transformers safetensors wav2vec2 automatic-speech-recognition generated_from_trainer base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: 1-bpe20k-freeze_cnn-drop.1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 1-bpe20k-freeze_cnn-drop.1

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0929
- Wer: 1.0076

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 91.1741       | 0.05  | 2000   | 179.2776        | 1.0    |
| 56.7966       | 0.09  | 4000   | 111.7828        | 1.0    |
| 11.4825       | 0.14  | 6000   | 16.6212         | 1.0    |
| 7.5731        | 0.19  | 8000   | 8.3634          | 1.0    |
| 7.3291        | 0.23  | 10000  | 8.3149          | 1.0    |
| 7.1995        | 0.28  | 12000  | 8.1837          | 1.0    |
| 7.1092        | 0.33  | 14000  | 8.1877          | 1.0    |
| 6.9362        | 0.38  | 16000  | 7.8934          | 1.0    |
| 6.7999        | 0.42  | 18000  | 7.7947          | 1.0    |
| 6.7773        | 0.47  | 20000  | 7.8173          | 1.0    |
| 6.7489        | 0.52  | 22000  | 7.8197          | 1.0117 |
| 6.7139        | 0.56  | 24000  | 7.6664          | 1.0078 |
| 6.6024        | 0.61  | 26000  | 7.8397          | 1.0    |
| 6.3451        | 0.66  | 28000  | 7.2039          | 1.0044 |
| 5.7315        | 0.7   | 30000  | 6.3228          | 1.0039 |
| 4.8494        | 0.75  | 32000  | 5.3767          | 1.0058 |
| 4.4103        | 0.8   | 34000  | 4.5216          | 1.0073 |
| 3.8434        | 0.85  | 36000  | 3.9239          | 1.0125 |
| 3.3268        | 0.89  | 38000  | 3.4547          | 1.0074 |
| 2.9034        | 0.94  | 40000  | 3.0617          | 1.0079 |
| 2.5747        | 0.99  | 42000  | 2.7700          | 1.0108 |
| 2.4532        | 1.03  | 44000  | 2.5497          | 1.0113 |
| 2.3169        | 1.08  | 46000  | 2.3780          | 1.0105 |
| 2.2304        | 1.13  | 48000  | 2.2581          | 1.0075 |
| 2.0592        | 1.17  | 50000  | 2.1189          | 1.0051 |
| 2.023         | 1.22  | 52000  | 2.0325          | 1.0049 |
| 1.8011        | 1.27  | 54000  | 1.9284          | 1.0081 |
| 1.817         | 1.31  | 56000  | 1.8635          | 1.0091 |
| 1.6902        | 1.36  | 58000  | 1.7967          | 1.0076 |
| 1.6398        | 1.41  | 60000  | 1.7413          | 1.0081 |
| 1.8097        | 1.46  | 62000  | 1.7066          | 1.0054 |
| 1.5504        | 1.5   | 64000  | 1.6525          | 1.0074 |
| 1.5759        | 1.55  | 66000  | 1.6402          | 1.0098 |
| 1.6652        | 1.6   | 68000  | 1.5884          | 1.0055 |
| 1.5074        | 1.64  | 70000  | 1.5423          | 1.0066 |
| 1.414         | 1.69  | 72000  | 1.5397          | 1.0095 |
| 1.4792        | 1.74  | 74000  | 1.4822          | 1.0062 |
| 1.4648        | 1.78  | 76000  | 1.5023          | 1.0083 |
| 1.4326        | 1.83  | 78000  | 1.4627          | 1.0070 |
| 1.3691        | 1.88  | 80000  | 1.4435          | 1.0073 |
| 1.3624        | 1.92  | 82000  | 1.4290          | 1.0066 |
| 1.5254        | 1.97  | 84000  | 1.4090          | 1.0104 |
| 1.3362        | 2.02  | 86000  | 1.3790          | 1.0065 |
| 1.3586        | 2.07  | 88000  | 1.3730          | 1.0061 |
| 1.29          | 2.11  | 90000  | 1.3825          | 1.0099 |
| 1.387         | 2.16  | 92000  | 1.3499          | 1.0087 |
| 1.356         | 2.21  | 94000  | 1.3591          | 1.0102 |
