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cstr/hubert-large-ls960-ft-GGUF overview

HuBERT Large GGUF GGUF conversion of facebook/hubert large ls960 ft https://huggingface.co/facebook/hubert large ls960 ft for use with CrispASR https://github.…

ggufaudiospeech-recognitionhubertwav2vec2ctcautomatic-speech-recognitionenbase_model:facebook/hubert-large-ls960-ftbase_model:quantized:facebook/hubert-large-ls960-ftlicense:apache-2.0region:us

Runs locally from ~211.7 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
563
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Pipeline
automatic-speech-recognition
Author

Repository Files & Downloads

2 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
hubert-large-ls960-ft-f16.ggufGGUFF16626.4 MBDownload
hubert-large-ls960-ft-q4_k.ggufGGUFQ4_K211.7 MBDownload

Model Details

Model IDcstr/hubert-large-ls960-ft-GGUF
Authorcstr
Pipelineautomatic-speech-recognition
Licenseapache-2.0
Base modelfacebook/hubert-large-ls960-ft
Last modified2026-08-02T15:26:14.000Z

Model README

---

license: apache-2.0

language:

  • en

tags:

  • gguf
  • audio
  • speech-recognition
  • hubert
  • wav2vec2
  • ctc
  • automatic-speech-recognition

base_model: facebook/hubert-large-ls960-ft

pipeline_tag: automatic-speech-recognition

---

HuBERT Large (GGUF)

GGUF conversion of facebook/hubert-large-ls960-ft for use with CrispASR.

Model Details

  • Architecture: HuBERT — wav2vec2-style CNN (7L, 512-dim) + 24-layer transformer (1024-dim, 16 heads, pre-norm) + CTC head
  • Parameters: ~316M
  • Training: Self-supervised pre-training on LibriSpeech 960h, fine-tuned with CTC loss
  • Language: English only
  • License: Apache 2.0

Usage

crispasr --backend wav2vec2 -m hubert-large-ls960-ft-q4_k.gguf -f audio.wav

Files

| File | Size | JFK Result |

|------|------|-----------|

| hubert-large-ls960-ft-f16.gguf | 627 MB | perfect |

| hubert-large-ls960-ft-q4_k.gguf | 212 MB | perfect |

Provenance and EU AI Act Art. 53 note

  • Upstream model: facebook/hubert-large-ls960-ft — published by facebook.
  • Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
  • What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
  • Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
  • Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

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