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# wav2vec2-large-xls-r-1b-frisian-cv-8-1h

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: 0.4120
- Wer: 0.2373

And on the test set:
- Wer: 0.2540

## Model description

This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 4 where 
I use as training set 1 hour 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 1 hour 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: 6e-05
- train_batch_size: 32
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.2987        | 4.35  | 100  | 3.0210          | 1.0    |
| 3.1424        | 8.7   | 200  | 2.9611          | 1.0    |
| 2.6299        | 13.04 | 300  | 0.9929          | 0.8377 |
| 1.3134        | 17.39 | 400  | 0.5679          | 0.5264 |
| 0.9747        | 21.74 | 500  | 0.4516          | 0.3764 |
| 0.8755        | 26.09 | 600  | 0.4515          | 0.3403 |
| 0.7227        | 30.43 | 700  | 0.4169          | 0.3211 |
| 0.6634        | 34.78 | 800  | 0.4159          | 0.2962 |
| 0.5568        | 39.13 | 900  | 0.4081          | 0.2795 |
| 0.7943        | 43.48 | 1000 | 0.4090          | 0.2709 |
| 0.5537        | 47.83 | 1100 | 0.4239          | 0.2649 |
| 0.5596        | 52.17 | 1200 | 0.4029          | 0.2561 |
| 0.5523        | 56.52 | 1300 | 0.4073          | 0.2524 |
| 0.4579        | 60.87 | 1400 | 0.4098          | 0.2470 |
| 0.6477        | 65.22 | 1500 | 0.4099          | 0.2446 |
| 0.4957        | 69.57 | 1600 | 0.4167          | 0.2475 |
| 0.3246        | 73.91 | 1700 | 0.4146          | 0.2389 |
| 0.3937        | 78.26 | 1800 | 0.4120          | 0.2373 |


### 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-8-1h", "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": 0.23732323953720896, "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": 0.25404682757623936, "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-8-1h
=======================================


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: 0.4120
* Wer: 0.2373


And on the test set:


* Wer: 0.2540


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


This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 4 where
I use as training set 1 hour 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 1 hour 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: 6e-05
* train\_batch\_size: 32
* 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: 6e-05
* train\_batch\_size: 32
* 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: 6e-05
* train\_batch\_size: 32
* 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