Downloads · 30 days
17
20% of all-time downloads
roman4work/ultravox-mamaylm-12b-uk-v2
ultravox-mamaylm-12b-uk-v2 is a audio-text-to-text model from roman4work. Use it for the audio-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as gemma.
Single-pass speech-language model for Ukrainian, built on top of INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0 with the Ultravox v0.6 architecture (Whisper-large-v3-turbo audio encoder + projector → frozen Gemma-3-12B…
Downloads · 30 days
17
20% of all-time downloads
All-time downloads
87
Public
Parameters
51.8M
142 MB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors104 MB · 72%
From the Hugging Face model README
Single-pass speech-language model for Ukrainian, built on top of
INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0
with the Ultravox v0.6 architecture
(Whisper-large-v3-turbo audio encoder + projector → frozen Gemma-3-12B LLM).
This is the v2 checkpoint (HF tag v2.0): trained on a broader UK + EN
dataset mix for 14 400 steps. v2 closes the verbatim-ASR WER gap with the
Whisper-large-v3-turbo cascade pipeline on Ukrainian read-aloud audio while
keeping the single-pass latency advantage.
Same 50-fixture Ukrainian benchmark, same MamayLM-12B backbone, same prompts:
| Pipeline | Verbatim WER | TTFT p50 |
|---|---|---|
| Cascade (Whisper-large-v3-turbo + MamayLM-12B) | 0.219 | 0.288 s |
| Ultravox v1 (single-dataset, 8 k steps) | 0.339 | 0.092 s |
| Ultravox v2 (multi-dataset, 14.4 k steps) | 0.222 | 0.091 s |
v2 vs v1: paired Δ = −0.117 (95 % CI [−0.150, −0.085], Cohen's d = −0.98, n = 51 fixtures, v2 better on 38/51, 2 verbatim rounds per version). v2's verbatim WER is statistically indistinguishable from the Whisper cascade's at 3.4× faster TTFT.
Full bench artifacts (v1 and v2):
roman4work/voice-bench-results
(bench-20260501T141534Z for v1, bench-20260502T081341Z for v2).
openai/whisper-large-v3-turbo (LoRA-adapted,
r = 8, target k/v/q/o_proj)stack_factor = 8, mid-LayerNormINSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0
(frozen during training, loaded automatically by the Ultravox model class —
you do not need to download it separately, but you must have access)This repository contains only the projector + Whisper-LoRA + tokenizer /
processor files (~130 MB). The base text model is referenced by config.json
(text_model_id) and fetched from HF Hub at load time.
| Dataset | Weight | Objective |
|---|---|---|
commonvoice-uk-transcription | 4 | UK ASR (verbatim) |
commonvoice-uk-continuation | 4 | UK reply / instruction-following |
fleurs-uk_ua-transcription | 8 | broader UK domain coverage |
librispeech-clean-transcription | 1 | EN audio anchor |
librispeech-clean-continuation | 1 | EN audio anchor |
commonvoice-en-transcription | 0.5 | EN audio anchor |
commonvoice-en-continuation | 0.5 | EN audio anchor |
EN data is anchored at low weight to prevent the projector from collapsing onto
UK-specific audio statistics. FLEURS contributes only its -transcription
form because the dataset registry does not provide a -continuation version
for FLEURS.
| Hardware | 4 × NVIDIA B200 (DDP via torchrun --nproc_per_node=4) |
| Steps | 14 400 (10 checkpoints saved every 1 440 steps) |
| Batch size | 4 per GPU × grad_accum 4 → effective 64 |
| Learning rate | 5e-4, 1 000-step warmup |
| Wall clock | 9 h 4 m |
| Final loss | 0.119 (train), 0.217 (train_loss aggregate) |
| Seed | 43 |
Recommended runtime: vLLM with the
vllm[audio] extras. Example deployment:
vllm serve roman4work/ultravox-mamaylm-12b-uk-v2 \
--served-model-name ultravox-mamaylm-uk \
--max-model-len 4096 \
--dtype bfloat16 \
--trust-remote-code \
--enforce-eager \
--block-size 64
The OpenAI-compatible Chat Completions endpoint accepts audio via the
input_audio content type:
{
"model": "ultravox-mamaylm-uk",
"messages": [
{"role": "system",
"content": "Ти український голосовий помічник. Відповідай коротко, природно і виключно українською мовою."},
{"role": "user", "content": [
{"type": "input_audio",
"input_audio": {"data": "<base64-encoded-wav>", "format": "wav"}}
]}
]
}
This model inherits from its components and you must comply with all of:
If you use this checkpoint, please credit: