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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_16_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-yoruba-test |
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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_16_1 |
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type: common_voice_16_1 |
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config: yo |
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split: test |
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args: yo |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.6802364381733245 |
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language: |
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- yo |
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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-yoruba-test |
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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_16_1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6682 |
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- Wer: 0.6802 |
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Finetuned by Daniel Ogbuigwe |
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## Model description |
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This checkpoint is a model fine-tuned for multi-lingual ASR using Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and makes use of adapter models to transcribe 1000+ languages. The checkpoint consists of 1 billion parameters and has been fine-tuned from facebook/mms-1b on Yoruba. |
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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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Common Voice 16.1 Yoruba data |
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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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- 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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| 4.8923 | 0.77 | 100 | 0.7710 | 0.7413 | |
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| 0.7507 | 1.54 | 200 | 0.7249 | 0.7585 | |
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| 0.7033 | 2.31 | 300 | 0.7105 | 0.7247 | |
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| 0.6888 | 3.08 | 400 | 0.6829 | 0.7229 | |
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| 0.6471 | 3.85 | 500 | 0.6682 | 0.6802 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
