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--- |
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library_name: transformers |
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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-CV17.0 |
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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: 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: 0.6538388264431321 |
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- name: Bleu |
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type: bleu |
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value: 0.14202013774436864 |
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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 |
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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: 0.6919 |
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- Wer: 0.6538 |
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- Cer: 0.2510 |
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- Bleu: 0.1420 |
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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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| 6.2938 | 3.0769 | 200 | 3.8350 | 0.9981 | 0.9092 | 0.0 | |
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| 2.0522 | 6.1538 | 400 | 0.7219 | 0.6997 | 0.2730 | 0.1116 | |
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| 0.7043 | 9.2308 | 600 | 0.7137 | 0.7419 | 0.2682 | 0.0933 | |
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| 0.6497 | 12.3077 | 800 | 0.6962 | 0.6664 | 0.2667 | 0.1318 | |
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| 0.614 | 15.3846 | 1000 | 0.6680 | 0.6586 | 0.2596 | 0.1356 | |
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| 0.5794 | 18.4615 | 1200 | 0.6798 | 0.6722 | 0.2599 | 0.1254 | |
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| 0.5439 | 21.5385 | 1400 | 0.6724 | 0.6665 | 0.2541 | 0.1287 | |
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| 0.5146 | 24.6154 | 1600 | 0.6906 | 0.6704 | 0.2513 | 0.1327 | |
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| 0.489 | 27.6923 | 1800 | 0.6886 | 0.6599 | 0.2509 | 0.1390 | |
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| 0.4668 | 30.7692 | 2000 | 0.6919 | 0.6538 | 0.2510 | 0.1420 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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