Use default audio resampling type
_The documentation is not available anymore as the PR was closed or merged._ <details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.008730 / 0.011353 (-0.002623) | 0.004551 / 0.011008 (-0.006457) | 0.100206 / 0.038508 (0.061698) | 0.030264 / 0.023109 (0.007154) | 0.303310 / 0.275898 (0.027412) | 0.339040 / 0.323480 (0.015560) | 0.006923 / 0.007986 (-0.001063) | 0.004707 / 0.004328 (0.000379) | 0.077822 / 0.004250 (0.073571) | 0.034368 / 0.037052 (-0.002684) | 0.303125 / 0.258489 (0.044636) | 0.348322 / 0.293841 (0.054481) | 0.033831 / 0.128546 (-0.094715) | 0.011459 / 0.075646 (-0.064187) | 0.322092 / 0.419271 (-0.097180) | 0.047720 / 0.043533 (0.004187) | 0.304849 / 0.255139 (0.049710) | 0.330767 / 0.283200 (0.047567) | 0.087362 / 0.141683 (-0.054321) | 1.536095 / 1.452155 (0.083941) | 1.599979 / 1.492716 (0.107263) |

### 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) | 0.188985 / 0.018006 (0.170979) | 0.410775 / 0.000490 (0.410286) | 0.004215 / 0.000200 (0.004015) | 0.000086 / 0.000054 (0.000032) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023124 / 0.037411 (-0.014287) | 0.096962 / 0.014526 (0.082436) | 0.104070 / 0.176557 (-0.072486) | 0.141248 / 0.737135 (-0.595887) | 0.108534 / 0.296338 (-0.187804) |

### 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) | 0.417118 / 0.215209 (0.201909) | 4.167808 / 2.077655 (2.090154) | 2.016540 / 1.504120 (0.512420) | 1.847812 / 1.541195 (0.306617) | 1.967023 / 1.468490 (0.498532) | 0.689262 / 4.584777 (-3.895515) | 3.378747 / 3.745712 (-0.366965) | 1.854126 / 5.269862 (-3.415735) | 1.152102 / 4.565676 (-3.413575) | 0.081839 / 0.424275 (-0.342437) | 0.012426 / 0.007607 (0.004819) | 0.521334 / 0.226044 (0.295289) | 5.230593 / 2.268929 (2.961664) | 2.269386 / 55.444624 (-53.175238) | 1.965631 / 6.876477 (-4.910846) | 2.028994 / 2.142072 (-0.113079) | 0.802142 / 4.805227 (-4.003085) | 0.147954 / 6.500664 (-6.352710) | 0.065031 / 0.075469 (-0.010438) |

### 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) | 1.235289 / 1.841788 (-0.606499) | 13.723507 / 8.074308 (5.649199) | 14.197923 / 10.191392 (4.006531) | 0.147950 / 0.680424 (-0.532473) | 0.028332 / 0.534201 (-0.505869) | 0.400180 / 0.579283 (-0.179103) | 0.418970 / 0.434364 (-0.015393) | 0.478381 / 0.540337 (-0.061957) | 0.576138 / 1.386936 (-0.810798) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.006548 / 0.011353 (-0.004805) | 0.004567 / 0.011008 (-0.006441) | 0.075658 / 0.038508 (0.037150) | 0.027190 / 0.023109 (0.004080) | 0.363417 / 0.275898 (0.087518) | 0.399575 / 0.323480 (0.076095) | 0.004982 / 0.007986 (-0.003004) | 0.003364 / 0.004328 (-0.000964) | 0.074392 / 0.004250 (0.070142) | 0.038839 / 0.037052 (0.001787) | 0.361133 / 0.258489 (0.102644) | 0.408557 / 0.293841 (0.114717) | 0.031468 / 0.128546 (-0.097078) | 0.011645 / 0.075646 (-0.064001) | 0.085145 / 0.419271 (-0.334126) | 0.041775 / 0.043533 (-0.001758) | 0.348624 / 0.255139 (0.093485) | 0.389610 / 0.283200 (0.106410) | 0.088576 / 0.141683 (-0.053107) | 1.511208 / 1.452155 (0.059054) | 1.560568 / 1.492716 (0.067852) |

### 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) | 0.185017 / 0.018006 (0.167011) | 0.407543 / 0.000490 (0.407053) | 0.002486 / 0.000200 (0.002286) | 0.000076 / 0.000054 (0.000021) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.025181 / 0.037411 (-0.012231) | 0.099056 / 0.014526 (0.084530) | 0.108597 / 0.176557 (-0.067959) | 0.144664 / 0.737135 (-0.592471) | 0.110417 / 0.296338 (-0.185922) |

