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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- automatic-speech-recognition |
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- NbAiLab/NPSC |
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- robust-speech-event |
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- "no" |
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- nn-NO |
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- hf-asr-leaderboard |
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datasets: |
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- NbAiLab/NPSC |
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language: |
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- nn-NO |
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model-index: |
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- name: wav2vec2-large-voxrex-npsc-nynorsk |
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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: NPSC |
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type: NbAiLab/NPSC |
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args: 16K_mp3_nynorsk |
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metrics: |
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- name: Test (Nynorsk) WER |
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type: wer |
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value: 0.12220762155059132 |
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- name: Test (Nynorsk) CER |
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type: cer |
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value: 0.04195612578778549 |
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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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# wav2vec2-large-voxrex-npsc-nynorsk |
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This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the NBAILAB/NPSC - 16K_MP3_NYNORSK dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4142 |
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- Wer: 0.1576 |
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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: 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: 40.0 |
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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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| 3.086 | 2.17 | 500 | 3.0773 | 1.0 | |
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| 2.8532 | 4.35 | 1000 | 2.8393 | 1.0 | |
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| 0.9738 | 6.52 | 1500 | 0.7283 | 0.4890 | |
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| 0.6763 | 8.7 | 2000 | 0.5340 | 0.3662 | |
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| 0.5303 | 10.87 | 2500 | 0.4521 | 0.3140 | |
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| 0.4765 | 13.04 | 3000 | 0.4181 | 0.2853 | |
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| 0.4219 | 15.22 | 3500 | 0.4156 | 0.2934 | |
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| 0.3564 | 17.39 | 4000 | 0.3925 | 0.2509 | |
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| 0.3282 | 19.57 | 4500 | 0.3824 | 0.2420 | |
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| 0.3118 | 21.74 | 5000 | 0.3636 | 0.2354 | |
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| 0.2919 | 23.91 | 5500 | 0.3615 | 0.2281 | |
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| 0.2961 | 26.09 | 6000 | 0.3548 | 0.2255 | |
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| 0.284 | 28.26 | 6500 | 0.3526 | 0.2209 | |
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| 0.2566 | 30.43 | 7000 | 0.3526 | 0.2205 | |
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| 0.2422 | 32.61 | 7500 | 0.3569 | 0.2173 | |
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| 0.2472 | 34.78 | 8000 | 0.3592 | 0.2166 | |
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| 0.2337 | 36.96 | 8500 | 0.3625 | 0.2172 | |
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| 0.2315 | 39.13 | 9000 | 0.3580 | 0.2155 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.0+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.10.3 |
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