---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Model_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_ALL_Wav2Vec2

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7779
- Wer: 0.1975
- Cer: 0.0813

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.8385        | 0.67  | 400   | 0.5656          | 0.3049 | 0.1100 |
| 0.3291        | 1.34  | 800   | 0.5395          | 0.3184 | 0.1128 |
| 0.258         | 2.01  | 1200  | 0.4904          | 0.2770 | 0.1030 |
| 0.217         | 2.68  | 1600  | 0.4673          | 0.2814 | 0.1073 |
| 0.1956        | 3.35  | 2000  | 0.5108          | 0.2697 | 0.1021 |
| 0.1872        | 4.02  | 2400  | 0.5531          | 0.2735 | 0.1050 |
| 0.168         | 4.69  | 2800  | 0.5113          | 0.2536 | 0.0967 |
| 0.1476        | 5.36  | 3200  | 0.6744          | 0.2420 | 0.0941 |
| 0.1531        | 6.04  | 3600  | 0.6433          | 0.2492 | 0.0962 |
| 0.1271        | 6.71  | 4000  | 0.5360          | 0.2392 | 0.0928 |
| 0.1362        | 7.38  | 4400  | 0.5451          | 0.2458 | 0.0958 |
| 0.1169        | 8.05  | 4800  | 0.6710          | 0.2470 | 0.0965 |
| 0.117         | 8.72  | 5200  | 0.5291          | 0.2480 | 0.0990 |
| 0.1146        | 9.39  | 5600  | 0.6168          | 0.2372 | 0.0927 |
| 0.1028        | 10.06 | 6000  | 0.5437          | 0.2294 | 0.0914 |
| 0.0918        | 10.73 | 6400  | 0.6350          | 0.2392 | 0.0947 |
| 0.1037        | 11.4  | 6800  | 0.6351          | 0.2346 | 0.0920 |
| 0.0926        | 12.07 | 7200  | 0.6677          | 0.2316 | 0.0924 |
| 0.0861        | 12.74 | 7600  | 0.5842          | 0.2301 | 0.0934 |
| 0.0791        | 13.41 | 8000  | 0.5862          | 0.2286 | 0.0916 |
| 0.08          | 14.08 | 8400  | 0.6183          | 0.2227 | 0.0900 |
| 0.0707        | 14.75 | 8800  | 0.5985          | 0.2351 | 0.0955 |
| 0.0719        | 15.42 | 9200  | 0.6327          | 0.2200 | 0.0897 |
| 0.0674        | 16.09 | 9600  | 0.6184          | 0.2193 | 0.0889 |
| 0.0612        | 16.76 | 10000 | 0.5501          | 0.2224 | 0.0912 |
| 0.0607        | 17.44 | 10400 | 0.5404          | 0.2233 | 0.0916 |
| 0.0612        | 18.11 | 10800 | 0.6111          | 0.2193 | 0.0889 |
| 0.0542        | 18.78 | 11200 | 0.6610          | 0.2196 | 0.0893 |
| 0.0517        | 19.45 | 11600 | 0.6083          | 0.2199 | 0.0905 |
| 0.0478        | 20.12 | 12000 | 0.6500          | 0.2130 | 0.0874 |
| 0.0464        | 20.79 | 12400 | 0.6671          | 0.2144 | 0.0863 |
| 0.0395        | 21.46 | 12800 | 0.7239          | 0.2113 | 0.0864 |
| 0.0391        | 22.13 | 13200 | 0.7791          | 0.2084 | 0.0851 |
| 0.0362        | 22.8  | 13600 | 0.6682          | 0.2083 | 0.0855 |
| 0.0396        | 23.47 | 14000 | 0.6608          | 0.2065 | 0.0848 |
| 0.0346        | 24.14 | 14400 | 0.7438          | 0.2065 | 0.0856 |
| 0.0368        | 24.81 | 14800 | 0.7382          | 0.2066 | 0.0842 |
| 0.0273        | 25.48 | 15200 | 0.7486          | 0.2020 | 0.0841 |
| 0.0286        | 26.15 | 15600 | 0.7566          | 0.2029 | 0.0838 |
| 0.0268        | 26.82 | 16000 | 0.7680          | 0.2015 | 0.0828 |
| 0.0248        | 27.49 | 16400 | 0.7499          | 0.1994 | 0.0813 |
| 0.0253        | 28.16 | 16800 | 0.7511          | 0.1998 | 0.0820 |
| 0.0228        | 28.83 | 17200 | 0.7686          | 0.1985 | 0.0820 |
| 0.0212        | 29.51 | 17600 | 0.7779          | 0.1975 | 0.0813 |


### Framework versions

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