README.md

metadata

license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-cv_vi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: vi
          split: test
          args: vi
        metrics:
          - name: Wer
            type: wer
            value: 0.663156740155753

wav2vec2-large-xls-r-300m-cv_vi

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:

• Loss: 2.3858
• Wer: 0.6632

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: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
14.1667	9.2	200	4.5633	1.0
3.6334	18.39	400	3.4332	1.0
1.938	27.59	600	1.2434	0.7082
0.3082	36.78	800	1.2288	0.6534
0.1766	45.98	1000	1.2915	0.6500
0.1287	55.17	1200	1.3452	0.6269
0.1043	64.37	1400	1.4746	0.6395
0.0834	73.56	1600	1.4731	0.6347
0.0837	82.76	1800	1.5893	0.6493
0.0711	91.95	2000	1.6205	0.6522
0.0672	101.15	2200	1.5513	0.6503
0.0745	110.34	2400	1.6509	0.6774
0.07	119.54	2600	1.6779	0.6543
0.0492	128.74	2800	1.7616	0.6611
0.0473	137.93	3000	1.7885	0.6634
0.0535	147.13	3200	1.8877	0.6806
0.0468	156.32	3400	1.7766	0.6671
0.0386	165.52	3600	1.7956	0.6494
0.0418	174.71	3800	1.9402	0.6851
0.0426	183.91	4000	1.9777	0.6927
0.0395	193.1	4200	1.8733	0.6689
0.0392	202.3	4400	1.8994	0.6774
0.0377	211.49	4600	1.9983	0.6889
0.0354	220.69	4800	1.8858	0.6645
0.0315	229.89	5000	1.9716	0.6805
0.0312	239.08	5200	2.0422	0.6825
0.0292	248.28	5400	2.0780	0.7019
0.0283	257.47	5600	1.9102	0.6743
0.025	266.67	5800	1.9745	0.6756
0.0246	275.86	6000	2.1289	0.6918
0.0234	285.06	6200	2.1775	0.7068
0.0219	294.25	6400	2.1755	0.6935
0.0182	303.45	6600	2.1602	0.6764
0.0174	312.64	6800	2.1359	0.6596
0.0157	321.84	7000	2.1958	0.6797
0.0147	331.03	7200	2.1460	0.6657
0.0135	340.23	7400	2.2716	0.6719
0.0124	349.43	7600	2.3556	0.6762
0.0109	358.62	7800	2.2520	0.6632
0.0115	367.82	8000	2.3112	0.6802
0.0108	377.01	8200	2.2925	0.6659
0.0106	386.21	8400	2.2950	0.6726
0.0088	395.4	8600	2.3078	0.6735
0.0084	404.6	8800	2.3538	0.6723
0.0079	413.79	9000	2.3212	0.6615
0.0074	422.99	9200	2.3908	0.6774
0.0094	432.18	9400	2.3164	0.6779
0.0077	441.38	9600	2.3105	0.6649
0.0066	450.57	9800	2.3599	0.6742
0.007	459.77	10000	2.3675	0.6709
0.0056	468.97	10200	2.3964	0.6677
0.0049	478.16	10400	2.3858	0.6632

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.13.3