README.md

metadata

language:
  - te
license: apache-2.0
tags:
  - automatic-speech-recognition
  - openslr_SLR66
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - openslr
  - SLR66
metrics:
  - wer
model-index:
  - name: xls-r-1B-te
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: openslr
          name: Open SLR
          args: SLR66
        metrics:
          - type: wer
            value: 20.624
            name: Test WER
          - type: cer
            value: 3.979
            name: Test CER
          - type: wer
            value: 26.14777618364419
            name: Test WER (without LM)
          - type: cer
            value: 4.932543184970369
            name: Test CER (without LM)

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the OPENSLR_SLR66 - NA dataset. It achieves the following results on the evaluation set:

• Loss: 0.3119
• Wer: 0.2613

Evaluation metrics

Metric	Split	Decode with LM	Value
WER	Train	No	5.36
CER	Train	No	1.11
WER	Test	No	26.14
CER	Test	No	4.93
WER	Train	Yes	5.04
CER	Train	Yes	1.07
WER	Test	Yes	20.69
CER	Test	Yes	3.986

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 4
• 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: 2000
• num_epochs: 150.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9038	4.8	500	3.0125	1.0
1.3777	9.61	1000	0.8681	0.8753
1.1436	14.42	1500	0.6256	0.7961
1.0997	19.23	2000	0.5244	0.6875
1.0363	24.04	2500	0.4585	0.6276
0.7996	28.84	3000	0.4072	0.5295
0.825	33.65	3500	0.3590	0.5222
0.8018	38.46	4000	0.3678	0.4671
0.7545	43.27	4500	0.3474	0.3962
0.7375	48.08	5000	0.3224	0.3869
0.6198	52.88	5500	0.3233	0.3630
0.6608	57.69	6000	0.3029	0.3308
0.645	62.5	6500	0.3195	0.3722
0.5249	67.31	7000	0.3004	0.3202
0.4875	72.11	7500	0.2826	0.2992
0.5171	76.92	8000	0.2962	0.2976
0.4974	81.73	8500	0.2990	0.2933
0.4387	86.54	9000	0.2834	0.2755
0.4511	91.34	9500	0.2886	0.2787
0.4112	96.15	10000	0.3093	0.2976
0.4064	100.96	10500	0.3123	0.2863
0.4047	105.77	11000	0.2968	0.2719
0.3519	110.57	11500	0.3106	0.2832
0.3719	115.38	12000	0.3030	0.2737
0.3669	120.19	12500	0.2964	0.2714
0.3386	125.0	13000	0.3101	0.2714
0.3137	129.8	13500	0.3063	0.2710
0.3008	134.61	14000	0.3082	0.2617
0.301	139.42	14500	0.3121	0.2628
0.3291	144.23	15000	0.3105	0.2612
0.3133	149.04	15500	0.3114	0.2624

Framework versions

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