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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-8-10h |
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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: 0.09612912441079846 |
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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: 0.08830755889579418 |
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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-8-10h |
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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: 0.1207 |
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- Wer: 0.0961 |
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And on the test set: |
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- Wer: 0.0883 |
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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 3 where |
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I use as training set 10 hours of Frisian speech randomly selected from all validated data except the test and evaluation sets. |
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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 evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split is 10 hours of Frisian randomly selected from validated data except for the recordings from test and evaluation splits. |
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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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| 5.6342 | 1.32 | 300 | 2.9760 | 1.0 | |
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| 2.2716 | 2.63 | 600 | 0.6877 | 0.6024 | |
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| 1.1303 | 3.95 | 900 | 0.3522 | 0.3450 | |
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| 0.9038 | 5.26 | 1200 | 0.2714 | 0.2603 | |
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| 0.846 | 6.58 | 1500 | 0.2143 | 0.2036 | |
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| 0.8044 | 7.89 | 1800 | 0.1829 | 0.1788 | |
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| 0.7069 | 9.21 | 2100 | 0.1751 | 0.1667 | |
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| 0.6995 | 10.53 | 2400 | 0.1741 | 0.1727 | |
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| 0.7115 | 11.84 | 2700 | 0.1591 | 0.1486 | |
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| 0.677 | 13.16 | 3000 | 0.1636 | 0.1459 | |
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| 0.6032 | 14.47 | 3300 | 0.1535 | 0.1439 | |
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| 0.6218 | 15.79 | 3600 | 0.1427 | 0.1406 | |
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| 0.6519 | 17.11 | 3900 | 0.1498 | 0.1488 | |
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| 0.5739 | 18.42 | 4200 | 0.1438 | 0.1319 | |
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| 0.567 | 19.74 | 4500 | 0.1379 | 0.1322 | |
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| 0.4982 | 21.05 | 4800 | 0.1315 | 0.1237 | |
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| 0.5825 | 22.37 | 5100 | 0.1349 | 0.1252 | |
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| 0.5085 | 23.68 | 5400 | 0.1297 | 0.1233 | |
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| 0.4946 | 25.0 | 5700 | 0.1343 | 0.1127 | |
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| 0.5677 | 26.32 | 6000 | 0.1323 | 0.1228 | |
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| 0.4858 | 27.63 | 6300 | 0.1292 | 0.1098 | |
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| 0.4709 | 28.95 | 6600 | 0.1267 | 0.1204 | |
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| 0.3241 | 30.26 | 6900 | 0.1315 | 0.1274 | |
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| 0.2796 | 31.58 | 7200 | 0.1315 | 0.1202 | |
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| 0.3171 | 32.89 | 7500 | 0.1315 | 0.1200 | |
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| 0.2591 | 34.21 | 7800 | 0.1322 | 0.1106 | |
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| 0.2716 | 35.53 | 8100 | 0.1233 | 0.1030 | |
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| 0.2446 | 36.84 | 8400 | 0.1273 | 0.1087 | |
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| 0.2377 | 38.16 | 8700 | 0.1243 | 0.1101 | |
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| 0.2183 | 39.47 | 9000 | 0.1230 | 0.1116 | |
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| 0.2059 | 40.79 | 9300 | 0.1240 | 0.1001 | |
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| 0.1916 | 42.11 | 9600 | 0.1223 | 0.1003 | |
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| 0.196 | 43.42 | 9900 | 0.1246 | 0.0965 | |
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| 0.1969 | 44.74 | 10200 | 0.1222 | 0.1038 | |
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| 0.1951 | 46.05 | 10500 | 0.1208 | 0.1003 | |
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| 0.1809 | 47.37 | 10800 | 0.1213 | 0.1003 | |
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| 0.1793 | 48.68 | 11100 | 0.1202 | 0.0959 | |
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| 0.1837 | 50.0 | 11400 | 0.1207 | 0.0961 | |
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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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