| 1.3145        | 2.25  | 96000  | 1.3154          | 1.0084 |
| 1.2747        | 2.3   | 98000  | 1.3095          | 1.0080 |
| 1.3444        | 2.35  | 100000 | 1.2910          | 1.0087 |
| 1.2304        | 2.39  | 102000 | 1.2850          | 1.0074 |
| 1.3306        | 2.44  | 104000 | 1.2698          | 1.0085 |
| 1.2431        | 2.49  | 106000 | 1.2645          | 1.0087 |
| 1.1938        | 2.54  | 108000 | 1.2497          | 1.0061 |
| 1.2476        | 2.58  | 110000 | 1.2416          | 1.0077 |
| 1.1743        | 2.63  | 112000 | 1.2460          | 1.0064 |
| 1.2554        | 2.68  | 114000 | 1.2369          | 1.0076 |
| 1.1518        | 2.72  | 116000 | 1.2212          | 1.0076 |
| 1.1812        | 2.77  | 118000 | 1.1954          | 1.0054 |
| 1.2447        | 2.82  | 120000 | 1.2144          | 1.0082 |
| 1.2839        | 2.86  | 122000 | 1.1976          | 1.0076 |
| 1.117         | 2.91  | 124000 | 1.1809          | 1.0060 |
| 1.1151        | 2.96  | 126000 | 1.1930          | 1.0087 |
| 1.182         | 3.0   | 128000 | 1.1750          | 1.0079 |
| 1.214         | 3.05  | 130000 | 1.1687          | 1.0047 |
| 1.1701        | 3.1   | 132000 | 1.1651          | 1.0071 |
| 1.1268        | 3.15  | 134000 | 1.1635          | 1.0068 |
| 1.193         | 3.19  | 136000 | 1.1550          | 1.0075 |
| 1.1245        | 3.24  | 138000 | 1.1436          | 1.0068 |
| 1.136         | 3.29  | 140000 | 1.1378          | 1.0059 |
| 1.1318        | 3.33  | 142000 | 1.1526          | 1.0074 |
| 1.1129        | 3.38  | 144000 | 1.1452          | 1.0074 |
| 1.1175        | 3.43  | 146000 | 1.1491          | 1.0092 |
| 1.0812        | 3.47  | 148000 | 1.1300          | 1.0075 |
| 1.0735        | 3.52  | 150000 | 1.1442          | 1.0083 |
| 1.0626        | 3.57  | 152000 | 1.1298          | 1.0083 |
| 1.0952        | 3.61  | 154000 | 1.1326          | 1.0087 |
| 1.0756        | 3.66  | 156000 | 1.1341          | 1.0089 |
| 1.0695        | 3.71  | 158000 | 1.1287          | 1.0092 |
| 1.0672        | 3.76  | 160000 | 1.1379          | 1.0085 |
| 1.1802        | 3.8   | 162000 | 1.1235          | 1.0074 |
| 1.094         | 3.85  | 164000 | 1.1155          | 1.0068 |
| 1.0811        | 3.9   | 166000 | 1.1172          | 1.0078 |
| 0.9901        | 3.94  | 168000 | 1.0964          | 1.0064 |
| 1.0907        | 3.99  | 170000 | 1.1031          | 1.0084 |
| 1.0799        | 4.04  | 172000 | 1.0924          | 1.0071 |
| 1.0271        | 4.08  | 174000 | 1.1020          | 1.0086 |
| 1.0482        | 4.13  | 176000 | 1.1067          | 1.0084 |
| 1.1078        | 4.18  | 178000 | 1.1083          | 1.0088 |
| 1.1798        | 4.23  | 180000 | 1.0964          | 1.0064 |
| 1.0933        | 4.27  | 182000 | 1.1034          | 1.0080 |
| 1.0272        | 4.32  | 184000 | 1.1036          | 1.0075 |
| 1.1125        | 4.37  | 186000 | 1.1022          | 1.0084 |
| 1.033         | 4.41  | 188000 | 1.0906          | 1.0070 |
| 1.1048        | 4.46  | 190000 | 1.0923          | 1.0079 |
| 1.1565        | 4.51  | 192000 | 1.0976          | 1.0078 |
| 1.0698        | 4.55  | 194000 | 1.0950          | 1.0073 |
| 1.0735        | 4.6   | 196000 | 1.0920          | 1.0074 |
| 1.0137        | 4.65  | 198000 | 1.0899          | 1.0073 |
| 1.0669        | 4.69  | 200000 | 1.0929          | 1.0076 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
