Skip to page content
SAVRN
Search Plan a project

Open-weight model · Speech recognition

VibeVoice-ASR-DFlash2

by Nikita Davidchuk Ar4ikov/VibeVoice-ASR-DFlash2

VibeVoice-ASR-DFlash2 is an open-weight model for speech recognition from Nikita Davidchuk, released under MIT License. It has 8.7B parameters. At 16-bit it needs about 20.8 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

VibeVoice-ASR (7B), the original BF16 weights as Microsoft published them, with its DFlash 2 drafter bundled in drafter/: one download, and vibevoice.c decodes with speculative decoding -- the drafter proposes 8 tokens in one pass, the model checks them in…

Parameters8.7B
Context—
Weights19.0 GB
Licensemit
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve VibeVoice-ASR-DFlash2 (8.7B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 17.3 GB 20.8 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 8.7 GB 10.4 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 4.3 GB 5.2 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.

VibeVoice-ASR-DFlash2 on every accelerator the SAVRN Index prices, at every precision

Model Card

By Nikita Davidchuk, published under mit, revision 39dd1c48d043.

VibeVoice-ASR (7B), the original BF16 weights as Microsoft published them, with its DFlash 2 drafter bundled in drafter/: one download, and vibevoice.c decodes with speculative decoding -- the drafter proposes 8 tokens in one pass, the model checks them in one pass and keeps the ones it agrees with. the model's own decode step, so the transcript is byte-for-byte the one without the drafter. (With a drafter vibevoice.c attends with flashinfer, which checks rows together; the transcript is then --attn flashinfer's.) Needs vibevoice.c with DFlash 2 support: branch dflash2 (PR #48), in the next release. A model directory's drafter/ is used without asking: vibevoice.c runs BF16 weights as dense…

Read Nikita Davidchuk's full model card

VibeVoice-ASR (7B), the original BF16 weights as Microsoft published them, with its DFlash 2 drafter bundled in drafter/: one download, and vibevoice.c decodes with speculative decoding -- the drafter proposes 8 tokens in one pass, the model checks them in one pass and keeps the ones it agrees with. The check is exact: every checked row is computed with the arithmetic of the model's own decode step, so the transcript is byte-for-byte the one without the drafter. (With a drafter vibevoice.c attends with flashinfer, which checks rows together; the transcript is then --attn flashinfer's.)

Use

Needs vibevoice.c with DFlash 2 support: branch dflash2 (PR #48), in the next release. A model directory's drafter/ is used without asking:

vv_cli --model ./VibeVoice-ASR-DFlash2 --audio talk.wav                  # with the drafter
vv_cli --model ./VibeVoice-ASR-DFlash2 --audio talk.wav --draft none     # plain decoding
vv_cli serve --model ./VibeVoice-ASR-DFlash2 --slots 4

vibevoice.c runs BF16 weights as dense FP16 (about 19 GB of VRAM for a 7B, drafter included); --quant int4 quantizes them at load, and the drafter works with that too.

Results

vibevoice.c b72be15 (branch dflash2), RTX 3090, greedy decoding, decode tokens per second:

plain drafted speedup tokens per block same transcript
2-minute file 57 tok/s 156 tok/s 2.73x 3.52 yes
32-minute file 52 tok/s 124 tok/s 2.36x 3.07 yes

BF16 weights run as dense FP16. Plain = the same model with --draft none --attn flashinfer (with a drafter vibevoice.c attends with flashinfer); against the default without a drafter (fa2): 2.79x on the 2-minute file, 2.51x on the 32-minute file. --draft-check exact, the check width measured as it runs.

