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wav2vec2-xlsr-lithuanian

Automatic Speech Recognition
Transformers
PyTorch
Lithuanian
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Community
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metadata
language:
  - lt
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - fi
  - robust-speech-event
  - model_for_talk
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: sammy786/wav2vec2-xlsr-lithuanian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: fi
        metrics:
          - name: Test WER
            type: wer
            value: 39.1
          - name: Test CER
            type: cer
            value: 11.38
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: lt
        metrics:
          - name: Test WER
            type: wer
            value: 39.1
          - name: Test CER
            type: cer
            value: 11.38

sammy786/wav2vec2-xlsr-lithuanian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - lt dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):

  • Loss: 13.1811
  • Wer: 24.2570

Model description

"facebook/wav2vec2-xls-r-1b" was finetuned.

Intended uses & limitations

More information needed

Training and evaluation data

Training data - Common voice Finnish train.tsv, dev.tsv and other.tsv

Training procedure

For creating the train dataset, all possible datasets were appended and 90-10 split was used.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.000045637994662983496
  • train_batch_size: 8
  • eval_batch_size: 16
  • seed: 13
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 40
  • mixed_precision_training: Native AMP

Training results

Step Training Loss Validation Loss Wer
200 5.718700 2.897032 1.000000
400 1.340000 0.309548 0.507284
600 0.799100 0.220205 0.402098
800 0.494400 0.185093 0.352855
1000 0.370800 0.165869 0.334207
1200 0.312500 0.159801 0.324009
1400 0.276100 0.148066 0.321678
1600 0.250100 0.153748 0.311626
1800 0.226400 0.147437 0.302885
2000 0.206900 0.141176 0.296037
2200 0.189900 0.142161 0.288170
2400 0.192100 0.138029 0.286568
2600 0.175600 0.139496 0.283654
2800 0.156900 0.138609 0.283217
3000 0.149400 0.140468 0.281906
3200 0.144600 0.132472 0.278263
3400 0.144100 0.141028 0.277535
3600 0.133000 0.134287 0.275495
3800 0.126600 0.149136 0.277681
4000 0.123500 0.132180 0.266463
4200 0.113000 0.137942 0.268211
4400 0.111700 0.140038 0.272873
4600 0.108600 0.136756 0.264132
4800 0.103600 0.137541 0.263403
5000 0.098000 0.140435 0.264860
5200 0.095800 0.136950 0.262383
5400 0.094000 0.128214 0.263986
5600 0.085300 0.125024 0.259761
5800 0.078900 0.128575 0.260198
6000 0.083300 0.135496 0.258887
6200 0.078800 0.131706 0.259178
6400 0.073800 0.128451 0.255390
6600 0.072600 0.131245 0.252768
6800 0.073300 0.131525 0.249417
7000 0.069000 0.128627 0.255536
7200 0.064400 0.127767 0.250583
7400 0.065400 0.129557 0.247815
7600 0.061200 0.129734 0.250146
7800 0.059100 0.135124 0.249709
8000 0.057000 0.132850 0.249126
8200 0.056100 0.128827 0.248252
8400 0.056400 0.130229 0.246795
8600 0.052800 0.128939 0.245775
8800 0.051100 0.131892 0.248543
9000 0.052900 0.132062 0.244464
9200 0.048200 0.130988 0.244172
9400 0.047700 0.131811 0.242570
9600 0.050000 0.133832 0.245484
9800 0.047500 0.134340 0.243881
10000 0.048400 0.133388 0.243590
10200 0.047800 0.132729 0.244464
10400 0.049000 0.131695 0.245047
10600 0.044400 0.132154 0.245484
10800 0.050100 0.131575 0.245192
11000 0.047700 0.131211 0.245192
11200 0.046000 0.131293 0.245047

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.0+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.10.3

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with split test
python eval.py --model_id sammy786/wav2vec2-xlsr-lithuanian --dataset mozilla-foundation/common_voice_8_0 --config lt --split test