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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-8-1h |
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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: 0.23732323953720896 |
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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: 0.25404682757623936 |
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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-8-1h |
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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: 0.4120 |
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- Wer: 0.2373 |
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And on the test set: |
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- Wer: 0.2540 |
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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 4 where |
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I use as training set 1 hour 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 1 hour 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: 6e-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: 80 |
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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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| 6.2987 | 4.35 | 100 | 3.0210 | 1.0 | |
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| 3.1424 | 8.7 | 200 | 2.9611 | 1.0 | |
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| 2.6299 | 13.04 | 300 | 0.9929 | 0.8377 | |
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| 1.3134 | 17.39 | 400 | 0.5679 | 0.5264 | |
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| 0.9747 | 21.74 | 500 | 0.4516 | 0.3764 | |
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| 0.8755 | 26.09 | 600 | 0.4515 | 0.3403 | |
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| 0.7227 | 30.43 | 700 | 0.4169 | 0.3211 | |
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| 0.6634 | 34.78 | 800 | 0.4159 | 0.2962 | |
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| 0.5568 | 39.13 | 900 | 0.4081 | 0.2795 | |
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| 0.7943 | 43.48 | 1000 | 0.4090 | 0.2709 | |
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| 0.5537 | 47.83 | 1100 | 0.4239 | 0.2649 | |
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| 0.5596 | 52.17 | 1200 | 0.4029 | 0.2561 | |
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| 0.5523 | 56.52 | 1300 | 0.4073 | 0.2524 | |
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| 0.4579 | 60.87 | 1400 | 0.4098 | 0.2470 | |
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| 0.6477 | 65.22 | 1500 | 0.4099 | 0.2446 | |
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| 0.4957 | 69.57 | 1600 | 0.4167 | 0.2475 | |
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| 0.3246 | 73.91 | 1700 | 0.4146 | 0.2389 | |
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| 0.3937 | 78.26 | 1800 | 0.4120 | 0.2373 | |
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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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