklearn.utils.fixes', 'threadpoolctl', 'sklearn.externals', 'sklearn.externals._packaging', 'sklearn.externals._packaging.version', 'sklearn.externals._packaging._structures', 'sklearn.utils._estimator_html_repr', 'sklearn.utils.validation', 'sklearn.utils._tags', 'sklearn.utils._show_versions', 'sklearn.utils._openmp_helpers', 'sklearn.decomposition', 'sklearn.decomposition._nmf', 'sklearn.decomposition._cdnmf_fast', '_cython_0_29_26', 'sklearn.utils.extmath', 'sklearn.utils._logistic_sigmoid', 'sklearn.utils.sparsefuncs_fast', 'sklearn.decomposition._pca', 'sklearn.decomposition._base', 'sklearn.utils._arpack', 'sklearn.decomposition._incremental_pca', 'sklearn.decomposition._kernel_pca', 'sklearn.preprocessing', 'sklearn.preprocessing._function_transformer', 'sklearn.preprocessing._data', 'sklearn.utils.sparsefuncs', 'sklearn.preprocessing._encoders', 'sklearn.utils._mask', 'sklearn.utils._encode', 'sklearn.preprocessing._label', 'sklearn.utils.multiclass', 'sklearn.preprocessing._discretization', 'sklearn.preprocessing._polynomial', 'sklearn.utils.stats', 'sklearn.preprocessing._csr_polynomial_expansion', 'sklearn.metrics', 'sklearn.metrics._ranking', 'sklearn.metrics._base', 'sklearn.metrics._classification', 'sklearn.metrics._dist_metrics', 'sklearn.utils._typedefs', 'sklearn.utils._readonly_array_wrapper', 'sklearn.metrics.cluster', 'sklearn.metrics.cluster._supervised', 'sklearn.metrics.cluster._expected_mutual_info_fast', 'sklearn.metrics.cluster._unsupervised', 'sklearn.metrics.pairwise', 'sklearn.metrics._pairwise_fast', 'sklearn.metrics.cluster._bicluster', 'sklearn.metrics._regression', 'sklearn._loss', 'sklearn._loss.glm_distribution', 'sklearn.metrics._scorer', 'sklearn.metrics._plot', 'sklearn.metrics._plot.det_curve', 'sklearn.metrics._plot.base', 'sklearn.metrics._plot.roc_curve', 'sklearn.metrics._plot.precision_recall_curve', 'sklearn.metrics._plot.confusion_matrix', 'sklearn.decomposition._sparse_pca', 'sklearn.linear_model', 'sklearn.linear_model._base', 'sklearn.utils._seq_dataset', 'sklearn.utils._random', 'sklearn.linear_model._bayes', 'sklearn.linear_model._least_angle', 'sklearn.utils.arrayfuncs', 'sklearn.utils._cython_blas', 'sklearn.model_selection', 'sklearn.model_selection._split', 'sklearn.model_selection._validation', 'sklearn.utils.metaestimators', 'sklearn.model_selection._search', 'sklearn.utils.random', 'sklearn.linear_model._coordinate_descent', 'sklearn.linear_model._cd_fast', 'sklearn.linear_model._glm', 'sklearn.linear_model._glm.glm', 'sklearn.utils.optimize', 'sklearn.linear_model._glm.link', 'sklearn.linear_model._huber', 'sklearn.linear_model._sgd_fast', 'sklearn.utils._weight_vector', 'sklearn.linear_model._stochastic_gradient', 'sklearn.linear_model._ridge', 'sklearn.linear_model._sag', 'sklearn.linear_model._sag_fast', 'sklearn.linear_model._logistic', 'sklearn.svm', 'sklearn.svm._classes', 'sklearn.svm._base', 'sklearn.svm._libsvm', 'sklearn.svm._liblinear', 'sklearn.svm._libsvm_sparse', 'sklearn.svm._bounds', 'sklearn.linear_model._omp', 'sklearn.linear_model._passive_aggressive', 'sklearn.linear_model._perceptron', 'sklearn.linear_model._quantile', 'sklearn.linear_model._ransac', 'sklearn.linear_model._theil_sen', 