metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-medium
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_myst
type: rishabhjain16/infer_myst
config: en
split: test
metrics:
- type: wer
value: 11.96
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_pfs
type: rishabhjain16/infer_pfs
config: en
split: test
metrics:
- type: wer
value: 3.12
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_cmu
type: rishabhjain16/infer_cmu
config: en
split: test
metrics:
- type: wer
value: 8.92
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/libritts_dev_clean
type: rishabhjain16/libritts_dev_clean
config: en
split: test
metrics:
- type: wer
value: 5.39
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_pf_swedish
type: rishabhjain16/infer_pf_swedish
config: en
split: test
metrics:
- type: wer
value: 7.74
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_pf_german
type: rishabhjain16/infer_pf_german
config: en
split: test
metrics:
- type: wer
value: 36.21
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_pf_italian
type: rishabhjain16/infer_pf_italian
config: en
split: test
metrics:
- type: wer
value: 4.16
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: rishabhjain16/infer_so_chinese
type: rishabhjain16/infer_so_chinese
config: en
split: test
metrics:
- type: wer
value: 14.4
name: WER
openai/whisper-medium
This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3622
- Wer: 10.6091
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
0.5314 | 0.12 | 500 | 0.2762 | 13.2758 |
0.1619 | 1.09 | 1000 | 0.2541 | 11.6909 |
0.069 | 2.05 | 1500 | 0.2768 | 10.2892 |
0.0756 | 3.02 | 2000 | 0.2756 | 11.6142 |
0.0324 | 3.14 | 2500 | 0.2961 | 11.1800 |
0.0171 | 4.11 | 3000 | 0.3322 | 11.1689 |
0.0046 | 5.07 | 3500 | 0.3653 | 10.5858 |
0.0091 | 6.03 | 4000 | 0.3622 | 10.6091 |
Framework versions
- Transformers 4.29.0
- Pytorch 1.14.0a0+44dac51
- Datasets 2.12.0
- Tokenizers 0.13.3