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wav2vec2-bloom-speech-bam

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 - BAM (Bambara) dataset. It achieves the following results on the evaluation set:

Loss: 1.0592
Wer: 0.3590
Cer: 0.1093

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	41.67	250	0.8321	0.5770	0.1769
2.495	83.33	500	0.7474	0.4692	0.1418
2.495	125.0	750	0.8151	0.4159	0.1241
0.07	166.67	1000	0.8168	0.4645	0.1439
0.07	208.33	1250	0.8188	0.3981	0.1186
0.0361	250.0	1500	0.8344	0.4028	0.1202
0.0361	291.67	1750	0.9109	0.4301	0.1306
0.023	333.33	2000	0.9366	0.4088	0.1210
0.023	375.0	2250	0.9443	0.4088	0.1158
0.0187	416.67	2500	1.0506	0.4289	0.1316
0.0187	458.33	2750	1.0120	0.3780	0.1137
0.0159	500.0	3000	1.0376	0.3768	0.1139
0.0159	541.67	3250	1.0098	0.4088	0.1202
0.0145	583.33	3500	1.0302	0.3993	0.1132
0.0145	625.0	3750	1.0513	0.3910	0.1134
0.0116	666.67	4000	1.0325	0.3934	0.1150
0.0116	708.33	4250	1.0285	0.3898	0.1124
0.0109	750.0	4500	0.9712	0.3626	0.1134
0.0109	791.67	4750	1.0263	0.3614	0.1113
0.0089	833.33	5000	1.0592	0.3590	0.1093
0.0089	875.0	5250	1.0888	0.3614	0.1093
0.0082	916.67	5500	1.0896	0.3661	0.1108
0.0082	958.33	5750	1.0890	0.3626	0.1108

Framework versions

Transformers 4.20.0.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 bam
self-reported

35.900
Test CER on Bloom Speech bam
self-reported

10.930

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