alt.freeweights.speech / nb-whisper-base-ONNX
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nb-whisper-base-ONNX

Date: 2026-11-15Commit: 9bdce3aSigned: 2DC28CC7B26A283B ✓

Automatic speech recognition model. Onnx weights, 3.52 GB in all. Published by onnx-community under Apache-2.0.

speech3.52GBnb-whisper-base-ONNXonnxpermissive
FactsParamsnot statedSize3.52 GBFormatsonnxTaskautomatic speech recognitionLicenseApache-2.0From sourcepermissive
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From sourceHosted (signed)onnx-community/nb-whisper-base-ONNXLast check: 2026-11-15 05:03 UTC

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Overview

Size
3.52 GB
Weights
14.1 MB (smallest of 32 ONNX variants)
Parameters
not stated
Formats
onnx
Task
automatic speech recognition
License
Apache-2.0 (permissive)
Files
45, each with a sha256
Publisher
onnx-community
Added
2026-11-15
Section
alt.freeweights.speech
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Original release from the publisher.

Base model: NbAiLab/nb-whisper-base

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Model card

From the publisher at commit 9bdce3a. Shown as plain text with links; images open at the publisher.

nb-whisper-base (ONNX)#

This is an ONNX version of NbAiLab/nb-whisper-base. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js#

See the pipeline documentation for automatic-speech-recognition: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AutomaticSpeechRecognitionPipeline


NB-Whisper Base#

Introducing the Norwegian NB-Whisper Base model, proudly developed by the National Library of Norway. NB-Whisper is a cutting-edge series of models designed for automatic speech recognition (ASR) and speech translation. These models are based on the work of OpenAI's Whisper. Each model in the series has been trained for 250,000 steps, utilizing a diverse dataset of 8 million samples. These samples consist of aligned audio clips, each 30 seconds long, culminating in a staggering 66,000 hours of speech. For an in-depth understanding of our training methodology and dataset composition, keep an eye out for our upcoming article.

Model SizeParametersModel
Tiny39MNB-Whisper Tiny
Base74MNB-Whisper Base
Small244MNB-Whisper Small
Medium769MNB-Whisper Medium
Large1550MNB-Whisper Large
Verbatim Model#

While the main models are suitable for most transcription task, we demonstrate how easy it is to change the output of the main model. The following models are trained 250 additional steps from the main models above, and might be suitable for more targetted use cases:

  • Verbatim version: This lower-cased variant is more literal and suitable for tasks requiring detailed transcription, such as linguistic analysis.
Model SizeParametersSemantic version
Tiny39MTiny - semantic
Base74MBase - semantic
Small244MSmall - semantic
Medium769MMedium - semantic
Large1550MLarge - semantic
Model Description#

How to Use the Models#

Online Demos#

You can try the models directly through the HuggingFace Inference API, accessible on the right side of this page. Be aware that initially, the model needs to load and will run on limited CPU capacity, which might be slow. To enhance your experience, we are temporarily hosting some models on TPUs for a few days, significantly boosting their performance. Explore these under the Spaces section on the Main Page.

Local Setup with HuggingFace#

Alternatively, you can run the models locally. The Tiny, Base, and Small models are optimized for CPU execution. For the Medium and Large models, we recommend a system equipped with a GPU to ensure efficient processing. Setting up and using these models with HuggingFace's Transformers is straightforward, provided you have Python installed on your machine. For practical demonstrations, refer to examples using this sample mp3 file.

# Download the sample file
$ wget -N https://github.com/NbAiLab/nb-whisper/raw/main/audio/king.mp3

# Install necessary libraries. 
$ pip install transformers>=4.35.2

After this is done, you should be able to run this in Python:

from transformers import pipeline

# Load the model
asr = pipeline("automatic-speech-recognition", "NbAiLabBeta/nb-whisper-base")

#transcribe
asr("king.mp3", generate_kwargs={'task': 'transcribe', 'language': 'no'})

Expected output

{
  {'text': ' Nordmenn er nordlendinger, trøndere, sørlendinger og folk fra alle andre regioner. Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra.'}
}
Extended HuggingFace#

Examining the output above, we see that there are multiple repetitions at the end. This is because the video is longer than 30 seconds. By passing the chunk_lengt_s argument, we can transcribe longer file. Our experience is that we get slightly better result by setting that to 28 seconds instead of the default 30 seconds. We also recommend setting the beam size to 5 if possible. This greatly increases the accuracy but takes a bit longer and requires slightly more memory. The examples below also illustrates how to transcribe to English or Nynorsk, and how to get timestamps for sentences and words.

