--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-1b tags: - generated_from_trainer datasets: - common_voice_14_0 metrics: - wer model-index: - name: XLS-R-SWAHILI-ASR-CV-14-1B results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_14_0 type: common_voice_14_0 config: sw split: test args: sw metrics: - name: Wer type: wer value: 0.2794303764906829 --- <!-- 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. --> # XLS-R-SWAHILI-ASR-CV-14-1B This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_14_0 dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.2794 - Cer: 0.0903 ## 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: 8 - 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 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:------:|:---------------:|:------:| | 1.9691 | 0.33 | 400 | 0.2374 | inf | 0.6776 | | 0.5464 | 0.66 | 800 | 0.1758 | inf | 0.5598 | | 0.4909 | 1.0 | 1200 | 0.1680 | inf | 0.5243 | | 0.4263 | 1.33 | 1600 | 0.1502 | inf | 0.4706 | | 0.4047 | 1.66 | 2000 | 0.1580 | inf | 0.4858 | | 0.4054 | 1.99 | 2400 | 0.1426 | inf | 0.4348 | | 0.3542 | 2.32 | 2800 | 0.1340 | inf | 0.4185 | | 0.3525 | 2.66 | 3200 | 0.1400 | inf | 0.4311 | | 0.3359 | 2.99 | 3600 | 0.1308 | inf | 0.4012 | | 0.3006 | 3.32 | 4000 | 0.1278 | inf | 0.3939 | | 0.326 | 1.83 | 4400 | inf | 0.4232 | 0.1362 | | 0.326 | 1.99 | 4800 | inf | 0.4136 | 0.1350 | | 0.3034 | 2.16 | 5200 | inf | 0.4282 | 0.1419 | | 0.2925 | 2.32 | 5600 | inf | 0.3901 | 0.1282 | | 0.2822 | 2.49 | 6000 | inf | 0.3876 | 0.1270 | | 0.2659 | 2.66 | 6400 | inf | 0.3586 | 0.1159 | | 0.2582 | 2.82 | 6800 | inf | 0.3536 | 0.1168 | | 0.2414 | 2.99 | 7200 | inf | 0.3327 | 0.1069 | | 0.208 | 3.15 | 7600 | inf | 0.3249 | 0.1053 | | 0.1934 | 3.32 | 8000 | inf | 0.3120 | 0.1015 | | 0.1881 | 3.49 | 8400 | inf | 0.3058 | 0.0993 | | 0.1774 | 3.65 | 8800 | inf | 0.2962 | 0.0959 | | 0.1736 | 3.82 | 9200 | inf | 0.2902 | 0.0935 | | 0.1679 | 3.98 | 9600 | inf | 0.2843 | 0.0917 | | 0.1436 | 4.15 | 10000 | inf | 0.2794 | 0.0903 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1 - Datasets 2.17.0 - Tokenizers 0.15.2