metadata

language:
  - yue
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
model-index:
  - name: Whisper Large V2 - Cantonese - Augmented
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0
          type: mozilla-foundation/common_voice_11_0
          config: yue
          split: test
        metrics:
          - type: cer
            value: 6.213317142278891
            name: CER

Whisper Large V2 - Cantonese - Augmented

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_11_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.1828
Cer: 6.2133

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Training:

mozilla-foundation/common_voice_11_0 (train+validation)

Evaluation:

mozilla-foundation/common_voice_11_0 (test)

Training procedure

Datasets were augmented on-the-fly using audiomentations via PitchShift and TimeStretch transformations at p=0.3.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.1126	1.21	200	0.1666	7.3103
0.0467	2.42	400	0.1610	6.9419
0.0217	3.63	600	0.1621	6.3874
0.008	4.85	800	0.1699	6.3064
0.0023	6.06	1000	0.1828	6.2133

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.8.1.dev0
Tokenizers 0.13.2