---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-mn-pretrain-42h-100-epochs
  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. -->

# wav2vec2-large-mn-pretrain-42h-100-epochs

This model is a fine-tuned version of [bayartsogt/wav2vec2-large-mn-pretrain-42h](https://huggingface.co/bayartsogt/wav2vec2-large-mn-pretrain-42h) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4172
- Wer: 1.0
- Cer: 0.9841

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:---:|:------:|
| 7.6418        | 1.59  | 400   | 6.4239          | 1.0 | 0.9841 |
| 5.5936        | 3.19  | 800   | 6.4154          | 1.0 | 0.9841 |
| 5.5208        | 4.78  | 1200  | 6.5248          | 1.0 | 0.9841 |
| 5.4869        | 6.37  | 1600  | 6.3805          | 1.0 | 0.9841 |
| 5.4757        | 7.97  | 2000  | 6.3988          | 1.0 | 0.9841 |
| 5.4624        | 9.56  | 2400  | 6.4058          | 1.0 | 0.9841 |
| 5.517         | 11.16 | 2800  | 6.3991          | 1.0 | 0.9841 |
| 5.4821        | 12.75 | 3200  | 6.4066          | 1.0 | 0.9841 |
| 5.487         | 14.34 | 3600  | 6.4281          | 1.0 | 0.9841 |
| 5.4786        | 15.93 | 4000  | 6.4174          | 1.0 | 0.9841 |
| 5.5017        | 17.53 | 4400  | 6.4338          | 1.0 | 0.9841 |
| 5.4967        | 19.12 | 4800  | 6.4653          | 1.0 | 0.9841 |
| 5.4619        | 20.72 | 5200  | 6.4499          | 1.0 | 0.9841 |
| 5.4883        | 22.31 | 5600  | 6.4345          | 1.0 | 0.9841 |
| 5.4899        | 23.9  | 6000  | 6.4224          | 1.0 | 0.9841 |
| 5.493         | 25.5  | 6400  | 6.4374          | 1.0 | 0.9841 |
| 5.4549        | 27.09 | 6800  | 6.4320          | 1.0 | 0.9841 |
| 5.4531        | 28.68 | 7200  | 6.4137          | 1.0 | 0.9841 |
| 5.4738        | 30.28 | 7600  | 6.4155          | 1.0 | 0.9841 |
| 5.4309        | 31.87 | 8000  | 6.4193          | 1.0 | 0.9841 |
| 5.4669        | 33.47 | 8400  | 6.4109          | 1.0 | 0.9841 |
| 5.47          | 35.06 | 8800  | 6.4111          | 1.0 | 0.9841 |
| 5.4623        | 36.65 | 9200  | 6.4102          | 1.0 | 0.9841 |
| 5.4583        | 38.25 | 9600  | 6.4150          | 1.0 | 0.9841 |
| 5.4551        | 39.84 | 10000 | 6.4172          | 1.0 | 0.9841 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1