### 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) | 0.434302 / 0.215209 (0.219093) | 4.327840 / 2.077655 (2.250185) | 2.059939 / 1.504120 (0.555819) | 1.853267 / 1.541195 (0.312072) | 1.906616 / 1.468490 (0.438126) | 0.700165 / 4.584777 (-3.884611) | 3.439216 / 3.745712 (-0.306496) | 2.792034 / 5.269862 (-2.477827) | 1.424852 / 4.565676 (-3.140824) | 0.083926 / 0.424275 (-0.340349) | 0.013943 / 0.007607 (0.006336) | 0.535964 / 0.226044 (0.309920) | 5.368671 / 2.268929 (3.099743) | 2.497027 / 55.444624 (-52.947597) | 2.166222 / 6.876477 (-4.710254) | 2.183766 / 2.142072 (0.041693) | 0.805886 / 4.805227 (-3.999341) | 0.152474 / 6.500664 (-6.348190) | 0.067354 / 0.075469 (-0.008115) |

### 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) | 1.284052 / 1.841788 (-0.557736) | 13.714066 / 8.074308 (5.639758) | 14.195212 / 10.191392 (4.003820) | 0.151815 / 0.680424 (-0.528609) | 0.016847 / 0.534201 (-0.517354) | 0.391174 / 0.579283 (-0.188109) | 0.409784 / 0.434364 (-0.024580) | 0.473880 / 0.540337 (-0.066458) | 0.561016 / 1.386936 (-0.825920) |

</details>
</details>

![](https://cml.dev/watermark.png#47ab08d9f06abd5bc23bddaa4875b93e926dd3b1 "CML watermark")
 <details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.010284 / 0.011353 (-0.001068) | 0.005654 / 0.011008 (-0.005355) | 0.100522 / 0.038508 (0.062014) | 0.039201 / 0.023109 (0.016092) | 0.320831 / 0.275898 (0.044933) | 0.365351 / 0.323480 (0.041871) | 0.009066 / 0.007986 (0.001080) | 0.005805 / 0.004328 (0.001476) | 0.076969 / 0.004250 (0.072719) | 0.045813 / 0.037052 (0.008760) | 0.327115 / 0.258489 (0.068626) | 0.362823 / 0.293841 (0.068982) | 0.040521 / 0.128546 (-0.088025) | 0.013166 / 0.075646 (-0.062481) | 0.358579 / 0.419271 (-0.060692) | 0.051753 / 0.043533 (0.008220) | 0.323741 / 0.255139 (0.068602) | 0.360211 / 0.283200 (0.077011) | 0.111534 / 0.141683 (-0.030149) | 1.594887 / 1.452155 (0.142732) | 1.651516 / 1.492716 (0.158799) |

### 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) | 0.012051 / 0.018006 (-0.005956) | 0.475316 / 0.000490 (0.474826) | 0.004804 / 0.000200 (0.004604) | 0.000100 / 0.000054 (0.000046) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027480 / 0.037411 (-0.009931) | 0.112022 / 0.014526 (0.097496) | 0.121539 / 0.176557 (-0.055017) | 0.166327 / 0.737135 (-0.570809) | 0.132575 / 0.296338 (-0.163763) |

### 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) | 0.418322 / 0.215209 (0.203113) | 4.149463 / 2.077655 (2.071808) | 1.890901 / 1.504120 (0.386781) | 1.682521 / 1.541195 (0.141327) | 1.716331 / 1.468490 (0.247841) | 0.729176 / 4.584777 (-3.855601) | 4.173303 / 3.745712 (0.427591) | 2.166249 / 5.269862 (-3.103612) | 1.384623 / 4.565676 (-3.181053) | 0.095486 / 0.424275 (-0.328789) | 0.013800 / 0.007607 (0.006193) | 0.573917 / 0.226044 (0.347872) | 5.348843 / 2.268929 (3.079914) | 2.421716 / 55.444624 (-53.022909) | 2.002048 / 6.876477 (-4.874428) | 2.079493 / 2.142072 (-0.062579) | 0.882818 / 4.805227 (-3.922409) | 0.172936 / 6.500664 (-6.327728) | 0.068384 / 0.075469 (-0.007085) |