# Long Transcripts
asr("king.mp3", chunk_length_s=28, generate_kwargs={'task': 'transcribe', 'language': 'no'})

# Increase accuracy by setting beam size to 5
asr("king.mp3", chunk_length_s=28, return_timestamps=True, generate_kwargs={'num_beams': 5, 'task': 'transcribe', 'language': 'no'})

# Return Timestamps
asr("king.mp3", chunk_length_s=28, return_timestamps=True, generate_kwargs={'task': 'transcribe', 'language': 'no'})

# Return Word Level Timestamps
asr("king.mp3", chunk_length_s=28, return_timestamps="word", generate_kwargs={'task': 'transcribe', 'language': 'no'})

# Transcribe to Nynorsk
asr("king.mp3", chunk_length_s=28, generate_kwargs={'task': 'transcribe', 'language': 'nn'})

# Transcribe to English
asr("king.mp3", chunk_length_s=28, generate_kwargs={'task': 'transcribe', 'language': 'en'})

Expected output

Long transcripts:

{
  {'text': ' Nordmenn er nordlendinger, trøndere, sørlendinger og folk fra alle andre regioner. Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra, hvilken nasjonalitet vi tilhører. Det vi kaller hjem, er der hjertet vårt er, og det kan ikke alltid plasseres innenfor landegrenser. Nordmenn er jenter som er glad i jenter, gutter som er glad i gutter, og jenter og gutter som er glad i hverandre. Nordmenn trommer på Gud, Allah, Altet og ingenting. Nordmenn liker Grieg, Kygo, Helbilis og Kari Bremnes. Med andre ord, Norge er dere. Norge er oss. Mitt største håp for Norge er at vi skal klare å ta vare på hverandre, at vi skal bygge dette landet videre på tillit, fellesskap og raushet.'}
}

Timestamps:

{
  {'text': ' Nordmenn er nordlendinger, trøndere, sørlendinger og folk fra alle andre regioner. Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi er fra. Hvilken nasjonalitet vi er fra. hvilken nasjonalitet vi tilhører. Det vi kaller hjem, er der hjertet vårt er, og det kan ikke alltid plasseres innenfor landegrenser. Nordmenn er jenter som er glad i jenter, gutter som er glad i gutter, og jenter og gutter som er glad i hverandre. Nordmenn trommer på Gud, Allah, Altet og ingenting. Nordmenn liker Grieg, Kygo, Helbiles og Kari Bremnes. Med andre ord, Norge er dere. Norge er oss. Mitt største håp for Norge er at vi skal klare å ta vare på hverandre, at vi skal bygge dette landet videre på tillit, fellesskap og raushet.',
 'chunks': [{'timestamp': (0.0, 5.46),
   'text': ' Nordmenn er nordlendinger, trøndere, sørlendinger'},
  {'timestamp': (5.52, 8.68), 'text': ' og folk fra alle andre regioner.'},
  {'timestamp': (8.68, 16.64),
   'text': ' Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria.'},
  {'timestamp': (16.64, 13.3),
   'text': ' Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi er fra.'},
  {'timestamp': (13.32, 30.28),
   'text': ' Hvilken nasjonalitet vi er fra. hvilken nasjonalitet vi tilhører.'},
  {'timestamp': (32.52, 39.16),
   'text': ' Det vi kaller hjem, er der hjertet vårt er, og det kan ikke alltid plasseres'},
  {'timestamp': (39.16, 42.0), 'text': ' innenfor landegrenser.'},
  {'timestamp': (42.0, 46.74),
   'text': ' Nordmenn er jenter som er glad i jenter, gutter som er glad i gutter,'},
  {'timestamp': (46.74, 51.12),
   'text': ' og jenter og gutter som er glad i hverandre.'},
  {'timestamp': (51.16, 57.42),
   'text': ' Nordmenn trommer på Gud, Allah, Altet og ingenting.'},
  {'timestamp': (57.42, 64.3),
   'text': ' Nordmenn liker Grieg, Kygo, Helbiles og Kari Bremnes.'},
  {'timestamp': (64.34, 71.24),
   'text': ' Med andre ord, Norge er dere. Norge er oss.'},
  {'timestamp': (71.24, 78.04),
   'text': ' Mitt største håp for Norge er at vi skal klare å ta vare på hverandre,'},
  {'timestamp': (78.12, 84.68),
   'text': ' at vi skal bygge dette landet videre på tillit, fellesskap og raushet.'}]}
}

