|
--- |
|
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 |
|
type: ymoslem/IWSLT2023-GA-EN |
|
metrics: |
|
- name: Bleu |
|
type: bleu |
|
value: 33.79 |
|
- name: Wer |
|
type: wer |
|
value: 61.68392615938766 |
|
--- |
|
|
|
<!-- 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 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.3818 |
|
- Bleu: 33.79 |
|
- Chrf: 51.67 |
|
- Wer: 61.6839 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.03 |
|
- training_steps: 10000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer | |
|
|:-------------:|:------:|:-----:|:-----:|:-----:|:---------------:|:--------:| |
|
| 2.4382 | 0.0109 | 100 | 3.07 | 16.85 | 2.1114 | 171.0491 | |
|
| 2.6151 | 0.0219 | 200 | 6.25 | 23.02 | 2.0207 | 126.9698 | |
|
| 2.5699 | 0.0328 | 300 | 5.71 | 24.03 | 1.8660 | 155.5606 | |
|
| 2.3084 | 0.0438 | 400 | 9.87 | 28.45 | 1.8084 | 129.0860 | |
|
| 2.3327 | 0.0547 | 500 | 12.01 | 31.92 | 1.7823 | 102.7915 | |
|
| 2.1495 | 0.0657 | 600 | 13.97 | 32.4 | 1.7238 | 98.6042 | |
|
| 2.2164 | 0.0766 | 700 | 11.21 | 33.19 | 1.6538 | 146.0153 | |
|
| 2.0071 | 0.0876 | 800 | 14.34 | 35.72 | 1.7038 | 96.9383 | |
|
| 1.8334 | 0.0985 | 900 | 16.51 | 37.23 | 1.6329 | 96.8032 | |
|
| 1.8359 | 0.1095 | 1000 | 17.87 | 35.94 | 1.6637 | 84.4665 | |
|
| 1.7703 | 0.1204 | 1100 | 19.54 | 39.02 | 1.5626 | 79.7839 | |
|
| 1.5805 | 0.1314 | 1200 | 20.19 | 40.4 | 1.5618 | 77.8028 | |
|
| 1.4545 | 0.1423 | 1300 | 13.88 | 35.53 | 1.5599 | 112.5619 | |
|
| 1.5177 | 0.1533 | 1400 | 18.79 | 40.11 | 1.4880 | 84.6916 | |
|
| 1.6335 | 0.1642 | 1500 | 16.41 | 38.64 | 1.4996 | 96.9833 | |
|
| 1.3809 | 0.1752 | 1600 | 18.3 | 40.17 | 1.4739 | 101.8910 | |
|
| 1.2694 | 0.1861 | 1700 | 22.53 | 43.15 | 1.4498 | 76.9923 | |
|
| 1.2321 | 0.1970 | 1800 | 19.92 | 42.59 | 1.4163 | 84.6015 | |
|
| 1.1969 | 0.2080 | 1900 | 21.63 | 44.92 | 1.4137 | 85.3670 | |
|
| 1.2023 | 0.2189 | 2000 | 20.42 | 41.57 | 1.3530 | 82.8906 | |
|
| 1.1676 | 0.2299 | 2100 | 22.82 | 44.23 | 1.3723 | 78.1180 | |
|
| 1.0332 | 0.2408 | 2200 | 26.73 | 44.75 | 1.3641 | 70.2386 | |
|
| 0.8589 | 0.2518 | 2300 | 26.94 | 46.89 | 1.3344 | 72.7600 | |
|
| 0.9829 | 0.2627 | 2400 | 28.15 | 47.21 | 1.3181 | 69.1130 | |
|
| 0.8228 | 0.2737 | 2500 | 26.98 | 47.41 | 1.3049 | 74.0207 | |
|
| 0.7667 | 0.2846 | 2600 | 30.0 | 49.42 | 1.2698 | 65.1058 | |
|
| 0.8749 | 0.2956 | 2700 | 27.91 | 47.67 | 1.2878 | 66.9518 | |
|
