|
--- |
|
language: |
|
- km |
|
license: apache-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- openslr |
|
- robust-speech-event |
|
- km |
|
- generated_from_trainer |
|
datasets: |
|
- openslr |
|
model-index: |
|
- name: wav2vec2-xls-r-1b-km |
|
results: |
|
- task: |
|
name: Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: OpenSLR km |
|
type: openslr |
|
args: km |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 32.13 |
|
- name: Test CER |
|
type: cer |
|
value: 9.35 |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Robust Speech Event - Dev Data |
|
type: speech-recognition-community-v2/dev_data |
|
args: km |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: 32.13 |
|
- name: Test CER |
|
type: cer |
|
value: 9.35 |
|
--- |
|
# |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the openslr dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4239 |
|
- Wer: 0.4221 |
|
|
|
# Evaluation results on OpenSLR "test" (self-split 10%) (Running ./eval.py): |
|
- WER: 0.4490281634272114 |
|
- CER: 0.12198285179047481 |
|
|
|
# Evaluation results on OpenSLR "test" with LM ngram (self-split 10%) (Running ./eval.py): |
|
- WER: 0.32130107100357 |
|
- CER: 0.09345053678218891 |
|
|
|
# Note |
|
- Since this dataset is small (4 hours of voice recording), we decided not to train that for too long to avoid overfitting and under-generalization. |
|
- This model performs worse than its 300M-variant. Probably, we don't explore the hyper-parameter enough? |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 1e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 2000 |
|
- num_epochs: 75 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 3.5671 | 5.47 | 400 | 12.0218 | 1.0 | |
|
| 3.5159 | 10.95 | 800 | 10.6337 | 1.0 | |
|
| 2.4543 | 16.43 | 1200 | 1.8256 | 0.9839 | |
|
| 1.9437 | 21.91 | 1600 | 1.1237 | 0.9173 | |
|
| 1.696 | 27.39 | 2000 | 0.8246 | 0.7700 | |
|
| 1.5342 | 32.87 | 2400 | 0.6433 | 0.6594 | |
|
| 1.4509 | 38.35 | 2800 | 0.5500 | 0.5787 | |
|
| 1.3478 | 43.83 | 3200 | 0.5070 | 0.4907 | |
|
| 1.3096 | 49.31 | 3600 | 0.4692 | 0.4726 | |
|
| 1.2532 | 54.79 | 4000 | 0.4448 | 0.4479 | |
|
| 1.2291 | 60.27 | 4400 | 0.4374 | 0.4366 | |
|
| 1.196 | 65.75 | 4800 | 0.4314 | 0.4310 | |
|
| 1.1862 | 71.23 | 5200 | 0.4239 | 0.4221 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.17.0.dev0 |
|
- Pytorch 1.10.2+cu102 |
|
- Datasets 1.18.2.dev0 |
|
- Tokenizers 0.11.0 |
|
|
