metadata

language:
  - bas
license: apache-2.0
tags:
  - automatic-speech-recognition
  - bas
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: XLS-R-300M - Basaa
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: bas
        metrics:
          - name: Test WER
            type: wer
            value: 38.057
          - name: Test CER
            type: cer
            value: 11.233

wav2vec2-large-xls-r-300m-basaa-cv8

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BAS dataset. It achieves the following results on the evaluation set:

Loss: 0.4648
Wer: 0.5472

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 7e-05
train_batch_size: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 100.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9421	12.82	500	2.8894	1.0
1.1872	25.64	1000	0.6688	0.7460
0.8894	38.46	1500	0.4868	0.6516
0.769	51.28	2000	0.4960	0.6507
0.6936	64.1	2500	0.4781	0.5384
0.624	76.92	3000	0.4643	0.5430
0.5966	89.74	3500	0.4530	0.5591

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.18.3
Tokenizers 0.11.0