metadata
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Medium MS - Augmented
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: ms_my
split: test
args: ms_my
metrics:
- type: wer
value: 9.578362255965294
name: WER
- type: cer
value: 2.8109053797929726
name: CER
Whisper Medium MS - Augmented
This model is a fine-tuned version of openai/whisper-medium on the google/fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.2066
- Wer: 9.5784
- Cer: 2.8109
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Training:
- google/fleurs (train+validation)
Evaluation:
- google/fleurs (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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- 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 | Wer | Cer |
|---|---|---|---|---|---|
| 0.0876 | 2.15 | 200 | 0.1949 | 10.3105 | 3.0685 |
| 0.0064 | 4.3 | 400 | 0.1974 | 9.7004 | 2.9596 |
| 0.0014 | 6.45 | 600 | 0.2031 | 9.6190 | 2.8955 |
| 0.001 | 8.6 | 800 | 0.2058 | 9.6055 | 2.8440 |
| 0.0009 | 10.75 | 1000 | 0.2066 | 9.5784 | 2.8109 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2