Rename audio_classificiation.py to audio_classification.py

[carlthome]

No description provided.

[lhoestq]

Oh good catch. Can you also rename it in src/datasets/tasks/__init__.py ?

[carlthome]

Fixed!

(I think, tough word to spell right TBH)

[HuggingFaceDocBuilderDev]

The documentation is not available anymore as the PR was closed or merged.

[lhoestq]

Thanks :)

[github-actions]

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric	read_batch_formatted_as_numpy after write_array2d	read_batch_formatted_as_numpy after write_flattened_sequence	read_batch_formatted_as_numpy after write_nested_sequence	read_batch_unformated after write_array2d	read_batch_unformated after write_flattened_sequence	read_batch_unformated after write_nested_sequence	read_col_formatted_as_numpy after write_array2d	read_col_formatted_as_numpy after write_flattened_sequence	read_col_formatted_as_numpy after write_nested_sequence	read_col_unformated after write_array2d	read_col_unformated after write_flattened_sequence	read_col_unformated after write_nested_sequence	read_formatted_as_numpy after write_array2d	read_formatted_as_numpy after write_flattened_sequence	read_formatted_as_numpy after write_nested_sequence	read_unformated after write_array2d	read_unformated after write_flattened_sequence	read_unformated after write_nested_sequence	write_array2d	write_flattened_sequence	write_nested_sequence
new / old (diff)	0.004737 / 0.011353 (-0.006616)	0.002446 / 0.011008 (-0.008563)	0.060928 / 0.038508 (0.022420)	0.030479 / 0.023109 (0.007370)	0.238385 / 0.275898 (-0.037513)	0.265563 / 0.323480 (-0.057917)	0.002910 / 0.007986 (-0.005076)	0.002325 / 0.004328 (-0.002004)	0.047817 / 0.004250 (0.043566)	0.044243 / 0.037052 (0.007191)	0.245190 / 0.258489 (-0.013299)	0.275449 / 0.293841 (-0.018392)	0.023384 / 0.128546 (-0.105162)	0.006820 / 0.075646 (-0.068826)	0.201488 / 0.419271 (-0.217783)	0.057758 / 0.043533 (0.014225)	0.245279 / 0.255139 (-0.009860)	0.266094 / 0.283200 (-0.017106)	0.019254 / 0.141683 (-0.122429)	1.107497 / 1.452155 (-0.344658)	1.161412 / 1.492716 (-0.331304)

Benchmark: benchmark_getitem_100B.json

metric	get_batch_of_1024_random_rows	get_batch_of_1024_rows	get_first_row	get_last_row
new / old (diff)	0.094909 / 0.018006 (0.076903)	0.305185 / 0.000490 (0.304695)	0.000221 / 0.000200 (0.000021)	0.000042 / 0.000054 (-0.000012)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018352 / 0.037411 (-0.019059)	0.062441 / 0.014526 (0.047915)	0.072386 / 0.176557 (-0.104171)	0.118836 / 0.737135 (-0.618299)	0.074514 / 0.296338 (-0.221824)

Benchmark: benchmark_iterating.json

metric	read 5000	read 50000	read_batch 50000 10	read_batch 50000 100	read_batch 50000 1000	read_formatted numpy 5000	read_formatted pandas 5000	read_formatted tensorflow 5000	read_formatted torch 5000	read_formatted_batch numpy 5000 10	read_formatted_batch numpy 5000 1000	shuffled read 5000	shuffled read 50000	shuffled read_batch 50000 10	shuffled read_batch 50000 100	shuffled read_batch 50000 1000	shuffled read_formatted numpy 5000	shuffled read_formatted_batch numpy 5000 10	shuffled read_formatted_batch numpy 5000 1000
new / old (diff)	0.283632 / 0.215209 (0.068423)	2.751845 / 2.077655 (0.674190)	1.478620 / 1.504120 (-0.025499)	1.357221 / 1.541195 (-0.183974)	1.415297 / 1.468490 (-0.053194)	0.400093 / 4.584777 (-4.184684)	2.404607 / 3.745712 (-1.341105)	2.617572 / 5.269862 (-2.652289)	1.587622 / 4.565676 (-2.978055)	0.045997 / 0.424275 (-0.378278)	0.004872 / 0.007607 (-0.002735)	0.338901 / 0.226044 (0.112856)	3.371362 / 2.268929 (1.102434)	1.870469 / 55.444624 (-53.574155)	1.561670 / 6.876477 (-5.314807)	1.573186 / 2.142072 (-0.568886)	0.478735 / 4.805227 (-4.326492)	0.098743 / 6.500664 (-6.401921)	0.041780 / 0.075469 (-0.033689)

Benchmark: benchmark_map_filter.json

metric	filter	map fast-tokenizer batched	map identity	map identity batched	map no-op batched	map no-op batched numpy	map no-op batched pandas	map no-op batched pytorch	map no-op batched tensorflow
new / old (diff)	0.945422 / 1.841788 (-0.896366)	11.563464 / 8.074308 (3.489156)	10.368731 / 10.191392 (0.177339)	0.129910 / 0.680424 (-0.550513)	0.014014 / 0.534201 (-0.520187)	0.269036 / 0.579283 (-0.310247)	0.265516 / 0.434364 (-0.168848)	0.311082 / 0.540337 (-0.229255)	0.431510 / 1.386936 (-0.955426)

