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---
language:
- ckb
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- ckb
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/Central_kurdish_xlsr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ckb
metrics:
- name: Test WER
type: wer
value: 0.36754389884276845
- name: Test CER
type: cer
value: 0.07827896768334217
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ckb
metrics:
- name: Test WER
type: wer
value: 0.36754389884276845
- name: Test CER
type: cer
value: 0.07827896768334217
---
# Akashpb13/Central_kurdish_xlsr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other and dev datasets):
- Loss: 0.348580
- Wer: 0.401147
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Central Kurdish train.tsv, dev.tsv, invalidated.tsv, reported.tsv, and other.tsv
Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0
## Training procedure
For creating the train dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000095637994662983496
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 2
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 200
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|-------|---------------|-----------------|----------|
| 500 | 5.097800 | 2.190326 | 1.001207 |
| 1000 | 0.797500 | 0.331392 | 0.576819 |
| 1500 | 0.405100 | 0.262009 | 0.549049 |
| 2000 | 0.322100 | 0.248178 | 0.479626 |
| 2500 | 0.264600 | 0.258866 | 0.488983 |
| 3000 | 0.228300 | 0.261523 | 0.469665 |
| 3500 | 0.201000 | 0.270135 | 0.451856 |
| 4000 | 0.180900 | 0.279302 | 0.448536 |
| 4500 | 0.163800 | 0.280921 | 0.459704 |
| 5000 | 0.147300 | 0.319249 | 0.471778 |
| 5500 | 0.137600 | 0.289546 | 0.449140 |
| 6000 | 0.132000 | 0.311350 | 0.458195 |
| 6500 | 0.117100 | 0.316726 | 0.432840 |
| 7000 | 0.109200 | 0.302210 | 0.439481 |
| 7500 | 0.104900 | 0.325913 | 0.439481 |
| 8000 | 0.097500 | 0.329446 | 0.431935 |
| 8500 | 0.088600 | 0.345259 | 0.425898 |
| 9000 | 0.084900 | 0.342891 | 0.428313 |
| 9500 | 0.080900 | 0.353081 | 0.424389 |
| 10000 | 0.075600 | 0.347063 | 0.424992 |
| 10500 | 0.072800 | 0.330086 | 0.424691 |
| 11000 | 0.068100 | 0.350658 | 0.421974 |
| 11500 | 0.064700 | 0.342949 | 0.413522 |
| 12000 | 0.061500 | 0.341704 | 0.415334 |
| 12500 | 0.059500 | 0.346279 | 0.411410 |
| 13000 | 0.057400 | 0.349901 | 0.407184 |
| 13500 | 0.056400 | 0.347733 | 0.402656 |
| 14000 | 0.053300 | 0.344899 | 0.405976 |
| 14500 | 0.052900 | 0.346708 | 0.402656 |
| 15000 | 0.050600 | 0.344118 | 0.400845 |
| 15500 | 0.050200 | 0.348396 | 0.402958 |
| 16000 | 0.049800 | 0.348312 | 0.401751 |
| 16500 | 0.051900 | 0.348372 | 0.401147 |
| 17000 | 0.049800 | 0.348580 | 0.401147 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.1
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Akashpb13/Central_kurdish_xlsr --dataset mozilla-foundation/common_voice_8_0 --config ckb --split test
```