metadata
language:
- dv
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: whisper-medium-dv
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_13_0 dv
type: mozilla-foundation/common_voice_13_0
config: dv
split: test
args: dv
metrics:
- type: wer
value: 8.957818965817019
name: Wer
whisper-medium-dv
This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_13_0 dv dataset. It achieves the following results on the evaluation set:
- Loss: 0.2998
- Wer: 8.9578
To reproduce this run, execute the command in run.sh. Note that you will require the DeepSpeed package, which can be pip installed with:
pip install --upgrade deepspeed
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0349 | 3.58 | 1000 | 0.1622 | 9.9437 |
| 0.0046 | 7.17 | 2000 | 0.2288 | 9.5090 |
| 0.0007 | 10.75 | 3000 | 0.2820 | 9.0952 |
| 0.0 | 14.34 | 4000 | 0.2998 | 8.9578 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1.dev0
- Tokenizers 0.13.3