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---
language:
- hy
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hy
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-1b-hy-cv
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice hy-AM
args: hy-AM
metrics:
- type: wer
value: 10.811865729898516
name: WER LM
- type: cer
value: 2.2205361659079412
name: CER LM
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hy
metrics:
- name: Test WER
type: wer
value: 18.219363037089988
- name: Test CER
type: cer
value: 7.075988867335752
---
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_4/ - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1693
- Wer: 0.2373
- Cer: 0.0429
## 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: 16
- eval_batch_size: 64
- seed: 842
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.255 | 7.24 | 500 | 0.2978 | 0.4294 | 0.0758 |
| 1.0058 | 14.49 | 1000 | 0.1883 | 0.2838 | 0.0483 |
| 0.9371 | 21.73 | 1500 | 0.1813 | 0.2627 | 0.0457 |
| 0.8999 | 28.98 | 2000 | 0.1693 | 0.2373 | 0.0429 |
| 0.8814 | 36.23 | 2500 | 0.1760 | 0.2420 | 0.0435 |
| 0.8364 | 43.47 | 3000 | 0.1765 | 0.2416 | 0.0419 |
| 0.8019 | 50.72 | 3500 | 0.1758 | 0.2311 | 0.0398 |
| 0.7665 | 57.96 | 4000 | 0.1745 | 0.2240 | 0.0399 |
| 0.7376 | 65.22 | 4500 | 0.1717 | 0.2190 | 0.0385 |
| 0.716 | 72.46 | 5000 | 0.1700 | 0.2147 | 0.0382 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
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