metadata

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:

python create_model.py

followed by

./run_librispeech.sh

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