---
license: apache-2.0
tags:
- automatic-speech-recognition
- google/xtreme_s
- generated_from_trainer
datasets:
- google/xtreme_s
model-index:
- name: xtreme_s_xlsr_mls
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xtreme_s_xlsr_300m_mls

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MLS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6215
- Wer: 0.3033
- Cer: 0.0951

## 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: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3000
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 3.0446        | 1.91  | 500   | 2.9866          | 1.0    | 1.0    |
| 0.8789        | 3.82  | 1000  | 0.8574          | 0.7225 | 0.2355 |
| 0.4766        | 5.72  | 1500  | 0.4813          | 0.4624 | 0.1394 |
| 0.3779        | 7.63  | 2000  | 0.4465          | 0.4154 | 0.1309 |
| 0.3244        | 9.54  | 2500  | 0.4213          | 0.3683 | 0.1163 |
| 0.346         | 11.45 | 3000  | 0.4606          | 0.4033 | 0.1299 |
| 0.3092        | 13.36 | 3500  | 0.4160          | 0.3585 | 0.1115 |
| 0.3287        | 15.27 | 4000  | 0.4364          | 0.3631 | 0.1165 |
| 0.3165        | 17.18 | 4500  | 0.4218          | 0.3451 | 0.1056 |
| 0.2874        | 19.08 | 5000  | 0.4583          | 0.3650 | 0.1151 |
| 0.3089        | 20.99 | 5500  | 0.4424          | 0.3485 | 0.1137 |
| 0.2689        | 22.9  | 6000  | 0.4427          | 0.3542 | 0.1128 |
| 0.234         | 24.81 | 6500  | 0.4204          | 0.3431 | 0.1069 |
| 0.2363        | 26.72 | 7000  | 0.4792          | 0.3689 | 0.1191 |
| 0.2796        | 28.62 | 7500  | 0.4867          | 0.3662 | 0.1154 |
| 0.2447        | 30.53 | 8000  | 0.4908          | 0.3584 | 0.1160 |
| 0.22          | 32.44 | 8500  | 0.5315          | 0.3626 | 0.1240 |
| 0.1961        | 34.35 | 9000  | 0.5121          | 0.3610 | 0.1168 |
| 0.1959        | 36.26 | 9500  | 0.5140          | 0.3648 | 0.1179 |
| 0.1748        | 38.17 | 10000 | 0.5464          | 0.3763 | 0.1206 |
| 0.197         | 40.08 | 10500 | 0.5199          | 0.3515 | 0.1128 |
| 0.2166        | 41.98 | 11000 | 0.5336          | 0.3607 | 0.1191 |
| 0.2078        | 43.89 | 11500 | 0.5389          | 0.3518 | 0.1136 |
| 0.1827        | 45.8  | 12000 | 0.5014          | 0.3287 | 0.1053 |
| 0.1783        | 47.71 | 12500 | 0.5408          | 0.3545 | 0.1121 |
| 0.1489        | 49.62 | 13000 | 0.5292          | 0.3472 | 0.1098 |
| 0.1665        | 51.53 | 13500 | 0.5052          | 0.3300 | 0.1033 |
| 0.1631        | 53.43 | 14000 | 0.5241          | 0.3362 | 0.1081 |
| 0.1943        | 55.34 | 14500 | 0.5453          | 0.3373 | 0.1076 |
| 0.1504        | 57.25 | 15000 | 0.5958          | 0.3594 | 0.1149 |
| 0.136         | 59.16 | 15500 | 0.5645          | 0.3367 | 0.1082 |
| 0.1224        | 61.07 | 16000 | 0.5322          | 0.3302 | 0.1039 |
| 0.1156        | 62.98 | 16500 | 0.5728          | 0.3332 | 0.1061 |
| 0.114         | 64.88 | 17000 | 0.5994          | 0.3410 | 0.1125 |
| 0.1445        | 66.79 | 17500 | 0.6048          | 0.3471 | 0.1098 |
| 0.1281        | 68.7  | 18000 | 0.5747          | 0.3278 | 0.1042 |
| 0.1233        | 70.61 | 18500 | 0.6021          | 0.3375 | 0.1082 |
| 0.1109        | 72.52 | 19000 | 0.5851          | 0.3188 | 0.1021 |
| 0.0943        | 74.43 | 19500 | 0.5944          | 0.3238 | 0.1033 |
| 0.1418        | 76.34 | 20000 | 0.5904          | 0.3143 | 0.0997 |
| 0.1317        | 78.24 | 20500 | 0.6291          | 0.3283 | 0.1047 |
| 0.1177        | 80.15 | 21000 | 0.6114          | 0.3190 | 0.1000 |
| 0.1138        | 82.06 | 21500 | 0.6155          | 0.3245 | 0.1023 |
| 0.1074        | 83.97 | 22000 | 0.6094          | 0.3153 | 0.1004 |
| 0.11          | 85.88 | 22500 | 0.6041          | 0.3141 | 0.0988 |
| 0.1096        | 87.78 | 23000 | 0.6243          | 0.3110 | 0.0986 |
| 0.1017        | 89.69 | 23500 | 0.6110          | 0.3121 | 0.0984 |
| 0.1015        | 91.6  | 24000 | 0.6385          | 0.3093 | 0.0978 |
| 0.0952        | 93.51 | 24500 | 0.6155          | 0.3036 | 0.0953 |
| 0.0896        | 95.42 | 25000 | 0.6215          | 0.3033 | 0.0951 |
| 0.0953        | 97.33 | 25500 | 0.6293          | 0.3037 | 0.0953 |
| 0.0834        | 99.24 | 26000 | 0.6302          | 0.3036 | 0.0952 |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6