Word Level Timestamps:

{
  {"text": "Nordmenn er nordlendinger, trøndere, sørlendinger og folk fra alle andre regioner. Nordmenn er også innvandret fra Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikke alltid så lett å si hvor vi er fra, hvilken nasjonalitet vi tilhører. Det vi kaller hjem, er der hjertet vårt er, og det kan ikke alltid plasseres innenfor landegrenser. Nordmenn er jenter som er glad i jenter, gutter som er glad i gutter, og jenter og gutter som er glad i hverandre. Nordmenn trommer på Gud, Allah, Altet og ingenting. Nordmenn liker Grieg, Kygo, Helbilis og Kari Bremnes. Med andre ord, Norge er dere. Norge er oss. Mitt største håp for Norge er at vi skal klare å ta vare på hverandre, at vi skal bygge dette landet videre på tillit, fellesskap og raushet.",
  "chunks": [
    {"text": "Nordmenn", "timestamp": [0.72, 1.42]},
    {"text": "er", "timestamp": [1.42, 1.74]},
    // ... more chunks ...
    {"text": "raushet.", "timestamp": [83.1, 84.88]}
  ]
  }
}

Nynorsk:

{
  {"text": "Nordmenn er nordlendingar, trøndarar, sørlendingar og folk frå alle andre regionar. Nordmenn er også innvandra frå Afghanistan, Pakistan, Polen, Sverige, Somalia og Syria. Det er ikkje alltid så lett å seie kvar vi er frå, kva nasjonalitet vi tilhøyrer. Det vi kallar heim, er der hjartet vårt er, og det kan ikkje alltid plasserast innanfor landegrenser. Nordmenn er jenter som er glad i jenter, gutar som erade i gutar, og jenter og gutar som er glade i kvarandre. Nordmenn trommar på Gud, Allah, Altet og ingenting. Nordmenn liker Grieg, Kygo, Helbiles og Kari Bremnes. Med andre ord, Noreg er dere! Noreg er oss. Mitt største håp for Noreg er at vi skal klare å ta vare på kvarandre, at vi skal byggje dette landet vidare på tillit, fellesskap og raushet."}
}

English:

{
  {"text": "Norwegians are Norwegians, trønders, southerners and people from all other regions. Norwegians are also invaded from Afghanistan, Pakistan, Poland, Sweden, Somalia and Suria. It is not always so easy to say where we are from, what nationality we belong to. What we call home is where our heart is, and it cannot always be placed within national borders. Norwegians are girls who like girls, boys who like boys, and girls and boys who like each other. Norwegians thrump on God, Allah, Altet and nothing. Norwegians like Grieg, Kygo, Helbilis and Kari Bremnes. In other words, Norway is you. Norway is us. My biggest hope for Norway is that we should be able to take care of each other, that we should build this country on trust, community and generosity."}
}
Whisper CPP#

Whisper CPP is a C++ implementation of the Whisper model, offering the same functionalities with the added benefits of C++ efficiency and performance optimizations. This allows embedding any Whisper model into a binary file, facilitating the development of real applications. However, it requires some familiarity with compiling C++ programs. Their homepage provides examples of how to build applications, including real-time transcription.

We have converted this model to the ggml-format model used by Whisper CPP binaries. The file can be downloaded here, and a q5_0 quantized version is also available here.

