Whisper Large v2 GGUF

whisper-large-v2: transcribe.cpp GGUF

GGUF conversions of openai/whisper-large-v2 for use with transcribe.cpp.

Ported from upstream commit ae46427, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 5.6.1 on 2026-04-26.

OpenAI Whisper large-v2 — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). Encoder-decoder transformer; 30-second windows with chunked long-form decoding.

Downloads

Quantization	Download	Size	WER (LibriSpeech test-clean)
F32	whisper-large-v2-F32.gguf	5.75 GB	2.68%
F16	whisper-large-v2-F16.gguf	2.89 GB	2.68%
Q8_0	whisper-large-v2-Q8_0.gguf	1.55 GB	2.65%
Q6_K	whisper-large-v2-Q6_K.gguf	1.21 GB	2.83%
Q5_K_M	whisper-large-v2-Q5_K_M.gguf	1.08 GB	2.72%
Q4_K_M	whisper-large-v2-Q4_K_M.gguf	950 MB	2.46%

WER measured on the full LibriSpeech test-clean split (2620 utterances) with the transcribe.cpp default decode (greedy, suppress_tokens, temperature fallback, segment timestamps enabled). OpenAI's self-reported number on the same split is 2.83%. We don't know upstream's exact eval config, but the most likely cause of any divergence is that OpenAI's model.generate() defaults to <|notimestamps|> while transcribe.cpp's pipeline runs with timestamps enabled. Numbers come from a single Metal-backed run; Metal's non-deterministic parallel reductions can shift corpus WER by ~0.1pp between runs, mostly driven by short-clip hallucination outcomes on the noise floor.

Usage

Build transcribe.cpp from source:

git clone git@github.com:handy-computer/transcribe.cpp.git
cd transcribe.cpp
cmake -B build && cmake --build build

Run on a 16 kHz mono WAV:

build/bin/transcribe-cli \
  -m whisper-large-v2-Q8_0.gguf \
  input.wav

If your audio isn't already 16 kHz mono WAV, convert it first:

ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav

See the transcribe.cpp model page for performance numbers, numerical validation, and reproduction steps.

License

Inherited from the base model: Apache-2.0. See the upstream model card for full terms.

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Original Model Card

The section below is reproduced from openai/whisper-large-v2 at commit ae46427 for offline reference. The upstream card is the authoritative source.

Whisper

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning.

Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. from OpenAI. The original code repository can be found here.

Compared to the Whisper large model, the large-v2 model is trained for 2.5x more epochs with added regularization for improved performance.

Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card.

Model details

Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision.

The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio.

Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the Hugging Face Hub. The checkpoints are summarised in the following table with links to the models on the Hub:

Size	Parameters	English-only	Multilingual
tiny	39 M	✓	✓
base	74 M	✓	✓
small	244 M	✓	✓
medium	769 M	✓	✓
large	1550 M	x	✓
large-v2	1550 M	x	✓

Usage

To transcribe audio samples, the model has to be used alongside a WhisperProcessor.

The WhisperProcessor is used to:

1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
2. Post-process the model outputs (converting them from tokens to text)

The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order:

1. The transcription always starts with the <|startoftranscript|> token
2. The second token is the language token (e.g. <|en|> for English)
3. The third token is the "task token". It can take one of two values: <|transcribe|> for speech recognition or <|translate|> for speech translation
4. In addition, a <|notimestamps|> token is added if the model should not include timestamp prediction

Thus, a typical sequence of context tokens might look as follows:

<|startoftranscript|> <|en|> <|transcribe|> <|notimestamps|>

Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.

These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself.

The context tokens can be set accordingly:

model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe")

Which forces the model to predict in English under the task of speech recognition.

Transcription

English to English

In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe).

>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset

>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
>>> model.config.forced_decoder_ids = None

>>> # load dummy dataset and read audio files
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]
>>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features 

>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>']

>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.']

The context tokens can be removed from the start of the transcription by setting skip_special_tokens=True.

French to French

The following example demonstrates French to French transcription by setting the decoder ids appropriately.

>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> from datasets import Audio, load_dataset

>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
>>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe")

>>> # load streaming dataset and read first audio sample
>>> ds = load_dataset("common_voice", "fr", split="test", streaming=True)
>>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
>>> input_speech = next(iter(ds))["audio"]
>>> input_features = processor(input_speech["array"],...