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# wav2vec2-large-xlsr-mn_en_v1

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9588
- Wer: 0.3868

## 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: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3016        | 0.31  | 400   | 1.8974          | 0.9524 |
| 1.4532        | 0.62  | 800   | 0.9980          | 0.7182 |
| 1.0379        | 0.93  | 1200  | 0.7873          | 0.6193 |
| 0.836         | 1.24  | 1600  | 0.7248          | 0.5773 |
| 0.7698        | 1.55  | 2000  | 0.6732          | 0.5495 |
| 0.7235        | 1.86  | 2400  | 0.6309          | 0.5178 |
| 0.6405        | 2.17  | 2800  | 0.6529          | 0.5105 |
| 0.6158        | 2.48  | 3200  | 0.6189          | 0.4981 |
| 0.5668        | 2.79  | 3600  | 0.6242          | 0.4865 |
| 0.5359        | 3.1   | 4000  | 0.6143          | 0.4703 |
| 0.5           | 3.41  | 4400  | 0.6288          | 0.4708 |
| 0.5006        | 3.72  | 4800  | 0.5983          | 0.4630 |
| 0.4874        | 4.03  | 5200  | 0.6253          | 0.4632 |
| 0.4425        | 4.34  | 5600  | 0.6035          | 0.4477 |
| 0.4364        | 4.65  | 6000  | 0.5864          | 0.4451 |
| 0.4342        | 4.96  | 6400  | 0.5920          | 0.4467 |
| 0.3932        | 5.27  | 6800  | 0.6110          | 0.4483 |
| 0.3919        | 5.58  | 7200  | 0.6228          | 0.4411 |
| 0.3996        | 5.89  | 7600  | 0.6215          | 0.4401 |
| 0.3724        | 6.2   | 8000  | 0.6375          | 0.4463 |
| 0.3541        | 6.51  | 8400  | 0.5837          | 0.4332 |
| 0.3573        | 6.82  | 8800  | 0.6098          | 0.4482 |
| 0.3495        | 7.13  | 9200  | 0.5830          | 0.4364 |
| 0.3238        | 7.44  | 9600  | 0.5945          | 0.4343 |
| 0.3244        | 7.75  | 10000 | 0.6099          | 0.4375 |
| 0.3296        | 8.06  | 10400 | 0.6191          | 0.4339 |
| 0.2991        | 8.37  | 10800 | 0.6058          | 0.4273 |
| 0.3043        | 8.68  | 11200 | 0.6060          | 0.4289 |
| 0.3034        | 8.99  | 11600 | 0.6172          | 0.4328 |
| 0.2711        | 9.3   | 12000 | 0.6674          | 0.4241 |
| 0.2869        | 9.6   | 12400 | 0.6451          | 0.4186 |
| 0.2828        | 9.91  | 12800 | 0.6250          | 0.4198 |
| 0.2586        | 10.22 | 13200 | 0.6477          | 0.4177 |
| 0.2625        | 10.53 | 13600 | 0.6563          | 0.4173 |
| 0.2588        | 10.84 | 14000 | 0.6566          | 0.4277 |
| 0.2431        | 11.15 | 14400 | 0.6822          | 0.4230 |
| 0.246         | 11.46 | 14800 | 0.6835          | 0.4188 |
| 0.2372        | 11.77 | 15200 | 0.6824          | 0.4319 |
| 0.2427        | 12.08 | 15600 | 0.7079          | 0.4188 |
| 0.2232        | 12.39 | 16000 | 0.6642          | 0.4131 |
| 0.2186        | 12.7  | 16400 | 0.6705          | 0.4158 |
| 0.2268        | 13.01 | 16800 | 0.6710          | 0.4179 |
| 0.2037        | 13.32 | 17200 | 0.6766          | 0.4152 |
| 0.2082        | 13.63 | 17600 | 0.6658          | 0.4155 |
| 0.2122        | 13.94 | 18000 | 0.6845          | 0.4248 |
| 0.1933        | 14.25 | 18400 | 0.7068          | 0.4107 |
| 0.1933        | 14.56 | 18800 | 0.7056          | 0.4149 |
