transformers tensorboard 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
model-index:
- name: ft_0117_korean
  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. -->

# ft_0117_korean

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8178
- Cer: 0.1942

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 68.3088       | 0.03  | 100   | 125.2977        | 1.0    |
| 42.6071       | 0.07  | 200   | 75.7330         | 1.0    |
| 25.0826       | 0.1   | 300   | 35.9474         | 1.0    |
| 10.1653       | 0.14  | 400   | 7.3490          | 1.0    |
| 4.9322        | 0.17  | 500   | 5.3703          | 1.0    |
| 4.7973        | 0.21  | 600   | 5.2150          | 1.0    |
| 4.7712        | 0.24  | 700   | 5.1904          | 1.0    |
| 4.7076        | 0.28  | 800   | 5.0821          | 1.0    |
| 4.6923        | 0.31  | 900   | 4.9610          | 1.0    |
| 4.6677        | 0.34  | 1000  | 4.9497          | 1.0    |
| 4.7168        | 0.38  | 1100  | 5.0000          | 1.0    |
| 4.632         | 0.41  | 1200  | 4.9656          | 1.0    |
| 4.6183        | 0.45  | 1300  | 4.8375          | 1.0    |
| 4.5395        | 0.48  | 1400  | 4.6795          | 1.0    |
| 4.5385        | 0.52  | 1500  | 4.7110          | 1.0    |
| 4.5216        | 0.55  | 1600  | 4.6153          | 1.0    |
| 4.4442        | 0.59  | 1700  | 4.5397          | 1.0    |
| 4.3119        | 0.62  | 1800  | 4.2205          | 1.0    |
| 4.0832        | 0.66  | 1900  | 3.8678          | 0.9388 |
| 3.7389        | 0.69  | 2000  | 3.3075          | 0.6898 |
| 3.2707        | 0.72  | 2100  | 2.9589          | 0.5771 |
| 3.0099        | 0.76  | 2200  | 2.6719          | 0.5310 |
| 2.8581        | 0.79  | 2300  | 2.5290          | 0.5090 |
| 2.677         | 0.83  | 2400  | 2.3971          | 0.4737 |
| 2.5064        | 0.86  | 2500  | 2.2446          | 0.4643 |
| 2.4691        | 0.9   | 2600  | 2.1015          | 0.4373 |
| 2.2864        | 0.93  | 2700  | 2.0987          | 0.4290 |
| 2.2526        | 0.97  | 2800  | 2.0082          | 0.4223 |
| 2.156         | 1.0   | 2900  | 1.9078          | 0.4063 |
| 2.1556        | 1.03  | 3000  | 1.8029          | 0.3970 |
| 1.9783        | 1.07  | 3100  | 1.7724          | 0.3876 |
| 1.9678        | 1.1   | 3200  | 1.7115          | 0.3705 |
| 1.9242        | 1.14  | 3300  | 1.6834          | 0.3634 |
| 1.8216        | 1.17  | 3400  | 1.6559          | 0.3532 |
| 1.7855        | 1.21  | 3500  | 1.6106          | 0.3556 |
| 1.8123        | 1.24  | 3600  | 1.6309          | 0.3465 |
| 1.7609        | 1.28  | 3700  | 1.5353          | 0.3403 |
| 1.7131        | 1.31  | 3800  | 1.5067          | 0.3320 |
| 1.6954        | 1.35  | 3900  | 1.4273          | 0.3228 |
| 1.6219        | 1.38  | 4000  | 1.3992          | 0.3198 |
| 1.5606        | 1.41  | 4100  | 1.4247          | 0.3164 |
| 1.6549        | 1.45  | 4200  | 1.3775          | 0.3135 |
| 1.5869        | 1.48  | 4300  | 1.3162          | 0.3043 |
| 1.531         | 1.52  | 4400  | 1.2849          | 0.2980 |
| 1.4833        | 1.55  | 4500  | 1.3072          | 0.2989 |
| 1.4852        | 1.59  | 4600  | 1.2669          | 0.2984 |
| 1.4379        | 1.62  | 4700  | 1.2259          | 0.2897 |
| 1.4085        | 1.66  | 4800  | 1.2219          | 0.2828 |
| 1.4005        | 1.69  | 4900  | 1.1980          | 0.2791 |
| 1.3868        | 1.72  | 5000  | 1.2399          | 0.2874 |
| 1.3646        | 1.76  | 5100  | 1.2098          | 0.2829 |
| 1.3728        | 1.79  | 5200  | 1.2053          | 0.2779 |
| 1.2867        | 1.83  | 5300  | 1.1602          | 0.2737 |
| 1.3263        | 1.86  | 5400  | 1.1363          | 0.2650 |
| 1.2914        | 1.9   | 5500  | 1.1249          | 0.2611 |
| 1.2629        | 1.93  | 5600  | 1.0774          | 0.2559 |
