README.md

metadata

language:
  - nl
license: apache-2.0
tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  - hf-asr-leaderboard
  - model_for_talk
  - nl
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-nl
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice
          type: common_voice
          args: nl
        metrics:
          - name: Test WER
            type: wer
            value: 17.17
          - name: Test CER
            type: cer
            value: 5.13
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: nl
        metrics:
          - name: Test WER
            type: wer
            value: 35.76
          - name: Test CER
            type: cer
            value: 13.99
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: nl
        metrics:
          - name: Test WER
            type: wer
            value: 37.19

wav2vec2-large-xls-r-300m-nl

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

• Loss: 0.3923
• Wer: 0.1748

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: 7.5e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.5787	0.89	400	0.6354	0.5643
0.3036	1.78	800	0.3690	0.3552
0.188	2.67	1200	0.3239	0.2958
0.1434	3.56	1600	0.3093	0.2515
0.1245	4.44	2000	0.3024	0.2433
0.1095	5.33	2400	0.3249	0.2643
0.0979	6.22	2800	0.3191	0.2281
0.0915	7.11	3200	0.3152	0.2216
0.0829	8.0	3600	0.3419	0.2218
0.0777	8.89	4000	0.3432	0.2132
0.073	9.78	4400	0.3223	0.2131
0.0688	10.67	4800	0.3094	0.2152
0.0647	11.56	5200	0.3411	0.2152
0.0639	12.44	5600	0.3762	0.2135
0.0599	13.33	6000	0.3790	0.2137
0.0572	14.22	6400	0.3693	0.2118
0.0563	15.11	6800	0.3495	0.2139
0.0521	16.0	7200	0.3800	0.2023
0.0508	16.89	7600	0.3678	0.2033
0.0513	17.78	8000	0.3845	0.1987
0.0476	18.67	8400	0.3511	0.2037
0.045	19.56	8800	0.3794	0.1994
0.044	20.44	9200	0.3525	0.2050
0.043	21.33	9600	0.4082	0.2007
0.0409	22.22	10000	0.3866	0.2004
0.0393	23.11	10400	0.3899	0.2008
0.0382	24.0	10800	0.3626	0.1951
0.039	24.89	11200	0.3936	0.1953
0.0361	25.78	11600	0.4262	0.1928
0.0362	26.67	12000	0.3796	0.1934
0.033	27.56	12400	0.3616	0.1934
0.0321	28.44	12800	0.3742	0.1933
0.0325	29.33	13200	0.3582	0.1869
0.0309	30.22	13600	0.3717	0.1874
0.029	31.11	14000	0.3814	0.1894
0.0296	32.0	14400	0.3698	0.1877
0.0281	32.89	14800	0.3976	0.1899
0.0275	33.78	15200	0.3854	0.1858
0.0264	34.67	15600	0.4021	0.1889
0.0261	35.56	16000	0.3850	0.1830
0.0242	36.44	16400	0.4091	0.1878
0.0245	37.33	16800	0.4012	0.1846
0.0243	38.22	17200	0.3996	0.1833
0.0223	39.11	17600	0.3962	0.1815
0.0223	40.0	18000	0.3898	0.1832
0.0219	40.89	18400	0.4019	0.1822
0.0211	41.78	18800	0.4035	0.1809
0.021	42.67	19200	0.3915	0.1826
0.0208	43.56	19600	0.3934	0.1784
0.0188	44.44	20000	0.3912	0.1787
0.0195	45.33	20400	0.3989	0.1766
0.0186	46.22	20800	0.3887	0.1773
0.0188	47.11	21200	0.3982	0.1758
0.0175	48.0	21600	0.3933	0.1755
0.0172	48.89	22000	0.3921	0.1749
0.0187	49.78	22400	0.3923	0.1748

Framework versions

• Transformers 4.16.0.dev0
• Pytorch 1.10.1+cu102
• Datasets 1.17.1.dev0
• Tokenizers 0.11.0