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# wavlm-libri-clean-100h-large

This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the AHAZEEMI/LIBRISPEECH10H - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0893
- Wer: 0.0655

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0144        | 0.42  | 300  | 0.0947          | 0.0749 |
| 0.1408        | 0.84  | 600  | 0.1347          | 0.1363 |
| 0.0396        | 1.26  | 900  | 0.1090          | 0.0935 |
| 0.0353        | 1.68  | 1200 | 0.1032          | 0.0832 |
| 0.051         | 2.1   | 1500 | 0.0969          | 0.0774 |
| 0.0254        | 2.52  | 1800 | 0.0930          | 0.0715 |
| 0.0579        | 2.94  | 2100 | 0.0894          | 0.0660 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.0+cpu
- Datasets 2.9.0
- Tokenizers 0.13.2
{"language": ["en"], "tags": ["automatic-speech-recognition", "ahazeemi/librispeech10h", "generated_from_trainer"], "datasets": ["ahazeemi/librispeech10h"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "wavlm-libri-clean-100h-large", "results": []}]}
transformers pytorch wavlm automatic-speech-recognition ahazeemi/librispeech10h generated_from_trainer en dataset:ahazeemi/librispeech10h endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #wavlm #automatic-speech-recognition #ahazeemi/librispeech10h #generated_from_trainer #en #dataset-ahazeemi/librispeech10h #endpoints_compatible #has_space #region-us 

wavlm-libri-clean-100h-large
============================


This model is a fine-tuned version of microsoft/wavlm-large on the AHAZEEMI/LIBRISPEECH10H - CLEAN dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0893
* Wer: 0.0655


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: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* 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


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cpu
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* 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 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cpu
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wavlm #automatic-speech-recognition #ahazeemi/librispeech10h #generated_from_trainer #en #dataset-ahazeemi/librispeech10h #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* 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 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cpu
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wavlm #automatic-speech-recognition #ahazeemi/librispeech10h #generated_from_trainer #en #dataset-ahazeemi/librispeech10h #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* 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### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cpu
* Datasets 2.9.0
* Tokenizers 0.13.2