PyArrow==latest

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric	read_batch_formatted_as_numpy after write_array2d	read_batch_formatted_as_numpy after write_flattened_sequence	read_batch_formatted_as_numpy after write_nested_sequence	read_batch_unformated after write_array2d	read_batch_unformated after write_flattened_sequence	read_batch_unformated after write_nested_sequence	read_col_formatted_as_numpy after write_array2d	read_col_formatted_as_numpy after write_flattened_sequence	read_col_formatted_as_numpy after write_nested_sequence	read_col_unformated after write_array2d	read_col_unformated after write_flattened_sequence	read_col_unformated after write_nested_sequence	read_formatted_as_numpy after write_array2d	read_formatted_as_numpy after write_flattened_sequence	read_formatted_as_numpy after write_nested_sequence	read_unformated after write_array2d	read_unformated after write_flattened_sequence	read_unformated after write_nested_sequence	write_array2d	write_flattened_sequence	write_nested_sequence
new / old (diff)	0.005068 / 0.011353 (-0.006284)	0.002989 / 0.011008 (-0.008019)	0.048213 / 0.038508 (0.009705)	0.056133 / 0.023109 (0.033024)	0.283347 / 0.275898 (0.007449)	0.307505 / 0.323480 (-0.015975)	0.004041 / 0.007986 (-0.003944)	0.002477 / 0.004328 (-0.001852)	0.047771 / 0.004250 (0.043521)	0.039361 / 0.037052 (0.002309)	0.283764 / 0.258489 (0.025275)	0.320644 / 0.293841 (0.026803)	0.024972 / 0.128546 (-0.103575)	0.007599 / 0.075646 (-0.068048)	0.054732 / 0.419271 (-0.364539)	0.032774 / 0.043533 (-0.010759)	0.285594 / 0.255139 (0.030455)	0.301500 / 0.283200 (0.018300)	0.018181 / 0.141683 (-0.123501)	1.126311 / 1.452155 (-0.325843)	1.187147 / 1.492716 (-0.305569)

Benchmark: benchmark_getitem_100B.json

metric	get_batch_of_1024_random_rows	get_batch_of_1024_rows	get_first_row	get_last_row
new / old (diff)	0.097397 / 0.018006 (0.079391)	0.315112 / 0.000490 (0.314622)	0.000224 / 0.000200 (0.000024)	0.000056 / 0.000054 (0.000001)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.021529 / 0.037411 (-0.015882)	0.073208 / 0.014526 (0.058682)	0.081683 / 0.176557 (-0.094874)	0.120475 / 0.737135 (-0.616660)	0.083265 / 0.296338 (-0.213073)

Benchmark: benchmark_iterating.json

metric	read 5000	read 50000	read_batch 50000 10	read_batch 50000 100	read_batch 50000 1000	read_formatted numpy 5000	read_formatted pandas 5000	read_formatted tensorflow 5000	read_formatted torch 5000	read_formatted_batch numpy 5000 10	read_formatted_batch numpy 5000 1000	shuffled read 5000	shuffled read 50000	shuffled read_batch 50000 10	shuffled read_batch 50000 100	shuffled read_batch 50000 1000	shuffled read_formatted numpy 5000	shuffled read_formatted_batch numpy 5000 10	shuffled read_formatted_batch numpy 5000 1000
new / old (diff)	0.289976 / 0.215209 (0.074767)	2.839860 / 2.077655 (0.762205)	1.592635 / 1.504120 (0.088515)	1.466722 / 1.541195 (-0.074472)	1.552850 / 1.468490 (0.084360)	0.418693 / 4.584777 (-4.166084)	2.526620 / 3.745712 (-1.219093)	2.706182 / 5.269862 (-2.563680)	1.618514 / 4.565676 (-2.947162)	0.046303 / 0.424275 (-0.377972)	0.004873 / 0.007607 (-0.002734)	0.345146 / 0.226044 (0.119102)	3.378448 / 2.268929 (1.109520)	1.986393 / 55.444624 (-53.458231)	1.681838 / 6.876477 (-5.194639)	1.738093 / 2.142072 (-0.403980)	0.484386 / 4.805227 (-4.320842)	0.100693 / 6.500664 (-6.399971)	0.043084 / 0.075469 (-0.032385)

Benchmark: benchmark_map_filter.json

metric	filter	map fast-tokenizer batched	map identity	map identity batched	map no-op batched	map no-op batched numpy	map no-op batched pandas	map no-op batched pytorch	map no-op batched tensorflow
new / old (diff)	0.976399 / 1.841788 (-0.865389)	13.122968 / 8.074308 (5.048660)	11.245031 / 10.191392 (1.053639)	0.134433 / 0.680424 (-0.545991)	0.017439 / 0.534201 (-0.516762)	0.274083 / 0.579283 (-0.305200)	0.287353 / 0.434364 (-0.147011)	0.309231 / 0.540337 (-0.231106)	0.418003 / 1.386936 (-0.968933)