metadata

language:
  - lv
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - lv
  - robust-speech-event
  - model_for_talk
datasets:
  - common_voice
model-index:
  - name: XLS-R-300M - Latvian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: lv
        metrics:
          - name: Test WER
            type: wer
            value: 16.977
          - name: Test CER
            type: cer
            value: 4.23
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: lv
        metrics:
          - name: Test WER
            type: wer
            value: 45.247
          - name: Test CER
            type: cer
            value: 16.924

wav2vec2-large-xls-r-300m-latvian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - LV dataset. It achieves the following results on the evaluation set:

Loss: 0.1892
Wer: 0.1698

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: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2000
num_epochs: 100.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.4235	12.82	2000	0.4475	0.4551
0.9383	25.64	4000	0.2235	0.2328
0.8359	38.46	6000	0.2004	0.2098
0.7633	51.28	8000	0.1960	0.1882
0.7001	64.1	10000	0.1902	0.1809
0.652	76.92	12000	0.1979	0.1775
0.6025	89.74	14000	0.1866	0.1696

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.17.1.dev0
Tokenizers 0.11.0