wav2vec2-bloom-speech-quc

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 - QUC (K’iche’) dataset. It achieves the following results on the evaluation set:

• Loss: 0.7370
• Wer: 0.3508
• Cer: 0.1010

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

Framework versions

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