README.md

metadata

language:
  - sv-SE
license: apache-2.0
tags:
  - automatic-speech-recognition
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
  - cer
model-index:
  - name: wav2vec2-xls-r-300m-swedish
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice sv-SE
          args: sv-SE
        metrics:
          - type: wer
            value: 24.73
            name: Test WER
            args:
              - learning_rate: 0.000075
              - train_batch_size: 64
              - eval_batch_size: 8
              - seed: 42
              - gradient_accumulation_steps: 2
              - total_train_batch_size: 128
              - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
              - lr_scheduler_type: linear
              - lr_scheduler_warmup_steps: 1000
              - num_epochs: 50
              - mixed_precision_training: Native AMP
          - type: cer
            value: 7.58
            name: Test CER
            args:
              - learning_rate: 0.000075
              - train_batch_size: 64
              - eval_batch_size: 8
              - seed: 42
              - gradient_accumulation_steps: 2
              - total_train_batch_size: 128
              - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
              - lr_scheduler_type: linear
              - lr_scheduler_warmup_steps: 1000
              - num_epochs: 50
              - mixed_precision_training: Native AMP

wav2vec2-large-xls-r-300m-Swedish

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

• Loss: 0.3641
• Wer: 0.2473
• Cer: 0.0758

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 7.5e-05
• train_batch_size: 64
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
6.1097	5.49	500	3.1422	1.0	1.0
2.985	10.98	1000	1.7357	0.9876	0.4125
1.0363	16.48	1500	0.4773	0.3510	0.1047
0.6111	21.97	2000	0.3937	0.2998	0.0910
0.4942	27.47	2500	0.3779	0.2776	0.0844
0.4421	32.96	3000	0.3745	0.2630	0.0807
0.4018	38.46	3500	0.3685	0.2553	0.0781
0.3759	43.95	4000	0.3618	0.2488	0.0761
0.3646	49.45	4500	0.3641	0.2473	0.0758

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0