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--- |
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license: mit |
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base_model: facebook/w2v-bert-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- audiofolder |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec-bert-2.0-even-pakendorf-0406-1347 |
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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: audiofolder |
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type: audiofolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.5968606805108706 |
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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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# wav2vec-bert-2.0-even-pakendorf-0406-1347 |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Cer: 0.2128 |
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- Loss: inf |
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- Wer: 0.5969 |
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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: 5e-05 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 500 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:------:|:---------------:|:------:| |
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| 4.5767 | 0.5051 | 200 | 0.4932 | inf | 0.9973 | |
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| 1.8775 | 1.0101 | 400 | 0.3211 | inf | 0.8494 | |
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| 1.6006 | 1.5152 | 600 | 0.3017 | inf | 0.8040 | |
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| 1.4476 | 2.0202 | 800 | 0.2896 | inf | 0.7534 | |
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| 1.2213 | 2.5253 | 1000 | 0.2610 | inf | 0.7080 | |
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| 1.1485 | 3.0303 | 1200 | 0.2684 | inf | 0.6800 | |
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| 0.9554 | 3.5354 | 1400 | 0.2459 | inf | 0.6732 | |
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| 0.9379 | 4.0404 | 1600 | 0.2275 | inf | 0.6251 | |
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| 0.7644 | 4.5455 | 1800 | 0.2235 | inf | 0.6224 | |
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| 0.7891 | 5.0505 | 2000 | 0.2180 | inf | 0.6053 | |
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| 0.633 | 5.5556 | 2200 | 0.2130 | inf | 0.5996 | |
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| 0.6197 | 6.0606 | 2400 | 0.2126 | inf | 0.6032 | |
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| 0.5212 | 6.5657 | 2600 | 0.2196 | inf | 0.6019 | |
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| 0.4881 | 7.0707 | 2800 | 0.2125 | inf | 0.5894 | |
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| 0.4 | 7.5758 | 3000 | 0.2066 | inf | 0.5852 | |
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| 0.4008 | 8.0808 | 3200 | 0.2076 | inf | 0.5790 | |
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| 0.3304 | 8.5859 | 3400 | 0.2096 | inf | 0.5884 | |
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| 0.3446 | 9.0909 | 3600 | 0.2124 | inf | 0.5983 | |
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| 0.3237 | 9.5960 | 3800 | 0.2128 | inf | 0.5969 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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