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---
language:
- ga
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
base_model: facebook/wav2vec2-xls-r-1b
model-index:
- name: wav2vec2-large-xls-r-1b-Irish-Abid
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: Common Voice ga-IE
type: mozilla-foundation/common_voice_8_0
args: ga-IE
metrics:
- type: wer
value: 38.45
name: Test WER With LM
- type: cer
value: 16.52
name: Test CER With LM
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-1b-Irish
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 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3599
- Wer: 0.4236
- Cer: 0.1768
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Irish --dataset mozilla-foundation/common_voice_8_0 --config ga-IE --split test
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "kingabzpro/wav2vec2-large-xls-r-1b-Irish"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ga-IE", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 6.3955 | 12.48 | 100 | 2.9897 | 1.0 | 1.0 |
| 2.3811 | 24.97 | 200 | 1.2304 | 0.7140 | 0.3106 |
| 1.0476 | 37.48 | 300 | 1.0661 | 0.5597 | 0.2407 |
| 0.7014 | 49.97 | 400 | 1.1788 | 0.4799 | 0.1947 |
| 0.4409 | 62.48 | 500 | 1.2649 | 0.4658 | 0.1997 |
| 0.4839 | 74.97 | 600 | 1.3259 | 0.4450 | 0.1868 |
| 0.3643 | 87.48 | 700 | 1.3506 | 0.4312 | 0.1760 |
| 0.3468 | 99.97 | 800 | 1.3599 | 0.4236 | 0.1768 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
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