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# wav2vec2-large-xls-r-1b-frisian-cv-8-10m

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9269
- Wer: 0.5262

And on the test set:
- Wer: 0.6225

## Model description

This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 3 where 
I use as training set 10 minutes of Frisian speech randomly selected from all validated data except the test and evaluation sets.

## Intended uses & limitations

The intended use is for recognizing Frisian speech.

Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0.

## Training and evaluation data

The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split is 10 minutes of Frisian randomly selected from validated data except for the recordings from test and evaluation splits.

## Training procedure

The script used for training this model can be found in this GitHub repository: [link](https://github.com/greenw0lf/MSc-VT-Thesis/).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.2929        | 6.25  | 50   | 3.0514          | 1.0    |
| 3.315         | 12.5  | 100  | 3.2255          | 1.0    |
| 3.1506        | 18.75 | 150  | 2.9924          | 1.0    |
| 2.9773        | 25.0  | 200  | 2.2199          | 1.0    |
| 2.1616        | 31.25 | 250  | 1.1423          | 0.8603 |
| 1.6887        | 37.5  | 300  | 0.9730          | 0.7020 |
| 1.1178        | 43.75 | 350  | 0.8971          | 0.6323 |
| 0.9512        | 50.0  | 400  | 0.9040          | 0.5960 |
| 0.7696        | 56.25 | 450  | 0.9232          | 0.5713 |
| 0.7348        | 62.5  | 500  | 0.9203          | 0.5412 |
| 0.9312        | 68.75 | 550  | 0.9673          | 0.5376 |
| 0.6519        | 75.0  | 600  | 0.9269          | 0.5262 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-1b-frisian-cv-8-10m", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "common_voice_8_0", "type": "common_voice_8_0", "config": "fy-NL", "split": "validation", "args": "fy-NL"}, "metrics": [{"type": "wer", "value": 0.5262462505356378, "name": "Wer"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "common_voice_8_0", "type": "common_voice_8_0", "config": "fy-NL", "split": "test", "args": "fy-NL"}, "metrics": [{"type": "wer", "value": 0.6225249313484608, "name": "Wer"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice_8_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

wav2vec2-large-xls-r-1b-frisian-cv-8-10m
========================================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice\_8\_0 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9269
* Wer: 0.5262


And on the test set:


* Wer: 0.6225


Model description
-----------------


This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 3 where
I use as training set 10 minutes of Frisian speech randomly selected from all validated data except the test and evaluation sets.


Intended uses & limitations
---------------------------


The intended use is for recognizing Frisian speech.


Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0.


Training and evaluation data
----------------------------


The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split is 10 minutes of Frisian randomly selected from validated data except for the recordings from test and evaluation splits.


Training procedure
------------------


The script used for training this model can be found in this GitHub repository: link.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 80
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 80
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 80
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3