metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: b21-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: 0.6304145319049836

b21-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: 0.8091
Wer: 0.6304

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: 0.0004
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
6.5829	0.76	100	2.9564	1.0
2.9568	1.52	200	3.0768	1.0
2.9578	2.29	300	3.0654	1.0
2.957	3.05	400	2.9377	1.0
2.9419	3.81	500	2.9408	1.0
2.9567	4.58	600	2.9395	1.0
2.9625	5.34	700	2.9388	1.0
2.9395	6.11	800	2.9374	1.0
2.9285	6.87	900	2.9240	1.0
2.9187	7.63	1000	2.9057	1.0
2.9251	8.4	1100	2.8985	1.0
2.9033	9.16	1200	2.8942	1.0
2.8877	9.92	1300	2.8917	1.0
2.8586	10.68	1400	2.7719	1.0
2.5777	11.45	1500	2.2424	1.0
1.9243	12.21	1600	1.7068	0.9772
1.4534	12.97	1700	1.2780	0.9585
1.1793	13.74	1800	1.1482	0.9360
1.0026	14.5	1900	1.0673	0.8852
0.8879	15.27	2000	0.9651	0.8433
0.7933	16.03	2100	0.8973	0.8216
0.6895	16.79	2200	0.8396	0.8034
0.6531	17.56	2300	0.8131	0.7713
0.5753	18.32	2400	0.8388	0.7531
0.5621	19.08	2500	0.7844	0.7632
0.5076	19.84	2600	0.7629	0.7485
0.4672	20.61	2700	0.7777	0.7497
0.443	21.37	2800	0.8001	0.7292
0.4129	22.14	2900	0.7902	0.7094
0.3767	22.9	3000	0.7569	0.6784
0.357	23.66	3100	0.7726	0.6903
0.3378	24.43	3200	0.8016	0.6882
0.3199	25.19	3300	0.7854	0.6677
0.3144	25.95	3400	0.7792	0.6509
0.3025	26.71	3500	0.8157	0.6695
0.2919	27.48	3600	0.8215	0.6633
0.2762	28.24	3700	0.8167	0.6500
0.2679	29.01	3800	0.8144	0.6311
0.2671	29.77	3900	0.8091	0.6304

Framework versions

Transformers 4.26.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3