wav2vec2-base-960h — GGUF
GGUF conversion of facebook/wav2vec2-base-960h for CrispASR.
This model is primarily used as a CTC forced aligner — it produces word-level timestamps by aligning a known transcript to audio. It can also perform standalone ASR via CTC greedy decode.
Architecture
- wav2vec2-base: 12-layer transformer encoder (768-dim, 12 heads)
- CTC head: Linear(768, 32) — character-level English vocabulary
- Feature extractor: 7-layer CNN (raw waveform -> 512-dim features)
- Input: 16 kHz mono audio
- Training: 960 hours of LibriSpeech (supervised CTC fine-tune)
Files
| File | Size | Description |
|---|---|---|
wav2vec2-base-960h.gguf | 197 MB | Full precision (F16 weights, F32 norms) |
Usage with CrispASR
Forced alignment (word-level timestamps)
# Align a transcript to audio — produces word-level SRT:
crispasr --align-only \
-am wav2vec2-base-960h.gguf \
-f audio.wav \
--ref-text "The quick brown fox jumps over the lazy dog." \
--align-format srt --align-output aligned.srt
# Or align from an existing SRT (re-time the cues):
crispasr --align-only \
-am wav2vec2-base-960h.gguf \
-f audio.wav \
--text-file subtitles.srt \
--align-output retimed.srt
As an aligner companion for LLM ASR backends
# Word timestamps for any backend that lacks native ones:
crispasr --backend granite -m auto \
-am wav2vec2-base-960h.gguf --force-aligner \
-f audio.wav -osrt
Auto-download
# The aligner is auto-downloaded when using -am auto:
crispasr --backend granite -m auto \
-am auto --force-aligner \
-f audio.wav -osrt
License
Apache 2.0 (same as the original model).
Credits
- Original model: Facebook AI / Meta
- GGUF conversion & runtime: CrispASR
Provenance and EU AI Act Art. 53 note
- Upstream model: facebook/wav2vec2-base-960h — 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.