| 0.1868        | 14.87 | 19200 | 0.6932          | 0.4152 |
| 0.1891        | 15.18 | 19600 | 0.7369          | 0.4104 |
| 0.1741        | 15.49 | 20000 | 0.7334          | 0.4095 |
| 0.1785        | 15.8  | 20400 | 0.7364          | 0.4142 |
| 0.1794        | 16.11 | 20800 | 0.7588          | 0.4146 |
| 0.167         | 16.42 | 21200 | 0.7607          | 0.4115 |
| 0.1666        | 16.73 | 21600 | 0.7603          | 0.4137 |
| 0.1715        | 17.04 | 22000 | 0.7678          | 0.4087 |
| 0.1641        | 17.35 | 22400 | 0.7855          | 0.4092 |
| 0.1514        | 17.66 | 22800 | 0.7811          | 0.4116 |
| 0.1648        | 17.97 | 23200 | 0.7801          | 0.4111 |
| 0.1421        | 18.28 | 23600 | 0.8001          | 0.4123 |
| 0.1528        | 18.59 | 24000 | 0.7936          | 0.4043 |
| 0.1516        | 18.9  | 24400 | 0.8146          | 0.4115 |
| 0.145         | 19.21 | 24800 | 0.7926          | 0.4093 |
| 0.1405        | 19.52 | 25200 | 0.8165          | 0.4083 |
| 0.1406        | 19.83 | 25600 | 0.8154          | 0.4060 |
| 0.1354        | 20.14 | 26000 | 0.8114          | 0.4026 |
| 0.1307        | 20.45 | 26400 | 0.8073          | 0.4023 |
| 0.1314        | 20.76 | 26800 | 0.8122          | 0.4018 |
| 0.1236        | 21.07 | 27200 | 0.8162          | 0.4043 |
| 0.1245        | 21.38 | 27600 | 0.8339          | 0.4002 |
| 0.1207        | 21.69 | 28000 | 0.8461          | 0.4059 |
| 0.1228        | 22.0  | 28400 | 0.8508          | 0.3984 |
| 0.1127        | 22.31 | 28800 | 0.8681          | 0.4026 |
| 0.1178        | 22.62 | 29200 | 0.8610          | 0.4006 |
| 0.1169        | 22.93 | 29600 | 0.8775          | 0.3996 |
| 0.1115        | 23.24 | 30000 | 0.8591          | 0.3999 |
| 0.105         | 23.55 | 30400 | 0.8945          | 0.3958 |
| 0.1085        | 23.86 | 30800 | 0.8954          | 0.3933 |
| 0.1054        | 24.17 | 31200 | 0.8982          | 0.3940 |
| 0.1007        | 24.48 | 31600 | 0.9025          | 0.3962 |
| 0.1018        | 24.79 | 32000 | 0.8998          | 0.3962 |
| 0.0973        | 25.1  | 32400 | 0.9150          | 0.3917 |
| 0.0969        | 25.41 | 32800 | 0.9348          | 0.3915 |
| 0.0934        | 25.72 | 33200 | 0.8974          | 0.3930 |
| 0.0983        | 26.03 | 33600 | 0.9129          | 0.3960 |
| 0.0962        | 26.34 | 34000 | 0.9215          | 0.3901 |
| 0.092         | 26.65 | 34400 | 0.9314          | 0.3881 |
| 0.0894        | 26.96 | 34800 | 0.9286          | 0.3885 |
| 0.087         | 27.27 | 35200 | 0.9277          | 0.3882 |
| 0.0906        | 27.58 | 35600 | 0.9400          | 0.3887 |
| 0.0886        | 27.89 | 36000 | 0.9285          | 0.3890 |
| 0.086         | 28.2  | 36400 | 0.9455          | 0.3890 |
| 0.0821        | 28.51 | 36800 | 0.9504          | 0.3874 |
| 0.0843        | 28.81 | 37200 | 0.9445          | 0.3875 |
| 0.0802        | 29.12 | 37600 | 0.9565          | 0.3872 |
| 0.0821        | 29.43 | 38000 | 0.9587          | 0.3871 |
| 0.0852        | 29.74 | 38400 | 0.9588          | 0.3868 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu102
- Datasets 1.18.3
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xlsr-mn_en_v1", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us