đ Akashpb13/Hausa_xlsr
This model is a fine - tuned version of [facebook/wav2vec2 - xls - r - 300m](https://huggingface.co/facebook/wav2vec2 - xls - r - 300m). It is designed for automatic speech recognition in the Hausa language, achieving excellent results on relevant evaluation datasets.
đ Quick Start
Evaluation
To evaluate on mozilla - foundation/common_voice_8_0 with split test, you can use the following command:
python eval.py --model_id Akashpb13/Hausa_xlsr --dataset mozilla - foundation/common_voice_8_0 --config ha --split test
⨠Features
- Fine - tuned Model: Based on [facebook/wav2vec2 - xls - r - 300m], it has been fine - tuned for better performance in Hausa automatic speech recognition.
- Good Performance: Achieves low WER (Word Error Rate) and CER (Character Error Rate) on evaluation sets.
đĻ Installation
No installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
No code examples are provided in the original document, so this section is skipped.
đ Documentation
Model description
"facebook/wav2vec2 - xls - r - 300m" was finetuned.
Intended uses & limitations
More information needed
Training and evaluation data
- Training data: Common voice Hausa train.tsv, dev.tsv, invalidated.tsv, reported.tsv and other.tsv. 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 a 90 - 10 split was used.
Training hyperparameters
The following hyperparameters were used during training:
| Property |
Details |
| learning_rate |
0.000096 |
| train_batch_size |
16 |
| eval_batch_size |
16 |
| seed |
13 |
| gradient_accumulation_steps |
2 |
| lr_scheduler_type |
cosine_with_restarts |
| lr_scheduler_warmup_steps |
500 |
| num_epochs |
50 |
| mixed_precision_training |
Native AMP |
Training results
| Step |
Training Loss |
Validation Loss |
Wer |
| 500 |
5.175900 |
2.750914 |
1.000000 |
| 1000 |
1.028700 |
0.338649 |
0.497999 |
| 1500 |
0.332200 |
0.246896 |
0.402241 |
| 2000 |
0.227300 |
0.239640 |
0.395839 |
| 2500 |
0.175000 |
0.239577 |
0.373966 |
| 3000 |
0.140400 |
0.243272 |
0.356095 |
| 3500 |
0.119200 |
0.263761 |
0.365164 |
| 4000 |
0.099300 |
0.265954 |
0.353428 |
| 4500 |
0.084400 |
0.276367 |
0.349693 |
| 5000 |
0.073700 |
0.282631 |
0.343825 |
| 5500 |
0.068000 |
0.282344 |
0.341158 |
| 6000 |
0.064500 |
0.281591 |
0.342491 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
Model Index
| Task |
Dataset |
Metrics (WER) |
Metrics (CER) |
| Automatic Speech Recognition |
Common Voice 8 (mozilla - foundation/common_voice_8_0, args: ha) |
0.20614541257934219 |
0.04358048053214061 |
| Automatic Speech Recognition |
Robust Speech Event - Dev Data (speech - recognition - community - v2/dev_data, args: ha) |
0.20614541257934219 |
0.04358048053214061 |
đ§ Technical Details
No detailed technical implementation details are provided in the original document, so this section is skipped.
đ License
The model is released under the Apache - 2.0 license.
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