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

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: 0.0444
- Wer: 0.0421

And on the test set:

- Wer: 0.0411

## Model description

This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 2 where 
I use as training set all validated data (~ 50 hours) except the test and evaluation sets (~ 4.5 hours each). The number of training hours adds up to 41 hours of Frisian speech.

## 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 corresponds to all of the validated data except for the recordings found in the evaluation and test 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: 3e-05
- train_batch_size: 36
- 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: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 7.2522        | 0.48  | 400   | 3.1028          | 1.0    |
| 3.0052        | 0.97  | 800   | 2.9334          | 1.0    |
| 2.0865        | 1.45  | 1200  | 0.7288          | 0.6646 |
| 1.1654        | 1.93  | 1600  | 0.4298          | 0.4196 |
| 0.9665        | 2.41  | 2000  | 0.3134          | 0.3162 |
| 0.7891        | 2.9   | 2400  | 0.2378          | 0.2587 |
| 0.8366        | 3.38  | 2800  | 0.1896          | 0.2016 |
| 0.8606        | 3.86  | 3200  | 0.1647          | 0.1903 |
| 0.7536        | 4.34  | 3600  | 0.1486          | 0.1573 |
| 0.632         | 4.83  | 4000  | 0.1341          | 0.1450 |
| 0.5198        | 5.31  | 4400  | 0.1223          | 0.1415 |
| 0.4998        | 5.79  | 4800  | 0.1155          | 0.1388 |
| 0.4273        | 6.27  | 5200  | 0.1132          | 0.1302 |
| 0.3982        | 6.76  | 5600  | 0.1036          | 0.1102 |
| 0.3964        | 7.24  | 6000  | 0.0988          | 0.1209 |
| 0.3848        | 7.72  | 6400  | 0.0995          | 0.0985 |
| 0.3702        | 8.2   | 6800  | 0.0969          | 0.0945 |
| 0.3612        | 8.69  | 7200  | 0.0899          | 0.0967 |
| 0.3518        | 9.17  | 7600  | 0.0856          | 0.1061 |
| 0.3371        | 9.65  | 8000  | 0.0902          | 0.0875 |
| 0.3295        | 10.13 | 8400  | 0.0819          | 0.0914 |
| 0.3157        | 10.62 | 8800  | 0.0785          | 0.0937 |
| 0.3025        | 11.1  | 9200  | 0.0782          | 0.0804 |
| 0.3092        | 11.58 | 9600  | 0.0758          | 0.0845 |
| 0.301         | 12.06 | 10000 | 0.0775          | 0.0847 |
| 0.3016        | 12.55 | 10400 | 0.0730          | 0.0776 |
| 0.2892        | 13.03 | 10800 | 0.0719          | 0.0735 |
| 0.283         | 13.51 | 11200 | 0.0728          | 0.0727 |
| 0.2806        | 13.99 | 11600 | 0.0694          | 0.0710 |
| 0.2639        | 14.48 | 12000 | 0.0705          | 0.0703 |
| 0.2606        | 14.96 | 12400 | 0.0652          | 0.0668 |
| 0.2595        | 15.44 | 12800 | 0.0638          | 0.0691 |
| 0.2611        | 15.92 | 13200 | 0.0636          | 0.0713 |
| 0.246         | 16.41 | 13600 | 0.0632          | 0.0653 |
| 0.2544        | 16.89 | 14000 | 0.0605          | 0.0638 |
| 0.2509        | 17.37 | 14400 | 0.0640          | 0.0646 |
| 0.2381        | 17.85 | 14800 | 0.0604          | 0.0663 |
| 0.2336        | 18.34 | 15200 | 0.0590          | 0.0628 |
| 0.2285        | 18.82 | 15600 | 0.0580          | 0.0612 |
| 0.2362        | 19.3  | 16000 | 0.0655          | 0.0638 |
| 0.2279        | 19.78 | 16400 | 0.0611          | 0.0669 |
| 0.2228        | 20.27 | 16800 | 0.0606          | 0.0621 |
| 0.2242        | 20.75 | 17200 | 0.0560          | 0.0575 |
| 0.2053        | 21.23 | 17600 | 0.0571          | 0.0572 |
| 0.2097        | 21.71 | 18000 | 0.0557          | 0.0555 |
| 0.2072        | 22.2  | 18400 | 0.0563          | 0.0576 |
| 0.2076        | 22.68 | 18800 | 0.0532          | 0.0562 |
| 0.2026        | 23.16 | 19200 | 0.0531          | 0.0540 |
