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--- |
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language: |
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- sv-SE |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- mozilla-foundation/common_voice_8_0 |
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- generated_from_trainer |
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- robust-speech-event |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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model-index: |
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- name: XLS-R-300M - Swedish - CV8 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice 8 |
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type: mozilla-foundation/common_voice_8_0 |
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args: sv-SE |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 17.10 |
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- name: Test CER |
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type: cer |
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value: 5.70 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: sv |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 26.92 |
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- name: Test CER |
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type: cer |
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value: 12.53 |
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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 MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. |
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It achieves the following results on the evaluation set: |
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**Without LM**: |
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- Wer: 0.2465 |
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- Cer: 0.0717 |
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**With LM**: |
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- Wer: 0.1710 |
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- Cer: 0.0569 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: 50.0 |
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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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| 3.3224 | 1.37 | 500 | 3.2676 | 1.0 | |
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| 2.9319 | 2.74 | 1000 | 2.9287 | 1.0000 | |
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| 2.1173 | 4.11 | 1500 | 1.1478 | 0.8788 | |
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| 1.6973 | 5.48 | 2000 | 0.6749 | 0.6547 | |
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| 1.5865 | 6.85 | 2500 | 0.5500 | 0.5634 | |
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| 1.5094 | 8.22 | 3000 | 0.4840 | 0.5430 | |
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| 1.4644 | 9.59 | 3500 | 0.4844 | 0.4142 | |
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| 1.4061 | 10.96 | 4000 | 0.4356 | 0.3808 | |
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| 1.3584 | 12.33 | 4500 | 0.4192 | 0.3698 | |
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| 1.3438 | 13.7 | 5000 | 0.3980 | 0.3584 | |
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| 1.3332 | 15.07 | 5500 | 0.3896 | 0.3572 | |
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| 1.3025 | 16.44 | 6000 | 0.3835 | 0.3487 | |
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| 1.2979 | 17.81 | 6500 | 0.3781 | 0.3417 | |
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| 1.2736 | 19.18 | 7000 | 0.3734 | 0.3270 | |
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| 1.2415 | 20.55 | 7500 | 0.3637 | 0.3316 | |
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| 1.2255 | 21.92 | 8000 | 0.3546 | 0.3147 | |
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| 1.2193 | 23.29 | 8500 | 0.3524 | 0.3196 | |
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| 1.2104 | 24.66 | 9000 | 0.3403 | 0.3097 | |
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| 1.1965 | 26.03 | 9500 | 0.3508 | 0.3093 | |
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| 1.1976 | 27.4 | 10000 | 0.3419 | 0.3071 | |
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| 1.182 | 28.77 | 10500 | 0.3364 | 0.2963 | |
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| 1.158 | 30.14 | 11000 | 0.3338 | 0.2932 | |
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| 1.1414 | 31.51 | 11500 | 0.3376 | 0.2940 | |
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| 1.1402 | 32.88 | 12000 | 0.3370 | 0.2891 | |
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| 1.1213 | 34.25 | 12500 | 0.3201 | 0.2874 | |
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| 1.1207 | 35.62 | 13000 | 0.3261 | 0.2826 | |
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| 1.1074 | 36.98 | 13500 | 0.3117 | 0.2786 | |
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| 1.0818 | 38.36 | 14000 | 0.3194 | 0.2776 | |
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| 1.0889 | 39.73 | 14500 | 0.3188 | 0.2738 | |
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| 1.0672 | 41.1 | 15000 | 0.3196 | 0.2773 | |
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| 1.0838 | 42.47 | 15500 | 0.3130 | 0.2739 | |
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| 1.0553 | 43.83 | 16000 | 0.3165 | 0.2704 | |
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| 1.0786 | 45.21 | 16500 | 0.3108 | 0.2706 | |
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| 1.0546 | 46.57 | 17000 | 0.3102 | 0.2677 | |
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| 1.0425 | 47.94 | 17500 | 0.3115 | 0.2679 | |
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| 1.0398 | 49.31 | 18000 | 0.3131 | 0.2666 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu113 |
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- Datasets 1.18.1.dev0 |
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- Tokenizers 0.10.3 |
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