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# Dansk-wav2vec2-stt

This model is a fine-tuned version of [Siyam/Dansk-wav2vec21](https://huggingface.co/Siyam/Dansk-wav2vec21) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7500
- Wer: 0.3929

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0298        | 4.26  | 400  | 0.8420          | 0.4579 |
| 0.0479        | 8.51  | 800  | 0.8713          | 0.4461 |
| 0.0387        | 12.77 | 1200 | 0.8307          | 0.4404 |
| 0.0336        | 17.02 | 1600 | 0.8322          | 0.4144 |
| 0.0322        | 21.28 | 2000 | 0.7493          | 0.4081 |
| 0.0288        | 25.53 | 2400 | 0.7361          | 0.3951 |
| 0.0264        | 29.79 | 2800 | 0.7500          | 0.3929 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "Dansk-wav2vec2-stt", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 

Dansk-wav2vec2-stt
==================


This model is a fine-tuned version of Siyam/Dansk-wav2vec21 on the common\_voice dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7500
* Wer: 0.3929


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: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_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: 30


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 2.1.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_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: 30 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 2.1.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_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: 30 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 2.1.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_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: 30### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 2.1.0
* Tokenizers 0.10.3