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--- |
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language: |
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- ga |
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- en |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ymoslem/IWSLT2023-GA-EN |
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- ymoslem/FLEURS-GA-EN |
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- ymoslem/BitesizeIrish-GA-EN |
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- ymoslem/SpokenWords-GA-EN-MTed |
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- ymoslem/Tatoeba-Speech-Irish |
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- ymoslem/Wikimedia-Speech-Irish |
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metrics: |
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- bleu |
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- wer |
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model-index: |
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- name: Whisper Medium GA-EN Speech Translation |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia |
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type: ymoslem/IWSLT2023-GA-EN |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 30.79 |
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- name: Wer |
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type: wer |
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value: 66.59162539396668 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Medium GA-EN Speech Translation |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1869 |
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- Bleu: 30.79 |
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- Chrf: 52.18 |
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- Wer: 66.5916 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.02 |
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- training_steps: 4000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:-----:|:-----:|:--------:| |
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| 2.7614 | 0.0328 | 100 | 2.1538 | 3.55 | 19.32 | 146.1954 | |
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| 2.6486 | 0.0657 | 200 | 1.9662 | 7.4 | 25.58 | 132.4178 | |
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| 2.5302 | 0.0985 | 300 | 1.8689 | 6.8 | 24.76 | 172.0846 | |
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| 2.4506 | 0.1314 | 400 | 1.8244 | 13.88 | 32.7 | 95.9478 | |
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| 2.3631 | 0.1642 | 500 | 1.6759 | 14.35 | 32.54 | 95.6776 | |
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| 2.173 | 0.1970 | 600 | 1.7051 | 13.14 | 34.29 | 93.2463 | |
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| 2.3489 | 0.2299 | 700 | 1.6077 | 15.85 | 36.52 | 87.7082 | |
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| 2.0183 | 0.2627 | 800 | 1.5894 | 15.72 | 36.54 | 94.1018 | |
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| 2.1502 | 0.2956 | 900 | 1.5739 | 16.58 | 36.4 | 96.7132 | |
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| 2.016 | 0.3284 | 1000 | 1.5470 | 18.99 | 39.22 | 83.9712 | |
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| 1.747 | 0.3612 | 1100 | 1.5428 | 15.49 | 38.02 | 101.5759 | |
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| 1.6728 | 0.3941 | 1200 | 1.5129 | 19.45 | 39.24 | 89.4642 | |
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| 1.6476 | 0.4269 | 1300 | 1.4747 | 21.77 | 40.53 | 82.8906 | |
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| 1.6764 | 0.4598 | 1400 | 1.4672 | 16.58 | 39.94 | 95.0923 | |
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| 1.5683 | 0.4926 | 1500 | 1.4116 | 22.0 | 42.55 | 77.8028 | |
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| 1.3607 | 0.5255 | 1600 | 1.4290 | 24.15 | 43.36 | 74.1108 | |
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| 1.4888 | 0.5583 | 1700 | 1.3684 | 22.61 | 42.39 | 83.5660 | |
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| 1.4222 | 0.5911 | 1800 | 1.3791 | 25.68 | 46.64 | 70.1036 | |
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| 1.3456 | 0.6240 | 1900 | 1.3312 | 26.77 | 46.59 | 68.1225 | |
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| 1.1232 | 0.6568 | 2000 | 1.3433 | 27.24 | 44.86 | 72.2197 | |
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| 1.1674 | 0.6897 | 2100 | 1.3228 | 23.2 | 45.04 | 84.1513 | |
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| 1.0711 | 0.7225 | 2200 | 1.2771 | 25.23 | 46.41 | 76.5421 | |
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| 1.2015 | 0.7553 | 2300 | 1.2549 | 24.98 | 47.79 | 84.4214 | |
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| 1.1339 | 0.7882 | 2400 | 1.2758 | 27.01 | 48.04 | 72.2197 | |
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| 1.0196 | 0.8210 | 2500 | 1.2501 | 26.74 | 48.19 | 72.8050 | |
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| 0.9275 | 0.8539 | 2600 | 1.2430 | 32.42 | 50.41 | 62.2692 | |
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| 0.8328 | 0.8867 | 2700 | 1.2413 | 30.63 | 50.63 | 65.5110 | |
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| 0.7923 | 0.9195 | 2800 | 1.2441 | 26.18 | 48.19 | 74.0207 | |
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| 0.8887 | 0.9524 | 2900 | 1.2109 | 30.91 | 50.87 | 62.3593 | |
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| 0.7954 | 0.9852 | 3000 | 1.2233 | 31.63 | 49.93 | 64.5205 | |
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| 0.2886 | 1.0181 | 3100 | 1.2340 | 28.74 | 49.67 | 72.9401 | |
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| 0.2889 | 1.0509 | 3200 | 1.2369 | 31.74 | 49.23 | 63.6650 | |
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| 0.2812 | 1.0837 | 3300 | 1.2589 | 32.95 | 50.09 | 63.6200 | |
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| 0.2634 | 1.1166 | 3400 | 1.2428 | 30.14 | 49.93 | 69.9685 | |
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| 0.2248 | 1.1494 | 3500 | 1.2486 | 33.38 | 50.48 | 62.0441 | |
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| 0.2266 | 1.1823 | 3600 | 1.2089 | 29.8 | 50.46 | 67.2220 | |
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| 0.2148 | 1.2151 | 3700 | 1.1988 | 31.57 | 51.49 | 64.0252 | |
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| 0.2345 | 1.2479 | 3800 | 1.1889 | 32.46 | 52.24 | 64.1603 | |
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| 0.1924 | 1.2808 | 3900 | 1.1888 | 29.38 | 51.57 | 72.6700 | |
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| 0.2056 | 1.3136 | 4000 | 1.1869 | 30.79 | 52.18 | 66.5916 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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