Hausa Asr

🚀 Cdial/Hausa_xlsr

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m. It is designed for automatic speech recognition tasks, achieving high performance on Hausa language datasets.

🚀 Quick Start

Evaluation

1. To evaluate on mozilla-foundation/common_voice_8_0 with split test

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, fine - tuned for better performance on Hausa language tasks.
• High Performance: Achieves good results on multiple evaluation metrics such as WER and CER on relevant datasets.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Model Information

Evaluation Results

The model achieves the following results on different evaluation sets:

• Common Voice 8 (mozilla - foundation/common_voice_8_0, ha):
  • Test WER: 0.20614541257934219
  • Test CER: 0.04358048053214061
• Robust Speech Event - Dev Data (speech - recognition - community - v2/dev_data, ha):
  • Test WER: 0.20614541257934219
  • Test CER: 0.04358048053214061

On the evaluation set (which is 10 percent of train data set merged with invalidated data, reported, other, and dev datasets):

• Loss: 0.275118
• Wer: 0.329955

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

• Dataset Creation: All possible datasets were appended and a 90 - 10 split was used.
• Training Hyperparameters:
  • 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

🔧 Technical Details

The model is a fine - tuned version of facebook/wav2vec2-xls-r-300m. The fine - tuning process involves adjusting the model's parameters on Hausa language datasets to improve its performance on automatic speech recognition tasks.

📄 License

This model is released under the Apache - 2.0 license.