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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-300m-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: 24.695121951219512 |
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name: Test WER |
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- type: cer |
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value: 4.861934182322532 |
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name: Test CER |
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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-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR66 - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2680 |
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- Wer: 0.3467 |
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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: 7.5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 10.0 |
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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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| 3.0304 | 4.81 | 500 | 1.5676 | 1.0554 | |
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| 1.5263 | 9.61 | 1000 | 0.4693 | 0.8023 | |
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| 1.5299 | 14.42 | 1500 | 0.4368 | 0.7311 | |
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| 1.5063 | 19.23 | 2000 | 0.4360 | 0.7302 | |
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| 1.455 | 24.04 | 2500 | 0.4213 | 0.6692 | |
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| 1.4755 | 28.84 | 3000 | 0.4329 | 0.5943 | |
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| 1.352 | 33.65 | 3500 | 0.4074 | 0.5765 | |
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| 1.3122 | 38.46 | 4000 | 0.3866 | 0.5630 | |
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| 1.2799 | 43.27 | 4500 | 0.3860 | 0.5480 | |
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| 1.212 | 48.08 | 5000 | 0.3590 | 0.5317 | |
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| 1.1645 | 52.88 | 5500 | 0.3283 | 0.4757 | |
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| 1.0854 | 57.69 | 6000 | 0.3162 | 0.4687 | |
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| 1.0292 | 62.5 | 6500 | 0.3126 | 0.4416 | |
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| 0.9607 | 67.31 | 7000 | 0.2990 | 0.4066 | |
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| 0.9156 | 72.12 | 7500 | 0.2870 | 0.4009 | |
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| 0.8329 | 76.92 | 8000 | 0.2791 | 0.3909 | |
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| 0.7979 | 81.73 | 8500 | 0.2770 | 0.3670 | |
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| 0.7144 | 86.54 | 9000 | 0.2841 | 0.3661 | |
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| 0.6997 | 91.35 | 9500 | 0.2721 | 0.3485 | |
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| 0.6568 | 96.15 | 10000 | 0.2681 | 0.3437 | |
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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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