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--- |
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tags: |
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- automatic-speech-recognition |
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- librispeech_asr |
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- generated_from_trainer |
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- asr_seq2esq |
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model-index: |
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- name: wav2vec2-2-bart-base |
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results: [] |
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widget: |
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- example_title: Librispeech sample 1 |
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src: https://cdn-media.huggingface.co/speech_samples/sample1.flac |
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- example_title: Librispeech sample 2 |
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src: https://cdn-media.huggingface.co/speech_samples/sample2.flac |
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- example_title: Common Voice sample |
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src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3 |
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--- |
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To rerun this experiment, please clone this directory and run: |
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```bash |
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python create_model.py |
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``` |
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followed by |
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```bash |
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./run_librispeech.sh |
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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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# wav2vec2-2-bart-base |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) and [bart-base](https://huggingface.co/facebook/bart-base) on the librispeech_asr - clean dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.405 |
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- Wer: 0.0728 |
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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: 0.0003 |
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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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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 64 |
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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: 400 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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See Training Metrics Tab. |
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### Framework versions |
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- Transformers 4.15.0.dev0 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.16.2.dev0 |
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- Tokenizers 0.10.3 |
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