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# wav2vec2-large-voxrex-npsc-nynorsk

This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the NBAILAB/NPSC - 16K_MP3_NYNORSK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4142
- Wer: 0.1576

## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 2000
- num_epochs: 40.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.086         | 2.17  | 500  | 3.0773          | 1.0    |
| 2.8532        | 4.35  | 1000 | 2.8393          | 1.0    |
| 0.9738        | 6.52  | 1500 | 0.7283          | 0.4890 |
| 0.6763        | 8.7   | 2000 | 0.5340          | 0.3662 |
| 0.5303        | 10.87 | 2500 | 0.4521          | 0.3140 |
| 0.4765        | 13.04 | 3000 | 0.4181          | 0.2853 |
| 0.4219        | 15.22 | 3500 | 0.4156          | 0.2934 |
| 0.3564        | 17.39 | 4000 | 0.3925          | 0.2509 |
| 0.3282        | 19.57 | 4500 | 0.3824          | 0.2420 |
| 0.3118        | 21.74 | 5000 | 0.3636          | 0.2354 |
| 0.2919        | 23.91 | 5500 | 0.3615          | 0.2281 |
| 0.2961        | 26.09 | 6000 | 0.3548          | 0.2255 |
| 0.284         | 28.26 | 6500 | 0.3526          | 0.2209 |
| 0.2566        | 30.43 | 7000 | 0.3526          | 0.2205 |
| 0.2422        | 32.61 | 7500 | 0.3569          | 0.2173 |
| 0.2472        | 34.78 | 8000 | 0.3592          | 0.2166 |
| 0.2337        | 36.96 | 8500 | 0.3625          | 0.2172 |
| 0.2315        | 39.13 | 9000 | 0.3580          | 0.2155 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3

{"language": ["nn-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", "no", "nn-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-large-voxrex-npsc-nynorsk", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_nynorsk"}, "metrics": [{"type": "wer", "value": 0.12220762155059132, "name": "Test (Nynorsk) WER"}, {"type": "cer", "value": 0.04195612578778549, "name": "Test (Nynorsk) CER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer NbAiLab/NPSC robust-speech-event no nn-NO hf-asr-leaderboard dataset:NbAiLab/NPSC license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #NbAiLab/NPSC #robust-speech-event #no #nn-NO #hf-asr-leaderboard #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us 

wav2vec2-large-voxrex-npsc-nynorsk
==================================


This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K\_MP3\_NYNORSK dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4142
* Wer: 0.1576


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: 7.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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: 2000
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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: 2000
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #NbAiLab/NPSC #robust-speech-event #no #nn-NO #hf-asr-leaderboard #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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: 2000
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #NbAiLab/NPSC #robust-speech-event #no #nn-NO #hf-asr-leaderboard #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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: 2000
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3