metadata

language:
  - stk
license: other
tags:
  - automatic-speech-recognition
  - sil-ai/bloom-speech
  - generated_from_trainer
datasets:
  - bloom_speech
model-index:
  - name: wav2vec2-bloom-speech-stk
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Bloom Speech stk
          type: sil-ai/bloom-speech
          args: stk
        metrics:
          - name: Test WER
            type: wer
            value: 53.12
          - name: Test CER
            type: cer
            value: 16.4
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wav2vec2-bloom-speech-stk

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 - STK (Aramba) dataset. It achieves the following results on the evaluation set:

Loss: 1.8616
Wer: 0.5312
Cer: 0.1640

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	2.9562	1.1641	1.0317
4.9676	83.33	500	1.3412	0.6641	0.1928
4.9676	125.0	750	1.5549	0.625	0.1856
0.1326	166.67	1000	1.4986	0.6094	0.1856
0.1326	208.33	1250	1.5501	0.5938	0.1554
0.0597	250.0	1500	1.8616	0.5312	0.1640
0.0597	291.67	1750	1.7009	0.5312	0.1496
0.0352	333.33	2000	1.9785	0.5781	0.1770
0.0352	375.0	2250	1.7492	0.625	0.1871

Framework versions

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