'sklearn.decomposition._dict_learning', 'sklearn.decomposition._truncated_svd', 'sklearn.decomposition._fastica', 'sklearn.decomposition._factor_analysis', 'sklearn.decomposition._lda', 'sklearn.decomposition._online_lda_fast', 'librosa.segment', 'sklearn.cluster', 'sklearn.cluster._spectral', 'sklearn.neighbors', 'sklearn.neighbors._ball_tree', 'sklearn.neighbors._partition_nodes', 'sklearn.neighbors._kd_tree', 'sklearn.neighbors._distance_metric', 'sklearn.neighbors._graph', 'sklearn.neighbors._base', 'sklearn.neighbors._unsupervised', 'sklearn.neighbors._classification', 'sklearn.neighbors._regression', 'sklearn.neighbors._nearest_centroid', 'sklearn.neighbors._kde', 'sklearn.neighbors._lof', 'sklearn.neighbors._nca', 'sklearn.manifold', 'sklearn.manifold._locally_linear', 'sklearn.manifold._isomap', 'sklearn.utils.graph', 'sklearn.manifold._mds', 'sklearn.isotonic', 'sklearn._isotonic', 'sklearn.manifold._spectral_embedding', 'sklearn.manifold._t_sne', 'sklearn.manifold._utils', 'sklearn.manifold._barnes_hut_tsne', 'sklearn.neighbors._quad_tree', 'sklearn.tree', 'sklearn.tree._classes', 'sklearn.tree._criterion', 'sklearn.tree._splitter', 'sklearn.tree._tree', 'sklearn.tree._utils', 'sklearn.tree._export', 'sklearn.tree._reingold_tilford', 'sklearn.cluster._kmeans', 'sklearn.cluster._k_means_common', 'sklearn.cluster._k_means_minibatch', 'sklearn.cluster._k_means_lloyd', 'sklearn.cluster._k_means_elkan', 'sklearn.cluster._mean_shift', 'sklearn.cluster._affinity_propagation', 'sklearn.cluster._agglomerative', 'sklearn.utils._fast_dict', 'sklearn.cluster._hierarchical_fast', 'sklearn.cluster._feature_agglomeration', 'sklearn.cluster._dbscan', 'sklearn.cluster._dbscan_inner', 'sklearn.cluster._optics', 'sklearn.cluster._bicluster', 'sklearn.cluster._birch', 'sklearn.feature_extraction', 'sklearn.feature_extraction._dict_vectorizer', 'sklearn.feature_extraction._hash', 'sklearn.feature_extraction._hashing_fast', 'sklearn.feature_extraction.image', 'sklearn.feature_extraction.text', 'sklearn.feature_extraction._stop_words', 'librosa.effects', 'commons', 'utils', 'scipy.io', 'scipy.io.matlab', 'scipy.io.matlab.mio', 'scipy.io.matlab.miobase', 'scipy.io.matlab.byteordercodes', 'scipy.io.matlab.mio4', 'scipy.io.matlab.mio_utils', 'scipy.io.matlab.mio5', 'scipy.io.matlab.mio5_utils', 'scipy.io.matlab.streams', 'scipy.io.matlab.mio5_params', 'scipy.io.netcdf', 'scipy.io._fortran', 'scipy.io.mmio', 'scipy.io.idl', 'scipy.io.harwell_boeing', 'scipy.io.harwell_boeing.hb', 'scipy.io.harwell_boeing._fortran_format_parser', 'scipy.io.wavfile', 'data_utils', 'mel_processing', 'text', 'text.cleaners', 'text.korean', 'jamo', 'jamo.jamo', 'ko_pron', 'ko_pron.ko_pron', 'ko_pron.data', 'text.symbols', 'models', 'modules', 'transforms', 'attentions', 'monotonic_align', 'monotonic_align.monotonic_align', 'monotonic_align.monotonic_align.core', 'monotonic_align.core', 'losses', 'multiprocessing.popen_spawn_win32', 'matplotlib', 'matplotlib.cbook', 'matplotlib.cbook.deprecation', 'matplotlib.rcsetup', 'matplotlib.animation', 'matplotlib._animation_data', 'matplotlib.fontconfig_pattern', 'pyparsing', 'pyparsing.util', 'pyparsing.exceptions', 'pyparsing.unicode', 'pyparsing.actions', 'pyparsing.core', 'pyparsing.results', 'pyparsing.helpers', 'pyparsing.testing', 'pyparsing.common', 'matplotlib.colors', 'matplotlib.docstring', 'matplotlib._color_data', 'cycler', 'matplotlib._version', 'matplotlib.ft2font', 'dateutil', 'dateutil._version', 'kiwisolver', 'kiwisolver._cext']
