---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: 1-char-based-freeze_cnn-dropout0.1
  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. -->

# 1-char-based-freeze_cnn-dropout0.1

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: 0.2454
- Wer: 0.1804

## 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: 12
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 48
- total_eval_batch_size: 16
- 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

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 5.3032        | 0.09  | 2500   | 7.0530          | 1.0    |
| 3.4521        | 0.18  | 5000   | 3.6019          | 1.0    |
| 3.3037        | 0.26  | 7500   | 3.4931          | 1.0    |
| 3.2012        | 0.35  | 10000  | 3.4193          | 1.0    |
| 2.3026        | 0.44  | 12500  | 1.9423          | 0.9873 |
| 1.4238        | 0.53  | 15000  | 0.8772          | 0.6695 |
| 1.1592        | 0.62  | 17500  | 0.6630          | 0.5011 |
| 0.861         | 0.7   | 20000  | 0.5460          | 0.4239 |
| 0.8123        | 0.79  | 22500  | 0.4794          | 0.3830 |
| 0.7568        | 0.88  | 25000  | 0.4369          | 0.3463 |
| 0.7182        | 0.97  | 27500  | 0.4111          | 0.3289 |
| 0.6896        | 1.06  | 30000  | 0.4041          | 0.3102 |
| 0.6655        | 1.14  | 32500  | 0.3933          | 0.2986 |
| 0.5738        | 1.23  | 35000  | 0.3676          | 0.2829 |
| 0.6361        | 1.32  | 37500  | 0.3533          | 0.2727 |
| 0.6142        | 1.41  | 40000  | 0.3545          | 0.2716 |
| 0.6346        | 1.5   | 42500  | 0.3428          | 0.2615 |
| 0.5739        | 1.58  | 45000  | 0.3470          | 0.2578 |
| 0.544         | 1.67  | 47500  | 0.3207          | 0.2490 |
| 0.5283        | 1.76  | 50000  | 0.3202          | 0.2424 |
| 0.5552        | 1.85  | 52500  | 0.3187          | 0.2379 |
| 0.5218        | 1.94  | 55000  | 0.3242          | 0.2383 |
| 0.4939        | 2.02  | 57500  | 0.3277          | 0.2418 |
| 0.5141        | 2.11  | 60000  | 0.3058          | 0.2329 |
| 0.5189        | 2.2   | 62500  | 0.3086          | 0.2273 |
| 0.4993        | 2.29  | 65000  | 0.3005          | 0.2245 |
| 0.5156        | 2.38  | 67500  | 0.2998          | 0.2223 |
| 0.4787        | 2.46  | 70000  | 0.2940          | 0.2173 |
| 0.5296        | 2.55  | 72500  | 0.3003          | 0.2225 |
| 0.4759        | 2.64  | 75000  | 0.2995          | 0.2144 |
| 0.485         | 2.73  | 77500  | 0.2882          | 0.2126 |
| 0.4888        | 2.82  | 80000  | 0.2893          | 0.2189 |
| 0.51          | 2.9   | 82500  | 0.2767          | 0.2046 |
| 0.4703        | 2.99  | 85000  | 0.2899          | 0.2124 |
| 0.4406        | 3.08  | 87500  | 0.2787          | 0.2068 |
| 0.4328        | 3.17  | 90000  | 0.2823          | 0.2070 |
| 0.4399        | 3.26  | 92500  | 0.2802          | 0.2058 |
| 0.4788        | 3.34  | 95000  | 0.2741          | 0.2084 |
| 0.4621        | 3.43  | 97500  | 0.2817          | 0.2038 |
| 0.523         | 3.52  | 100000 | 0.2735          | 0.2015 |
| 0.4689        | 3.61  | 102500 | 0.2631          | 0.1995 |
| 0.4502        | 3.7   | 105000 | 0.2689          | 0.1986 |
| 0.4402        | 3.78  | 107500 | 0.2726          | 0.1987 |
| 0.4189        | 3.87  | 110000 | 0.2724          | 0.1994 |
| 0.4526        | 3.96  | 112500 | 0.2596          | 0.1918 |
| 0.4755        | 4.05  | 115000 | 0.2583          | 0.1900 |
| 0.4374        | 4.14  | 117500 | 0.2590          | 0.1944 |
| 0.4155        | 4.23  | 120000 | 0.2695          | 0.1961 |
| 0.4463        | 4.31  | 122500 | 0.2605          | 0.1909 |
| 0.4007        | 4.4   | 125000 | 0.2529          | 0.1891 |
| 0.4156        | 4.49  | 127500 | 0.2568          | 0.1913 |
| 0.4124        | 4.58  | 130000 | 0.2559          | 0.1889 |
| 0.4085        | 4.67  | 132500 | 0.2610          | 0.1922 |
| 0.4474        | 4.75  | 135000 | 0.2588          | 0.1961 |
| 0.4098        | 4.84  | 137500 | 0.2512          | 0.1877 |
| 0.3941        | 4.93  | 140000 | 0.2549          | 0.1891 |
| 0.3917        | 5.02  | 142500 | 0.2544          | 0.1863 |
| 0.4324        | 5.11  | 145000 | 0.2564          | 0.1882 |
| 0.4255        | 5.19  | 147500 | 0.2536          | 0.1885 |
| 0.3894        | 5.28  | 150000 | 0.2538          | 0.1860 |
| 0.4108        | 5.37  | 152500 | 0.2539          | 0.1860 |
| 0.4312        | 5.46  | 155000 | 0.2526          | 0.1849 |
| 0.3786        | 5.55  | 157500 | 0.2504          | 0.1837 |
| 0.4033        | 5.63  | 160000 | 0.2516          | 0.1852 |
| 0.3973        | 5.72  | 162500 | 0.2570          | 0.1870 |
| 0.3994        | 5.81  | 165000 | 0.2499          | 0.1846 |
| 0.4183        | 5.9   | 167500 | 0.2489          | 0.1835 |
| 0.3826        | 5.99  | 170000 | 0.2468          | 0.1847 |
| 0.4103        | 6.07  | 172500 | 0.2477          | 0.1806 |
| 0.4291        | 6.16  | 175000 | 0.2492          | 0.1835 |
| 0.4417        | 6.25  | 177500 | 0.2464          | 0.1824 |
| 0.3962        | 6.34  | 180000 | 0.2476          | 0.1815 |
| 0.4633        | 6.43  | 182500 | 0.2447          | 0.1818 |
| 0.422         | 6.51  | 185000 | 0.2455          | 0.1802 |
| 0.4098        | 6.6   | 187500 | 0.2488          | 0.1814 |
| 0.4018        | 6.69  | 190000 | 0.2453          | 0.1804 |
| 0.4559        | 6.78  | 192500 | 0.2458          | 0.1823 |
| 0.4033        | 6.87  | 195000 | 0.2451          | 0.1794 |
| 0.3829        | 6.95  | 197500 | 0.2453          | 0.1804 |
| 0.3676        | 7.04  | 200000 | 0.2454          | 0.1804 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.1