pytorch tensorboard wavlm automatic-speech-recognition transformers librispeech_asr generated_from_trainer wavlm_libri_finetune model-index
---
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
- wavlm_libri_finetune
model-index:
- name: wavlm-libri-clean-100h-base-plus
  results: []
---

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# wavlm-libri-clean-100h-base-plus

This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0819
- Wer: 0.0683

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_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: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8877        | 0.34  | 300  | 2.8649          | 1.0    |
| 0.2852        | 0.67  | 600  | 0.2196          | 0.1830 |
| 0.1198        | 1.01  | 900  | 0.1438          | 0.1273 |
| 0.0906        | 1.35  | 1200 | 0.1145          | 0.1035 |
| 0.0729        | 1.68  | 1500 | 0.1055          | 0.0955 |
| 0.0605        | 2.02  | 1800 | 0.0936          | 0.0859 |
| 0.0402        | 2.35  | 2100 | 0.0885          | 0.0746 |
| 0.0421        | 2.69  | 2400 | 0.0848          | 0.0700 |


### Framework versions

- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
