|
--- |
|
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 |
|
|
