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---
language:
- km
license: apache-2.0
tags:
- automatic-speech-recognition
- openslr
- robust-speech-event
- km
- generated_from_trainer
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: 44.90
- name: Test CER
type: cer
value: 12.20
- 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: 44.90
- name: Test CER
type: cer
value: 12.20
---
#
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
# 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
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