Model reference · open weights
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.
Summary of the g-group-ai-lab/gipformer1.5-68M-rnnt model card, 2026-10-31
What it is
| Released by | g-group-ai-lab |
|---|---|
| Released | 2026-08-21 |
| VRAM | 279 MB for the weights |
What it runs on
| Card | One stream | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
Source code: github.com/ggroup-ai-lab/gipformer
Demo: Try gipformer in your browser
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.
| Model | Params | tele-medium | tele-hard-north | tele-hard-middle | tele-hard-south | vi-asr-tech | vi-asr-edu | vi-asr-finance | vi-asr-pubadmin | vivos | Common-Voice | vlsp-t1 | VietMed | MultiMED | LSVSC | Fleurs | ViMD |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| vinai/PhoWhisper-small | 244M | 33.96 | 55.88 | 65.41 | 62.35 | 56.81 | 51.87 | 47.36 | 41.46 | 6.23 | 11.55 | 15.99 | 25.50 | 26.02 | 11.23 | 16.11 | 14.09 |
| vinai/PhoWhisper-medium | 769M | 26.46 | 51.20 | 59.04 | 55.39 | 47.93 | 43.00 | 41.60 | 34.81 | 4.93 | 8.37 | 14.06 | 24.90 | 24.76 | 10.25 | 14.44 | 11.34 |
| vinai/PhoWhisper-large | 1.5B | 26.82 | 50.39 | 59.44 | 56.70 | 42.94 | 44.99 | 40.54 | 33.78 | 4.73 | 8.60 | 13.70 | 24.37 | 24.47 | 10.08 | 12.62 | 11.18 |
| Qualcomm-AI-Research/PhoASR-whisper-small | 244M | 30.73 | 50.30 | 56.66 | 54.12 | 44.35 | 41.65 | — | 35.99 | 5.87 | 9.73 | 14.60 | 23.10 | 22.78 | 7.59 | 13.97 | 9.75 |
| nvidia/parakeet-ctc-0.6b-Vietnamese | 600M | 31.82 | 55.33 | 61.65 | 56.70 | 48.69 | 45.86 | 43.16 | 38.84 | 7.76 | 11.40 | 17.00 | 23.53 | 23.79 | 10.46 | 16.11 | 12.95 |
| khanhld/chunkformer-large-vie | 110M | 27.60 | 46.30 | 51.91 | 49.09 | 39.81 | 37.20 | 34.31 | 29.30 | 4.18 | 6.94 | 14.09 | 19.59 | 22.60 | 8.85 | 14.17 | 11.77 |
| nguyenvulebinh/wav2vec2-base-vi | 95M | 23.71 | 40.49 | 48.90 | 46.33 | 59.86 | 53.06 | 49.66 | 39.82 | 6.60 | 12.61 | 13.14 | 22.96 | 23.03 | 9.89 | 20.09 | 11.42 |
| zipformer-rnnt | 68M | 20.30 | 42.21 | 49.01 | 47.86 | 36.81 | 30.26 | 29.13 | 18.62 | 6.92 | 11.48 | 14.54 | 21.90 | 22.05 | 10.23 | 14.76 | 10.15 |
| Qwen/Qwen3-ASR-1.7B | 1.7B | 26.34 | 46.80 | 59.85 | 51.84 | 27.95 | 29.93 | 34.09 | 31.83 | 7.17 | 10.76 | 16.29 | 20.21 | 20.11 | 9.64 | 10.13 | 11.16 |
| Qwen/Qwen3-ASR-0.6B | 600M | 32.29 | 48.57 | 61.88 | 55.43 | 37.52 | 36.97 | 38.31 | 38.65 | 10.23 | 16.68 | 18.62 | 22.51 | 22.65 | 10.96 | 13.11 | 14.37 |
| hynt/Zipformer-30M-RNNT-6000h | 30M | 19.53 | 38.13 | 44.73 | 41.58 | 29.77 | 25.91 | 25.86 | 17.92 | 4.55 | 4.16 | 11.78 | 19.91 | 19.88 | 9.04 | 13.03 | 7.18 |
| g-group-ai-lab/gipformer-68M-rnnt | 68M | 15.53 | 25.10 | 32.27 | 32.62 | 36.59 | 29.81 | 29.67 | 20.09 | 4.12 | 6.63 | 13.39 | 19.41 | 19.35 | 8.96 | 12.92 | 7.17 |
| g-group-ai-lab/gipformer1.5-68M-rnnt | 68M | 15.44 | 26.24 | 33.31 | 32.48 | 27.49 | 23.32 | 22.34 | 14.82 | 4.25 | 6.45 | 13.37 | 19.23 | 19.17 | 8.97 | 12.65 | 7.00 |
Private test sets (call center domain):
Public test sets:
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.