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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- wer
model-index:
- name: whisper-small-Eng-1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: speech_commands
type: speech_commands
config: v0.01
split: test
args: v0.01
metrics:
- name: Wer
type: wer
value: 239.6
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-Eng-1
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0620
- Wer: 239.6
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 7.156 | 0.01 | 5 | 6.9727 | 256.4 |
| 7.5392 | 0.02 | 10 | 5.0620 | 239.6 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|