---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-large-ll60k-librispeech-clean-100h-demo-dist
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# hubert-large-ll60k-librispeech-clean-100h-demo-dist

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Cer: 0.0316
- Loss: 0.2143
- Wer: 0.0995

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
| 2.911         | 0.89  | 100  | 1.0    | 2.9202          | 1.0    |
| 2.6638        | 1.79  | 200  | 1.0    | 2.6310          | 1.0    |
| 0.3898        | 2.68  | 300  | 0.0968 | 0.3892          | 0.3366 |
| 0.2156        | 3.57  | 400  | 0.0591 | 0.2250          | 0.2090 |
| 0.1517        | 4.46  | 500  | 0.0474 | 0.1834          | 0.1695 |
| 0.1059        | 5.36  | 600  | 0.0428 | 0.1668          | 0.1502 |
| 0.0825        | 6.25  | 700  | 0.0393 | 0.1662          | 0.1406 |
| 0.0679        | 7.14  | 800  | 0.0393 | 0.1747          | 0.1357 |
| 0.0602        | 8.04  | 900  | 0.0390 | 0.1767          | 0.1334 |
| 0.0587        | 8.93  | 1000 | 0.0376 | 0.1708          | 0.1292 |
| 0.0517        | 9.82  | 1100 | 0.0372 | 0.1677          | 0.1255 |
| 0.0413        | 10.71 | 1200 | 0.0361 | 0.1771          | 0.1234 |
| 0.0418        | 11.61 | 1300 | 0.0358 | 0.1731          | 0.1229 |
| 0.0424        | 12.5  | 1400 | 0.0348 | 0.1796          | 0.1191 |
| 0.0469        | 13.39 | 1500 | 0.0358 | 0.1848          | 0.1207 |
| 0.0414        | 14.29 | 1600 | 0.0367 | 0.1863          | 0.1213 |
| 0.0338        | 15.18 | 1700 | 0.0347 | 0.1889          | 0.1177 |
| 0.0334        | 16.07 | 1800 | 0.0360 | 0.1900          | 0.1188 |
| 0.0315        | 16.96 | 1900 | 0.0346 | 0.1901          | 0.1158 |
| 0.0317        | 17.86 | 2000 | 0.0341 | 0.1790          | 0.1134 |
| 0.0264        | 18.75 | 2100 | 0.0356 | 0.1864          | 0.1159 |
| 0.0271        | 19.64 | 2200 | 0.0341 | 0.1861          | 0.1150 |
| 0.0272        | 20.54 | 2300 | 0.0339 | 0.1945          | 0.1129 |
| 0.0278        | 21.43 | 2400 | 0.0343 | 0.1950          | 0.1131 |
| 0.0254        | 22.32 | 2500 | 0.0330 | 0.2015          | 0.1097 |
| 0.0204        | 23.21 | 2600 | 0.0326 | 0.1952          | 0.1069 |
| 0.0259        | 24.11 | 2700 | 0.0330 | 0.1976          | 0.1103 |
| 0.0325        | 25.0  | 2800 | 0.0328 | 0.1958          | 0.1088 |
| 0.0359        | 25.89 | 2900 | 0.0346 | 0.1908          | 0.1105 |
| 0.0265        | 26.79 | 3000 | 0.0337 | 0.1991          | 0.1096 |
| 0.0223        | 27.68 | 3100 | 0.0345 | 0.1948          | 0.1107 |
| 0.025         | 28.57 | 3200 | 0.0330 | 0.2046          | 0.1077 |
| 0.0242        | 29.46 | 3300 | 0.0335 | 0.2055          | 0.1072 |
| 0.0187        | 30.36 | 3400 | 0.0307 | 0.1980          | 0.1021 |
| 0.0219        | 31.25 | 3500 | 0.0322 | 0.1998          | 0.1054 |
| 0.0198        | 32.14 | 3600 | 0.0322 | 0.2104          | 0.1048 |
| 0.0181        | 33.04 | 3700 | 0.0325 | 0.2093          | 0.1050 |
| 0.0166        | 33.93 | 3800 | 0.0315 | 0.2120          | 0.1032 |
| 0.0212        | 34.82 | 3900 | 0.0300 | 0.2021          | 0.1003 |
| 0.0214        | 35.71 | 4000 | 0.0316 | 0.2045          | 0.1033 |
| 0.016         | 36.61 | 4100 | 0.0302 | 0.2022          | 0.1000 |
| 0.0169        | 37.5  | 4200 | 0.0299 | 0.2060          | 0.0996 |
| 0.0191        | 38.39 | 4300 | 0.0307 | 0.2114          | 0.1006 |
| 0.0218        | 39.29 | 4400 | 0.0314 | 0.2066          | 0.1015 |
| 0.0182        | 40.18 | 4500 | 0.0300 | 0.2054          | 0.0988 |
| 0.0185        | 41.07 | 4600 | 0.0303 | 0.2050          | 0.0994 |
| 0.0171        | 41.96 | 4700 | 0.0306 | 0.2136          | 0.0994 |
| 0.0171        | 42.86 | 4800 | 0.0318 | 0.2062          | 0.1007 |
| 0.0161        | 43.75 | 4900 | 0.0319 | 0.2101          | 0.1013 |
| 0.0168        | 44.64 | 5000 | 0.0306 | 0.2111          | 0.0985 |
| 0.015         | 45.54 | 5100 | 0.0318 | 0.2110          | 0.1003 |
| 0.0126        | 46.43 | 5200 | 0.0319 | 0.2086          | 0.0999 |
| 0.0153        | 47.32 | 5300 | 0.0310 | 0.2095          | 0.0981 |
| 0.0172        | 48.21 | 5400 | 0.0310 | 0.2130          | 0.0985 |
| 0.017         | 49.11 | 5500 | 0.0316 | 0.2137          | 0.0994 |
| 0.0152        | 50.0  | 5600 | 0.0316 | 0.2143          | 0.0995 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.8.0
- Tokenizers 0.15.2