Inside

  • The model: the files of microsoft/VibeVoice-ASR at revision d0c9efdb, unchanged (17.37 GB) -- its card has the model, the evaluation and the license.
  • tokenizer.json, tokenizer_config.json, vocab.json, merges.txt, special_tokens_map.json, preprocessor_config.json: from Ar4ikov/VibeVoice-ASR-AWQ-W4A16-ASYM at revision 22b44c85 -- the upstream repository leaves them out (they are Qwen2.5's tokenizer and the audio preprocessor's settings), and vibevoice.c needs them next to the weights.
  • drafter/: Ar4ikov/VibeVoice-ASR-DFlash2-Drafter at revision 795e2a5d (1.66 GB): 5 Qwen3-style layers reading the model's layers 1/7/13/19/25, a candidate selector, a 32768-id draft vocabulary; BF16, held as INT4 by default at load (--draft-quant f16 keeps FP16). Its card has the architecture and the training.

License

MIT, like VibeVoice.

Configuration

Architecture
VibeVoiceForASRTraining
Model type
vibevoice

Identity and Version

Repository
Ar4ikov/VibeVoice-ASR-DFlash2
Publisher
Nikita Davidchuk
Task
Speech recognition
Modality
Audio
Library
vibevoice.c
Parameters
8.7B parameters
Languages
Not stated by the source
Revision
39dd1c48d043abb94b016c16d18aa4d99ac18ada
First published
2026-09-25
Last updated
2026-09-26

Files and Weights

25 files, 19.0 GB in total. The weights are 9 files totalling 19.0 GB in safetensors.

Weights9 files · 19.0 GB
Configuration5 files · 125.6 KB
Tokenizer4 files · 15.9 MB
Documentation1 file · 3.2 KB
Other5 files · 1.2 MB
Repository1 file · 1.7 KB
Every file
FileTypeSizeSHA-256
drafter/model.safetensorsWeights1.7 GB 68e8e7471c01
model-00001-of-00008.safetensorsWeights2.5 GB 5548c67885d4
model-00002-of-00008.safetensorsWeights2.4 GB 163023c61a3f
model-00003-of-00008.safetensorsWeights2.5 GB 4e021702dfac
model-00004-of-00008.safetensorsWeights2.5 GB b17657bb151d
model-00005-of-00008.safetensorsWeights2.5 GB 0ed4e457268f
model-00006-of-00008.safetensorsWeights2.5 GB 6de8246bb042
model-00007-of-00008.safetensorsWeights1.5 GB a2ba6960d994
model-00008-of-00008.safetensorsWeights1.1 GB 1b9d9b328f85
config.jsonConfiguration3.5 KB —
drafter/config.jsonConfiguration979 B —
model.safetensors.index.jsonConfiguration120.2 KB —
preprocessor_config.jsonConfiguration182 B —
special_tokens_map.jsonConfiguration785 B —
README.mdDocumentation3.2 KB —
figures/DER.jpgOther62.7 KB —
figures/VibeVoice_ASR_archi.pngOther149.5 KB c5973094212c
figures/cpWER.jpgOther68.5 KB —
figures/language_distribution_horizontal.pngOther888.4 KB 1151d7587c66
figures/tcpWER.jpgOther64.3 KB —
.gitattributesRepository1.7 KB —
merges.txtTokenizer1.7 MB —
tokenizer.jsonTokenizer11.4 MB 9c5ae00e602b
tokenizer_config.jsonTokenizer4.7 KB —
vocab.jsonTokenizer2.8 MB —

License and Download

License
mit
Access
Open weights, no gate
Download size
19.0 GB
Download from Nikita Davidchuk

Released by Nikita Davidchuk through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published19.0 GB
16-bit17.3 GB
8-bit8.7 GB
4-bit4.3 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About VibeVoice-ASR-DFlash2

How much GPU memory does VibeVoice-ASR-DFlash2 need?

About 20.8 GB at 16-bit and 5.2 GB at 4-bit: the weights (8.7B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run VibeVoice-ASR-DFlash2 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use VibeVoice-ASR-DFlash2 commercially?

