File size: 2,016 Bytes
0733bae 9a74a44 0733bae 9a74a44 0733bae 9a74a44 0733bae 1b8dc92 0733bae 9a74a44 0733bae 9a74a44 0733bae 9a74a44 1b8dc92 74fe304 feb112a 0733bae c17e695 0733bae a9dd21f 1b8dc92 feb112a 1b8dc92 4dcf62c 0733bae feb112a 52c0183 feb112a d2e7fed |
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 |
---
language:
- en
license: apache-2.0
base_model: openai/whisper-medium.en
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Base EN
results: []
---
<!-- 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 Base EN
This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the ADLINK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
- Wer: 1.5152
## 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: 8
- eval_batch_size: 8
- 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: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0495 | 25.0 | 100 | 1.0270 | 2.1212 |
| 0.3802 | 50.0 | 200 | 0.3923 | 1.8182 |
| 0.0205 | 75.0 | 300 | 0.0130 | 1.8182 |
| 0.0012 | 100.0 | 400 | 0.0012 | 0.9091 |
| 0.0006 | 125.0 | 500 | 0.0006 | 0.9091 |
| 0.0004 | 150.0 | 600 | 0.0004 | 0.9091 |
| 0.0003 | 175.0 | 700 | 0.0003 | 0.9091 |
| 0.0003 | 200.0 | 800 | 0.0003 | 2.1212 |
| 0.0003 | 225.0 | 900 | 0.0003 | 2.1212 |
| 0.0003 | 250.0 | 1000 | 0.0003 | 1.5152 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0a0+ebedce2
- Datasets 2.19.2
- Tokenizers 0.19.1
|