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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_17_0 |
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metrics: |
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- wer |
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- bleu |
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model-index: |
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- name: wav2vec2-mms-1b-malayalam-colab-CV17.0-v2 |
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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_17_0 |
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type: common_voice_17_0 |
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config: ml |
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split: test |
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args: ml |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.5283687943262412 |
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- name: Bleu |
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type: bleu |
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value: 0.1996948603256558 |
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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-malayalam-colab-CV17.0-v2 |
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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: 0.2965 |
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- Wer: 0.5284 |
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- Cer: 0.0934 |
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- Bleu: 0.1997 |
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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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| 5.5563 | 3.1496 | 200 | 0.3157 | 0.5580 | 0.1055 | 0.1800 | |
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| 0.3888 | 6.2992 | 400 | 0.2983 | 0.5471 | 0.1003 | 0.1906 | |
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| 0.3328 | 9.4488 | 600 | 0.3008 | 0.5542 | 0.1002 | 0.1634 | |
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| 0.3006 | 12.5984 | 800 | 0.2821 | 0.5368 | 0.0984 | 0.1888 | |
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| 0.2743 | 15.7480 | 1000 | 0.2913 | 0.5329 | 0.0968 | 0.1813 | |
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| 0.2461 | 18.8976 | 1200 | 0.2822 | 0.5319 | 0.0957 | 0.1937 | |
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| 0.2346 | 22.0472 | 1400 | 0.2933 | 0.5335 | 0.0942 | 0.1848 | |
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| 0.2112 | 25.1969 | 1600 | 0.2885 | 0.5300 | 0.0947 | 0.1900 | |
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| 0.2006 | 28.3465 | 1800 | 0.2944 | 0.5329 | 0.0939 | 0.1870 | |
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| 0.1879 | 31.4961 | 2000 | 0.2965 | 0.5284 | 0.0934 | 0.1997 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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