YassineBenlaria's picture
using niger-mali feature extractor
db14d37
---
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-arabic
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: tamasheq-99-2.feature_ext
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tamasheq-99-2.feature_ext
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-arabic](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-arabic) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9535
- Wer: 0.9815
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.2797 | 15.79 | 300 | 2.8964 | 1.0 |
| 2.9763 | 31.58 | 600 | 2.7486 | 1.0 |
| 2.011 | 47.37 | 900 | 1.5549 | 0.9778 |
| 0.8448 | 63.16 | 1200 | 1.6495 | 0.9852 |
| 0.6122 | 78.95 | 1500 | 1.7794 | 0.9852 |
| 0.5039 | 94.74 | 1800 | 1.9535 | 0.9815 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3