---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Model_G_ALL_Wav2Vec2
  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. -->

# Model_G_ALL_Wav2Vec2

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7956
- Wer: 0.1972
- Cer: 0.0811

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.8395        | 0.67  | 400   | 0.5656          | 0.3303 | 0.1236 |
| 0.3196        | 1.34  | 800   | 0.5007          | 0.2922 | 0.1071 |
| 0.2491        | 2.01  | 1200  | 0.5008          | 0.2830 | 0.1056 |
| 0.2012        | 2.68  | 1600  | 0.5177          | 0.2689 | 0.1003 |
| 0.1882        | 3.35  | 2000  | 0.5517          | 0.2622 | 0.0986 |
| 0.1811        | 4.02  | 2400  | 0.5225          | 0.2543 | 0.0980 |
| 0.1648        | 4.69  | 2800  | 0.5504          | 0.2477 | 0.0948 |
| 0.1451        | 5.36  | 3200  | 0.5181          | 0.2346 | 0.0908 |
| 0.149         | 6.04  | 3600  | 0.5204          | 0.2375 | 0.0941 |
| 0.1328        | 6.71  | 4000  | 0.5780          | 0.2375 | 0.0920 |
| 0.1256        | 7.38  | 4400  | 0.5406          | 0.2443 | 0.0960 |
| 0.1193        | 8.05  | 4800  | 0.5489          | 0.2317 | 0.0928 |
| 0.1099        | 8.72  | 5200  | 0.5864          | 0.2363 | 0.0925 |
| 0.1125        | 9.39  | 5600  | 0.5749          | 0.2267 | 0.0902 |
| 0.1044        | 10.06 | 6000  | 0.5698          | 0.2279 | 0.0905 |
| 0.0925        | 10.73 | 6400  | 0.6051          | 0.2337 | 0.0933 |
| 0.0951        | 11.4  | 6800  | 0.6785          | 0.2286 | 0.0907 |
| 0.0926        | 12.07 | 7200  | 0.5937          | 0.2337 | 0.0919 |
| 0.0838        | 12.74 | 7600  | 0.5918          | 0.2233 | 0.0893 |
| 0.0775        | 13.41 | 8000  | 0.5642          | 0.2227 | 0.0888 |
| 0.0742        | 14.08 | 8400  | 0.5927          | 0.2249 | 0.0898 |
| 0.0687        | 14.75 | 8800  | 0.6647          | 0.2265 | 0.0900 |
| 0.0685        | 15.42 | 9200  | 0.7438          | 0.2164 | 0.0885 |
| 0.0645        | 16.09 | 9600  | 0.6351          | 0.2128 | 0.0858 |
| 0.0582        | 16.76 | 10000 | 0.6164          | 0.2169 | 0.0878 |
| 0.0604        | 17.44 | 10400 | 0.6327          | 0.2146 | 0.0867 |
| 0.0557        | 18.11 | 10800 | 0.6790          | 0.2148 | 0.0879 |
| 0.0552        | 18.78 | 11200 | 0.6859          | 0.2101 | 0.0848 |
| 0.0474        | 19.45 | 11600 | 0.6648          | 0.2071 | 0.0847 |
| 0.048         | 20.12 | 12000 | 0.7172          | 0.2136 | 0.0873 |
| 0.0475        | 20.79 | 12400 | 0.6451          | 0.2058 | 0.0845 |
| 0.041         | 21.46 | 12800 | 0.6826          | 0.2074 | 0.0839 |
| 0.0405        | 22.13 | 13200 | 0.6738          | 0.2110 | 0.0842 |
| 0.0355        | 22.8  | 13600 | 0.7020          | 0.2050 | 0.0839 |
| 0.0325        | 23.47 | 14000 | 0.7085          | 0.2117 | 0.0854 |
| 0.0308        | 24.14 | 14400 | 0.7418          | 0.2077 | 0.0854 |
| 0.0321        | 24.81 | 14800 | 0.7371          | 0.2051 | 0.0840 |
| 0.0274        | 25.48 | 15200 | 0.7611          | 0.2082 | 0.0848 |
| 0.0287        | 26.15 | 15600 | 0.7208          | 0.2021 | 0.0836 |
| 0.0253        | 26.82 | 16000 | 0.7432          | 0.2025 | 0.0831 |
| 0.0256        | 27.49 | 16400 | 0.7435          | 0.2011 | 0.0824 |
| 0.0243        | 28.16 | 16800 | 0.7543          | 0.1991 | 0.0818 |
| 0.0241        | 28.83 | 17200 | 0.7676          | 0.1986 | 0.0814 |
| 0.0204        | 29.51 | 17600 | 0.7956          | 0.1972 | 0.0811 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 1.18.3
- Tokenizers 0.13.3