cstr/omnivoice-GGUF
22.5k
OmniVoice GGUF
GGUF conversions of k2-fsa/OmniVoice for the CrispASR omnivoice backend.
Files
Usage
# Auto-download
./crispasr --backend omnivoice -m auto --tts "Hello world."
# Manual
./crispasr --backend omnivoice --model omnivoice-q8_0.gguf \
--codec-model omnivoice-tokenizer-f16.gguf --tts "Hello world."Status
- Main model GGUF conversion (F16 + Q8_0)
- Qwen3 LLM forward pass (28L, flash_attn)
- Masked iterative code generation (32 steps)
- HiggsAudioV2 DAC decoder (codes to 24 kHz PCM)
- Special token handling (textstart/end, langstart/end, etc.)
- Audio output: end-to-end text to WAV
Parity note: The C++ generation loop implements the basic masked iterative algorithm. Classifier-free guidance (the unconditional branch that OmniVoice uses for quality) is not yet implemented -- output quality does not yet match the Python reference. The Kaggle parity test confirmed the Python pipeline produces correct speech (ASR roundtrip: exact match).
License
Apache-2.0 (same as k2-fsa/OmniVoice).
Provenance and EU AI Act Art. 53 note
- Upstream model: k2-fsa/OmniVoice — published by
k2-fsa. - 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.