| 0.7504 | 0.3065 | 2800 | 32.03 | 50.35 | 1.2670 | 63.6650 | |
|
| 0.7069 | 0.3175 | 2900 | 30.7 | 49.53 | 1.2771 | 64.4304 | |
|
| 0.7199 | 0.3284 | 3000 | 30.21 | 48.93 | 1.2658 | 65.5561 | |
|
| 0.6207 | 0.3394 | 3100 | 30.82 | 49.11 | 1.2687 | 66.0063 | |
|
| 0.5995 | 0.3503 | 3200 | 31.99 | 50.94 | 1.2207 | 62.9446 | |
|
| 0.6294 | 0.3612 | 3300 | 31.05 | 50.85 | 1.2422 | 64.7006 | |
|
| 0.4612 | 0.3722 | 3400 | 33.1 | 51.82 | 1.2203 | 61.9090 | |
|
| 0.5138 | 0.3831 | 3500 | 32.08 | 51.86 | 1.2007 | 63.0797 | |
|
| 0.5059 | 0.3941 | 3600 | 31.8 | 51.19 | 1.2130 | 63.9352 | |
|
| 0.417 | 0.4050 | 3700 | 32.45 | 51.41 | 1.1975 | 62.2692 | |
|
| 0.2958 | 0.4160 | 3800 | 29.29 | 51.39 | 1.2046 | 62.7645 | |
|
| 0.393 | 0.4269 | 3900 | 28.95 | 51.45 | 1.1968 | 63.1697 | |
|
| 0.3858 | 0.4379 | 4000 | 29.54 | 51.58 | 1.1929 | 62.4043 | |
|
| 0.5416 | 0.4488 | 4100 | 1.3522| 27.29 | 43.94 | 67.9424 | |
|
| 0.6644 | 0.4598 | 4200 | 1.4191| 23.16 | 44.45 | 77.3976 | |
|
| 0.5246 | 0.4707 | 4300 | 1.4221| 22.26 | 44.91 | 77.2625 | |
|
| 0.614 | 0.4817 | 4400 | 1.3956| 26.9 | 46.15 | 70.4638 | |
|
| 0.5973 | 0.4926 | 4500 | 1.4152| 25.55 | 45.51 | 76.7222 | |
|
| 0.544 | 0.5036 | 4600 | 1.4091| 23.54 | 47.87 | 79.1085 | |
|
| 0.5975 | 0.5145 | 4700 | 1.4644| 21.85 | 42.69 | 78.5682 | |
|
| 0.4675 | 0.5255 | 4800 | 1.4598| 22.93 | 43.69 | 76.9023 | |
|
| 0.7959 | 0.5364 | 4900 | 1.3884| 24.91 | 44.98 | 74.5610 | |
|
| 0.5936 | 0.5473 | 5000 | 1.4235| 26.91 | 44.88 | 69.0680 | |
|
| 0.4631 | 0.5583 | 5100 | 1.4002| 25.77 | 45.81 | 74.0207 | |
|
| 0.5188 | 0.5692 | 5200 | 1.4405| 28.37 | 45.48 | 66.2765 | |
|
| 0.4675 | 0.5802 | 5300 | 1.4045| 21.1 | 43.11 | 92.1207 | |
|
| 0.4214 | 0.5911 | 5400 | 1.4250| 25.62 | 44.82 | 72.2197 | |
|
| 0.4592 | 0.6021 | 5500 | 1.4107| 27.24 | 46.44 | 70.0585 | |
|
| 0.4809 | 0.6130 | 5600 | 1.3896| 27.93 | 47.42 | 69.5182 | |
|
| 0.4364 | 0.6240 | 5700 | 1.3808| 25.84 | 47.47 | 77.6227 | |
|
| 0.3333 | 0.6349 | 5800 | 1.4203| 26.46 | 47.08 | 72.4899 | |
|
| 0.3345 | 0.6459 | 5900 | 1.4763| 23.1 | 44.6 | 81.2247 | |
|
| 0.3368 | 0.6568 | 6000 | 1.4182| 24.55 | 45.76 | 80.5493 | |
|
| 0.3061 | 0.6678 | 6100 | 1.4218| 23.1 | 45.97 | 81.3597 | |
|
| 0.324 | 0.6787 | 6200 | 1.4453| 28.26 | 47.06 | 67.5822 | |
|
| 0.2667 | 0.6897 | 6300 | 1.4494| 27.87 | 46.14 | 69.0230 | |
|
| 0.2845 | 0.7006 | 6400 | 1.4448| 26.39 | 46.72 | 71.4543 | |
|
