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visualears-fastconformer-fa-full-ab-fp8
Reza2kn/visualears-fastconformer-fa-full-ab-fp8
visualears-fastconformer-fa-full-ab-fp8 is a automatic speech recognition model from Reza2kn. Use it when you need speech turned into text. It is set up for nemo. The card lists the license as apache-2.0.
FP8 post-training quantization of Reza2kn/visualears-fastconformer-fa-full-ab via NVIDIA modelopt.
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At a glance
- Task
- Automatic Speech Recognition
- Library
- nemo
- License
- apache-2.0
- Access
- Public
- Created
- Jun 11, 2026
- Updated
- Jun 11, 2026
- SHA
- 4aac0f19
Base models
Datasets
Model card
From the Hugging Face model README
visualears-fastconformer-fa-full-ab-fp8
FP8 post-training quantization of Reza2kn/visualears-fastconformer-fa-full-ab via NVIDIA modelopt.
- Base architecture: EncDecHybridRNNTCTCBPEModel (NeMo)
- Calibration: 32 Persian clips from
Reza2kn/persian-asr-eval-v0(held out from eval). - Hardware target: NVIDIA GPUs with FP8/TensorRT-family runtime support.
Eval — Reza2kn/persian-asr-eval-v0 (FLEURS-fa, 200 clips)
| Variant | WER ↓ | CER ↓ | per-clip latency | peak VRAM |
|---|---|---|---|---|
| FP base | 18.38% | 6.58% | 31 ms | 588 MiB |
| FP8 (this repo) | 18.48% | 6.69% | 51 ms | 662 MiB |
Usage
import nemo.collections.asr as nemo_asr
m = nemo_asr.models.ASRModel.restore_from("visualears-fastconformer-fa-full-ab-FP8.nemo").cuda().eval()
transcripts = m.transcribe(["clip.wav"])
print(transcripts[0])
License
Inherits the base model's license.
Base Comparison
On the same 200 FLEURS-fa clips, FP8 WER retention vs the FP base was 99.47% and CER retention was 98.34%. Exact normalized transcript match was 54.0%; rough word-position agreement was 93.13%. See validation/fp8_vs_base_eval_summary.json.
- Task
- Automatic Speech Recognition
- Library
- nemo
- License
- apache-2.0
- Languages
- fa
- Created
- Jun 11, 2026
- Updated
- Jun 11, 2026