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README.md
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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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- Loss: 1.0240
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- Bleu:
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- Chrf: 50.
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- Wer:
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## Model description
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- training_steps: 2000
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
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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 |
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| 1.125 | 0.56 | 1700 |
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| 1.1348 | 0.59 | 1800 |
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| 1.14 | 0.62 | 1900 |
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| 1.1059 | 0.66 | 2000 |
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### Framework versions
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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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- 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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| 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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