File size: 1,873 Bytes
a878b8c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
---
license: apache-2.0
tags:
- whisper-small
- asr
- zh-TW
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small TW
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: zh-TW
split: test
metrics:
- type: wer
value: 9.78
name: WER
---
<!-- 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 Medium TW
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 dataset.
## Training and evaluation data
Training:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation)
Evaluation:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (test)
## Training procedure
- Datasets were augmented using [audiomentations](https://github.com/iver56/audiomentations) via PitchShift, TimeStretch, Gain, AddGaussianNoise transformations at `p=0.3`.
- A space is added between each Chinese character, as demonstrated in the original paper. Effectively, WER == CER in this case.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: Adam
- generation_max_length: 225
- warmup_steps: 500
- max_steps: 2400
- fp16: True
- evaluation_strategy: "steps"
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu120
- Datasets 2.13.1
|