license: apache-2.0
language:
- fa
library_name: nemo
pipeline_tag: automatic-speech-recognition
tags:
- nemo
- automatic-speech-recognition
- speech
- persian
- farsi
- fastconformer
- ctc
- on-device
- shenava
- shenava-1
- visualears
- liteasr
- compression
- low-rank
- dhh
base_model: Reza2kn/Shenava-Koochik-v1.0
base_model_relation: quantized
datasets:
- Reza2kn/visualears-golden-6669
- Reza2kn/fleurs-fa-benchmark
🪶🎙️ Shenava Koochik Lite v1.0
A LITEASR-compressed encoder for Shenava Koochik v1.0. Post-training low-rank factorization reduces the encoder from 108.9M to 85.4M parameters (21.6%) without retraining.
This repository is not a standalone ASR checkpoint. It contains a replacement encoder state dict and must be loaded on top of the base .nemo model; the decoder, CTC head, and tokenizer still come from Koochik.
✨ At a glance | معرفی سریع
| English | فارسی | |
|---|---|---|
| 🪶 Role | Compressed Koochik encoder | encoder فشردهشدهٔ کوچیک |
| 📉 Reduction | 108.9M → 85.4M encoder parameters | کاهش ۲۱٫۶ درصدی پارامترهای encoder |
| 🧪 Method | Post-training LITEASR low-rank factorization | فشردهسازی low-rank بدون آموزش مجدد |
| 🧩 Requirement | Base Koochik .nemo is required |
فایل NeMo مدل اصلی الزامی است |
| ⚠️ Scope | Not a standalone checkpoint | checkpoint مستقل نیست |
- Canonical repository:
Reza2kn/Shenava-Koochik-Lite-v1.0 - PersianML mirror:
PersianML/Shenava-Koochik-Lite-v1.0
📦 Files
koochik_lite099_enc.pt: compressed FP32 encoder state dict.koochik_lite099_kmap.json: retained rank for each factorized layer.load_koochik_lite.py: reconstructs the low-rank modules and loads the state dict into the base model.
🚀 Load
from huggingface_hub import hf_hub_download, snapshot_download
from load_koochik_lite import load_koochik_lite
base = hf_hub_download(
"Reza2kn/Shenava-Koochik-v1.0",
"shenava-koochik-v1.0.nemo",
)
repo = snapshot_download("Reza2kn/Shenava-Koochik-Lite-v1.0")
model = load_koochik_lite(
base,
f"{repo}/koochik_lite099_enc.pt",
f"{repo}/koochik_lite099_kmap.json",
)
print(model.transcribe(["speech.wav"])[0].text)
📊 Published trade-off
The release evaluated both greedy decoding and an optional Vosk-guided hotword beam. Lower is better.
| Decode | golden-6669 keyword-band WER | FLEURS keyword-band WER | golden-6669 overall WER | FLEURS overall WER |
|---|---|---|---|---|
| Full Koochik, greedy | 8.0 | 13.1 | 4.64 | 5.36 |
| Koochik Lite, greedy | 12.5 | 18.0 | 6.92 | 7.23 |
| Koochik Lite + Vosk guide | 6.4 | 11.7 | 5.30 | 5.31 |
Compression alone reduces quality; the Vosk-guided result requires a separate Vosk first pass plus hotword-aware pyctcdecode beam search. Do not compare the guided row to a greedy-only deployment as though they used the same runtime.
🇮🇷 خلاصهٔ فارسی
این مخزن یک مدل کامل و مستقل نیست؛ فقط encoder فشردهشده را نگه میدارد و برای اجرا به فایل NeMo مدل اصلی نیاز دارد. نسخهٔ greedy سبکتر است ولی افت دقت دارد؛ ردیف Vosk-guided به یک مرحلهٔ جداگانهٔ Vosk و beam search نیاز دارد.
🌌 Explore Shenava-1
🧠 Full Koochik · 🪶 Koochik Lite · ⚖️ Rizeh 32M · 🐣 Rizeh-Pizeh 6.9M
Apache-2.0. Compression method: LITEASR.