| 1.2031        | 1.97  | 5700  | 1.1048          | 0.2570 |
| 1.2491        | 2.0   | 5800  | 1.0966          | 0.2655 |
| 1.159         | 2.04  | 5900  | 1.0593          | 0.2566 |
| 1.134         | 2.07  | 6000  | 1.0350          | 0.2475 |
| 1.1207        | 2.1   | 6100  | 1.0544          | 0.2455 |
| 1.116         | 2.14  | 6200  | 1.0340          | 0.2470 |
| 1.0947        | 2.17  | 6300  | 1.0177          | 0.2431 |
| 1.0844        | 2.21  | 6400  | 1.0166          | 0.2394 |
| 1.0679        | 2.24  | 6500  | 1.0666          | 0.2457 |
| 1.1139        | 2.28  | 6600  | 0.9607          | 0.2333 |
| 1.1074        | 2.31  | 6700  | 0.9982          | 0.2315 |
| 1.0263        | 2.35  | 6800  | 0.9937          | 0.2326 |
| 1.0264        | 2.38  | 6900  | 0.9521          | 0.2294 |
| 0.999         | 2.41  | 7000  | 0.9542          | 0.2306 |
| 1.0688        | 2.45  | 7100  | 0.9294          | 0.2251 |
| 1.0357        | 2.48  | 7200  | 0.9602          | 0.2231 |
| 1.0218        | 2.52  | 7300  | 0.9285          | 0.2264 |
| 0.9932        | 2.55  | 7400  | 0.9392          | 0.2224 |
| 1.0133        | 2.59  | 7500  | 0.9092          | 0.2225 |
| 1.0369        | 2.62  | 7600  | 0.9226          | 0.2185 |
| 0.9927        | 2.66  | 7700  | 0.9695          | 0.2236 |
| 1.0042        | 2.69  | 7800  | 0.9115          | 0.2200 |
| 0.9954        | 2.73  | 7900  | 0.8979          | 0.2151 |
| 0.9775        | 2.76  | 8000  | 0.9016          | 0.2161 |
| 0.9078        | 2.79  | 8100  | 0.9009          | 0.2177 |
| 0.9196        | 2.83  | 8200  | 0.9006          | 0.2149 |
| 0.9177        | 2.86  | 8300  | 0.8777          | 0.2125 |
| 0.8992        | 2.9   | 8400  | 0.8889          | 0.2097 |
| 0.911         | 2.93  | 8500  | 0.8693          | 0.2087 |
| 0.888         | 2.97  | 8600  | 0.8735          | 0.2134 |
| 0.9566        | 3.0   | 8700  | 0.8586          | 0.2078 |
| 0.8704        | 3.04  | 8800  | 0.8686          | 0.2079 |
| 0.8203        | 3.07  | 8900  | 0.8537          | 0.2064 |
| 0.8425        | 3.1   | 9000  | 0.8827          | 0.2065 |
| 0.8615        | 3.14  | 9100  | 0.8392          | 0.2030 |
| 0.8364        | 3.17  | 9200  | 0.8474          | 0.2041 |
| 0.8011        | 3.21  | 9300  | 0.8441          | 0.2031 |
| 0.8624        | 3.24  | 9400  | 0.8491          | 0.2046 |
| 0.8525        | 3.28  | 9500  | 0.8359          | 0.1995 |
| 0.8235        | 3.31  | 9600  | 0.8370          | 0.2008 |
| 0.8155        | 3.35  | 9700  | 0.8495          | 0.2015 |
| 0.7683        | 3.38  | 9800  | 0.8514          | 0.2014 |
| 0.8514        | 3.41  | 9900  | 0.8179          | 0.1981 |
| 0.8574        | 3.45  | 10000 | 0.8201          | 0.1992 |
| 0.819         | 3.48  | 10100 | 0.8437          | 0.1998 |
| 0.8078        | 3.52  | 10200 | 0.8292          | 0.1984 |
| 0.7888        | 3.55  | 10300 | 0.8253          | 0.1971 |
| 0.8105        | 3.59  | 10400 | 0.8180          | 0.1961 |
| 0.8006        | 3.62  | 10500 | 0.8111          | 0.1951 |
| 0.7583        | 3.66  | 10600 | 0.8265          | 0.1960 |
| 0.8425        | 3.69  | 10700 | 0.8248          | 0.1943 |
| 0.8564        | 3.73  | 10800 | 0.8250          | 0.1953 |
| 0.772         | 3.76  | 10900 | 0.8287          | 0.1970 |
| 0.8058        | 3.79  | 11000 | 0.8243          | 0.1961 |
| 0.7974        | 3.83  | 11100 | 0.8162          | 0.1944 |
| 0.7292        | 3.86  | 11200 | 0.8227          | 0.1950 |
| 0.7915        | 3.9   | 11300 | 0.8160          | 0.1941 |
| 0.7891        | 3.93  | 11400 | 0.8193          | 0.1945 |
| 0.7766        | 3.97  | 11500 | 0.8178          | 0.1942 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.13.0
- Tokenizers 0.15.0