Yes. VibeVoice-ASR-DFlash2 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

Similar Models

Model · Speech recognition

VibeVoice-ASR

Microsoft

VibeVoice-ASR is a unified speech-to-text model designed to handle 60-minute long-form audio in a single pass, generating structured transcriptions containing Who (Speaker), When (Timestamps), and What (Content), with support for Customized Hotwords and over 50 languages. - 60-minute Single-Pass Processing: Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to 60 minutes of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour. Users can provide customized hotwords (e.g., specific names, technical terms, or background info) to guide the…

Open weights mit 8.7B parameters transformers

VibeVoice-ASR (7B), AWQ W4A16 (asymmetric, groups of 128), with its DFlash 2 drafter bundled in drafter/: one download, and vibevoice.c decodes with speculative decoding -- the drafter proposes 8 tokens in one pass, the model checks them in one pass and keeps the ones it agrees with. the model's own decode step, so the transcript is byte-for-byte the one without the drafter. (With a drafter vibevoice.c attends with flashinfer, which checks rows together; the transcript is then --attn flashinfer's.) Needs vibevoice.c with DFlash 2 support: branch dflash2 (PR #48), in the next release. A model directory's drafter/ is used without asking: vibevoice.c b72be15 (branch dflash2), RTX 3090, greedy…

Open weights mit 8.3B parameters vibevoice.c

Model · Speech recognition

Voxtral-Mini-4B-Realtime-2602

Mistral AI_

Voxtral Mini 4B Realtime 2602 is a multilingual, realtime speech-transcription model and among the first open-source solutions to achieve accuracy comparable to offline systems with a delay of = 3600 / 0.8 = 45000. In theory, you should be able to record with no limit; in practice, pre-allocations of RoPE parameters among other things limits --max-model-len. For the best user experience, we recommend to simply instantiate vLLM with the default parameters which will automatically set a maximum model length of 131072 (~ca. 3h).

Open weights apache-2.0 4.4B parameters 131,072 tokens vllm

Model · Speech recognition

Audio8-ASR-Infinite

Edge0

Audio8 ASR Infinite is a native streaming speech recognition model built to be as responsive as possible. It offers a selectable audio clock (80/120/160 ms) and a transcription delay (240–560 ms). With our adapted vLLM build it transcribes unlimited-length audio 24/7 without drifting. - Super responsive — the native streaming architecture decodes 12.5 times per second. - Unlimited-length transcription — a rolling KV Cache keeps both memory and latency constant, even in 24/7 operation. - Selectable streaming clock — one text token per clock step (12.5 / 8.3 / 6.25 decisions per second), balancing perception granularity and resource cost. - Configurable transcription delay — set how much…

Open weights apache-2.0 4.1B parameters 32,768 tokens transformers

Model · Speech recognition

qwen3-asr-1.7b-int8-mlx

App Automaton

MLX-native int8 conversion of Qwen3-ASR-1.7B for local automatic speech recognition on Apple Silicon — English, Chinese, and mixed Chinese/English. It runs through the mlx-speech runtime with no PyTorch and no cloud API at inference time. Weights ship as plain.safetensors. Install mlx-speech, then load by alias (downloads on first use): Or download the weights once and load by local path: - This repo contains the quantized MLX runtime artifact only. - The quantization mode (affine, groupsize 64, 8-bit) is stored in config.json and re-applied automatically on load, so it cannot desync from the weights. - v0 is single-pass offline ASR; streaming, timestamps, and long-audio chunking are…

Open weights apache-2.0 2.4B parameters mlx

Model · Speech recognition

Qwen3-ASR-1.7B

Qwen

The Qwen3-ASR family includes Qwen3-ASR-1.7B and Qwen3-ASR-0.6B, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. Experiments show that the 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs. Here are the main features: Novel and strong forced alignment Solution: We introduce Qwen3-ForcedAligner-0.6B, which supports timestamp prediction for arbitrary units within up to 5 minutes of speech in 11 languages. Evaluations show its timestamp…

Open weights apache-2.0 2.3B parameters