---
base_model: facebook/wav2vec2-xls-r-300m
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: bambara-5-hours-bambara-asr-hf
  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. -->

# bambara-5-hours-bambara-asr-hf

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8042
- Wer: 0.5257
- Cer: 0.2399

## 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: 16
- eval_batch_size: 16
- 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 5.6981        | 1.13  | 200  | 2.9919          | 1.0    | 1.0    |
| 2.9496        | 2.27  | 400  | 2.9333          | 1.0    | 1.0    |
| 2.4081        | 3.4   | 600  | 1.4375          | 0.8869 | 0.4174 |
| 1.5268        | 4.53  | 800  | 1.1942          | 0.7734 | 0.3602 |
| 1.3291        | 5.67  | 1000 | 1.1261          | 0.6712 | 0.2994 |
| 1.2301        | 6.8   | 1200 | 1.0859          | 0.6470 | 0.2892 |
| 1.1379        | 7.93  | 1400 | 1.0552          | 0.6314 | 0.2813 |
| 1.0664        | 9.07  | 1600 | 1.0684          | 0.6079 | 0.2731 |
| 0.9866        | 10.2  | 1800 | 1.0584          | 0.6110 | 0.2756 |
| 0.9625        | 11.33 | 2000 | 1.0776          | 0.5757 | 0.2590 |
| 0.8828        | 12.46 | 2200 | 1.1372          | 0.5865 | 0.2596 |
| 0.8451        | 13.6  | 2400 | 1.0821          | 0.5645 | 0.2574 |
| 0.8016        | 14.73 | 2600 | 1.1293          | 0.5754 | 0.2608 |
| 0.7615        | 15.86 | 2800 | 1.1312          | 0.5586 | 0.2519 |
| 0.715         | 17.0  | 3000 | 1.1657          | 0.5635 | 0.2558 |
| 0.6792        | 18.13 | 3200 | 1.2197          | 0.5521 | 0.2517 |
| 0.6498        | 19.26 | 3400 | 1.1157          | 0.5606 | 0.2533 |
| 0.6221        | 20.4  | 3600 | 1.2632          | 0.5501 | 0.2507 |
| 0.5912        | 21.53 | 3800 | 1.1686          | 0.5520 | 0.2505 |
| 0.5614        | 22.66 | 4000 | 1.3080          | 0.5547 | 0.2542 |
| 0.5485        | 23.8  | 4200 | 1.2349          | 0.5601 | 0.2583 |
| 0.5235        | 24.93 | 4400 | 1.2541          | 0.5458 | 0.2489 |
| 0.5084        | 26.06 | 4600 | 1.2414          | 0.5500 | 0.2514 |
| 0.4741        | 27.2  | 4800 | 1.5119          | 0.5444 | 0.2470 |
| 0.4699        | 28.33 | 5000 | 1.2778          | 0.5525 | 0.2524 |
| 0.4511        | 29.46 | 5200 | 1.5488          | 0.5502 | 0.2494 |
| 0.4181        | 30.59 | 5400 | 1.3489          | 0.5522 | 0.2536 |
| 0.4149        | 31.73 | 5600 | 1.5685          | 0.5460 | 0.2454 |
| 0.3998        | 32.86 | 5800 | 1.4369          | 0.5434 | 0.2494 |
| 0.401         | 33.99 | 6000 | 1.5961          | 0.5376 | 0.2433 |
| 0.3744        | 35.13 | 6200 | 1.5695          | 0.5361 | 0.2452 |
| 0.36          | 36.26 | 6400 | 1.5968          | 0.5400 | 0.2445 |
| 0.3435        | 37.39 | 6600 | 1.6238          | 0.5334 | 0.2424 |
| 0.3337        | 38.53 | 6800 | 1.6619          | 0.5340 | 0.2440 |
| 0.3232        | 39.66 | 7000 | 1.6444          | 0.5345 | 0.2446 |
| 0.3224        | 40.79 | 7200 | 1.7175          | 0.5417 | 0.2451 |
| 0.303         | 41.93 | 7400 | 1.6966          | 0.5273 | 0.2417 |
| 0.3028        | 43.06 | 7600 | 1.7403          | 0.5325 | 0.2431 |
| 0.2899        | 44.19 | 7800 | 1.7688          | 0.5255 | 0.2398 |
| 0.2861        | 45.33 | 8000 | 1.7705          | 0.5269 | 0.2405 |
| 0.2744        | 46.46 | 8200 | 1.7792          | 0.5244 | 0.2396 |
| 0.2731        | 47.59 | 8400 | 1.7876          | 0.5286 | 0.2412 |
| 0.27          | 48.73 | 8600 | 1.8060          | 0.5264 | 0.2403 |
| 0.2643        | 49.86 | 8800 | 1.8042          | 0.5257 | 0.2399 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2