fix typo in Audio dataset documentation
State: closed | Author: prassanna-ravishankar (CONTRIBUTOR)

There is a typo in the section of the documentation dedicated to creating an audio dataset. The Dataset is incorrectly suffixed with a `Config`


https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/blob/main/librivox-indonesia.py#L59

Comments: _The documentation is not available anymore as the PR was closed or merged._

<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<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) | 0.006655 / 0.011353 (-0.004698) | 0.004115 / 0.011008 (-0.006893) | 0.083895 / 0.038508 (0.045387) | 0.072770 / 0.023109 (0.049661) | 0.311401 / 0.275898 (0.035503) | 0.341079 / 0.323480 (0.017599) | 0.005488 / 0.007986 (-0.002497) | 0.003530 / 0.004328 (-0.000799) | 0.064691 / 0.004250 (0.060441) | 0.053096 / 0.037052 (0.016044) | 0.314969 / 0.258489 (0.056480) | 0.358245 / 0.293841 (0.064404) | 0.030789 / 0.128546 (-0.097757) | 0.008868 / 0.075646 (-0.066779) | 0.288022 / 0.419271 (-0.131249) | 0.052092 / 0.043533 (0.008559) | 0.310061 / 0.255139 (0.054922) | 0.345369 / 0.283200 (0.062170) | 0.024100 / 0.141683 (-0.117582) | 1.520573 / 1.452155 (0.068418) | 1.593750 / 1.492716 (0.101033) |

### 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) | 0.242520 / 0.018006 (0.224514) | 0.567963 / 0.000490 (0.567473) | 0.003183 / 0.000200 (0.002983) | 0.000074 / 0.000054 (0.000020) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029473 / 0.037411 (-0.007939) | 0.083012 / 0.014526 (0.068486) | 0.262386 / 0.176557 (0.085830) | 0.155131 / 0.737135 (-0.582004) | 0.099880 / 0.296338 (-0.196458) |

### 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) | 0.382388 / 0.215209 (0.167179) | 3.816538 / 2.077655 (1.738884) | 1.863422 / 1.504120 (0.359302) | 1.694652 / 1.541195 (0.153457) | 1.738738 / 1.468490 (0.270248) | 0.477073 / 4.584777 (-4.107704) | 3.539244 / 3.745712 (-0.206468) | 3.238469 / 5.269862 (-2.031392) | 2.026154 / 4.565676 (-2.539523) | 0.056111 / 0.424275 (-0.368164) | 0.007615 / 0.007607 (0.000008) | 0.460620 / 0.226044 (0.234576) | 4.596383 / 2.268929 (2.327455) | 2.348645 / 55.444624 (-53.095979) | 1.977465 / 6.876477 (-4.899011) | 2.222828 / 2.142072 (0.080755) | 0.588065 / 4.805227 (-4.217162) | 0.132175 / 6.500664 (-6.368489) | 0.061322 / 0.075469 (-0.014147) |

### 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) | 1.260623 / 1.841788 (-0.581164) | 19.976475 / 8.074308 (11.902167) | 14.346488 / 10.191392 (4.155096) | 0.145614 / 0.680424 (-0.534810) | 0.018309 / 0.534201 (-0.515892) | 0.393644 / 0.579283 (-0.185639) | 0.405355 / 0.434364 (-0.029009) | 0.458355 / 0.540337 (-0.081982) | 0.630147 / 1.386936 (-0.756789) |

</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) | 0.006769 / 0.011353 (-0.004584) | 0.004172 / 0.011008 (-0.006836) | 0.064863 / 0.038508 (0.026355) | 0.076831 / 0.023109 (0.053722) | 0.419391 / 0.275898 (0.143493) | 0.439912 / 0.323480 (0.116432) | 0.006249 / 0.007986 (-0.001737) | 0.003571 / 0.004328 (-0.000757) | 0.064877 / 0.004250 (0.060626) | 0.056023 / 0.037052 (0.018971) | 0.419899 / 0.258489 (0.161410) | 0.459334 / 0.293841 (0.165493) | 0.032217 / 0.128546 (-0.096329) | 0.008628 / 0.075646 (-0.067019) | 0.071089 / 0.419271 (-0.348183) | 0.047463 / 0.043533 (0.003930) | 0.414961 / 0.255139 (0.159822) | 0.431408 / 0.283200 (0.148209) | 0.022406 / 0.141683 (-0.119277) | 1.511890 / 1.452155 (0.059735) | 1.580268 / 1.492716 (0.087551) |

### 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) | 0.280805 / 0.018006 (0.262799) | 0.553766 / 0.000490 (0.553276) | 0.006155 / 0.000200 (0.005955) | 0.000102 / 0.000054 (0.000047) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.032980 / 0.037411 (-0.004431) | 0.092981 / 0.014526 (0.078456) | 0.108820 / 0.176557 (-0.067737) | 0.161709 / 0.737135 (-0.575426) | 0.109772 / 0.296338 (-0.186566) |

### 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) | 0.433659 / 0.215209 (0.218450) | 4.328577 / 2.077655 (2.250923) | 2.316899 / 1.504120 (0.812779) | 2.142645 / 1.541195 (0.601451) | 2.245518 / 1.468490 (0.777028) | 0.489448 / 4.584777 (-4.095329) | 3.630074 / 3.745712 (-0.115638) | 3.322749 / 5.269862 (-1.947112) | 2.062307 / 4.565676 (-2.503370) | 0.058153 / 0.424275 (-0.366122) | 0.007453 / 0.007607 (-0.000154) | 0.507234 / 0.226044 (0.281190) | 5.071830 / 2.268929 (2.802902) | 2.839374 / 55.444624 (-52.605250) | 2.429583 / 6.876477 (-4.446893) | 2.671940 / 2.142072 (0.529868) | 0.588256 / 4.805227 (-4.216972) | 0.135135 / 6.500664 (-6.365530) | 0.060963 / 0.075469 (-0.014506) |

### 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) | 1.337462 / 1.841788 (-0.504326) | 20.292912 / 8.074308 (12.218604) | 14.871809 / 10.191392 (4.680417) | 0.169214 / 0.680424 (-0.511209) | 0.020450 / 0.534201 (-0.513751) | 0.397094 / 0.579283 (-0.182189) | 0.411623 / 0.434364 (-0.022741) | 0.471560 / 0.540337 (-0.068777) | 0.647293 / 1.386936 (-0.739643) |

</details>
</details>

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