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--- |
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license: cc-by-nc-4.0 |
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base_model: facebook/mms-1b-all |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_6_1 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-mms-1b-thai-colab |
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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_6_1 |
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type: common_voice_6_1 |
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config: th |
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split: test |
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args: th |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.7234125438254773 |
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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-mms-1b-thai-colab |
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_6_1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2452 |
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- Wer: 0.7234 |
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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: 0.001 |
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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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- 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: 100 |
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- num_epochs: 4 |
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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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| 8.0794 | 0.17 | 100 | 0.3832 | 0.8329 | |
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| 0.561 | 0.33 | 200 | 0.3162 | 0.8099 | |
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| 0.5132 | 0.5 | 300 | 0.2907 | 0.7842 | |
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| 0.5015 | 0.66 | 400 | 0.2954 | 0.7998 | |
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| 0.5126 | 0.83 | 500 | 0.2812 | 0.7924 | |
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| 0.5182 | 0.99 | 600 | 0.2782 | 0.7631 | |
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| 0.4459 | 1.16 | 700 | 0.2735 | 0.7526 | |
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| 0.4694 | 1.32 | 800 | 0.2716 | 0.7628 | |
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| 0.4576 | 1.49 | 900 | 0.2649 | 0.7538 | |
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| 0.4749 | 1.65 | 1000 | 0.2614 | 0.7503 | |
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| 0.4282 | 1.82 | 1100 | 0.2687 | 0.7464 | |
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| 0.4009 | 1.98 | 1200 | 0.2622 | 0.7480 | |
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| 0.3976 | 2.15 | 1300 | 0.2619 | 0.7421 | |
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| 0.4306 | 2.31 | 1400 | 0.2620 | 0.7538 | |
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| 0.4413 | 2.48 | 1500 | 0.2551 | 0.7515 | |
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| 0.3888 | 2.64 | 1600 | 0.2545 | 0.7339 | |
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| 0.4213 | 2.81 | 1700 | 0.2541 | 0.7316 | |
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| 0.3945 | 2.98 | 1800 | 0.2507 | 0.7246 | |
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| 0.3765 | 3.14 | 1900 | 0.2495 | 0.7234 | |
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| 0.3859 | 3.31 | 2000 | 0.2498 | 0.7269 | |
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| 0.3931 | 3.47 | 2100 | 0.2469 | 0.7250 | |
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| 0.3737 | 3.64 | 2200 | 0.2470 | 0.7242 | |
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| 0.3716 | 3.8 | 2300 | 0.2454 | 0.7219 | |
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| 0.3582 | 3.97 | 2400 | 0.2452 | 0.7234 | |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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