File size: 4,775 Bytes
2fe4457 8c87fbd 2fe4457 8c87fbd 2fe4457 8c87fbd 2fe4457 8c87fbd 2fe4457 8c87fbd 7679d73 2fe4457 8c87fbd 2fe4457 |
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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- ymoslem/IWSLT2023-GA-EN
- ymoslem/FLEURS-GA-EN
- ymoslem/BitesizeIrish-GA-EN
- ymoslem/SpokenWords-GA-EN-MTed
- ymoslem/Tatoeba-Speech-Irish
- ymoslem/Wikimedia-Speech-Irish
metrics:
- bleu
- wer
model-index:
- name: Whisper Medium GA-EN Speech Translation
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia, augmented
with noise
type: ymoslem/IWSLT2023-GA-EN
metrics:
- name: Bleu
type: bleu
value: 32.01
- name: Wer
type: wer
value: 62.76452048626745
---
<!-- 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 GA-EN Speech Translation
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia, augmented with noise dataset.
The datasets are augmented in two ways: noise augmentation, and truncating low-magnitude samples.
The best model checkpoint (this version) based on ChrF is at step 2900, epoch 0.6349,
and it achieves the following results on the evaluation set:
- Loss: 1.1883
- Bleu: 32.88
- Chrf: 51.52
- Wer: 62.0441
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.02
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer |
|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:|
| 2.4487 | 0.0219 | 100 | 1.9518 | 8.34 | 24.49 | 117.2445 |
| 2.11 | 0.0438 | 200 | 1.6630 | 15.32 | 32.12 | 84.0612 |
| 1.9757 | 0.0657 | 300 | 1.5366 | 10.86 | 33.42 | 131.7875 |
| 1.7964 | 0.0876 | 400 | 1.4825 | 19.81 | 36.71 | 81.9451 |
| 1.6422 | 0.1095 | 500 | 1.4432 | 18.83 | 40.4 | 84.0162 |
| 1.3839 | 0.1314 | 600 | 1.4169 | 24.91 | 40.87 | 69.0230 |
| 1.352 | 0.1533 | 700 | 1.4340 | 25.01 | 41.57 | 71.5894 |
| 1.2434 | 0.1752 | 800 | 1.3813 | 24.05 | 41.29 | 73.7506 |
| 1.2223 | 0.1970 | 900 | 1.3578 | 25.89 | 41.61 | 70.5988 |
| 1.0414 | 0.2189 | 1000 | 1.3075 | 27.45 | 44.17 | 68.2575 |
| 0.9199 | 0.2408 | 1100 | 1.3022 | 23.14 | 44.3 | 84.1513 |
| 0.8648 | 0.2627 | 1200 | 1.3050 | 23.36 | 43.37 | 72.4448 |
| 0.8469 | 0.2846 | 1300 | 1.2853 | 28.37 | 45.97 | 67.1319 |
| 0.7649 | 0.3065 | 1400 | 1.2755 | 28.56 | 46.76 | 66.0964 |
| 0.7321 | 0.3284 | 1500 | 1.2750 | 27.23 | 46.1 | 69.3381 |
| 0.6541 | 0.3503 | 1600 | 1.2557 | 30.02 | 48.06 | 65.6011 |
| 0.6107 | 0.3722 | 1700 | 1.2520 | 30.41 | 49.23 | 64.2954 |
| 0.5738 | 0.3941 | 1800 | 1.2435 | 32.45 | 50.27 | 63.4399 |
| 0.4983 | 0.4160 | 1900 | 1.2007 | 31.17 | 48.58 | 64.0702 |
| 0.4439 | 0.4379 | 2000 | 1.2140 | 32.29 | 50.37 | 60.6033 |
| 0.367 | 0.4598 | 2100 | 1.2230 | 29.54 | 49.14 | 67.7172 |
| 0.2807 | 0.4817 | 2200 | 1.2277 | 33.1 | 51.21 | 62.9446 |
| 0.2621 | 0.5036 | 2300 | 1.2441 | 30.59 | 49.49 | 64.8807 |
| 0.2965 | 0.5255 | 2400 | 1.1969 | 31.82 | 49.67 | 63.5299 |
| 0.236 | 0.5473 | 2500 | 1.2275 | 31.17 | 50.29 | 65.1959 |
| 0.229 | 0.5692 | 2600 | 1.2008 | 30.02 | 50.27 | 70.6439 |
| 0.164 | 0.5911 | 2700 | 1.2192 | 31.37 | 50.57 | 63.6200 |
| 0.1786 | 0.6130 | 2800 | 1.1965 | 31.81 | 50.13 | 62.8546 |
| 0.1987 | 0.6349 | 2900 | 1.1883 | 32.88 | 51.52 | 62.0441 |
| 0.1633 | 0.6568 | 3000 | 1.1903 | 32.01 | 50.38 | 62.7645 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.0.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|