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---
language: ga
datasets:
- common_voice
- google/fleurs
- living_audio_irish
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- ga-IE
- speech
- Irish
model-index:
- name: Wav2vec 2.0 300m XLS-R
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 10.0
type: common_voice
args: ga-IE
metrics:
- name: Test WER (Without LM)
type: wer
value: 19.98
- name: Test WER (With LM)
type: wer
value: 13.87
- name: Common Voice Irish Invalidated 281 utterances (Without LM)
type: wer
value: 39.19
- name: Common Voice Irish Invalidated 281 utterances (With LM)
type: wer
value: 30.85
---
# wav2vec2-Irish-common-voice-Fleurs-living-audio-300m
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/FLEURS - GA-IE, Common Voice Irish (Validated - (minus) Test) and Living audio Irish Speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3361
- Wer: 0.1963
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 18.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.56 | 200 | 2.8832 | 1.0 |
| No log | 1.11 | 400 | 1.1705 | 0.7788 |
| 3.3987 | 1.67 | 600 | 0.7739 | 0.5895 |
| 3.3987 | 2.23 | 800 | 0.6045 | 0.4902 |
| 0.8313 | 2.78 | 1000 | 0.5235 | 0.4394 |
| 0.8313 | 3.34 | 1200 | 0.4824 | 0.4002 |
| 0.8313 | 3.9 | 1400 | 0.4378 | 0.3754 |
| 0.5342 | 4.46 | 1600 | 0.4433 | 0.3634 |
| 0.5342 | 5.01 | 1800 | 0.4103 | 0.3485 |
| 0.3792 | 5.57 | 2000 | 0.3816 | 0.3310 |
| 0.3792 | 6.13 | 2200 | 0.3953 | 0.3225 |
| 0.3792 | 6.68 | 2400 | 0.3995 | 0.3132 |
| 0.2924 | 7.24 | 2600 | 0.3907 | 0.2930 |
| 0.2924 | 7.8 | 2800 | 0.3517 | 0.2740 |
| 0.2217 | 8.36 | 3000 | 0.3361 | 0.2591 |
| 0.2217 | 8.91 | 3200 | 0.3340 | 0.2451 |
| 0.2217 | 9.47 | 3400 | 0.3126 | 0.2448 |
| 0.1714 | 10.03 | 3600 | 0.3441 | 0.2556 |
| 0.1714 | 10.58 | 3800 | 0.3404 | 0.2521 |
| 0.1395 | 11.14 | 4000 | 0.3728 | 0.2518 |
| 0.1395 | 11.7 | 4200 | 0.3829 | 0.2396 |
| 0.1395 | 12.26 | 4400 | 0.3466 | 0.2361 |
| 0.1069 | 12.81 | 4600 | 0.3188 | 0.2241 |
| 0.1069 | 13.37 | 4800 | 0.3396 | 0.2197 |
| 0.0845 | 13.93 | 5000 | 0.3365 | 0.2206 |
| 0.0845 | 14.48 | 5200 | 0.3459 | 0.2209 |
| 0.0845 | 15.04 | 5400 | 0.3429 | 0.2194 |
| 0.0675 | 15.6 | 5600 | 0.3434 | 0.2182 |
| 0.0675 | 16.16 | 5800 | 0.3434 | 0.2083 |
| 0.0561 | 16.71 | 6000 | 0.3375 | 0.2036 |
| 0.0561 | 17.27 | 6200 | 0.3446 | 0.1987 |
| 0.0561 | 17.83 | 6400 | 0.3362 | 0.1978 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
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