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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- timit_asr |
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model-index: |
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- name: wav2vec2-base_phoneme-timit_english_timit-4k_001 |
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results: [] |
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language: |
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- en |
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metrics: |
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- wer |
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library_name: transformers |
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pipeline_tag: automatic-speech-recognition |
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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_phoneme-timit_english_timit-4k_001 |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the timit dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6361 |
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- Per: 0.1195 |
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## Model description |
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The wav2vec 2.0 base model is pre-trained on 960 hours of the LibriSpeech dataset. |
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- 12 Transformer blocks (Each block: 768 dimensions & 8 attention heads) |
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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: 16 |
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- eval_batch_size: 16 |
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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: 5000 |
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- training_steps: 10000 |
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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 | Per | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 5.2193 | 3.46 | 1000 | 3.5945 | 0.9617 | |
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| 1.5174 | 6.92 | 2000 | 0.5574 | 0.1665 | |
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| 0.5246 | 10.38 | 3000 | 0.4228 | 0.1503 | |
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| 0.3915 | 13.84 | 4000 | 0.4276 | 0.1512 | |
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| 0.3293 | 17.3 | 5000 | 0.4656 | 0.1517 | |
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| 0.2757 | 20.76 | 6000 | 0.4719 | 0.1486 | |
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| 0.209 | 24.22 | 7000 | 0.5314 | 0.1478 | |
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| 0.1589 | 27.68 | 8000 | 0.6102 | 0.1484 | |
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| 0.1207 | 31.14 | 9000 | 0.6449 | 0.1484 | |
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| 0.0951 | 34.6 | 10000 | 0.6579 | 0.1471 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.1 |
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- Datasets 2.18.0 |
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- Tokenizers 0.13.3 |
