metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: b22-wav2vec2-large-xls-r-romansh-colab
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_13_0
          type: common_voice_13_0
          config: rm-vallader
          split: test
          args: rm-vallader
        metrics:
          - name: Wer
            type: wer
            value: 0.4990684676292501

b22-wav2vec2-large-xls-r-romansh-colab

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.7362
Wer: 0.4991

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: 0.0003
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.8045	0.76	100	2.9604	1.0
2.9578	1.52	200	3.0626	1.0
2.9565	2.29	300	3.0432	1.0
2.9533	3.05	400	2.9304	1.0
2.9263	3.81	500	2.9134	1.0
2.9174	4.58	600	2.9022	1.0
2.9282	5.34	700	2.8967	1.0
2.8973	6.11	800	2.8477	1.0
2.6047	6.87	900	2.0269	1.0
1.6468	7.63	1000	1.0780	0.9029
1.1006	8.4	1100	0.8305	0.8319
0.8708	9.16	1200	0.7704	0.8055
0.7708	9.92	1300	0.6815	0.7385
0.6608	10.68	1400	0.6738	0.7212
0.6014	11.45	1500	0.6535	0.6940
0.5419	12.21	1600	0.6608	0.6639
0.4961	12.97	1700	0.6568	0.6372
0.4462	13.74	1800	0.6557	0.6362
0.4169	14.5	1900	0.6487	0.5985
0.3951	15.27	2000	0.7126	0.6376
0.3643	16.03	2100	0.6539	0.5859
0.3243	16.79	2200	0.6803	0.5946
0.3243	17.56	2300	0.6619	0.5745
0.2869	18.32	2400	0.6826	0.5592
0.2895	19.08	2500	0.6980	0.5524
0.2612	19.84	2600	0.6599	0.5445
0.2492	20.61	2700	0.6533	0.5394
0.2485	21.37	2800	0.7103	0.5494
0.2352	22.14	2900	0.7339	0.5501
0.2136	22.9	3000	0.7154	0.5470
0.2079	23.66	3100	0.7360	0.5389
0.2011	24.43	3200	0.7481	0.5263
0.1925	25.19	3300	0.7409	0.5186
0.193	25.95	3400	0.7334	0.5091
0.1874	26.71	3500	0.7493	0.5075
0.1802	27.48	3600	0.7362	0.5102
0.1736	28.24	3700	0.7427	0.5033
0.1725	29.01	3800	0.7404	0.5033
0.1684	29.77	3900	0.7362	0.4991

Framework versions

Transformers 4.26.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3