| 0.1941        | 23.64 | 19600 | 0.0535          | 0.0534 |
| 0.1983        | 24.13 | 20000 | 0.0528          | 0.0541 |
| 0.2075        | 24.61 | 20400 | 0.0536          | 0.0538 |
| 0.1937        | 25.09 | 20800 | 0.0532          | 0.0569 |
| 0.1943        | 25.57 | 21200 | 0.0511          | 0.0507 |
| 0.1844        | 26.06 | 21600 | 0.0521          | 0.0521 |
| 0.181         | 26.54 | 22000 | 0.0506          | 0.0507 |
| 0.1877        | 27.02 | 22400 | 0.0529          | 0.0510 |
| 0.1825        | 27.5  | 22800 | 0.0527          | 0.0498 |
| 0.1872        | 27.99 | 23200 | 0.0506          | 0.0485 |
| 0.1857        | 28.47 | 23600 | 0.0497          | 0.0492 |
| 0.1766        | 28.95 | 24000 | 0.0504          | 0.0488 |
| 0.1756        | 29.43 | 24400 | 0.0496          | 0.0482 |
| 0.1701        | 29.92 | 24800 | 0.0479          | 0.0479 |
| 0.1717        | 30.4  | 25200 | 0.0499          | 0.0468 |
| 0.1624        | 30.88 | 25600 | 0.0492          | 0.0466 |
| 0.1671        | 31.36 | 26000 | 0.0490          | 0.0461 |
| 0.1704        | 31.85 | 26400 | 0.0482          | 0.0452 |
| 0.1653        | 32.33 | 26800 | 0.0467          | 0.0446 |
| 0.158         | 32.81 | 27200 | 0.0465          | 0.0449 |
| 0.1599        | 33.29 | 27600 | 0.0473          | 0.0445 |
| 0.1558        | 33.78 | 28000 | 0.0475          | 0.0453 |
| 0.1556        | 34.26 | 28400 | 0.0462          | 0.0445 |
| 0.1591        | 34.74 | 28800 | 0.0464          | 0.0431 |
| 0.1544        | 35.22 | 29200 | 0.0476          | 0.0433 |
| 0.1576        | 35.71 | 29600 | 0.0466          | 0.0434 |
| 0.1507        | 36.19 | 30000 | 0.0451          | 0.0435 |
| 0.1501        | 36.67 | 30400 | 0.0453          | 0.0429 |
| 0.1482        | 37.15 | 30800 | 0.0439          | 0.0432 |
| 0.1518        | 37.64 | 31200 | 0.0446          | 0.0424 |
| 0.1454        | 38.12 | 31600 | 0.0449          | 0.0417 |
| 0.145         | 38.6  | 32000 | 0.0440          | 0.0421 |
| 0.147         | 39.08 | 32400 | 0.0441          | 0.0424 |
| 0.141         | 39.57 | 32800 | 0.0444          | 0.0421 |


### 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-large-train", "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": 0.04206541922582488, "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": 0.04108252637664402, "name": "Wer"}]}]}]}
transformers pytorch safetensors 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 #safetensors #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-large-train
================================================


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: 0.0444
* Wer: 0.0421


And on the test set:


* Wer: 0.0411


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


This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 2 where
I use as training set all validated data (~ 50 hours) except the test and evaluation sets (~ 4.5 hours each). The number of training hours adds up to 41 hours of Frisian speech.


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 corresponds to all of the validated data except for the recordings found in the evaluation and test 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: 3e-05
* train\_batch\_size: 36
* 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: 40
* 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: 3e-05
* train\_batch\_size: 36
* 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: 40
* 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 #safetensors #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: 3e-05
* train\_batch\_size: 36
* 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: 40
* 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