---
license: apache-2.0
tags:
- automatic-speech-recognition
- abdusahmbzuai/arabic_speech_massive_sm
- generated_from_trainer
model-index:
- name: aradia-ctc-distilhubert-ft
  results: []
---

<!-- 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. -->

# aradia-ctc-distilhubert-ft

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_SM - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7114
- Wer: 0.8908

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.43  | 100  | 4.4129          | 1.0    |
| No log        | 0.87  | 200  | 3.5927          | 1.0    |
| No log        | 1.3   | 300  | 3.3780          | 1.0    |
| No log        | 1.74  | 400  | 3.0830          | 1.0    |
| 5.3551        | 2.17  | 500  | 2.6278          | 0.9999 |
| 5.3551        | 2.61  | 600  | 1.8359          | 1.0000 |
| 5.3551        | 3.04  | 700  | 1.7878          | 0.9914 |
| 5.3551        | 3.48  | 800  | 1.5219          | 0.9875 |
| 5.3551        | 3.91  | 900  | 1.4348          | 0.9879 |
| 1.7199        | 4.35  | 1000 | 1.4354          | 0.9644 |
| 1.7199        | 4.78  | 1100 | 1.5210          | 0.9519 |
| 1.7199        | 5.22  | 1200 | 1.3607          | 0.9475 |
| 1.7199        | 5.65  | 1300 | 1.3839          | 0.9343 |
| 1.7199        | 6.09  | 1400 | 1.2806          | 0.8944 |
| 1.2342        | 6.52  | 1500 | 1.3036          | 0.9011 |
| 1.2342        | 6.95  | 1600 | 1.3704          | 0.9072 |
| 1.2342        | 7.39  | 1700 | 1.2981          | 0.8891 |
| 1.2342        | 7.82  | 1800 | 1.2786          | 0.8733 |
| 1.2342        | 8.26  | 1900 | 1.2897          | 0.8867 |
| 0.9831        | 8.69  | 2000 | 1.4436          | 0.8780 |
| 0.9831        | 9.13  | 2100 | 1.3680          | 0.8873 |
| 0.9831        | 9.56  | 2200 | 1.3471          | 0.8692 |
| 0.9831        | 10.0  | 2300 | 1.3725          | 0.8729 |
| 0.9831        | 10.43 | 2400 | 1.4439          | 0.8771 |
| 0.8071        | 10.87 | 2500 | 1.5114          | 0.8928 |
| 0.8071        | 11.3  | 2600 | 1.6156          | 0.8958 |
| 0.8071        | 11.74 | 2700 | 1.4381          | 0.8749 |
| 0.8071        | 12.17 | 2800 | 1.5088          | 0.8717 |
| 0.8071        | 12.61 | 2900 | 1.5486          | 0.8813 |
| 0.6321        | 13.04 | 3000 | 1.4536          | 0.8884 |
| 0.6321        | 13.48 | 3100 | 1.4679          | 0.8947 |
| 0.6321        | 13.91 | 3200 | 1.5628          | 0.9117 |
| 0.6321        | 14.35 | 3300 | 1.5831          | 0.8716 |
| 0.6321        | 14.78 | 3400 | 1.6733          | 0.8702 |
| 0.4998        | 15.22 | 3500 | 1.8225          | 0.8665 |
| 0.4998        | 15.65 | 3600 | 1.8558          | 0.8732 |
| 0.4998        | 16.09 | 3700 | 1.7513          | 0.8766 |
| 0.4998        | 16.52 | 3800 | 1.8562          | 0.8753 |
| 0.4998        | 16.95 | 3900 | 1.9018          | 0.8704 |
| 0.4421        | 17.39 | 4000 | 1.9341          | 0.8789 |
| 0.4421        | 17.82 | 4100 | 1.9582          | 0.8781 |
| 0.4421        | 18.26 | 4200 | 1.8863          | 0.8821 |
| 0.4421        | 18.69 | 4300 | 1.9366          | 0.8847 |
| 0.4421        | 19.13 | 4400 | 2.1902          | 0.8721 |
| 0.3712        | 19.56 | 4500 | 2.1641          | 0.8670 |
| 0.3712        | 20.0  | 4600 | 2.1639          | 0.8776 |
| 0.3712        | 20.43 | 4700 | 2.2695          | 0.9030 |
| 0.3712        | 20.87 | 4800 | 2.1909          | 0.8937 |
| 0.3712        | 21.3  | 4900 | 2.1606          | 0.8959 |
| 0.3067        | 21.74 | 5000 | 2.1756          | 0.8943 |
| 0.3067        | 22.17 | 5100 | 2.4092          | 0.8773 |
| 0.3067        | 22.61 | 5200 | 2.4991          | 0.8721 |
| 0.3067        | 23.04 | 5300 | 2.3340          | 0.8910 |
| 0.3067        | 23.48 | 5400 | 2.3567          | 0.8946 |
| 0.2764        | 23.91 | 5500 | 2.3215          | 0.8897 |
| 0.2764        | 24.35 | 5600 | 2.4824          | 0.9002 |
| 0.2764        | 24.78 | 5700 | 2.4585          | 0.8963 |
| 0.2764        | 25.22 | 5800 | 2.5804          | 0.8879 |
| 0.2764        | 25.65 | 5900 | 2.5814          | 0.8903 |
| 0.2593        | 26.09 | 6000 | 2.5374          | 0.8868 |
| 0.2593        | 26.52 | 6100 | 2.5346          | 0.8922 |
| 0.2593        | 26.95 | 6200 | 2.5465          | 0.8873 |
| 0.2593        | 27.39 | 6300 | 2.6002          | 0.8919 |
| 0.2593        | 27.82 | 6400 | 2.6102          | 0.8928 |
| 0.227         | 28.26 | 6500 | 2.6925          | 0.8914 |
| 0.227         | 28.69 | 6600 | 2.6981          | 0.8913 |
| 0.227         | 29.13 | 6700 | 2.6872          | 0.8891 |
| 0.227         | 29.56 | 6800 | 2.7015          | 0.8897 |
| 0.227         | 30.0  | 6900 | 2.7114          | 0.8908 |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6