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--- |
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base_model: facebook/mms-1b-all |
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datasets: |
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- common_voice_17_0 |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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metrics: |
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- wer |
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- bleu |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-mms-1b-CV17.0-training_set_variations |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: common_voice_17_0 |
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type: common_voice_17_0 |
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config: ta |
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split: validation |
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args: ta |
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metrics: |
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- type: wer |
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value: 0.35028609973948643 |
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name: Wer |
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- type: bleu |
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value: 0.4335646189418293 |
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name: Bleu |
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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-mms-1b-CV17.0-training_set_variations |
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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_17_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1970 |
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- Wer: 0.3503 |
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- Cer: 0.0557 |
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- Bleu: 0.4336 |
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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: 16 |
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- eval_batch_size: 8 |
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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_ratio: 0.15 |
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- training_steps: 2000 |
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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 | Cer | Bleu | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:| |
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| 7.329 | 0.0977 | 100 | 0.3217 | 0.4338 | 0.0716 | 0.3272 | |
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| 0.2154 | 0.1953 | 200 | 0.2314 | 0.3794 | 0.0634 | 0.3999 | |
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| 0.189 | 0.2930 | 300 | 0.2188 | 0.3656 | 0.0592 | 0.4166 | |
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| 0.1975 | 0.3906 | 400 | 0.2212 | 0.3763 | 0.0608 | 0.4032 | |
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| 0.1813 | 0.4883 | 500 | 0.2117 | 0.3634 | 0.0585 | 0.4171 | |
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| 0.1791 | 0.5859 | 600 | 0.2074 | 0.3590 | 0.0578 | 0.4220 | |
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| 0.187 | 0.6836 | 700 | 0.2087 | 0.3607 | 0.0582 | 0.4188 | |
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| 0.1789 | 0.7812 | 800 | 0.2064 | 0.3542 | 0.0568 | 0.4327 | |
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| 0.1704 | 0.8789 | 900 | 0.2076 | 0.3661 | 0.0587 | 0.4095 | |
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| 0.1813 | 0.9766 | 1000 | 0.2044 | 0.3589 | 0.0574 | 0.4200 | |
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| 0.1633 | 1.0742 | 1100 | 0.2029 | 0.3582 | 0.0575 | 0.4220 | |
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| 0.1699 | 1.1719 | 1200 | 0.2034 | 0.3537 | 0.0566 | 0.4335 | |
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| 0.1822 | 1.2695 | 1300 | 0.2037 | 0.3589 | 0.0578 | 0.4227 | |
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| 0.1654 | 1.3672 | 1400 | 0.2028 | 0.3549 | 0.0568 | 0.4288 | |
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| 0.1696 | 1.4648 | 1500 | 0.2011 | 0.3579 | 0.0567 | 0.4199 | |
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| 0.1622 | 1.5625 | 1600 | 0.1999 | 0.3568 | 0.0570 | 0.4228 | |
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| 0.1742 | 1.6602 | 1700 | 0.1983 | 0.3490 | 0.0559 | 0.4365 | |
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| 0.1581 | 1.7578 | 1800 | 0.1973 | 0.3511 | 0.0558 | 0.4329 | |
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| 0.1616 | 1.8555 | 1900 | 0.1970 | 0.3482 | 0.0556 | 0.4381 | |
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| 0.1607 | 1.9531 | 2000 | 0.1970 | 0.3503 | 0.0557 | 0.4336 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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