|
--- |
|
language: |
|
- km |
|
license: apache-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- openslr |
|
- robust-speech-event |
|
- km |
|
- generated_from_trainer |
|
- hf-asr-leaderboard |
|
model-index: |
|
- name: xls-r-300m-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: 25.7 |
|
- name: Test CER |
|
type: cer |
|
value: 7.03 |
|
- 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: 25.7 |
|
- name: Test CER |
|
type: cer |
|
value: 7.03 |
|
--- |
|
|
|
# |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the openslr dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3281 |
|
- Wer: 0.3462 |
|
|
|
# Evaluation results on OpenSLR "test" (self-split 10%) (Running ./eval.py): |
|
- WER: 0.3216977389924633 |
|
- CER: 0.08653361193169537 |
|
|
|
# Evaluation results with language model on OpenSLR "test" (self-split 10%) (Running ./eval.py): |
|
- WER: 0.257040856802856 |
|
- CER: 0.07025001801282513 |
|
|
|
## 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: 5e-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: 1000 |
|
- num_epochs: 100 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 5.0795 | 5.47 | 400 | 4.4121 | 1.0 | |
|
| 3.5658 | 10.95 | 800 | 3.5203 | 1.0 | |
|
| 3.3689 | 16.43 | 1200 | 2.8984 | 0.9996 | |
|
| 2.01 | 21.91 | 1600 | 1.0041 | 0.7288 | |
|
| 1.6783 | 27.39 | 2000 | 0.6941 | 0.5989 | |
|
| 1.527 | 32.87 | 2400 | 0.5599 | 0.5282 | |
|
| 1.4278 | 38.35 | 2800 | 0.4827 | 0.4806 | |
|
| 1.3458 | 43.83 | 3200 | 0.4429 | 0.4532 | |
|
| 1.2893 | 49.31 | 3600 | 0.4156 | 0.4330 | |
|
| 1.2441 | 54.79 | 4000 | 0.4020 | 0.4040 | |
|
| 1.188 | 60.27 | 4400 | 0.3777 | 0.3866 | |
|
| 1.1628 | 65.75 | 4800 | 0.3607 | 0.3858 | |
|
| 1.1324 | 71.23 | 5200 | 0.3534 | 0.3604 | |
|
| 1.0969 | 76.71 | 5600 | 0.3428 | 0.3624 | |
|
| 1.0897 | 82.19 | 6000 | 0.3387 | 0.3567 | |
|
| 1.0625 | 87.66 | 6400 | 0.3339 | 0.3499 | |
|
| 1.0601 | 93.15 | 6800 | 0.3288 | 0.3446 | |
|
| 1.0474 | 98.62 | 7200 | 0.3281 | 0.3462 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.17.0.dev0 |
|
- Pytorch 1.10.2+cu102 |
|
- Datasets 1.18.2.dev0 |
|
- Tokenizers 0.11.0 |
|
|
