Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
Norwegian
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabArchive/scream_small_beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/scream_small_beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabArchive/scream_small_beta")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabArchive/scream_small_beta") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabArchive/scream_small_beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - 'no' | |
| license: apache-2.0 | |
| tags: | |
| - audio | |
| - asr | |
| - automatic-speech-recognition | |
| - hf-asr-leaderboard | |
| model-index: | |
| - name: scream_small_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_small_beta | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the NbAiLab/ncc_speech dataset. | |
| ## 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: 5e-05 | |
| - lr_scheduler_type: linear | |
| - per_device_train_batch_size: 32 | |
| - total_train_batch_size_per_node: 128 | |
| - 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: 8 | |
| - total_num_training_examples: 25,600,000 | |
| - steps_per_epoch: _To be computed after first epoch_ | |
| - 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 | 1.2013 | 2.7117 | 32.3914 | 9.8343 | 35.7228 | 10.9398 | 1.4988 | 44.0673 | 22.9444 | 48.2612 | 24.2595 | 1.8165 | 79.9390 | 54.6020 | 89.7612 | 56.8482 | | |
| | 1000 | 0.5796 | 1.0147 | 16.1214 | 5.2624 | 19.9821 | 6.2962 | 0.4822 | 22.0502 | 13.3652 | 25.7586 | 14.0827 | 1.0170 | 51.9187 | 37.4011 | 59.7853 | 39.0187 | | |
| ### Framework versions | |
| - Transformers 4.31.0.dev0 | |
| - Datasets 2.13.0 | |
| - Tokenizers 0.13.3 | |