| 0.3125 | 0.7115 | 6500 | 1.4643| 27.81 | 46.45 | 70.0135 | |
|
| 0.264 | 0.7225 | 6600 | 1.4244| 26.27 | 47.75 | 72.7600 | |
|
| 0.2426 | 0.7334 | 6700 | 1.4081| 25.84 | 46.68 | 76.4070 | |
|
| 0.2174 | 0.7444 | 6800 | 1.4036| 30.67 | 47.92 | 65.8262 | |
|
| 0.2265 | 0.7553 | 6900 | 1.4174| 28.11 | 49.12 | 71.2292 | |
|
| 0.2016 | 0.7663 | 7000 | 1.4341| 30.43 | 49.47 | 65.9163 | |
|
| 0.1865 | 0.7772 | 7100 | 1.3690| 32.05 | 49.5 | 63.1697 | |
|
| 0.2148 | 0.7882 | 7200 | 1.3603| 32.29 | 49.91 | 63.8901 | |
|
| 0.2126 | 0.7991 | 7300 | 1.4046| 32.07 | 49.31 | 63.6650 | |
|
| 0.1594 | 0.8101 | 7400 | 1.4122| 29.94 | 47.48 | 65.5110 | |
|
| 0.1295 | 0.8210 | 7500 | 1.4243| 30.14 | 49.79 | 65.7812 | |
|
| 0.1378 | 0.8320 | 7600 | 1.4334| 31.23 | 49.42 | 65.9613 | |
|
| 0.1701 | 0.8429 | 7700 | 1.4149| 31.04 | 49.95 | 65.6461 | |
|
| 0.1102 | 0.8539 | 7800 | 1.4082| 31.37 | 50.2 | 63.7100 | |
|
| 0.1267 | 0.8648 | 7900 | 1.3642| 32.86 | 50.83 | 60.8285 | |
|
| 0.1384 | 0.8758 | 8000 | 1.3860| 33.47 | 49.61 | 59.8829 | |
|
| 0.1128 | 0.8867 | 8100 | 1.3840| 32.78 | 50.04 | 61.8190 | |
|
| 0.1197 | 0.8976 | 8200 | 1.3641| 33.69 | 50.94 | 61.8190 | |
|
| 0.1181 | 0.9086 | 8300 | 1.3913| 32.0 | 49.65 | 63.5299 | |
|
| 0.0866 | 0.9195 | 8400 | 1.4171| 30.39 | 48.48 | 68.0324 | |
|
| 0.0784 | 0.9305 | 8500 | 1.3850| 32.27 | 49.32 | 63.3949 | |
|
| 0.092 | 0.9414 | 8600 | 1.3880| 33.78 | 51.13 | 61.2787 | |
|
| 0.0685 | 0.9524 | 8700 | 1.3876| 34.33 | 51.23 | 61.1887 | |
|
| 0.0783 | 0.9633 | 8800 | 1.4010| 33.4 | 48.9 | 62.5844 | |
|
| 0.0735 | 0.9743 | 8900 | 1.4035| 33.72 | 49.01 | 61.5038 | |
|
| 0.0875 | 0.9852 | 9000 | 1.4064| 30.44 | 49.06 | 67.5371 | |
|
| 0.0822 | 0.9962 | 9100 | 1.3803| 34.64 | 51.51 | 60.5133 | |
|
| 0.041 | 1.0071 | 9200 | 1.3678| 34.66 | 52.06 | 59.4327 | |
|
| 0.0351 | 1.0181 | 9300 | 1.3739| 33.88 | 51.16 | 61.3688 | |
|
| 0.0368 | 1.0290 | 9400 | 1.3846| 35.2 | 51.73 | 60.4232 | |
|
| 0.035 | 1.0400 | 9500 | 1.3753| 34.23 | 51.32 | 60.8735 | |
|
| 0.0277 | 1.0509 | 9600 | 1.3788| 35.0 | 52.59 | 60.0180 | |
|
| 0.0247 | 1.0619 | 9700 | 1.3914| 34.69 | 51.7 | 60.2882 | |
|
| 0.0321 | 1.0728 | 9800 | 1.3804| 34.63 | 51.91 | 60.6033 | |
|
| 0.0286 | 1.0837 | 9900 | 1.3795| 33.92 | 51.64 | 61.8640 | |
|
| 0.0239 | 1.0947 | 10000 | 1.3818| 33.79 | 51.67 | 61.6839 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.2.0+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
