---
library_name: transformers
base_model: microsoft/wavlm-base
tags:
- automatic-speech-recognition
- Sunbird/salt
- generated_from_trainer
metrics:
- wer
model-index:
- name: wavlm-salt-eng
  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. -->

# wavlm-salt-eng

This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the SUNBIRD/SALT - MULTISPEAKER-ENG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2244
- Wer: 0.2118

## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500.0
- training_steps: 30000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 0.3803        | 3.3228  | 1000  | 0.3107          | 0.3624 |
| 0.1663        | 6.6456  | 2000  | 0.2740          | 0.3097 |
| 0.1206        | 9.9684  | 3000  | 0.2486          | 0.2903 |
| 0.0938        | 13.2895 | 4000  | 0.2588          | 0.2828 |
| 0.0816        | 16.6123 | 5000  | 0.2769          | 0.2785 |
| 0.0689        | 19.9351 | 6000  | 0.2457          | 0.2882 |
| 0.0642        | 23.2562 | 7000  | 0.2639          | 0.2914 |
| 0.0566        | 26.5790 | 8000  | 0.2954          | 0.2828 |
| 0.049         | 29.9018 | 9000  | 0.3172          | 0.2763 |
| 0.0454        | 33.2230 | 10000 | 0.3186          | 0.2828 |
| 0.0395        | 36.5458 | 11000 | 0.2782          | 0.2817 |
| 0.0389        | 39.8686 | 12000 | 0.2857          | 0.2828 |
| 0.0321        | 43.1897 | 13000 | 0.2692          | 0.2527 |
| 0.0282        | 46.5125 | 14000 | 0.2570          | 0.2559 |
| 0.0269        | 49.8353 | 15000 | 0.2446          | 0.2624 |
| 0.0233        | 53.1564 | 16000 | 0.2383          | 0.2473 |
| 0.0224        | 56.4792 | 17000 | 0.2805          | 0.2473 |
| 0.0198        | 59.8020 | 18000 | 0.2555          | 0.2516 |
| 0.0159        | 63.1231 | 19000 | 0.2097          | 0.2409 |
| 0.015         | 66.4459 | 20000 | 0.2367          | 0.2505 |
| 0.015         | 69.7687 | 21000 | 0.2486          | 0.2527 |
| 0.0122        | 73.0899 | 22000 | 0.2475          | 0.2527 |
| 0.0104        | 76.4126 | 23000 | 0.2377          | 0.2344 |
| 0.008         | 79.7354 | 24000 | 0.2363          | 0.2441 |
| 0.0081        | 83.0566 | 25000 | 0.2347          | 0.2333 |
| 0.0072        | 86.3794 | 26000 | 0.2232          | 0.2290 |
| 0.0064        | 89.7022 | 27000 | 0.2212          | 0.2280 |
| 0.0044        | 93.0233 | 28000 | 0.2287          | 0.2258 |
| 0.004         | 96.3461 | 29000 | 0.2295          | 0.2344 |
| 0.0037        | 99.6689 | 30000 | 0.2243          | 0.2204 |


### Framework versions

- Transformers 5.0.0.dev0
- Pytorch 2.9.1+cu128
- Datasets 3.6.0
- Tokenizers 0.22.1