alt.freeweights.speech / nb-whisper-base
From: NbAiLab <huggingface.co/NbAiLab>Newsgroups: alt.freeweights.speechSubject:

nb-whisper-base

Date: 2026-11-15Commit: 2ab372bSigned: 2DC28CC7B26A283B ✓

Automatic speech recognition model with 73M parameters. Ctranslate2 and ggml and onnx and safetensors weights, 2.74 GB in all. Published by NbAiLab under Apache-2.0.

73M params2.74GBnb-whisper-basectranslate2 ggml onnx safetensorspermissive
FactsParams73MSize2.74 GBFormatsctranslate2, ggml, onnx, safetensorsTaskautomatic speech recognitionLicenseApache-2.0From sourcepermissive
$ fw get NbAiLab/nb-whisper-base
# manifest sha256 62ceda714536… · 2.74 GB · 68 files
$ fw verify ./nb-whisper-base NbAiLab/nb-whisper-base
From sourceHosted (signed)NbAiLab/nb-whisper-baseLast check: 2026-11-15 05:06 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Quantized of whisper-base.

Works with

Overview

Size
2.74 GB
Weights
290 MB (safetensors)
Parameters
73M
Formats
ctranslate2, ggml, onnx, safetensors
Task
automatic speech recognition
License
Apache-2.0 (permissive)
Files
68, each with a sha256
Publisher
NbAiLab
Added
2026-11-15
Section
alt.freeweights.speech
Lineage

Original release from the publisher.

Base model: openai/whisper-base

Where these bytes come from
Publisher at the pinned commit→You

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 05:06 UTCpassed

Model card

From the publisher at commit 2ab372b. Shown as plain text with links; images open at the publisher.

