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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-medium-en
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: myst-test
type: asr
config: en
split: test
metrics:
- type: wer
value: 8.85
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: cslu_scripted
type: asr
config: en
split: test
metrics:
- type: wer
value: 2.38
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: cslu_spontaneous
type: asr
config: en
split: test
metrics:
- type: wer
value: 16.53
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: librispeech
type: asr
config: en
split: testclean
metrics:
- type: wer
value: 3.52
name: WER
---
# openai/whisper-medium-en
This model is a fine-tuned version of [openai/whisper-medium-en](https://huggingface.co/openai/whisper-medium-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.22987066209316254
- Wer: 7.945455976651671`
## Training and evaluation data
- Training data: Myst Train (125 hours) + CSLU Scripted train (35 hours)
- Evaluation data: Myst Dev (20.9 hours) + CSLU Scripted Dev(4.8)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- 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: 10000
- converged_after: 1000
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