README.md

metadata

language:
  - id
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: XLS-R-300M - Indonesia
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: sv-SE
        metrics:
          - name: Test WER
            type: wer
            value: 38.098
          - name: Test CER
            type: cer
            value: 14.261

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ID dataset. It achieves the following results on the evaluation set:

• Loss: 0.3975
• Wer: 0.2633

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: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 30.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.78	100	4.5645	1.0
No log	1.55	200	2.9016	1.0
No log	2.33	300	2.2666	1.0982
No log	3.1	400	0.6079	0.6376
3.2188	3.88	500	0.4985	0.5008
3.2188	4.65	600	0.4477	0.4469
3.2188	5.43	700	0.3953	0.3915
3.2188	6.2	800	0.4319	0.3921
3.2188	6.98	900	0.4171	0.3698
0.2193	7.75	1000	0.3957	0.3600
0.2193	8.53	1100	0.3730	0.3493
0.2193	9.3	1200	0.3780	0.3348
0.2193	10.08	1300	0.4133	0.3568
0.2193	10.85	1400	0.3984	0.3193
0.1129	11.63	1500	0.3845	0.3174
0.1129	12.4	1600	0.3882	0.3162
0.1129	13.18	1700	0.3982	0.3008
0.1129	13.95	1800	0.3902	0.3198
0.1129	14.73	1900	0.4082	0.3237
0.0765	15.5	2000	0.3732	0.3126
0.0765	16.28	2100	0.3893	0.3001
0.0765	17.05	2200	0.4168	0.3083
0.0765	17.83	2300	0.4193	0.3044
0.0765	18.6	2400	0.4006	0.3013
0.0588	19.38	2500	0.3836	0.2892
0.0588	20.16	2600	0.3761	0.2903
0.0588	20.93	2700	0.3895	0.2930
0.0588	21.71	2800	0.3885	0.2791
0.0588	22.48	2900	0.3902	0.2891
0.0448	23.26	3000	0.4200	0.2849
0.0448	24.03	3100	0.4013	0.2799
0.0448	24.81	3200	0.4039	0.2731
0.0448	25.58	3300	0.3970	0.2647
0.0448	26.36	3400	0.4081	0.2690
0.0351	27.13	3500	0.4090	0.2674
0.0351	27.91	3600	0.3953	0.2663
0.0351	28.68	3700	0.4044	0.2650
0.0351	29.46	3800	0.3969	0.2646

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.1+cu102
• Datasets 1.17.1.dev0
• Tokenizers 0.11.0