---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
- ASR
- Hassaniya
- Mauritanian Arabic
- Arabic Dialects
metrics:
- wer
- cer

pipeline_tag: automatic-speech-recognition

model-index:
- name: whisper-samll-hassaniya
  results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Hassaniya Audio Dataset
        type: private
      metrics:
        - name: Word Error Rate
          value: 52.3635
          type: wer
        - name: Character Error Rate
          value: 19.0151
          type: cer
---
<!-- 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. -->

# whisper-small-hassaniya

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Wer: 52.3635
- Cer: 19.0151


## Model description

This model utilizes state-of-the-art techniques in AI and NLP to provide efficient and accurate automatic speech recognition for the Hassanya dialect. This initiative addresses both a technological need and a cultural imperative to preserve a linguistically unique form of Arabic.

## Intended Uses & Limitations

This model is intended for use in professional transcription services and linguistic research. It can facilitate the creation of accurate textual representations of Hassanya speech, contributing to digital heritage preservation and linguistic studies. Users should note that performance may vary based on the audio quality and the speaker's accent.

## Training and Evaluation Data

The model was trained on a curated dataset of Hassanya audio recordings collected through AudioScribe, an application dedicated to high-quality data collection. The dataset is divided into three subsets with the following total audio lengths:

- **Training set**: 5 hours 30 minutes
- **Testing set**: 4 minutes
- **Evaluation set**: 18 minutes

This diverse dataset includes various speech samples from native speakers across different age groups and genders to ensure robust model performance.

## Model Performance

The model has been evaluated at several stages to assess its accuracy and efficiency both before and after training. Below are the key performance metrics:

- **Pre-training Evaluation on Eval Set**
  - WER: 108.7612
  - CER: 63.9705

- **Post-training Evaluation on Eval Set**
  - WER: 52.3634
  - CER: 19.0151

- **Post-training Evaluation on Test Set**
  - WER: 53.4791
  - CER: 19.6108

These results show a significant improvement in all metrics over the course of training, especially in terms of the Word Error Rate and Character Error Rate, demonstrating the model's growing accuracy and efficiency in recognizing Hassanya speech.

## Training Procedure

### Resource Capacity During Training

The training session was conducted on Google Colab Pro, which provided the following resource capacities:

- **System RAM**: The environment was equipped with 51 GB of system memory, offering ample capacity for handling data processing and model operations during training.
- **GPU RAM**: The GPU provided had a capacity of 15 GB of memory, which is well-suited for training large models or handling substantial batch sizes, especially when using advanced techniques like mixed-precision training.
- **Disk Space**: A total of 201.2 GB of disk space was available, ensuring sufficient storage for datasets, model checkpoints, and logs throughout the training process.

### Training Duration
The training of the model was completed in  160.32 minutes (approximately  2 hours 40 minutes).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training Results

| Step  | Epoch   | Training Loss | Validation Loss | Wer     | Cer     |
|:-----:|:-------:|:-------------:|:---------------:|:-------:|:-------:|
| 100   | 2.2472  | 0.9126        | 0.8441          | 65.8109 | 25.6766 |
| 200   | 4.4944  | 0.202         | 0.9230          | 57.5795 | 21.3726 |
| 300   | 6.7416  | 0.0854        | 1.0011          | 58.8020 | 21.1788 |
| 400   | 8.9888  | 0.0497        | 1.0513          | 57.2535 | 20.7988 |
| 500   | 11.2360 | 0.0358        | 1.0700          | 57.9055 | 21.7216 |
| 600   | 13.4831 | 0.026         | 1.0964          | 56.2755 | 20.8918 |
| 700   | 15.7303 | 0.0199        | 1.1063          | 55.9495 | 20.2482 |
| 800   | 17.9775 | 0.0114        | 1.1709          | 56.0717 | 20.8530 |
| 900   | 20.2247 | 0.0084        | 1.1633          | 56.6830 | 20.4653 |
| 1000  | 22.4719 | 0.005         | 1.1659          | 54.8900 | 20.4110 |
| 1100  | 24.7191 | 0.0026        | 1.1591          | 54.6455 | 20.2714 |
| 1200  | 26.9663 | 0.001         | 1.1771          | 54.3602 | 19.6278 |
| 1300  | 29.2135 | 0.0005        | 1.1900          | 53.9527 | 19.4959 |
| 1400  | 31.4607 | 0.0004        | 1.1971          | 53.6675 | 19.3641 |
| 1500  | 33.7079 | 0.0003        | 1.2049          | 52.5672 | 19.0694 |
| 1600  | 35.9551 | 0.0003        | 1.2069          | 52.6895 | 19.1082 |
| 1700  | 38.2022 | 0.0002        | 1.2107          | 52.6487 | 19.0151 |
| 1800  | 40.4494 | 0.0002        | 1.2125          | 52.4450 | 19.0151 |
| 1900  | 42.6966 | 0.0002        | 1.2145          | 52.4450 | 19.0539 |
| 2000  | 44.9438 | 0.0002        | 1.2149          | 52.3635 | 19.0151 |

### Framework versions

- Transformers 4.44.1
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1