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Fix docs
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@albertvillanova
albertvillanova committed Nov 9, 2021
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1 change: 1 addition & 0 deletions src/datasets/features/audio.py
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Expand Up @@ -16,6 +16,7 @@ class Audio:
- path: String with relative path of the audio file to the archive file.
- bytes: Bytes of the audio file.
This is useful for archived files with sequential access.
Args:
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Show benchmarks

PyArrow==3.0.0

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) 0.077594 / 0.011353 (0.066241) 0.003944 / 0.011008 (-0.007064) 0.034622 / 0.038508 (-0.003886) 0.040346 / 0.023109 (0.017237) 0.334893 / 0.275898 (0.058995) 0.373055 / 0.323480 (0.049575) 0.093498 / 0.007986 (0.085512) 0.004577 / 0.004328 (0.000249) 0.010316 / 0.004250 (0.006066) 0.042898 / 0.037052 (0.005846) 0.338151 / 0.258489 (0.079662) 0.376516 / 0.293841 (0.082675) 0.095605 / 0.128546 (-0.032941) 0.009549 / 0.075646 (-0.066097) 0.279485 / 0.419271 (-0.139786) 0.053522 / 0.043533 (0.009989) 0.343338 / 0.255139 (0.088199) 0.358843 / 0.283200 (0.075643) 0.098321 / 0.141683 (-0.043362) 1.905776 / 1.452155 (0.453622) 1.913249 / 1.492716 (0.420533)

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) 0.248252 / 0.018006 (0.230246) 0.451042 / 0.000490 (0.450553) 0.002219 / 0.000200 (0.002019) 0.000203 / 0.000054 (0.000149)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.046138 / 0.037411 (0.008727) 0.025584 / 0.014526 (0.011058) 0.032302 / 0.176557 (-0.144255) 0.233082 / 0.737135 (-0.504054) 0.031817 / 0.296338 (-0.264521)

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) 0.446935 / 0.215209 (0.231725) 4.462219 / 2.077655 (2.384565) 1.942437 / 1.504120 (0.438317) 1.698913 / 1.541195 (0.157718) 1.894817 / 1.468490 (0.426327) 0.494094 / 4.584777 (-4.090683) 5.466604 / 3.745712 (1.720892) 4.051745 / 5.269862 (-1.218116) 0.967414 / 4.565676 (-3.598262) 0.054360 / 0.424275 (-0.369915) 0.011525 / 0.007607 (0.003918) 0.580919 / 0.226044 (0.354875) 5.876298 / 2.268929 (3.607370) 2.676725 / 55.444624 (-52.767900) 2.265876 / 6.876477 (-4.610601) 2.401601 / 2.142072 (0.259529) 0.629521 / 4.805227 (-4.175706) 0.139519 / 6.500664 (-6.361145) 0.068012 / 0.075469 (-0.007458)

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) 1.672191 / 1.841788 (-0.169597) 13.233209 / 8.074308 (5.158901) 30.209254 / 10.191392 (20.017862) 0.902405 / 0.680424 (0.221981) 0.576220 / 0.534201 (0.042019) 0.407817 / 0.579283 (-0.171467) 0.560288 / 0.434364 (0.125924) 0.273954 / 0.540337 (-0.266383) 0.289468 / 1.386936 (-1.097468)
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) 0.082999 / 0.011353 (0.071646) 0.004579 / 0.011008 (-0.006429) 0.033887 / 0.038508 (-0.004621) 0.040063 / 0.023109 (0.016954) 0.394170 / 0.275898 (0.118272) 0.389988 / 0.323480 (0.066508) 0.100067 / 0.007986 (0.092081) 0.003874 / 0.004328 (-0.000455) 0.008563 / 0.004250 (0.004313) 0.044141 / 0.037052 (0.007088) 0.406477 / 0.258489 (0.147988) 0.411001 / 0.293841 (0.117160) 0.100802 / 0.128546 (-0.027744) 0.010405 / 0.075646 (-0.065242) 0.293173 / 0.419271 (-0.126098) 0.053426 / 0.043533 (0.009894) 0.400435 / 0.255139 (0.145296) 0.404123 / 0.283200 (0.120923) 0.092415 / 0.141683 (-0.049268) 1.984493 / 1.452155 (0.532339) 1.978907 / 1.492716 (0.486191)

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) 0.256913 / 0.018006 (0.238907) 0.448588 / 0.000490 (0.448099) 0.002764 / 0.000200 (0.002564) 0.000118 / 0.000054 (0.000063)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.035023 / 0.037411 (-0.002389) 0.023590 / 0.014526 (0.009064) 0.027075 / 0.176557 (-0.149482) 0.209455 / 0.737135 (-0.527680) 0.028790 / 0.296338 (-0.267549)

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) 0.462216 / 0.215209 (0.247007) 5.027885 / 2.077655 (2.950230) 2.179758 / 1.504120 (0.675638) 1.932384 / 1.541195 (0.391189) 2.025727 / 1.468490 (0.557237) 0.507412 / 4.584777 (-4.077365) 5.540885 / 3.745712 (1.795173) 4.120284 / 5.269862 (-1.149577) 1.064266 / 4.565676 (-3.501411) 0.054589 / 0.424275 (-0.369686) 0.012638 / 0.007607 (0.005030) 0.586297 / 0.226044 (0.360253) 5.582769 / 2.268929 (3.313841) 2.457951 / 55.444624 (-52.986674) 2.050124 / 6.876477 (-4.826353) 2.134511 / 2.142072 (-0.007561) 0.559762 / 4.805227 (-4.245465) 0.123424 / 6.500664 (-6.377240) 0.060901 / 0.075469 (-0.014568)

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) 1.867711 / 1.841788 (0.025924) 18.211506 / 8.074308 (10.137198) 30.508823 / 10.191392 (20.317431) 0.794382 / 0.680424 (0.113959) 0.577578 / 0.534201 (0.043377) 0.427695 / 0.579283 (-0.151588) 0.545650 / 0.434364 (0.111286) 0.293794 / 0.540337 (-0.246543) 0.294478 / 1.386936 (-1.092458)

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