--- 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 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: 32.14 - name: Wer type: wer value: 65.96127870328681 --- # 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: 1.0240 - Bleu: 32.14 - Chrf: 50.84 - Wer: 65.9613 ## 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_steps: 0.03 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer | |:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:| | 2.9468 | 0.03 | 100 | 4.72 | 20.55 | 2.2829 | 120.6213 | | 2.5074 | 0.07 | 200 | 7.81 | 25.23 | 2.0136 | 114.8131 | | 2.2406 | 0.1 | 300 | 11.24 | 29.39 | 1.8224 | 95.9928 | | 2.2466 | 0.13 | 400 | 16.01 | 34.73 | 1.6530 | 83.4309 | | 2.0276 | 0.16 | 500 | 16.69 | 34.76 | 1.5344 | 94.2368 | | 1.8429 | 0.2 | 600 | 21.37 | 37.48 | 1.4923 | 78.5682 | | 1.7621 | 0.23 | 700 | 23.4 | 40.89 | 1.3666 | 74.3359 | | 1.5629 | 0.26 | 800 | 24.76 | 44.63 | 1.2876 | 76.6321 | | 1.5458 | 0.3 | 900 | 25.81 | 44.59 | 1.2178 | 72.6249 | | 1.2971 | 0.33 | 1000 | 27.63 | 46.91 | 1.1823 | 70.2837 | | 1.3852 | 0.36 | 1100 | 27.18 | 46.16 | 1.2303 | 70.6889 | | 1.309 | 0.39 | 1200 | 27.65 | 47.41 | 1.1573 | 72.0396 | | 1.1818 | 0.43 | 1300 | 31.17 | 48.36 | 1.1304 | 61.6389 | | 1.2711 | 0.46 | 1400 | 33.55 | 50.95 | 1.0839 | 60.1981 | | 1.1305 | 0.49 | 1500 | 30.37 | 50.78 | 1.0718 | 68.6628 | | 1.0544 | 0.53 | 1600 | 1.1109| 26.98 | 48.1 | 73.7506 | | 1.125 | 0.56 | 1700 | 1.0709| 30.76 | 50.19 | 61.7740 | | 1.1348 | 0.59 | 1800 | 1.0530| 33.71 | 50.6 | 59.9280 | | 1.14 | 0.62 | 1900 | 1.0392| 31.45 | 50.16 | 66.9068 | | 1.1059 | 0.66 | 2000 | 1.0240| 32.14 | 50.84 | 65.9613 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2