KorinM/Corbin
Audio-to-Audio
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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 datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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"
] |
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