metadata

language: fi
datasets:
  - common_voice
metrics:
  - wer
  - cer
tags:
  - generated_from_trainer
  - audio
  - automatic-speech-recognition
  - speech
  - robust-speech-event
model-index:
  - name: XLS-R 1B Wav2Vec2 Finnish by Rasmus Toivanen
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice fi_7_0
          type: common_voice
          args: fi
        metrics:
          - name: Test WER
            type: wer
            value: 10.96
          - name: Test CER
            type: cer
            value: 2.81

wav2vec2-xlsr-fi-train-aug-lm-1B

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1499
Wer: 0.1955

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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.6473	0.29	400	0.2857	0.3825
0.6039	0.58	800	0.2459	0.3476
0.4757	0.87	1200	0.2338	0.3274
0.4473	1.15	1600	0.2246	0.3128
0.4322	1.44	2000	0.1962	0.2805
0.3961	1.73	2400	0.2070	0.2797
0.3642	2.02	2800	0.1790	0.2473
0.3561	2.31	3200	0.1769	0.2375
0.282	2.6	3600	0.1672	0.2263
0.2978	2.89	4000	0.1636	0.2192
0.2722	3.17	4400	0.1637	0.2102
0.2924	3.46	4800	0.1506	0.2021
0.2631	3.75	5200	0.1499	0.1955

Framework versions

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