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--- |
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language: |
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- te |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- openslr_SLR66 |
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- generated_from_trainer |
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- robust-speech-event |
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- hf-asr-leaderboard |
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datasets: |
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- openslr |
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- SLR66 |
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metrics: |
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- wer |
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model-index: |
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- name: xls-r-1B-te |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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type: openslr |
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name: Open SLR |
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args: SLR66 |
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metrics: |
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- type: wer |
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value: 20.624 |
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name: Test WER |
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- type: cer |
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value: 3.979 |
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name: Test CER |
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- type: wer |
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value: 26.14777618364419 |
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name: Test WER (without LM) |
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- type: cer |
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value: 4.932543184970369 |
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name: Test CER (without LM) |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OPENSLR_SLR66 - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3119 |
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- Wer: 0.2613 |
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### Evaluation metrics |
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| Metric | Split | Decode with LM | Value | |
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|:------:|:------:|:--------------:|:---------:| |
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| WER | Train | No | 5.36 | |
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| CER | Train | No | 1.11 | |
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| WER | Test | No | 26.14 | |
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| CER | Test | No | 4.93 | |
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| WER | Train | Yes | 5.04 | |
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| CER | Train | Yes | 1.07 | |
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| WER | Test | Yes | 20.69 | |
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| CER | Test | Yes | 3.986 | |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2000 |
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- num_epochs: 150.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:-----:|:---------------:|:------:| |
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| 2.9038 | 4.8 | 500 | 3.0125 | 1.0 | |
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| 1.3777 | 9.61 | 1000 | 0.8681 | 0.8753 | |
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| 1.1436 | 14.42 | 1500 | 0.6256 | 0.7961 | |
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| 1.0997 | 19.23 | 2000 | 0.5244 | 0.6875 | |
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| 1.0363 | 24.04 | 2500 | 0.4585 | 0.6276 | |
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| 0.7996 | 28.84 | 3000 | 0.4072 | 0.5295 | |
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| 0.825 | 33.65 | 3500 | 0.3590 | 0.5222 | |
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| 0.8018 | 38.46 | 4000 | 0.3678 | 0.4671 | |
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| 0.7545 | 43.27 | 4500 | 0.3474 | 0.3962 | |
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| 0.7375 | 48.08 | 5000 | 0.3224 | 0.3869 | |
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| 0.6198 | 52.88 | 5500 | 0.3233 | 0.3630 | |
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| 0.6608 | 57.69 | 6000 | 0.3029 | 0.3308 | |
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| 0.645 | 62.5 | 6500 | 0.3195 | 0.3722 | |
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| 0.5249 | 67.31 | 7000 | 0.3004 | 0.3202 | |
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| 0.4875 | 72.11 | 7500 | 0.2826 | 0.2992 | |
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| 0.5171 | 76.92 | 8000 | 0.2962 | 0.2976 | |
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| 0.4974 | 81.73 | 8500 | 0.2990 | 0.2933 | |
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| 0.4387 | 86.54 | 9000 | 0.2834 | 0.2755 | |
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| 0.4511 | 91.34 | 9500 | 0.2886 | 0.2787 | |
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| 0.4112 | 96.15 | 10000 | 0.3093 | 0.2976 | |
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| 0.4064 | 100.96 | 10500 | 0.3123 | 0.2863 | |
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| 0.4047 | 105.77 | 11000 | 0.2968 | 0.2719 | |
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| 0.3519 | 110.57 | 11500 | 0.3106 | 0.2832 | |
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| 0.3719 | 115.38 | 12000 | 0.3030 | 0.2737 | |
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| 0.3669 | 120.19 | 12500 | 0.2964 | 0.2714 | |
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| 0.3386 | 125.0 | 13000 | 0.3101 | 0.2714 | |
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| 0.3137 | 129.8 | 13500 | 0.3063 | 0.2710 | |
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| 0.3008 | 134.61 | 14000 | 0.3082 | 0.2617 | |
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| 0.301 | 139.42 | 14500 | 0.3121 | 0.2628 | |
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| 0.3291 | 144.23 | 15000 | 0.3105 | 0.2612 | |
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| 0.3133 | 149.04 | 15500 | 0.3114 | 0.2624 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
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