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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-medium |
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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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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: 32.14 |
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- name: Wer |
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type: wer |
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value: 65.96127870328681 |
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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-medium](https://huggingface.co./openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset. |
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The best model checkpoint (this version) is at step 1400, epoch 1.84 (4 x 0.46), and it achieves the following results on the evaluation set: |
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- Loss: 1.0240 |
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- Bleu: 33.55 |
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- Chrf: 50.95 |
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- Wer: 60.1981 |
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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_steps: 0.03 |
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- training_steps: 2000 |
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- mixed_precision_training: Native AMP |
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### Hardware |
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4 x A40 48GB VRAM, with batch size 4 per machine (total: 16) |
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### Training results |
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| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.9468 | 0.03 | 100 | 4.72 | 20.55 | 2.2829 | 120.6213 | |
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| 2.5074 | 0.07 | 200 | 7.81 | 25.23 | 2.0136 | 114.8131 | |
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| 2.2406 | 0.1 | 300 | 11.24 | 29.39 | 1.8224 | 95.9928 | |
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| 2.2466 | 0.13 | 400 | 16.01 | 34.73 | 1.6530 | 83.4309 | |
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| 2.0276 | 0.16 | 500 | 16.69 | 34.76 | 1.5344 | 94.2368 | |
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| 1.8429 | 0.2 | 600 | 21.37 | 37.48 | 1.4923 | 78.5682 | |
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| 1.7621 | 0.23 | 700 | 23.4 | 40.89 | 1.3666 | 74.3359 | |
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| 1.5629 | 0.26 | 800 | 24.76 | 44.63 | 1.2876 | 76.6321 | |
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| 1.5458 | 0.3 | 900 | 25.81 | 44.59 | 1.2178 | 72.6249 | |
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| 1.2971 | 0.33 | 1000 | 27.63 | 46.91 | 1.1823 | 70.2837 | |
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| 1.3852 | 0.36 | 1100 | 27.18 | 46.16 | 1.2303 | 70.6889 | |
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| 1.309 | 0.39 | 1200 | 27.65 | 47.41 | 1.1573 | 72.0396 | |
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| 1.1818 | 0.43 | 1300 | 31.17 | 48.36 | 1.1304 | 61.6389 | |
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| 1.2711 | 0.46 | 1400 | 33.55 | 50.95 | 1.0839 | 60.1981 | |
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| 1.1305 | 0.49 | 1500 | 30.37 | 50.78 | 1.0718 | 68.6628 | |
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| 1.0544 | 0.53 | 1600 | 26.98 | 48.1 | 1.1109 | 73.7506 | |
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| 1.125 | 0.56 | 1700 | 30.76 | 50.19 | 1.0709 | 61.7740 | |
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| 1.1348 | 0.59 | 1800 | 33.71 | 50.6 | 1.0530 | 59.9280 | |
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| 1.14 | 0.62 | 1900 | 31.45 | 50.16 | 1.0392 | 66.9068 | |
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| 1.1059 | 0.66 | 2000 | 32.14 | 50.84 | 1.0240 | 65.9613 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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