Model reference · open weights

gipformer1.5-rnnt

Audio g-group-ai-lab Speech→text 1 build Open weights 520 dl/mo

gipformer1.5-rnnt is an open-weight audio or speech model from g-group-ai-lab. gipformer1.5-68M-rnnt (BF16) weighs 279 MB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • gipformer1.5-rnnt is an automatic speech recognition model developed by g-group-ai-lab for the Vietnamese language.
  • It features 68 million parameters and is based on the Zipformer Transducer architecture.
  • The model is released under the MIT license.

Summary of the g-group-ai-lab/gipformer1.5-68M-rnnt model card, 2026-10-31

What it is

Released byg-group-ai-lab
Released2026-08-21
VRAM279 MB for the weights

What it runs on

Memory and cards for gipformer1.5-68M-rnnt (BF16)

279 MBweights, file size
1.6 GBruntime overhead
CardOne streamMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What g-group-ai-lab says about gipformer1.5-rnnt

Read the model card

Source code: github.com/ggroup-ai-lab/gipformer

Demo: Try gipformer in your browser

Highlights

  • State-of-the-art accuracy — Demonstrates top-tier performance across major Vietnamese ASR benchmarks, delivering highly precise and reliable transcription quality.
  • Robust handling of telephonic domains — Excels in processing challenging, noisy real-world call center recordings across all major Vietnamese regional accents.
  • Domain-leading accuracy — Best WER on the technology, finance, education, public administration and medical benchmarks.
  • Outstanding parameter efficiency — Ranks among the smallest ASR models currently available.
  • Seamless edge deployment — Its naturally low resource requirements enable ultra-fast inference on mobile and embedded systems, making it perfectly suited for offline, on-device applications.
  • Built-in data privacy — By supporting full local execution, the model ensures sensitive audio data is processed securely on-device, eliminating the need for third-party cloud services.
  • gipformer1.5-68M-rnnt is based on the Zipformer Transducer architecture.

Benchmark Results (WER%)

Lower is better; bold = best in each column.

Normalization: Both predictions and labels are normalized before computing WER — lowercased, punctuation removed, and numbers converted to spoken form.

ModelParamstele-mediumtele-hard-northtele-hard-middletele-hard-southvi-asr-techvi-asr-eduvi-asr-financevi-asr-pubadminvivosCommon-Voicevlsp-t1VietMedMultiMEDLSVSCFleursViMD
vinai/PhoWhisper-small244M33.9655.8865.4162.3556.8151.8747.3641.466.2311.5515.9925.5026.0211.2316.1114.09
vinai/PhoWhisper-medium769M26.4651.2059.0455.3947.9343.0041.6034.814.938.3714.0624.9024.7610.2514.4411.34
vinai/PhoWhisper-large1.5B26.8250.3959.4456.7042.9444.9940.5433.784.738.6013.7024.3724.4710.0812.6211.18
Qualcomm-AI-Research/PhoASR-whisper-small244M30.7350.3056.6654.1244.3541.65—35.995.879.7314.6023.1022.787.5913.979.75
nvidia/parakeet-ctc-0.6b-Vietnamese600M31.8255.3361.6556.7048.6945.8643.1638.847.7611.4017.0023.5323.7910.4616.1112.95
khanhld/chunkformer-large-vie110M27.6046.3051.9149.0939.8137.2034.3129.304.186.9414.0919.5922.608.8514.1711.77
nguyenvulebinh/wav2vec2-base-vi95M23.7140.4948.9046.3359.8653.0649.6639.826.6012.6113.1422.9623.039.8920.0911.42
zipformer-rnnt68M20.3042.2149.0147.8636.8130.2629.1318.626.9211.4814.5421.9022.0510.2314.7610.15
Qwen/Qwen3-ASR-1.7B1.7B26.3446.8059.8551.8427.9529.9334.0931.837.1710.7616.2920.2120.119.6410.1311.16
Qwen/Qwen3-ASR-0.6B600M32.2948.5761.8855.4337.5236.9738.3138.6510.2316.6818.6222.5122.6510.9613.1114.37
hynt/Zipformer-30M-RNNT-6000h30M19.5338.1344.7341.5829.7725.9125.8617.924.554.1611.7819.9119.889.0413.037.18
g-group-ai-lab/gipformer-68M-rnnt68M15.5325.1032.2732.6236.5929.8129.6720.094.126.6313.3919.4119.358.9612.927.17
g-group-ai-lab/gipformer1.5-68M-rnnt68M15.4426.2433.3132.4827.4923.3222.3414.824.256.4513.3719.2319.178.9712.657.00

Dataset Descriptions

Private test sets (call center domain):

  • tele-medium — Call center recordings with medium difficulty
  • tele-hard-north — Low-quality call center audio, hard-to-hear speakers — Northern Vietnamese accent
  • tele-hard-middle — Low-quality call center audio, hard-to-hear speakers — Central Vietnamese accent
  • tele-hard-south — Low-quality call center audio, hard-to-hear speakers — Southern Vietnamese accent

Public test sets:

  • vi-asr-tech-test — Technology: consumer electronics reviews, software tutorials, programming and IT walkthroughs
  • vi-asr-edu-test — Education: study-abroad consulting, exam guidance, university and training-course introductions
  • vi-asr-finance-test — Finance: stock market commentary, trading platforms, banking and crypto
  • vi-asr-pubadmin-test — Public administration: administrative procedures, licensing guidance, civil records
  • vivos — Vietnamese read speech corpus
  • Common-Voice — Mozilla Common Voice, Vietnamese subset
  • vlsp-t1 — VLSP 2020 ASR Shared Task 1
  • VietMed — Vietnamese medical domain
  • MultiMED — Multi-domain medical conversations
  • LSVSC — Large-Scale Vietnamese Speech Corpus
  • Fleurs — Google's Few-shot Learning Evaluation of Universal Representations of Speech, Vietnamese subset
  • ViMD — Vietnamese Multi-Domain

Call Center Domain: Where It Matters Most

Call center ASR is one of the most challenging real-world domains — noisy phone lines, overlapping speech, diverse regional accents, and spontaneous conversation. The gipformer family holds the best result on all four call center test sets, and no other model comes within 9

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms