
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

{"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": []}]}
transformers pytorch tensorboard speech-encoder-decoder automatic-speech-recognition librispeech_asr generated_from_trainer asr_seq2esq endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #librispeech_asr #generated_from_trainer #asr_seq2esq #endpoints_compatible #region-us 


To rerun this experiment, please clone this directory and run:


followed by





# wav2vec2-2-bart-base

This model is a fine-tuned version of facebook/wav2vec2-base and 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

# wav2vec2-2-bart-base

This model is a fine-tuned version of facebook/wav2vec2-base and 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
TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #librispeech_asr #generated_from_trainer #asr_seq2esq #endpoints_compatible #region-us 
 # wav2vec2-2-bart-base

This model is a fine-tuned version of facebook/wav2vec2-base and 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
TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #librispeech_asr #generated_from_trainer #asr_seq2esq #endpoints_compatible #region-us 
# wav2vec2-2-bart-base

This model is a fine-tuned version of facebook/wav2vec2-base and 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