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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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datasets: |
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- common_voice_8_0 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xls-r-1b-frisian-cv-8 |
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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_0 |
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type: common_voice_8_0 |
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config: fy-NL |
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split: validation |
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args: fy-NL |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.14290815597771747 |
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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_0 |
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type: common_voice_8_0 |
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config: fy-NL |
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split: test |
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args: fy-NL |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.1413499060557884 |
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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-xls-r-1b-frisian-cv-8 |
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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 common_voice_8_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2131 |
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- Wer: 0.1429 |
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And on the test set: |
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- Wer: 0.1413 |
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## Model description |
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This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 1 where |
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I use the same training set as the XLSR-53 baseline. |
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## Intended uses & limitations |
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The intended use is for recognizing Frisian speech. |
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Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0. |
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## Training and evaluation data |
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The training and evaluation splits used are the ones available in the Common Voice 8.0 Frisian subset. |
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## Training procedure |
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The script used for training this model can be found in this GitHub repository: [link](https://github.com/greenw0lf/MSc-VT-Thesis/). |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 50 |
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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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| 6.0565 | 1.72 | 200 | 3.1053 | 1.0 | |
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| 2.7675 | 3.45 | 400 | 1.1551 | 0.8611 | |
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| 1.3474 | 5.17 | 600 | 0.4770 | 0.4397 | |
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| 0.9617 | 6.9 | 800 | 0.3218 | 0.3343 | |
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| 0.9058 | 8.62 | 1000 | 0.2741 | 0.2768 | |
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| 0.9712 | 10.34 | 1200 | 0.2619 | 0.2505 | |
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| 0.6908 | 12.07 | 1400 | 0.2288 | 0.2243 | |
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| 0.745 | 13.79 | 1600 | 0.2288 | 0.2095 | |
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| 0.7742 | 15.52 | 1800 | 0.2289 | 0.1979 | |
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| 0.7231 | 17.24 | 2000 | 0.2198 | 0.1940 | |
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| 0.6475 | 18.97 | 2200 | 0.2180 | 0.1992 | |
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| 0.6421 | 20.69 | 2400 | 0.2133 | 0.1741 | |
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| 0.5925 | 22.41 | 2600 | 0.1998 | 0.1747 | |
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| 0.5608 | 24.14 | 2800 | 0.2212 | 0.1950 | |
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| 0.5315 | 25.86 | 3000 | 0.2187 | 0.1624 | |
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| 0.5362 | 27.59 | 3200 | 0.2057 | 0.1718 | |
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| 0.563 | 29.31 | 3400 | 0.2090 | 0.1613 | |
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| 0.4218 | 31.03 | 3600 | 0.2126 | 0.1531 | |
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| 0.3826 | 32.76 | 3800 | 0.2084 | 0.1538 | |
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| 0.356 | 34.48 | 4000 | 0.2115 | 0.1612 | |
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| 0.2966 | 36.21 | 4200 | 0.2093 | 0.1536 | |
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| 0.3377 | 37.93 | 4400 | 0.2061 | 0.1527 | |
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| 0.321 | 39.66 | 4600 | 0.2121 | 0.1463 | |
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| 0.2942 | 41.38 | 4800 | 0.2158 | 0.1441 | |
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| 0.2931 | 43.1 | 5000 | 0.2173 | 0.1446 | |
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| 0.2346 | 44.83 | 5200 | 0.2152 | 0.1436 | |
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| 0.2543 | 46.55 | 5400 | 0.2066 | 0.1445 | |
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| 0.2385 | 48.28 | 5600 | 0.2108 | 0.1432 | |
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| 0.2726 | 50.0 | 5800 | 0.2131 | 0.1429 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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