metadata

tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: microsoft-wavlm-fleurs-ur
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: ur_pk
          split: test
          args: ur_pk
        metrics:
          - name: Wer
            type: wer
            value: 0.4026467344688151

microsoft-wavlm-fleurs-ur

This model is a fine-tuned version of microsoft/wavlm-large on the fleurs dataset. It achieves the following results on the evaluation set:

Loss: 0.7294
Wer: 0.4026

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: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 8
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 15.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.911	0.35	100	3.7784	1.0
3.0833	0.71	200	3.0964	1.0
3.028	1.06	300	3.0377	1.0
2.5114	1.41	400	2.4941	0.9922
1.0583	1.77	500	1.0753	0.7579
0.715	2.12	600	0.8524	0.6410
0.6779	2.47	700	0.7711	0.6063
0.6123	2.83	800	0.7170	0.5706
0.8183	3.18	900	0.6897	0.5368
0.5195	3.53	1000	0.6586	0.5303
0.4774	3.89	1100	0.6306	0.5014
0.4242	4.24	1200	0.6138	0.4817
0.4549	4.59	1300	0.6027	0.4678
0.2576	4.95	1400	0.5878	0.4600
0.1578	5.3	1500	0.6144	0.4585
0.3556	5.65	1600	0.5884	0.4582
0.2427	6.01	1700	0.6071	0.4572
0.267	6.36	1800	0.6303	0.4514
0.2468	6.71	1900	0.6358	0.4495
0.159	7.07	2000	0.6242	0.4312
0.1527	7.42	2100	0.6372	0.4400
0.1401	7.77	2200	0.6252	0.4292
0.1211	8.13	2300	0.6358	0.4251
0.1022	8.48	2400	0.6529	0.4356
0.0818	8.83	2500	0.6773	0.4200
0.0918	9.19	2600	0.6879	0.4267
0.119	9.54	2700	0.6948	0.4254
0.1615	9.89	2800	0.6920	0.4259
0.0953	10.25	2900	0.7019	0.4218
0.1008	10.6	3000	0.6933	0.4133
0.0729	10.95	3100	0.6950	0.4164
0.0636	11.31	3200	0.7151	0.4121
0.0395	11.66	3300	0.7053	0.4098
0.0391	12.01	3400	0.7081	0.3984
0.0507	12.37	3500	0.7012	0.4111
0.0598	12.72	3600	0.7169	0.4035
0.0515	13.07	3700	0.7358	0.4102
0.0429	13.43	3800	0.7236	0.4013
0.0398	13.78	3900	0.7404	0.4026
0.0946	14.13	4000	0.7285	0.4029
0.0428	14.49	4100	0.7271	0.3991
0.0329	14.84	4200	0.7294	0.4026

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.1
Datasets 2.8.0
Tokenizers 0.13.2