File size: 2,236 Bytes
0e79e65 |
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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
language:
- en
license: mit
base_model: distil-whisper/distil-small.en
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: Distil Whisper Small finetuned on PolyAI Minds14 English US.
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Speech Transcription in English from e-banking domain.
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3318442884492661
---
<!-- 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. -->
# Distil Whisper Small finetuned on PolyAI Minds14 English US.
This model is a fine-tuned version of [distil-whisper/distil-small.en](https://huggingface.co/distil-whisper/distil-small.en) on the Speech Transcription in English from e-banking domain. dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0182
- Wer Ortho: 0.3371
- Wer: 0.3318
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.2325 | 3.57 | 100 | 0.6222 | 0.3557 | 0.3472 |
| 0.0196 | 7.14 | 200 | 0.8475 | 0.3757 | 0.3689 |
| 0.0014 | 10.71 | 300 | 0.9729 | 0.3630 | 0.3555 |
| 0.0006 | 14.29 | 400 | 1.0182 | 0.3371 | 0.3318 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|