Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
Safetensors
Norwegian
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabArchive/scream_medium_beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/scream_medium_beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabArchive/scream_medium_beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabArchive/scream_medium_beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabArchive/scream_medium_beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - 'no' | |
| license: apache-2.0 | |
| tags: | |
| - audio | |
| - asr | |
| - automatic-speech-recognition | |
| - hf-asr-leaderboard | |
| base_model: openai/whisper-medium | |
| model-index: | |
| - name: scream_medium_beta | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # scream_medium_beta | |
| This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the NbAiLab/ncc_speech dataset. | |
| It achieves the following results on the evaluation set: | |
| - step: 24999 | |
| - validation_fleurs_loss: 1.4171 | |
| - train_loss: 0.5400 | |
| - validation_fleurs_wer: 8.8638 | |
| - validation_fleurs_cer: 3.8370 | |
| - validation_fleurs_exact_wer: 14.1278 | |
| - validation_fleurs_exact_cer: 5.1993 | |
| - validation_stortinget_loss: 0.3369 | |
| - validation_stortinget_wer: 14.2120 | |
| - validation_stortinget_cer: 10.2972 | |
| - validation_stortinget_exact_wer: 17.4640 | |
| - validation_stortinget_exact_cer: 10.8352 | |
| - validation_nrk_tv_loss: 0.8259 | |
| - validation_nrk_tv_wer: 39.9035 | |
| - validation_nrk_tv_cer: 31.1762 | |
| - validation_nrk_tv_exact_wer: 47.4289 | |
| - validation_nrk_tv_exact_cer: 32.3674 | |
| ## 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: 2.5e-05 | |
| - lr_scheduler_type: linear | |
| - per_device_train_batch_size: 16 | |
| - total_train_batch_size_per_node: 64 | |
| - total_train_batch_size: 1024 | |
| - total_optimization_steps: 25,000 | |
| - starting_optimization_step: None | |
| - finishing_optimization_step: 25,000 | |
| - num_train_dataset_workers: 32 | |
| - num_hosts: 16 | |
| - total_num_training_examples: 25,600,000 | |
| - steps_per_epoch: 6271 | |
| - num_beams: None | |
| - dropout: True | |
| - bpe_dropout_probability: 0.1 | |
| ### Training results | |
| | step | validation_fleurs_loss | train_loss | validation_fleurs_wer | validation_fleurs_cer | validation_fleurs_exact_wer | validation_fleurs_exact_cer | validation_stortinget_loss | validation_stortinget_wer | validation_stortinget_cer | validation_stortinget_exact_wer | validation_stortinget_exact_cer | validation_nrk_tv_loss | validation_nrk_tv_wer | validation_nrk_tv_cer | validation_nrk_tv_exact_wer | validation_nrk_tv_exact_cer | | |
| |:-----:|:----------------------:|:----------:|:---------------------:|:---------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:-------------------------:|:-------------------------:|:-------------------------------:|:-------------------------------:|:----------------------:|:---------------------:|:---------------------:|:---------------------------:|:---------------------------:| | |
| | 0 | 3.6595 | 2.4764 | 17.4301 | 5.4794 | 21.6249 | 6.3977 | 1.3465 | 33.9515 | 19.1377 | 38.4072 | 20.3275 | 1.8386 | 66.2133 | 48.0904 | 75.6490 | 49.8313 | | |
| | 5000 | 1.2828 | 0.6841 | 8.8638 | 3.8864 | 13.2318 | 4.9529 | 0.3311 | 14.5798 | 10.4664 | 17.7786 | 11.0119 | 0.8229 | 41.0824 | 31.7759 | 48.7519 | 33.0088 | | |
| | 10000 | 1.1134 | 0.6019 | 8.3284 | 3.6990 | 13.1123 | 4.9287 | 0.3132 | 14.0485 | 10.1394 | 17.2099 | 10.6785 | 0.7856 | 39.0957 | 30.3740 | 46.7798 | 31.5896 | | |
| | 15000 | 1.1605 | 0.5821 | 8.5068 | 3.7631 | 13.5603 | 5.0157 | 0.3181 | 13.7633 | 10.0236 | 16.9465 | 10.5585 | 0.7864 | 39.4419 | 30.9142 | 46.7507 | 32.1012 | | |
| | 20000 | 1.0986 | 0.5395 | 8.7448 | 3.9456 | 14.4863 | 5.3733 | 0.3226 | 14.2469 | 10.3402 | 17.4640 | 10.8776 | 0.7884 | 39.8129 | 31.0325 | 47.3332 | 32.2280 | | |
| ### Framework versions | |
| - Transformers 4.31.0.dev0 | |
| - Datasets 2.13.0 | |
| - Tokenizers 0.13.3 | |