metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_13_0
metrics:
  - wer
model-index:
  - name: b24-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: 0.2624592454587797

b24-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: 0.3401
Wer: 0.2625

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: 0.0001
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
9.4471	0.76	100	3.3151	1.0
3.0392	1.52	200	3.0118	1.0
2.9633	2.29	300	3.0023	1.0
2.9643	3.05	400	2.9365	1.0
2.9381	3.81	500	2.9319	1.0
2.9411	4.58	600	2.9264	1.0
2.9407	5.34	700	2.9141	1.0
2.9027	6.11	800	2.8848	1.0
2.8833	6.87	900	2.8796	0.9988
2.8805	7.63	1000	2.8679	0.9956
2.7051	8.4	1100	1.8944	1.0
1.343	9.16	1200	0.7785	0.6970
0.8156	9.92	1300	0.5659	0.5824
0.591	10.68	1400	0.4982	0.5163
0.488	11.45	1500	0.4421	0.4299
0.4056	12.21	1600	0.3927	0.3959
0.3488	12.97	1700	0.4095	0.3910
0.2977	13.74	1800	0.3833	0.3687
0.273	14.5	1900	0.3690	0.3388
0.2601	15.27	2000	0.3505	0.3121
0.2258	16.03	2100	0.3577	0.3121
0.2122	16.79	2200	0.3467	0.3018
0.2095	17.56	2300	0.3361	0.2951
0.1719	18.32	2400	0.3572	0.2948
0.1722	19.08	2500	0.3380	0.2857
0.1634	19.84	2600	0.3516	0.2883
0.1592	20.61	2700	0.3374	0.2846
0.153	21.37	2800	0.3395	0.2783
0.1479	22.14	2900	0.3336	0.2729
0.1443	22.9	3000	0.3234	0.2669
0.1339	23.66	3100	0.3345	0.2664
0.1149	24.43	3200	0.3369	0.2664
0.1205	25.19	3300	0.3470	0.2660
0.1251	25.95	3400	0.3319	0.2629
0.1201	26.71	3500	0.3381	0.2667
0.1107	27.48	3600	0.3538	0.2655
0.1117	28.24	3700	0.3423	0.2625
0.1104	29.01	3800	0.3398	0.2608
0.104	29.77	3900	0.3401	0.2625

Framework versions

Transformers 4.26.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3