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Whisper Large v3 Turbo GGUF

Run high-quality, fully local speech-to-text dictation in Unsloth Studio. Whisper Large v3 Turbo offers the strongest speed and accuracy balance in the default Whisper lineup and is the recommended choice for capable...

Fontehuggingface.co/unslothai/whisper-large-v3-turbo-GGUF/tree/325c85b683627d65ea72d90b5d627d8de1a78d80
TipoOtherModelo baseunslothai/whisper-large-v3-turboVersãoWhisper large v3 turboCnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
1,51 GB
Modelo base
unslothai/whisper-large-v3-turbo
Versão
vWhisper large v3 turbo
Atualizado
31 de out. de 2026
Fonte
Hugging Face

Descrição da fonte

Whisper Large v3 Turbo F16 for Unsloth Studio

Run high-quality, fully local speech-to-text dictation in Unsloth Studio. Whisper Large v3 Turbo offers the strongest speed and accuracy balance in the default Whisper lineup and is the recommended choice for capable hardware.

Run in Unsloth Studio

  1. Install or update Unsloth Studio.
  2. Open Settings > Voice.
  3. Open the local dictation model picker and select Whisper Large v3 Turbo.
  4. Let Studio download and cache the model.
  5. Use the microphone button in the chat composer to dictate locally.

The model runs on your device through whisper.cpp. Your recorded audio does not need to be sent to a hosted transcription service.

Model file

  • whisper-large-v3-turbo.bin: native F16 model for whisper.cpp
  • Download size: approximately 1.62 GB
  • Best for: the recommended balance of high accuracy and fast transcription

whisper.cpp uses a custom GGML binary format for Whisper. The model file is therefore named .bin, not .gguf, even though this repository follows the common -GGUF repository naming convention.

No low-bit quantization was applied. Matrix weights are stored as F16, while tensors that whisper.cpp requires in F32 remain F32.

Manual whisper.cpp usage

whisper-cli -m whisper-large-v3-turbo.bin -f audio.wav

Integrity

  • Source model.safetensors SHA-256: 542566a422ae4f3fd23f1ba11add198fca01bbf82e66e6a2857b3f608b1eb9d1
  • Converted model SHA-256: 0dc8bab01bd6b0ab68e8ebb607bf3c960ff1bc136644a23b3ad59f7c00e78d09
  • Conversion tool: ggml-org/whisper.cpp commit 080bbbe85230f624f0b52127f1ae1218247989f9

The converted model was loaded by whisper.cpp and passed an end-to-end transcription test.

Fontes

1 fonte
Hugging FacePrimárioVersão 325c85b683627d65ea72d90b5d627d8de1a78d80
huggingface.co/unslothai/whisper-large-v3-turbo-GGUF/tree/325c85b683627d65ea72d90b5d627d8de1a78d80

Verificado 31 de out.

Artefatos de modelo

1 artefato

whisper-large-v3-turbo.bin

bin · 1,51 GB · SHA-256 0dc8bab01bd6…8d09 · Hugging Face

Download file

Trechos de fonte

2 trechos
Readme15 de ago.huggingface.co/unslothai/whisper-large-v3-turbo-GGUF

--- base_model: unslothai/whisper-large-v3-turbo license: apache-2.0 library_name: whisper.cpp pipeline_tag: automatic-speech-recognition tags: - whisper - whisper.cpp - ggml - f16 - unsloth-studio - unsloth --- # Whisper Large v3 Turbo F16 for Unsloth Studio Run high-quality, fully local speech-to-text dictation in [Unsloth Studio](https://unsloth.ai/docs). Whisper Large v3 Turbo offers the strongest speed and accuracy balance in the default Whisper lineup and is the recommended choice for capable hardware. ## Run in Unsloth Studio 1. Install or update [Unsloth Studio](https://unsloth.ai/docs). 2. Open **Settings > Voice**. 3. Open the local dictation model picker and select **Whisper Large v3 Turbo**. 4. Let Studio download and cache the model. 5. Use the microphone button in the chat composer to dictate locally. The model runs on your device through `whisper.cpp`. Your recorded audio does not need to be sent to a hosted transcription service. ## Model file - `whisper-large-v3-turbo.bin`: native F16 model for `whisper.cpp` - Download size: approximately 1.62 GB - Best for: the recommended balance of high accuracy and fast transcription `whisper.cpp` uses a custom GGML binary format for Whisper. The model file is therefore named `.bin`, not `.gguf`, even though this repository follows the common `-GGUF` repository naming convention. No low-bit quantization was applied. Matrix weights are stored as F16, while tensors that `whisper.cpp` requires in F32 remain F32. ## Manual whisper.cpp usage ```bash whisper-cli -m whisper-large-v3-turbo.bin -f audio.wav ``` ## Integrity - Source `model.safetensors` SHA-256: `542566a422ae4f3fd23f1ba11add198fca01bbf82e66e6a2857b3f608b1eb9d1` - Converted model SHA-256: `0dc8bab01bd6b0ab68e8ebb607bf3c960ff1bc136644a23b3ad59f7c00e78d09` - Conversion tool: `ggml-org/whisper.cpp` commit `080bbbe85230f624f0b52127f1ae1218247989f9` The converted model was loaded by `whisper.cpp` and passed an end-to-end transcription test.

Note15 de ago.huggingface.co/unslothai/whisper-large-v3-turbo-GGUF

Source context: 0 downloads · 0 likes · Pipeline automatic-speech-recognition · Library whisper.cpp · Repo unslothai/whisper-large-v3-turbo-GGUF