wav2vec2-conformer-rel-pos-jv-openslr

This model is a fine-tuned version of facebook/wav2vec2-conformer-rel-pos-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2470
• Wer: 0.1227

Model description

The model is a fine-tuned version of wav2vec2-conformer-rel-pos-large, specifically adapted using the OpenSLR 41 dataset, which is focused on the Javanese language domain. This adaptation enables the model to effectively recognize and process spoken Javanese, leveraging the robust capabilities of the wav2vec2-conformer-rel-pos-large architecture combined with domain-specific training data.

Intended uses & limitations

This model is intended for transcribing spoken Javanese language from audio recordings. It achieves a Word Error Rate (WER) of 12%, indicating that while the model performs reasonably well, it still produces significant transcription errors. Users should be aware that the accuracy may vary, particularly in cases with challenging audio conditions or less common dialects. Additionally, this model requires input audio at a sample rate of 16kHz, which may limit its applicability for recordings at different sample rates or lower quality audio files.

Training and evaluation data

The model use OpenSLR41 datasets, and split into 2 section (training and testing), then the model is trained using 1xA100 GPU with a training duration of NaN hours.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 75
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.5826	2.8329	2000	0.4733	0.4445
0.3478	5.6657	4000	0.3538	0.3191
0.2532	8.4986	6000	0.3085	0.2646
0.2028	11.3314	8000	0.2799	0.2467
0.1628	14.1643	10000	0.2623	0.2095
0.1407	16.9972	12000	0.2510	0.2068
0.1154	19.8300	14000	0.2922	0.1937
0.1044	22.6629	16000	0.2660	0.1730
0.0929	25.4958	18000	0.2818	0.1868
0.0798	28.3286	20000	0.2573	0.1633
0.074	31.1615	22000	0.2398	0.1647
0.0678	33.9943	24000	0.2601	0.1606
0.0628	36.8272	26000	0.2627	0.1613
0.057	39.6601	28000	0.2393	0.1468
0.0547	42.4929	30000	0.2662	0.1585
0.0512	45.3258	32000	0.2544	0.1502
0.0446	48.1586	34000	0.2542	0.1502
0.045	50.9915	36000	0.2624	0.1516
0.0403	53.8244	38000	0.2487	0.1420
0.0378	56.6572	40000	0.2498	0.1330
0.0353	59.4901	42000	0.2495	0.1309
0.0337	62.3229	44000	0.2505	0.1316
0.029	65.1558	46000	0.2373	0.1247
0.0277	67.9887	48000	0.2543	0.1282
0.0283	70.8215	50000	0.2547	0.1234
0.0275	73.6544	52000	0.2470	0.1227

Framework versions

• Transformers 4.44.0
• Pytorch 2.2.1+cu118
• Datasets 2.20.0
• Tokenizers 0.19.1