--- license: cc-by-4.0 language: - 'no' - nb - nn - en datasets: - NbAiLab/ncc_speech - NbAiLab/NST - NbAiLab/NPSC tags: - audio - asr - automatic-speech-recognition - hf-asr-leaderboard metrics: - wer - cer library_name: transformers pipeline_tag: automatic-speech-recognition --- # NB-Whisper small (beta) This is a **_public beta_** of the Norwegian NB-Whisper model released by the National Library of Norway. NB-Whisper is a series of models for automatic speech recognition (ASR) and speech translation, building upon the foundation laid by [OpenAI's Whisper](https://arxiv.org/abs/2212.04356). All models are trained on 20,000 hours of labeled data.
Speech given by His Majesty The King of Norway at the garden party hosted by Their Majesties The King and Queen at the Palace Park on 1 September 2016.
## Model Details NB-Whisper models will be available in five different sizes: | Model Size | Parameters | Availability | |------------|------------|--------------| | tiny | 39M | _Will be released in public beta shortly_ | | base | 74M | _Will be released in public beta shortly_ | | small | 244M | This model, available in public beta | | medium | 769M | _Will be released in public beta later this summer_ | | large | 1550M | _Will be released in public beta later this summer_ | An official release of NB-Whisper models is planned for the Fall 2023. Please refer to the OpenAI Whisper model card for more details about the backbone model. ### Model Description - **Developed by:** [NB AI-Lab](https://ai.nb.no/) - **Shared by:** [NB AI-Lab](https://ai.nb.no/) - **Model type:** `whisper` - **Language(s) (NLP):** Norwegian, Norwegian Bokmål, Norwegian Nynorsk, English - **License:** [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) - **Finetuned from model:** [openai/whisper-small](https://huggingface.co/openai/whisper-small) ### Model Sources - **Repository:** https://github.com/NbAiLab/nb-whisper/ - **Paper:** _Coming soon_ - **Demo:** http://ai.nb.no/demo/nb-whisper ## Uses ### Direct Use This is a **_public beta_** release. The models published in this repository are intended for a generalist purpose and are available to third parties. ### Downstream Use For Norwegian transcriptions we are confident that this public beta will give you State-of-the-Art results compared to currently available Norwegian ASR models of the same size. However, it is still known to show some hallucinations, as well as a tendency to drop part of the transcript from time to time. Please also note that the transcripts are typically not word by word. Spoken language and written language are often very different, and the model aims to "translate" spoken utterances into grammatically correct written sentences. We strongly believe that the best way to understand these models is to try them yourself. A significant part of the training material comes from TV subtitles. Subtitles often shorten the content to make it easier to read. Typically, non-essential parts of the utterance can be also dropped. In some cases, this is a desired ability, in other cases, this is undesired. The final release of these model will provida a mechanism to control for this beaviour. ## Bias, Risks, and Limitations This is a public beta that is not intended for production. Production use without adequate assessment of risks and mitigation may be considered irresponsible or harmful. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (The National Library of Norway) be liable for any results arising from the use made by third parties of these models. ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import pipeline asr = pipeline( "automatic-speech-recognition", "NbAiLab/nb-whisper-small-beta" ) asr( "audio.mp3", generate_kwargs={'task': 'transcribe', 'language': 'no'} ) # {'text': ' Så mange anga kører seg i så viktig sak, så vi får du kører det tilbake med. Om kabaret gudam i at vi skal hjælge. Kør seg vi gjør en uda? Nei noe skal å abelistera sonvorne skrifer. Det er sak, så kjent det bare handling i samtatsen til bargører. Trudet første lask. På den å først så å køre og en gange samme, og så får vi gjør å vorte vorte vorte når vi kjent dit.'} ``` Timestamps can also be retrieved by passing in the right parameter. ```python asr( "audio.mp3", generate_kwargs={'task': 'transcribe', 'language': 'no'}, return_timestamps=True, ) # {'text': ' at så mange angar til seg så viktig sak, så vi får jo kjølget klare tilbakemeldingen om hva valget dem gjør at vi skal gjøre. Hva skjer vi gjøre nå da? Nei, nå skal jo administrationen vår skrivferdige sak, så kjem til behandling i samfærdshetshøyvalget, tror det første # r. Først så kan vi ta og henge dem kjemme, og så får vi gjøre vårt valget når vi kommer dit.', # 'chunks': [{'timestamp': (0.0, 5.34), # 'text': ' at så mange angar til seg så viktig sak, så vi får jo kjølget klare tilbakemeldingen om'}, # {'timestamp': (5.34, 8.64), # 'text': ' hva valget dem gjør at vi skal gjøre.'}, # {'timestamp': (8.64, 10.64), 'text': ' Hva skjer vi gjøre nå da?'}, # {'timestamp': (10.64, 17.44), # 'text': ' Nei, nå skal jo administrationen vår skrivferdige sak, så kjem til behandling i samfærdshetshøyvalget,'}, # {'timestamp': (17.44, 19.44), 'text': ' tror det første år.'}, # {'timestamp': (19.44, 23.94), # 'text': ' Først så kan vi ta og henge dem kjemme, og så får vi gjøre vårt valget når vi kommer dit.'}]} ``` ## Environmental Impact Carbon emissions estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** TPUv4 - **Hours used:** 1,536 - **Cloud Provider:** Google Cloud - **Compute Region:** `us-central1` - **Carbon Emitted:** Total emissions are estimated to be 247.77 kgCO₂ of which 100 percents were directly offset by the cloud provider. #### Software The model is trained using Jax/Flax. The final model is converted to Pytorch, whisper.cpp and ONXX. Please tell us if you would like future models to be converted to other format. ## Citation & Authors This model was developed within the scope of the _NoSTram_ project, led by _Per Egil Kummervold_. The Jax code and training scripts were crafted by _Javier de la Rosa_, _Freddy Wetjen_, _Rolv-Arild Braaten_, and _Per Egil Kummervold_. Dataset curation was carried out by _Freddy Wetjen_, _Rolv-Arild Braaten_, and _Per Egil Kummervold_. Documentation was composed by _Javier de la Rosa_ and _Per Egil Kummervold_. The AiLab is under the direction of _Svein Arne Brygfjeld_. Each author contributed to the development and deliberations on the optimal way to train a Norwegian ASR model using Whisper. The work on this model was conducted as part of their professional roles at the National Library of Norway. _A paper is coming soon!_ If you plan on using this model in your research, we st ## Acknowledgements Thanks to [Google TPU Research Cloud](https://sites.research.google/trc/about/) for supporting this project with extensive training resources. Thanks to Google Cloud for supporting us with credits for translating large parts of the corpus. A special thanks to [Sanchit Ghandi](https://huggingface.co/sanchit-gandhi) for providing thorough technical advice in debugging and with the work of getting this to train on Google TPUs. A special thanks to Per Erik Solberg at Språkbanken for the collaboration with regard to the Stortinget corpus. ## Contact We are releasing this ASR Whisper model as a public beta to garner constructive feedback on its performance. Please do not hesitate to contact us with any experiences, insights, or suggestions that you may have. Your input is invaluable in helping us to improve the model and ensure that it effectively serves the needs of users. Whether you have technical concerns, usability suggestions, or ideas for future enhancements, we welcome your input. Thank you for participating in this critical stage of our model's development. ailab@nb.no