cstr/omniasr-llm-unlimited-300m-v2-GGUF

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.