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---
language:
- kab
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- sw
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
base_model: facebook/wav2vec2-xls-r-300m
model-index:
- name: Akashpb13/Kabyle_xlsr
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: kab
metrics:
- type: wer
value: 0.3188425282720088
name: Test WER
- type: cer
value: 0.09443079928558358
name: Test CER
---
# Akashpb13/Kabyle_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 the evaluation set (which is 10 percent of train data set merged with dev datasets):
- Loss: 0.159032
- Wer: 0.187934
## Model description
"facebook/wav2vec2-xls-r-300m" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training data -
Common voice Kabyle train.tsv. Only 50,000 records were sampled randomly and trained due to huge size of dataset.
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 training dataset, all possible datasets were appended and 90-10 split was used.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000096
- train_batch_size: 8
- seed: 13
- gradient_accumulation_steps: 4
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss | Wer |
|-------|---------------|-----------------|----------|
| 500 | 7.199800 | 3.130564 | 1.000000 |
| 1000 | 1.570200 | 0.718097 | 0.734682 |
| 1500 | 0.850800 | 0.524227 | 0.640532 |
| 2000 | 0.712200 | 0.468694 | 0.603454 |
| 2500 | 0.651200 | 0.413833 | 0.573025 |
| 3000 | 0.603100 | 0.403680 | 0.552847 |
| 3500 | 0.553300 | 0.372638 | 0.541719 |
| 4000 | 0.537200 | 0.353759 | 0.531191 |
| 4500 | 0.506300 | 0.359109 | 0.519601 |
| 5000 | 0.479600 | 0.343937 | 0.511336 |
| 5500 | 0.479800 | 0.338214 | 0.503948 |
| 6000 | 0.449500 | 0.332600 | 0.495221 |
| 6500 | 0.439200 | 0.323905 | 0.492635 |
| 7000 | 0.434900 | 0.310417 | 0.484555 |
| 7500 | 0.403200 | 0.311247 | 0.483262 |
| 8000 | 0.401500 | 0.295637 | 0.476566 |
| 8500 | 0.397000 | 0.301321 | 0.471672 |
| 9000 | 0.371600 | 0.295639 | 0.468440 |
| 9500 | 0.370700 | 0.294039 | 0.468902 |
| 10000 | 0.364900 | 0.291195 | 0.468440 |
| 10500 | 0.348300 | 0.284898 | 0.461098 |
| 11000 | 0.350100 | 0.281764 | 0.459805 |
| 11500 | 0.336900 | 0.291022 | 0.461606 |
| 12000 | 0.330700 | 0.280467 | 0.455234 |
| 12500 | 0.322500 | 0.271714 | 0.452694 |
| 13000 | 0.307400 | 0.289519 | 0.455465 |
| 13500 | 0.309300 | 0.281922 | 0.451217 |
| 14000 | 0.304800 | 0.271514 | 0.452186 |
| 14500 | 0.288100 | 0.286801 | 0.446830 |
| 15000 | 0.293200 | 0.276309 | 0.445399 |
| 15500 | 0.289800 | 0.287188 | 0.446230 |
| 16000 | 0.274800 | 0.286406 | 0.441243 |
| 16500 | 0.271700 | 0.284754 | 0.441520 |
| 17000 | 0.262500 | 0.275431 | 0.442167 |
| 17500 | 0.255500 | 0.276575 | 0.439858 |
| 18000 | 0.260200 | 0.269911 | 0.435425 |
| 18500 | 0.250600 | 0.270519 | 0.434686 |
| 19000 | 0.243300 | 0.267655 | 0.437826 |
| 19500 | 0.240600 | 0.277109 | 0.431731 |
| 20000 | 0.237200 | 0.266622 | 0.433994 |
| 20500 | 0.231300 | 0.273015 | 0.428868 |
| 21000 | 0.227200 | 0.263024 | 0.430161 |
| 21500 | 0.220400 | 0.272880 | 0.429607 |
| 22000 | 0.218600 | 0.272340 | 0.426883 |
| 22500 | 0.213100 | 0.277066 | 0.428407 |
| 23000 | 0.205000 | 0.278404 | 0.424020 |
| 23500 | 0.200900 | 0.270877 | 0.418987 |
| 24000 | 0.199000 | 0.289120 | 0.425821 |
| 24500 | 0.196100 | 0.275831 | 0.424066 |
| 25000 | 0.191100 | 0.282822 | 0.421850 |
| 25500 | 0.190100 | 0.275820 | 0.418248 |
| 26000 | 0.178800 | 0.279208 | 0.419125 |
| 26500 | 0.183100 | 0.271464 | 0.419218 |
| 27000 | 0.177400 | 0.280869 | 0.419680 |
| 27500 | 0.171800 | 0.279593 | 0.414924 |
| 28000 | 0.172900 | 0.276949 | 0.417648 |
| 28500 | 0.164900 | 0.283491 | 0.417786 |
| 29000 | 0.164800 | 0.283122 | 0.416078 |
| 29500 | 0.165500 | 0.281969 | 0.415801 |
| 30000 | 0.163800 | 0.283319 | 0.412753 |
| 30500 | 0.153500 | 0.285702 | 0.414046 |
| 31000 | 0.156500 | 0.285041 | 0.412615 |
| 31500 | 0.150900 | 0.284336 | 0.413723 |
| 32000 | 0.151800 | 0.285922 | 0.412292 |
| 32500 | 0.149200 | 0.289461 | 0.412153 |
| 33000 | 0.145400 | 0.291322 | 0.409567 |
| 33500 | 0.145600 | 0.294361 | 0.409614 |
| 34000 | 0.144200 | 0.290686 | 0.409059 |
| 34500 | 0.143400 | 0.289474 | 0.409844 |
| 35000 | 0.143500 | 0.290340 | 0.408367 |
| 35500 | 0.143200 | 0.289581 | 0.407351 |
| 36000 | 0.138400 | 0.292782 | 0.408736 |
| 36500 | 0.137900 | 0.289108 | 0.408044 |
| 37000 | 0.138200 | 0.292127 | 0.407166 |
| 37500 | 0.134600 | 0.291797 | 0.408413 |
| 38000 | 0.139800 | 0.290056 | 0.408090 |
| 38500 | 0.136500 | 0.291198 | 0.408090 |
| 39000 | 0.137700 | 0.289696 | 0.408044 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- 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/Kabyle_xlsr --dataset mozilla-foundation/common_voice_8_0 --config kab --split test
```
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