# We can download and compile whisper.cpp
$ git clone --depth 1 https://github.com/ggerganov/whisper.cpp --branch v1.5.1
$ cd whisper.cpp/
$ make

# We also need to convert the audio to WAV as that is the only format supported by whisper.cpp
$ wget -N https://github.com/NbAiLab/nb-whisper/raw/main/audio/king.mp3
$ ffmpeg -i king.mp3 -ar 16000 -ac 1 -c:a pcm_s16le king.wav                                        

# Lets download the two ggml-files from this site
wget -N https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/ggml-model.bin -O models/nb-base-ggml-model.bin
wget -N https://huggingface.co/NbAiLab/nb-whisper-base/resolve/main/ggml-model-q5_0.bin -O models/nb-base-ggml-model-q5_0.bin

# And run it with the f16 default model
$ ./main -l no -m models/nb-base-ggml-model.bin king.wav

# Or the quantized version
$ ./main -l no -m models/nb-base-ggml-model-q5_0.bin king.wav
WhisperX and Speaker Diarization#

Speaker diarization is a technique in natural language processing and automatic speech recognition that identifies and separates different speakers in an audio recording. It segments the audio into parts based on who is speaking, enhancing the quality of transcribing meetings or phone calls. We find that WhisperX is the easiest way to use our models for diarizing speech. In addition, WhisperX is using phoneme-based Wav2Vec-models for improving the alignment of the timestamps. As of December 2023 it also has native support for using the nb-wav2vec-models. It currently uses PyAnnote-audio for doing the actual diarization. This package has a fairly strict licence where you have to agree to user terms. Follow the instructions below.

# Follow the install instructions on https://github.com/m-bain/whisperX
# Make sure you have a HuggingFace account and have agreed to the pyannote terms

# Log in (or supply HF Token in command line)
huggingface-cli login

# Download a test file
wget -N https://github.com/NbAiLab/nb-whisper/raw/main/audio/knuthamsun.mp3

# Optional. If you get complains about not support for Norwegian, do:
pip uninstall whisperx && pip install git+https://github.com/m-bain/whisperx.git@8540ff5985fceee764acbed94f656063d7f56540

# Transcribe the test file. All transcripts will end up in the directory of the mp3-file
whisperx knuthamsun.mp3 --model NbAiLabBeta/nb-whisper-base --language no --diarize

You can also run WhisperX from Python. Please take a look at the instructions on WhisperX homepage.

API#

Instructions for accessing the models via a simple API are included in the demos under Spaces. Note that these demos are temporary and will only be available for a few weeks.

Training Data#

The training data originates from Språkbanken and the National Library of Norway's digital collection, including:

  • NST Norwegian ASR Database (16 kHz) and its corresponding dataset
  • Transcribed speeches from the Norwegian Parliament by Språkbanken
  • TV broadcast (NRK) subtitles (NLN digital collection)
  • Audiobooks (NLN digital collection)

Downstream Use#

The models, especially the smaller ones, may exhibit occasional hallucinations and may drop parts of the transcript. They are designed to convert spoken language into grammatically correct written sentences, which might not always be word-for-word translations. We have made two extra model variant for users that want a different transcription style. We encourage users to try the models themselves to get a better understanding.

Bias, Risks, and Limitations#

Using these models without adequate risk assessment and mitigation could be considered irresponsible. They may contain biases or other undesirable distortions. Users who deploy these models or integrate them into systems or services are responsible for mitigating risks and complying with applicable AI regulations. The National Library of Norway, as the model owner, disclaims liability for any outcomes resulting from third-party use of these models.

Software#

The model was trained using Jax/Flax and converted to PyTorch, Tensorflow, whisper.cpp, and ONXX formats. These are available under Files and versions. We welcome requests for conversion to other formats. All training code and scripts are released under the Apache License 2.0 in the GitHub repository nb-whisper.

