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wav2vec2-bloom-speech-jra

Model description

Homepage: SIL AI
Point of Contact: SIL AI email
Source Data: Bloom Library

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the SIL-AI/bloom-speech - JRA (Jarai) dataset. It achieves the following results on the evaluation set:

Loss: 0.5600
Wer: 0.1212
Cer: 0.0473

Users should refer to the original model for tutorials on using a trained model for inference.

Intended uses & limitations

Users of this model must abide by the SIL RAIL-M License.

This model is created as a proof of concept and no guarantees are made regarding the performance of the model is specific situations.

Training and evaluation data

Training, Validation, and Test datasets were generated from the same corpus, ensuring that no duplicate files were used.

Training procedure

Standard finetuning of XLS-R was used based on the examples in the Hugging Face Transformers Github

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: 250
num_epochs: 1000.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
No log	41.62	250	0.5465	0.3473	0.1059
2.1292	83.31	500	0.5108	0.2214	0.0731
2.1292	124.92	750	0.5218	0.1841	0.0629
0.0524	166.62	1000	0.4192	0.1550	0.0519
0.0524	208.31	1250	0.4669	0.1655	0.0529
0.0242	249.92	1500	0.5480	0.1667	0.0552
0.0242	291.62	1750	0.4986	0.1352	0.0496
0.0164	333.31	2000	0.5084	0.1364	0.0499
0.0164	374.92	2250	0.5017	0.1422	0.0527
0.0104	416.62	2500	0.4788	0.1282	0.0465
0.0104	458.31	2750	0.5268	0.1457	0.0519
0.0066	499.92	3000	0.5600	0.1212	0.0473
0.0066	541.62	3250	0.6352	0.1305	0.0476
0.0054	583.31	3500	0.6117	0.1224	0.0463
0.0054	624.92	3750	0.5915	0.1340	0.0476

Framework versions

Transformers 4.21.0.dev0
Pytorch 1.9.0+cu111
Datasets 2.2.2
Tokenizers 0.12.1

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Inference Examples

Automatic Speech Recognition

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Evaluation results

Test WER on Bloom Speech jra
self-reported

12.120
Test CER on Bloom Speech jra
self-reported

4.730

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