metadata

language:
  - sv
license: apache-2.0
tags:
  - automatic-speech-recognition
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
  - cer
base_model: facebook/wav2vec2-xls-r-300m
model-index:
  - name: wav2vec2-xls-r-300m-swedish
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          name: Common Voice sv-SE
          type: mozilla-foundation/common_voice_8_0
          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