D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
INFO:i:Saving model and optimizer state at iteration 1 to checkpoints\i\G_0.pth
INFO:i:Saving model and optimizer state at iteration 1 to checkpoints\i\D_0.pth
20%|████████████████▊ | 1/5 [00:17<01:08, 17.20s/it]INFO:torch.nn.parallel.distributed:Reducer buckets have been rebuilt in this iteration.
100%|████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:25<00:00, 5.09s/it]
INFO:i:====> Epoch: 1
C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\optim\lr_scheduler.py:143: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
0%| | 0/5 [00:00<?, ?it/s]D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
D:\vits\utils.py:138: WavFileWarning: Chunk (non-data) not understood, skipping it.
sampling_rate, data = read(full_path)
0%| | 0/5 [00:04<?, ?it/s]
Traceback (most recent call last):
File "train_ms.py", line 300, in <module>
main()
File "train_ms.py", line 56, in main
mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\multiprocessing\spawn.py", line 240, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\multiprocessing\spawn.py", line 198, in start_processes
while not context.join():
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\multiprocessing\spawn.py", line 160, in join
raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\multiprocessing\spawn.py", line 69, in _wrap
fn(i, *args)
File "D:\vits\train_ms.py", line 124, in run
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
File "D:\vits\train_ms.py", line 201, in train_and_evaluate
scaler.step(optim_g)
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\cuda\amp\grad_scaler.py", line 341, in step
retval = self._maybe_opt_step(optimizer, optimizer_state, *args, **kwargs)
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\cuda\amp\grad_scaler.py", line 288, in _maybe_opt_step
retval = optimizer.step(*args, **kwargs)
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\optim\lr_scheduler.py", line 68, in wrapper
return wrapped(*args, **kwargs)
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\optim\optimizer.py", line 140, in wrapper
out = func(*args, **kwargs)
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\optim\adamw.py", line 176, in step
capturable=group['capturable'])
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\optim\adamw.py", line 232, in adamw
capturable=capturable)
File "C:\ProgramData\anaconda3\envs\tts\lib\site-packages\torch\optim\adamw.py", line 273, in _single_tensor_adamw
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
RuntimeError: The size of tensor a (5) must match the size of tensor b (3) at non-singleton dimension 0
상황은 이렇습니다
3개의 새로운 음성을 학습하려고했습니다
대본 만들고 전처리를 하고 음성을 하나하나 들어가며 16bit 모노로 바꾸고 실행했습니다
다른건 다 잘되었는데 새로 학습하려는 것만 지금 오류가 계속 나옵니다
처음에는 대본에 없는 폴더를 요구하더니 대본 파일을 싹다 갈아 엎고 새로 작성해서 통과는 되었는데 .spec.pt 파일을 각 음성 폴더에 저장까지한후에 위의 오류가 계속 나옵니다
★필독★ AI 음성 채널 기본 통합 공지 (23-06-12) 