Citation & Contributors#

The NB-Whisper Base model is a product of the NoSTram project led by Per Egil Kummervold (@pere) at the National Library of Norway. Key contributors include Javier de la Rosa (@versae), Freddy Wetjen (@freddyw), and Rolv-Arild Braaten (@Rolv-Arild). NB AI-Lab, under the direction of Svein Arne Brygfjeld (@Brygfjeld), supported the project's successful completion. A detailed paper on our process and findings is forthcoming.

Disclaimer#

The models published in this repository are intended for a generalist purpose and are available to third parties. 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.

Acknowledgements#

Our gratitude extends to Google TPU Research Cloud for training resources, Google Cloud for translation credits, and HuggingFace's Sanchit Ghandi for technical support. A special thank you to Per Erik Solberg at Språkbanken for the collaboration on the Stortinget corpus.

Contact#

For feedback, technical concerns, or collaboration inquiries, please contact ailab@nb.no. If you plan to include this model in your research, contact us for the latest information on our upcoming paper for citation purposes.

Files

45 files · 3.52 GBWeights 14.1 MB (smallest of 32 ONNX variants). Sizes and sha256 from the signed manifest.
Show all 45 files with their sha256
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quantize_config.json312 B70f61db8708568fa3ee6e66ecbcfdb8a89b9069feadff679dea00b097e521c98
special_tokens_map.json2.19 KB4679897a50cc1aea98b7d63b8c928b407f252683d3248add11d091e66cf0d5f1
tokenizer.json3.93 MB1748d589785f2e50648a90dbf225bff0431f9e2556244d146909074fc38ed492
tokenizer_config.json283 KB67e1076baeba59ca5631fc6889bae5616b71a650e61e5d5feabcc7a8e6b9b302
vocab.json1.04 MB50d6a919f0a0601d56a04eb583c780d18553aa388254ba3158eb6a00f13e2c1a
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
9bdce3acae pinned now2026-11-15 05:03 UTC45 files · 3.52 GB

Use it

$ fw get onnx-community/nb-whisper-base-ONNX
# manifest sha256 7e87749feb48… · 3.52 GB · 45 files
$ fw verify ./nb-whisper-base-ONNX onnx-community/nb-whisper-base-ONNX
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/onnx-community/nb-whisper-base-ONNX.json --dir ./nb-whisper-base-ONNX $ python3 fw.py verify ./nb-whisper-base-ONNX ./onnx-community__nb-whisper-base-ONNX.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/nb-whisper-base-ONNX --revision 9bdce3acaeb772f968d0271cab8c72d883123cc9 --local-dir ./nb-whisper-base-ONNX

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
ce9013a370da7fe843a2863454ecdf189c3df480a718175d37f022eb6b631fc0
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.speechSubject: nb-whisper-base-ONNXDate: 2026-11-15Commit: 9bdce3aSigned: 2DC28CC7B26A283B ✓
Upstream
onnx-community/nb-whisper-base-ONNX
Pinned commit
9bdce3acaeb772f968d0271cab8c72d883123cc9
Manifest
nb-whisper-base-ONNX.json · .minisig
Manifest sha256
7e87749feb486bcfecd59fbe13cc15ae05141aaf29c0682a13d672077c987897
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 05:03 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/onnx-community/nb-whisper-base-ONNX.json $ curl -O https://freeweights.allternit.com/manifests/onnx-community/nb-whisper-base-ONNX.json.minisig $ minisign -Vm nb-whisper-base-ONNX.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

NbAiLab/nb-whisper-base · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/nb-whisper-base-ONNX
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. nb-whisper-base-ONNX.json. Provenance. Takedown history: none.
From: MIT <huggingface.co/MIT>Subject: ast-finetuned-audioset-10-10-0.4593
From: onnx-community <huggingface.co/onnx-community>Subject: ast-finetuned-audioset-10-10-0.4593-ONNX
From: Edge0 <huggingface.co/Edge0>Subject: Audio8-TTS-Preview-0.6b
From: Edge0 <huggingface.co/Edge0>Subject: Audio8-TTS-Realistic-Air
From: facebook <huggingface.co/facebook>Subject: audiobox-aesthetics
From: mlx-community <huggingface.co/mlx-community>Subject: chatterbox-4bit