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SayanoAI
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RVC-Studio

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<inter_channels: int64, hidden_channels: int64, filter_channels: int64, n_heads: int64, n_layers: int64, kernel_size: int64, p_dropout: double, resblock: string, resblock_kernel_sizes: list<item: int64>, resblock_dilation_sizes: list<item: list<item: int64>>, upsample_rates: list<item: int64>, upsample_initial_channel: int64, upsample_kernel_sizes: list<item: int64>, n_layers_q: int64, use_spectral_norm: bool, use_sdp: bool>
to
{'inter_channels': Value(dtype='int64', id=None), 'hidden_channels': Value(dtype='int64', id=None), 'filter_channels': Value(dtype='int64', id=None), 'n_heads': Value(dtype='int64', id=None), 'n_layers': Value(dtype='int64', id=None), 'kernel_size': Value(dtype='int64', id=None), 'p_dropout': Value(dtype='float64', id=None), 'resblock': Value(dtype='string', id=None), 'resblock_kernel_sizes': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'resblock_dilation_sizes': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'upsample_rates': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'upsample_initial_channel': Value(dtype='int64', id=None), 'upsample_kernel_sizes': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'n_layers_q': Value(dtype='int64', id=None), 'use_spectral_norm': Value(dtype='bool', id=None)}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 580, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2245, in cast_table_to_schema
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
                  cast_array_to_feature(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2108, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<inter_channels: int64, hidden_channels: int64, filter_channels: int64, n_heads: int64, n_layers: int64, kernel_size: int64, p_dropout: double, resblock: string, resblock_kernel_sizes: list<item: int64>, resblock_dilation_sizes: list<item: list<item: int64>>, upsample_rates: list<item: int64>, upsample_initial_channel: int64, upsample_kernel_sizes: list<item: int64>, n_layers_q: int64, use_spectral_norm: bool, use_sdp: bool>
              to
              {'inter_channels': Value(dtype='int64', id=None), 'hidden_channels': Value(dtype='int64', id=None), 'filter_channels': Value(dtype='int64', id=None), 'n_heads': Value(dtype='int64', id=None), 'n_layers': Value(dtype='int64', id=None), 'kernel_size': Value(dtype='int64', id=None), 'p_dropout': Value(dtype='float64', id=None), 'resblock': Value(dtype='string', id=None), 'resblock_kernel_sizes': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'resblock_dilation_sizes': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'upsample_rates': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'upsample_initial_channel': Value(dtype='int64', id=None), 'upsample_kernel_sizes': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'n_layers_q': Value(dtype='int64', id=None), 'use_spectral_norm': Value(dtype='bool', id=None)}
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1387, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1740, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1896, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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train dict	data dict	model dict	_class_name string	_diffusers_version string	act_fn string	attention_head_dim int64	block_out_channels sequence	center_input_sample bool	cross_attention_dim int64	down_block_types sequence	downsample_padding int64	flip_sin_to_cos bool	freq_shift int64	in_channels int64	layers_per_block int64	mid_block_scale_factor int64	norm_eps float64	norm_num_groups int64	out_channels int64	sample_size int64	up_block_types sequence
{ "log_interval": 200, "eval_interval": 1000, "seed": 1234, "epochs": 20000, "learning_rate": 0.0002, "betas": [ 0.8, 0.99 ], "eps": 1e-9, "batch_size": 64, "fp16_run": true, "lr_decay": 0.999875, "segment_size": 8192, "init_lr_ratio": 1, "warmup_epochs": 0, "c_mel": 45, "c_kl": 1 }	{ "training_files": "filelists/ljs_audio_text_train_filelist.txt.cleaned", "validation_files": "filelists/ljs_audio_text_val_filelist.txt.cleaned", "text_cleaners": [ "english_cleaners2" ], "max_wav_value": 32768, "sampling_rate": 22050, "filter_length": 1024, "hop_length": 256, "win_length": 1024, "n_mel_channels": 80, "mel_fmin": 0, "mel_fmax": null, "add_blank": true, "n_speakers": 0, "cleaned_text": true }	{ "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 }	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
{ "log_interval": 200, "eval_interval": 1000, "seed": 1234, "epochs": 20000, "learning_rate": 0.0002, "betas": [ 0.8, 0.99 ], "eps": 1e-9, "batch_size": 64, "fp16_run": true, "lr_decay": 0.999875, "segment_size": 8192, "init_lr_ratio": 1, "warmup_epochs": 0, "c_mel": 45, "c_kl": 1 }	{ "training_files": "filelists/ljs_audio_text_train_filelist.txt.cleaned", "validation_files": "filelists/ljs_audio_text_val_filelist.txt.cleaned", "text_cleaners": [ "english_cleaners2" ], "max_wav_value": 32768, "sampling_rate": 22050, "filter_length": 1024, "hop_length": 256, "win_length": 1024, "n_mel_channels": 80, "mel_fmin": 0, "mel_fmax": null, "add_blank": true, "n_speakers": 0, "cleaned_text": true }	{ "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, "use_sdp": false }	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
{ "log_interval": 200, "eval_interval": 1000, "seed": 1234, "epochs": 10000, "learning_rate": 0.0002, "betas": [ 0.8, 0.99 ], "eps": 1e-9, "batch_size": 64, "fp16_run": true, "lr_decay": 0.999875, "segment_size": 8192, "init_lr_ratio": 1, "warmup_epochs": 0, "c_mel": 45, "c_kl": 1 }	{ "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, "sampling_rate": 22050, "filter_length": 1024, "hop_length": 256, "win_length": 1024, "n_mel_channels": 80, "mel_fmin": 0, "mel_fmax": null, "add_blank": true, "n_speakers": 109, "cleaned_text": true }	{ "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 }	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
null	null	null	UNet2DConditionModel	0.6.0.dev0	silu	8	[ 320, 640, 1280, 1280 ]	false	384	[ "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D" ]	1	true	0	8	2	1	0.00001	32	4	64	[ "UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D" ]