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

# sh_sr_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: 2.0234
- Wer: 0.4765
- Cer: 0.8746

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.9917        | 20.0  | 100  | 1.3445          | 0.5882 | 0.8796 |
| 0.5644        | 40.0  | 200  | 1.3491          | 0.4941 | 0.875  |
| 0.3946        | 60.0  | 300  | 1.7289          | 0.5412 | 0.8762 |
| 0.2667        | 80.0  | 400  | 1.8795          | 0.5235 | 0.8762 |
| 0.2559        | 100.0 | 500  | 2.0205          | 0.5235 | 0.8772 |
| 0.2148        | 120.0 | 600  | 1.8615          | 0.4941 | 0.875  |
| 0.1694        | 140.0 | 700  | 1.9697          | 0.4765 | 0.8746 |
| 0.1793        | 160.0 | 800  | 1.9240          | 0.4706 | 0.8732 |
| 0.1598        | 180.0 | 900  | 2.0063          | 0.4765 | 0.8742 |
| 0.1569        | 200.0 | 1000 | 2.0234          | 0.4765 | 0.8746 |


### Framework versions

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