metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-1b-frisian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_13_0
          type: common_voice_13_0
          config: fy-NL
          split: validation
          args: fy-NL
        metrics:
          - name: Wer
            type: wer
            value: 0.1492598825428444
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_8_0
          type: common_voice_8_0
          config: fy-NL
          split: test
          args: fy-NL
        metrics:
          - name: Wer
            type: wer
            value: 0.15356265356265356
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_13_0
          type: common_voice_13_0
          config: fy-NL
          split: test
          args: fy-NL
        metrics:
          - name: Wer
            type: wer
            value: 0.14712316399874995

wav2vec2-large-xls-r-1b-frisian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.2204
Wer: 0.1493

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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.9606	2.45	300	2.6184	1.0
1.4992	4.9	600	0.4233	0.4143
0.9757	7.35	900	0.2765	0.3021
0.8773	9.8	1200	0.2529	0.2528
0.7448	12.24	1500	0.2363	0.2258
0.7039	14.69	1800	0.2258	0.2103
0.6811	17.14	2100	0.2217	0.2074
0.6279	19.59	2400	0.2050	0.1915
0.5938	22.04	2700	0.2229	0.1922
0.6227	24.49	3000	0.2088	0.2019
0.5682	26.94	3300	0.2127	0.1874
0.5939	29.39	3600	0.2044	0.1789
0.5427	31.84	3900	0.2185	0.1791
0.5551	34.41	4200	0.2097	0.1644
0.5021	36.86	4500	0.2180	0.1678
0.4589	39.31	4800	0.2076	0.1581
0.5204	41.76	5100	0.2181	0.1587
0.512	44.21	5400	0.2263	0.1607
0.465	46.66	5700	0.2204	0.1493

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3