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

68 files · 2.74 GBWeights 290 MB (safetensors). Sizes and sha256 from the signed manifest.
Show all 68 files with their sha256
FileSizesha256
.gitattributes1.61 KB50028c997e612f2373f076bde8d0e3ba1a63ef09353ac4620067248dbfa8aba1
README.md20.3 KB0aec5705636636fc12c50bd6b46d48f386d208a619898f672781632a858f56bf
added_tokens.json34.6 KB9715fd2243b6f06a5858b5e32950d2853f73dd5bc201aafcf76f5082a2d8acd1
config.json4.49 KB4991e1e104b10688f3cf127f3c0a10d2ace77d58419f2f3ef3e956d11a9817a4
config_hf.json2.25 KBd45c5f6f91abc662d83cde332d0dcce6978798aed076fdf7dfd01b021d7c4d7c
ct2/config.json2.25 KBa73a28cdfe1c43ccc7202fa333d1f89c202477271407ae9a7f19afa52039cac8
ct2/model.bin290 MB4c8483e9f7636023e740b3cbf68d27e2685a51276e797435786e7d380cf76d81
ct2/vocabulary.json1.07 MB5ad28279db5e546349708b9a74736e2c018737ac5a600f1048f1f26c99b85b47
export_models.sh2.14 KB01cbaa0b45aaa7d863580f177a2cedf70afece258260f1cfe78f7b173bab8175
flax_model.msgpack290 MBce183093b2aa8b51dafc7c8a0a05761dd002fa4109e69c67559282b3275ca41c
generation_config.json3.79 KB46c2bc2352277298d84f8385f4521b533a55270816ef63826cf5fd09145b6f2d
ggml-model-q5_0.bin55.3 MBdcb9f3ab963cd288974c826c1519ff73b78b2372e80d388a6ce94f29c6a5b40f
ggml-model.bin148 MB05fc04a7f16b634b99e0cbbe5e4e78a00469b834219e83df39ab4f8bca5c4b97
merges.txt494 KB78f27f801feb4283ae969d4cadacb28fc051b655647402b1a252c7079ea11c46
model.bin290 MB4c8483e9f7636023e740b3cbf68d27e2685a51276e797435786e7d380cf76d81
model.safetensors290 MB8033df8c4c1b1a96c9e569ca645ba25d5a5cc0f604a431e4a72683d81fb60bb3
model_def.json252 Bfb4660122b6890d9166f2c552178977994793433406d43bcb906a2fd669e81f4
normalizer.json52.7 KBbf1c507dc8724ca9cf9903640dacfb69dae2f00edee4f21ceba106a7392f26dd
onnx/added_tokens.json34.6 KB9715fd2243b6f06a5858b5e32950d2853f73dd5bc201aafcf76f5082a2d8acd1
onnx/config.json2.27 KBe501ffd339596a9d00e2d8c9b3d952cec1bb4154c0994e1b75eedd346576f289
onnx/decoder_model.onnx315 MB7c469f1b6f16a543c028ea94efded247d15d3a34c43191105ce0f35be610f16d
onnx/decoder_with_past_model.onnx302 MBcacafbc3e6435fde32b385f5692ad41ec054d8423ba321e8aa5ea84fc52beb39
onnx/encoder_model.onnx82.5 MB7f9a82b47fd1b82a5262c148d82b025df0c1ae7e1213f7db96f413e498fe2976
onnx/generation_config.json3.79 KB46c2bc2352277298d84f8385f4521b533a55270816ef63826cf5fd09145b6f2d
onnx/merges.txt494 KB78f27f801feb4283ae969d4cadacb28fc051b655647402b1a252c7079ea11c46
onnx/normalizer.json52.7 KBbf1c507dc8724ca9cf9903640dacfb69dae2f00edee4f21ceba106a7392f26dd
onnx/preprocessor_config.json339 Ba6a76d28c93edb273669eb9e0b0636a2bddbb1272c3261e47b7ca6dfdbac1b8d
onnx/special_tokens_map.json2.19 KB4679897a50cc1aea98b7d63b8c928b407f252683d3248add11d091e66cf0d5f1
onnx/tokenizer.json2.48 MB87b38f7a654dbbda293bcdda048d2fb18af241fdd41c62496f8e732209610ec1
onnx/tokenizer_config.json283 KBcbd58e0b0c29af58250917eb9f353c65204a5270b2a8d710f0ec5e3ee1c2b08e
onnx/vocab.json1.04 MB50d6a919f0a0601d56a04eb583c780d18553aa388254ba3158eb6a00f13e2c1a
predictions/validation_clean_stortinget_no/step_0.md123 KBa95be45a955cb33232d0067b23a2507c31e680ed4ff6e331f1c86930585f8137
predictions/validation_clean_stortinget_no/step_10000.md125 KB08feb43379de2c30d8272f95496ca8055229ac01f75ef6599ea520bcffb5599f
predictions/validation_clean_stortinget_no/step_15000.md125 KB38bed83f28798043c269e0c2608a68e9684e0132f94bb8ef80231676b2f320cf
predictions/validation_clean_stortinget_no/step_20000.md125 KB5ca0116a89af5417d0bbb688f73def34d1b9736c3e0b0ed98c56f848a8840b65
predictions/validation_clean_stortinget_no/step_25000.md124 KB8e37294f32426fa70981dbe98ce05a0a89473a3b58c1d738eac410da2c16c91b
predictions/validation_clean_stortinget_no/step_30000.md124 KB182b85b8f959261c9016c2126a1a02cc564f97d5fca3ac77256508e7802a2819
predictions/validation_clean_stortinget_no/step_35000.md124 KBee56a95f03a19d6bba321a7a1b15ca886d0ac2795d235f2134219509db802e78
predictions/validation_clean_stortinget_no/step_40000.md125 KBc733dc5f98adfc046650e8f06a28519e2ecdc8eb748969631899f0bd8018c0aa
predictions/validation_clean_stortinget_no/step_45000.md124 KB87816048493e6dd29c727526fa20d346d73f7a4293c1e88129a7f2f933af09e6
predictions/validation_clean_stortinget_no/step_49999.md124 KB0f42b6f86c0b3e5ccd61fdcdeb074f48aca657e394138056a69fca889a06758c
predictions/validation_clean_stortinget_no/step_5000.md124 KB7309e020c87d9518804b44f0b4acea97f3144f841cbcc23102331d4976c4196c
predictions/validation_nst/step_0.md36.6 KBb06506905b239a0e0b103684a362ad5ebcb62185d0b206c55fd8b5ff2b0a9425
predictions/validation_nst/step_10000.md36.8 KB696927a6fe5ef21994e00b8584d23b485e52a557648733f6420bea399ca586d2
predictions/validation_nst/step_15000.md36.7 KBcf3f1a6514e2c969a1829c92d45d59cf166a8fddf729abae1d793be4aee4030a
predictions/validation_nst/step_20000.md36.7 KBc6ddb5e825e492c4bb4862d886132bb04ce412b07876b2827db2c964023d9bde
predictions/validation_nst/step_25000.md36.7 KB2608027cd5e3e333e0e37d517841bc57e80ee31557e2e724af8084ab6cb4880e
predictions/validation_nst/step_30000.md36.8 KB06da6ea095448af8f909aa6ddcc930a09761a36452eb77b187700aca69dce7b3
predictions/validation_nst/step_35000.md36.7 KB42ff27f7fb78a4457baf87854df8d6055a2c050be0b495b59a44f3cb8e1295d9
predictions/validation_nst/step_40000.md36.6 KB5e2c5e2edcacc86b5ffebfdff3a41eb97963306f4692f2ca245010f06501535a
predictions/validation_nst/step_45000.md36.8 KBe41edaf7f00d50bc9ade5c25343ebdef23af43058380a7d9cdf640c37f4185bc
predictions/validation_nst/step_49999.md36.8 KB8fdcb7733f8f502e46b98e6b141b7a8c9a6588cc9f414bd82e35f8df5f133c8d
predictions/validation_nst/step_5000.md36.6 KBb04bf50efecac11fe733f257dd3ea81949513eb8de3dbd2f41fc558ba4a38fcd
preprocessor_config.json339 Ba6a76d28c93edb273669eb9e0b0636a2bddbb1272c3261e47b7ca6dfdbac1b8d
pytorch_model.bin290 MB3a3383d53a63f48c5e53231c8aac583366fbfc829a581f7da710f1ce3e398778
runs/Jan07_21-32-22_t1v-n-62dd7b00-w-1/events.out.tfevents.1704663143.t1v-n-62dd7b00-w-1.19954.0.v283.1 MBf7a25fc7aedeff037e8df06cfc37987521a8c2e4ee0a76805ae0a843dc54e954
special_tokens_map.json2.19 KB4679897a50cc1aea98b7d63b8c928b407f252683d3248add11d091e66cf0d5f1
stats.md6.38 KBda0b461dd7376b36900492287f061f722f1dd4a05bb3b07de977b146f4037c12
test_results.json365 Bf23976e6d44193a98de70b7bc97310542469f3d1ed3edc3bfd0dc2d729eb7da1
tf_model.h5291 MBf23d13b3065a01696c51f9d8c1923bf90d2740a6bf52d037c5df1a9eb57fca18
tokenizer.json2.48 MB87b38f7a654dbbda293bcdda048d2fb18af241fdd41c62496f8e732209610ec1
tokenizer_config.json283 KB348e80c42b59d4e005fdc7a68ad0244ed339e30eaaabcf805e8e28ef73e7d1e8
training_state.bin6.32 KBc5673a9c0eea5d0a137399a1a59e79cf0a7fb754305124b292d0fa1f1b6448aa
training_tokenizer-merges.txt494 KB78f27f801feb4283ae969d4cadacb28fc051b655647402b1a252c7079ea11c46
training_tokenizer-vocab.json836 KB8f680bba319e01a653d2e8a5dbc17a9157179e0576e6ce74ce0c06356c6e24f9
update_template.py822 Bd32f76dfc7d3feaece60a64de172710e78c9b3823be2abd31edb020d172379a2
vocab.json836 KB8f680bba319e01a653d2e8a5dbc17a9157179e0576e6ce74ce0c06356c6e24f9
vocabulary.json1.07 MB5ad28279db5e546349708b9a74736e2c018737ac5a600f1048f1f26c99b85b47
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
2ab372b6ba pinned now2026-11-15 05:06 UTC68 files · 2.74 GB