### 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) | 1.285704 / 1.841788 (-0.556084) | 16.036346 / 8.074308 (7.962038) | 15.181557 / 10.191392 (4.990165) | 0.194044 / 0.680424 (-0.486380) | 0.033128 / 0.534201 (-0.501073) | 0.480290 / 0.579283 (-0.098993) | 0.497525 / 0.434364 (0.063161) | 0.602304 / 0.540337 (0.061966) | 0.754273 / 1.386936 (-0.632663) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.007263 / 0.011353 (-0.004090) | 0.005164 / 0.011008 (-0.005845) | 0.079833 / 0.038508 (0.041325) | 0.033974 / 0.023109 (0.010865) | 0.382057 / 0.275898 (0.106159) | 0.402924 / 0.323480 (0.079444) | 0.007273 / 0.007986 (-0.000712) | 0.004378 / 0.004328 (0.000049) | 0.080556 / 0.004250 (0.076305) | 0.047376 / 0.037052 (0.010324) | 0.379044 / 0.258489 (0.120555) | 0.422135 / 0.293841 (0.128294) | 0.038294 / 0.128546 (-0.090252) | 0.013974 / 0.075646 (-0.061672) | 0.094936 / 0.419271 (-0.324335) | 0.051033 / 0.043533 (0.007501) | 0.368197 / 0.255139 (0.113058) | 0.409627 / 0.283200 (0.126427) | 0.107365 / 0.141683 (-0.034318) | 1.537501 / 1.452155 (0.085346) | 1.618021 / 1.492716 (0.125305) |

### 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) | 0.227187 / 0.018006 (0.209181) | 0.473226 / 0.000490 (0.472736) | 0.006532 / 0.000200 (0.006332) | 0.000121 / 0.000054 (0.000066) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029814 / 0.037411 (-0.007597) | 0.121113 / 0.014526 (0.106587) | 0.125107 / 0.176557 (-0.051450) | 0.167008 / 0.737135 (-0.570127) | 0.128720 / 0.296338 (-0.167619) |

### 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) | 0.452158 / 0.215209 (0.236949) | 4.507087 / 2.077655 (2.429433) | 2.193910 / 1.504120 (0.689790) | 1.991234 / 1.541195 (0.450039) | 2.120256 / 1.468490 (0.651766) | 0.726664 / 4.584777 (-3.858113) | 4.213148 / 3.745712 (0.467436) | 4.082857 / 5.269862 (-1.187005) | 1.741018 / 4.565676 (-2.824658) | 0.090176 / 0.424275 (-0.334099) | 0.013221 / 0.007607 (0.005614) | 0.558868 / 0.226044 (0.332824) | 5.617242 / 2.268929 (3.348313) | 2.985430 / 55.444624 (-52.459194) | 2.623136 / 6.876477 (-4.253341) | 2.383177 / 2.142072 (0.241105) | 0.917237 / 4.805227 (-3.887990) | 0.178774 / 6.500664 (-6.321890) | 0.064707 / 0.075469 (-0.010762) |

### 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) | 1.365402 / 1.841788 (-0.476385) | 16.035773 / 8.074308 (7.961465) | 13.917612 / 10.191392 (3.726220) | 0.152191 / 0.680424 (-0.528233) | 0.020734 / 0.534201 (-0.513467) | 0.442055 / 0.579283 (-0.137228) | 0.470588 / 0.434364 (0.036224) | 0.563433 / 0.540337 (0.023096) | 0.651161 / 1.386936 (-0.735775) |

</details>
</details>

![](https://cml.dev/watermark.png#6ab909a44b723fe0a8a586beafc8c5cbf9c91c21 "CML watermark")
 If it's good for you @polinaeterna I'd like to merge it and then run the `transformers` CI to see if it changes anything <details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.008829 / 0.011353 (-0.002524) | 0.004652 / 0.011008 (-0.006356) | 0.102505 / 0.038508 (0.063997) | 0.030164 / 0.023109 (0.007054) | 0.306551 / 0.275898 (0.030653) | 0.368920 / 0.323480 (0.045440) | 0.007084 / 0.007986 (-0.000902) | 0.003545 / 0.004328 (-0.000783) | 0.079402 / 0.004250 (0.075152) | 0.035296 / 0.037052 (-0.001756) | 0.312010 / 0.258489 (0.053520) | 0.348773 / 0.293841 (0.054932) | 0.034622 / 0.128546 (-0.093924) | 0.011727 / 0.075646 (-0.063920) | 0.326911 / 0.419271 (-0.092361) | 0.043832 / 0.043533 (0.000300) | 0.306357 / 0.255139 (0.051218) | 0.328744 / 0.283200 (0.045544) | 0.091954 / 0.141683 (-0.049729) | 1.563989 / 1.452155 (0.111834) | 1.591901 / 1.492716 (0.099185) |

### 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) | 0.194955 / 0.018006 (0.176948) | 0.412864 / 0.000490 (0.412374) | 0.003710 / 0.000200 (0.003510) | 0.000081 / 0.000054 (0.000026) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023132 / 0.037411 (-0.014279) | 0.099586 / 0.014526 (0.085060) | 0.105031 / 0.176557 (-0.071525) | 0.141206 / 0.737135 (-0.595929) | 0.111978 / 0.296338 (-0.184360) |

