metadata

language:
  - cak
license: other
tags:
  - automatic-speech-recognition
  - sil-ai/bloom-speech
  - generated_from_trainer
datasets:
  - bloom_speech
model-index:
  - name: wav2vec2-bloom-speech-cak
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Bloom Speech cak
          type: sil-ai/bloom-speech
          args: cak
        metrics:
          - name: Test WER
            type: wer
            value: 20.33
          - name: Test CER
            type: cer
            value: 3.62
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wav2vec2-bloom-speech-cak

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 - CAK (Kaqchikel) dataset. It achieves the following results on the evaluation set:

Loss: 0.2270
Wer: 0.2033
Cer: 0.0362

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	8.06	250	3.1322	1.0	1.0
4.2211	16.13	500	0.2734	0.3651	0.0628
4.2211	24.19	750	0.2225	0.2925	0.0524
0.2288	32.26	1000	0.2049	0.2593	0.0439
0.2288	40.32	1250	0.2162	0.2552	0.0463
0.1191	48.39	1500	0.2462	0.2344	0.0389
0.1191	56.45	1750	0.2959	0.2303	0.0436
0.0894	64.52	2000	0.2641	0.2510	0.0474
0.0894	72.58	2250	0.3067	0.2510	0.0478
0.0669	80.65	2500	0.2459	0.2054	0.0328
0.0669	88.71	2750	0.2425	0.2012	0.0351
0.0616	96.77	3000	0.2270	0.2033	0.0362
0.0616	104.84	3250	0.2975	0.2116	0.0382
0.0493	112.9	3500	0.2397	0.2054	0.0347
0.0493	120.97	3750	0.2718	0.2199	0.0447

Framework versions

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