---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: intit_model
  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. -->

# intit_model

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co./facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2486
- Wer: 0.4348
- Cer: 0.9047

## 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: 1e-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: 100
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.9753        | 20.0  | 100  | 1.3804          | 0.5072 | 0.9054 |
| 0.5395        | 40.0  | 200  | 1.5495          | 0.4444 | 0.9062 |
| 0.3735        | 60.0  | 300  | 1.7729          | 0.4396 | 0.9056 |
| 0.2427        | 80.0  | 400  | 1.9016          | 0.4348 | 0.9063 |
| 0.2389        | 100.0 | 500  | 2.0569          | 0.4348 | 0.9061 |
| 0.1822        | 120.0 | 600  | 2.0684          | 0.4300 | 0.9050 |
| 0.1578        | 140.0 | 700  | 2.1332          | 0.4396 | 0.9049 |
| 0.1547        | 160.0 | 800  | 2.2138          | 0.4444 | 0.9047 |
| 0.1807        | 180.0 | 900  | 2.2467          | 0.4348 | 0.9047 |
| 0.1427        | 200.0 | 1000 | 2.2486          | 0.4348 | 0.9047 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0