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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-base |
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tags: |
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- automatic-speech-recognition |
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- timit_asr |
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- generated_from_trainer |
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datasets: |
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- timit_asr |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-base-timit-fine-tuned |
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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: TIMIT_ASR - NA |
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type: timit_asr |
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config: clean |
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split: test |
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args: 'Config: na, Training split: train, Eval split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.4090867704634435 |
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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-base-timit-fine-tuned |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT_ASR - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4218 |
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- Wer: 0.4091 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 1 |
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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: 1000 |
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- num_epochs: 20.0 |
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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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| 3.1612 | 0.8621 | 100 | 3.1181 | 1.0 | |
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| 2.978 | 1.7241 | 200 | 2.9722 | 1.0 | |
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| 2.9185 | 2.5862 | 300 | 2.9098 | 1.0 | |
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| 2.1282 | 3.4483 | 400 | 2.0066 | 1.0247 | |
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| 1.1234 | 4.3103 | 500 | 1.0197 | 0.8393 | |
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| 0.602 | 5.1724 | 600 | 0.6714 | 0.6600 | |
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| 0.5032 | 6.0345 | 700 | 0.5285 | 0.5659 | |
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| 0.3101 | 6.8966 | 800 | 0.4819 | 0.5282 | |
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| 0.3432 | 7.7586 | 900 | 0.4653 | 0.5272 | |
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| 0.1922 | 8.6207 | 1000 | 0.4672 | 0.4918 | |
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| 0.2284 | 9.4828 | 1100 | 0.4834 | 0.4870 | |
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| 0.1372 | 10.3448 | 1200 | 0.4380 | 0.4727 | |
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| 0.1105 | 11.2069 | 1300 | 0.4509 | 0.4594 | |
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| 0.0992 | 12.0690 | 1400 | 0.4196 | 0.4544 | |
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| 0.1226 | 12.9310 | 1500 | 0.4237 | 0.4321 | |
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| 0.1013 | 13.7931 | 1600 | 0.4113 | 0.4298 | |
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| 0.0661 | 14.6552 | 1700 | 0.4038 | 0.4276 | |
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| 0.0901 | 15.5172 | 1800 | 0.4321 | 0.4225 | |
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| 0.053 | 16.3793 | 1900 | 0.4076 | 0.4236 | |
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| 0.0805 | 17.2414 | 2000 | 0.4336 | 0.4156 | |
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| 0.049 | 18.1034 | 2100 | 0.4193 | 0.4114 | |
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| 0.0717 | 18.9655 | 2200 | 0.4139 | 0.4091 | |
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| 0.0389 | 19.8276 | 2300 | 0.4216 | 0.4087 | |
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### Framework versions |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.3.0a0+git71dd2de |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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