--- language: - sv license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper Small Sv - Riksdag 100h results: [] --- # Whisper Small Sv - Riksdag 100h This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4977 - Wer: 1118.4718 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 20000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:---------:| | 0.1384 | 0.11 | 1000 | 0.4747 | 380.8335 | | 0.1186 | 0.22 | 2000 | 0.4513 | 1032.3900 | | 0.1056 | 0.33 | 3000 | 0.4385 | 582.0427 | | 0.0824 | 0.43 | 4000 | 0.4465 | 574.8907 | | 0.0961 | 0.54 | 5000 | 0.4199 | 1004.9138 | | 0.0939 | 0.65 | 6000 | 0.4478 | 866.2979 | | 0.0758 | 0.76 | 7000 | 0.4384 | 907.9496 | | 0.0741 | 0.87 | 8000 | 0.4264 | 641.1371 | | 0.0692 | 0.98 | 9000 | 0.4206 | 1142.6550 | | 0.0257 | 1.08 | 10000 | 0.4707 | 1152.4312 | | 0.0273 | 1.19 | 11000 | 0.4789 | 1100.2058 | | 0.021 | 1.3 | 12000 | 0.4763 | 1236.1719 | | 0.0163 | 1.41 | 13000 | 0.5035 | 924.8006 | | 0.0183 | 1.52 | 14000 | 0.4911 | 1285.1814 | | 0.024 | 1.63 | 15000 | 0.4861 | 1140.8284 | | 0.0158 | 1.73 | 16000 | 0.4793 | 1181.7597 | | 0.0167 | 1.84 | 17000 | 0.4759 | 1207.3064 | | 0.0231 | 1.95 | 18000 | 0.4801 | 1139.6964 | | 0.0054 | 2.06 | 19000 | 0.4934 | 1114.4842 | | 0.006 | 2.17 | 20000 | 0.4977 | 1118.4718 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.13.2