### 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) | 0.413729 / 0.215209 (0.198520) | 4.161713 / 2.077655 (2.084058) | 1.887442 / 1.504120 (0.383322) | 1.711847 / 1.541195 (0.170653) | 1.756833 / 1.468490 (0.288343) | 0.699239 / 4.584777 (-3.885538) | 3.346213 / 3.745712 (-0.399499) | 2.822289 / 5.269862 (-2.447573) | 1.475650 / 4.565676 (-3.090027) | 0.082800 / 0.424275 (-0.341475) | 0.012302 / 0.007607 (0.004695) | 0.523068 / 0.226044 (0.297024) | 5.242833 / 2.268929 (2.973904) | 2.310768 / 55.444624 (-53.133856) | 1.954629 / 6.876477 (-4.921847) | 2.015563 / 2.142072 (-0.126510) | 0.812613 / 4.805227 (-3.992614) | 0.149512 / 6.500664 (-6.351152) | 0.065162 / 0.075469 (-0.010307) |

### 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) | 1.270177 / 1.841788 (-0.571610) | 13.664765 / 8.074308 (5.590457) | 14.317968 / 10.191392 (4.126576) | 0.138135 / 0.680424 (-0.542289) | 0.028503 / 0.534201 (-0.505698) | 0.402921 / 0.579283 (-0.176362) | 0.400999 / 0.434364 (-0.033365) | 0.470983 / 0.540337 (-0.069355) | 0.544319 / 1.386936 (-0.842617) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.006841 / 0.011353 (-0.004512) | 0.004570 / 0.011008 (-0.006439) | 0.076398 / 0.038508 (0.037890) | 0.028136 / 0.023109 (0.005027) | 0.339864 / 0.275898 (0.063966) | 0.375496 / 0.323480 (0.052016) | 0.004967 / 0.007986 (-0.003019) | 0.003411 / 0.004328 (-0.000917) | 0.075727 / 0.004250 (0.071476) | 0.040025 / 0.037052 (0.002973) | 0.340473 / 0.258489 (0.081984) | 0.384396 / 0.293841 (0.090555) | 0.031683 / 0.128546 (-0.096863) | 0.011752 / 0.075646 (-0.063894) | 0.085635 / 0.419271 (-0.333636) | 0.042764 / 0.043533 (-0.000769) | 0.339417 / 0.255139 (0.084278) | 0.364190 / 0.283200 (0.080991) | 0.093842 / 0.141683 (-0.047841) | 1.480999 / 1.452155 (0.028844) | 1.549752 / 1.492716 (0.057036) |

### 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) | 0.174146 / 0.018006 (0.156140) | 0.415459 / 0.000490 (0.414970) | 0.002854 / 0.000200 (0.002654) | 0.000077 / 0.000054 (0.000023) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024671 / 0.037411 (-0.012740) | 0.101229 / 0.014526 (0.086703) | 0.108841 / 0.176557 (-0.067716) | 0.144530 / 0.737135 (-0.592606) | 0.112509 / 0.296338 (-0.183829) |

### 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) | 0.460561 / 0.215209 (0.245352) | 4.591139 / 2.077655 (2.513484) | 2.275535 / 1.504120 (0.771415) | 2.070976 / 1.541195 (0.529781) | 2.028766 / 1.468490 (0.560276) | 0.706166 / 4.584777 (-3.878611) | 3.408498 / 3.745712 (-0.337215) | 3.034665 / 5.269862 (-2.235197) | 1.586805 / 4.565676 (-2.978872) | 0.083355 / 0.424275 (-0.340920) | 0.012460 / 0.007607 (0.004853) | 0.565256 / 0.226044 (0.339212) | 5.662643 / 2.268929 (3.393715) | 2.697019 / 55.444624 (-52.747605) | 2.302044 / 6.876477 (-4.574433) | 2.373081 / 2.142072 (0.231009) | 0.809804 / 4.805227 (-3.995423) | 0.151481 / 6.500664 (-6.349183) | 0.066870 / 0.075469 (-0.008599) |

### 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) | 1.257293 / 1.841788 (-0.584495) | 14.059454 / 8.074308 (5.985146) | 13.783251 / 10.191392 (3.591859) | 0.140007 / 0.680424 (-0.540417) | 0.016624 / 0.534201 (-0.517577) | 0.381703 / 0.579283 (-0.197580) | 0.389032 / 0.434364 (-0.045332) | 0.466127 / 0.540337 (-0.074211) | 0.551052 / 1.386936 (-0.835884) |

</details>
</details>

![](https://cml.dev/watermark.png#4a767f7a3dffdf45886690b81c6e624146ae14da "CML watermark")

...instead of relying on the optional librosa dependency `resampy`.

It was only used for `_decode_non_mp3_file_like` anyway and not for the other ones - removing it fixes consistency between decoding methods (except torchaudio decoding)

Therefore I think it is a better solution than adding `resampy` as a dependency in https://github.com/huggingface/datasets/pull/5554

cc @polinaeterna 