metadata
language:
- hy
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- hy
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-hy
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: 13.192818110850899
name: WER LM
- type: cer
value: 2.787051087506323
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: 22.246048764990867
- name: Test CER
type: cer
value: 7.59406739840239
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_3/ - NA dataset. It achieves the following results on the evaluation set:
- Loss: 0.2293
- Wer: 0.3333
- Cer: 0.0602
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: 7e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 842
- gradient_accumulation_steps: 2
- 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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.1471 | 7.02 | 400 | 3.1599 | 1.0 | 1.0 |
| 1.8691 | 14.04 | 800 | 0.7674 | 0.7361 | 0.1686 |
| 1.3227 | 21.05 | 1200 | 0.3849 | 0.5336 | 0.1007 |
| 1.163 | 28.07 | 1600 | 0.3015 | 0.4559 | 0.0823 |
| 1.0768 | 35.09 | 2000 | 0.2721 | 0.4032 | 0.0728 |
| 1.0224 | 42.11 | 2400 | 0.2586 | 0.3825 | 0.0691 |
| 0.9817 | 49.12 | 2800 | 0.2458 | 0.3653 | 0.0653 |
| 0.941 | 56.14 | 3200 | 0.2306 | 0.3388 | 0.0605 |
| 0.9235 | 63.16 | 3600 | 0.2315 | 0.3380 | 0.0615 |
| 0.9141 | 70.18 | 4000 | 0.2293 | 0.3333 | 0.0602 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0