cstr/omniasr-llm-unlimited-300m-v2-GGUF overview
OmniASR LLM Unlimited 300M v2 — GGUF GGUF conversion of Meta's OmniASR LLM Unlimited 300M v2 from facebookresearch/omnilingual asr https://github.com/facebookr…
Runs locally from ~1.00 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | cstr/omniasr-llm-unlimited-300m-v2-GGUF |
|---|---|
| Author | cstr |
| Pipeline | automatic-speech-recognition |
| License | apache-2.0 |
| Base model | — |
| Last modified | 2026-08-02T15:34:27.000Z |
Model README
---
language:
- multilingual
license: apache-2.0
tags:
- gguf
- asr
- speech-recognition
- multilingual
- 1600-languages
- omniasr
- crispasr
pipeline_tag: automatic-speech-recognition
---
OmniASR-LLM-Unlimited-300M-v2 — GGUF
GGUF conversion of Meta's OmniASR-LLM-Unlimited-300M-v2 from facebookresearch/omnilingual-asr for the CrispASR omniasr backend.
1600+ language ASR with a 300M encoder + LLaMA decoder (~1.6B total params). The "Unlimited" variant handles arbitrarily long audio via a 15-second sliding-segment protocol.
Architecture
- Input: raw 16 kHz mono PCM (no mel features)
- Frontend: 7-layer CNN positional conv stack
- Encoder: 24-layer transformer (1024-dim, 4096 FFN, 16 heads), vocab=9812
- Projection: enc_proj bridge to decoder
- Decoder: 12-layer LLaMA (4096-dim, 2816 FFN, 8 heads)
- Tokenizer: SentencePiece; output: UTF-8 text
- Unlimited mode: 15s-segment streaming for audio of any length
Files
| File | Size | Description |
|------|------|-------------|
| omniasr-llm-unlimited-300m-v2-q4_k.gguf | 1.0 GB | Q4_K quantized (recommended) |
| omniasr-llm-unlimited-300m-v2-f16.gguf | 3.1 GB | F16 full precision |
Usage
crispasr --backend omniasr -m omniasr-llm-unlimited-300m-v2-q4_k.gguf \
-f audio.wav -l auto
License
Apache 2.0.
Credits
- Original model: Meta / facebookresearch
- GGUF conversion & runtime: CrispASR
Provenance and EU AI Act Art. 53 note
- Upstream model: facebookresearch/omnilingual-asr (Meta OmniASR-LLM-Unlimited-300M-v2).
- 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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