|
--- |
|
language: en |
|
tags: |
|
- automatic-speech-recognition |
|
- librispeech_asr |
|
- generated_from_trainer |
|
- asr_seq2esq |
|
widget: |
|
- example_title: Librispeech sample 1 |
|
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac |
|
- example_title: Librispeech sample 2 |
|
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac |
|
- example_title: Common Voice sample |
|
src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3 |
|
model-index: |
|
- name: wav2vec2-2-bart-base |
|
results: [] |
|
--- |
|
|
|
To rerun this experiment, please clone this directory and run: |
|
|
|
```bash |
|
python create_model.py |
|
``` |
|
followed by |
|
|
|
```bash |
|
./run_librispeech.sh |
|
``` |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# wav2vec2-2-bart-base |
|
|
|
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. |
|
|
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.405 |
|
- Wer: 0.0728 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.0003 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 8 |
|
- total_train_batch_size: 64 |
|
- total_eval_batch_size: 64 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 400 |
|
- num_epochs: 5 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
See Training Metrics Tab. |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.15.0.dev0 |
|
- Pytorch 1.9.0+cu111 |
|
- Datasets 1.16.2.dev0 |
|
- Tokenizers 0.10.3 |
|
|