Use it

$ fw get NbAiLab/nb-whisper-base
# manifest sha256 62ceda714536… · 2.74 GB · 68 files
$ fw verify ./nb-whisper-base NbAiLab/nb-whisper-base
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/NbAiLab/nb-whisper-base.json --dir ./nb-whisper-base $ python3 fw.py verify ./nb-whisper-base ./NbAiLab__nb-whisper-base.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download NbAiLab/nb-whisper-base --revision 2ab372b6baa181a22f54f18030cae3703402c59e --local-dir ./nb-whisper-base
transformersSpeech recognition with the transformers pipeline.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import pipeline asr = pipeline("automatic-speech-recognition", model="NbAiLab/nb-whisper-base", revision="2ab372b6baa181a22f54f18030cae3703402c59e") print(asr("audio.wav")["text"])

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
0aec5705636636fc12c50bd6b46d48f386d208a619898f672781632a858f56bf
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: NbAiLab <huggingface.co/NbAiLab>Newsgroups: alt.freeweights.speechSubject: nb-whisper-baseDate: 2026-11-15Commit: 2ab372bSigned: 2DC28CC7B26A283B ✓
Upstream
NbAiLab/nb-whisper-base
Pinned commit
2ab372b6baa181a22f54f18030cae3703402c59e
Manifest
nb-whisper-base.json · .minisig
Manifest sha256
62ceda7145361e329fa0a2ebbc720e9d1ce24a6aff7516c2d3357c791a5bca3f
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 05:06 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/NbAiLab/nb-whisper-base.json $ curl -O https://freeweights.allternit.com/manifests/NbAiLab/nb-whisper-base.json.minisig $ minisign -Vm nb-whisper-base.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

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

1 model in the catalog name this one as their parent.

Quantizations
1
Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download NbAiLab/nb-whisper-base
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.json. Provenance. Takedown history: none.

Variants

1 quantized or adapter build of nb-whisper-base. Each has its own license check and page.

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