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---
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
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