language:
- fa
license: apache-2.0
library_name: nemo
pipeline_tag: automatic-speech-recognition
base_model:
- Reza2kn/Shenava-Koochik-v1.0
base_model_relation: finetune
tags:
- automatic-speech-recognition
- speech
- persian
- farsi
- fastconformer
- ctc
- streaming
- on-device
- shenava
- shenava-1
- visualears
- rnnt
- nemo
- distillation
metrics:
- wer
- cer
datasets:
- Reza2kn/visualears-persian-asr-16k
- Reza2kn/visualears-golden-6669
- Reza2kn/fleurs-fa-benchmark
⚖️🎙️ Shenava Rizeh v1.0 · شنوا ریزه
The 32M-parameter middle tier of Shenava-1: a Persian FastConformer Hybrid RNNT/CTC model distilled with logit and feature knowledge distillation from the 114M Koochik teacher. This repository contains the FP32 NeMo source checkpoint; published deployment formats live in separate repositories.
✨ At a glance | معرفی سریع
| English | فارسی | |
|---|---|---|
| ⚖️ Role | Balanced 32M middle tier | مدل متعادل میانی با ۳۲M پارامتر |
| 🧠 Lineage | Distilled from 114M Koochik | تقطیرشده از کوچیک ۱۱۴M |
| 📦 Format | FP32 NeMo source | checkpoint اصلی FP32 و NeMo |
| 🎧 Input | 16 kHz mono Persian speech | گفتار فارسی تککانالهٔ ۱۶ کیلوهرتز |
| 🎯 Best for | Accuracy/footprint balance | تعادل دقت و اندازه |
- Canonical repository:
Reza2kn/Shenava-Rizeh-v1.0 - PersianML mirror:
PersianML/Shenava-Rizeh-v1.0
🧠 Model contract
- Audio: mono, 16 kHz Persian speech.
- Encoder:
d_model=256, 16 layers, 8x subsampling. - Contexts:
[70,13],[70,6],[70,1], and[70,0]. - Deployed head: CTC.
- Tokenizer: ve_tok_v4, SentencePiece BPE-1024 plus blank.
- Output: Persian text; numbers are spoken-form unless the display layer applies ITN.
📊 Published evaluation
Decoded with context [70,13] and the double-benchmark ITN/Persian-digit normalization convention.
| Set | WER | CER |
|---|---|---|
| visualears-golden-6669 | 12.11% | 3.94% |
| FLEURS-fa | 14.45% | 5.10% |
🚀 Load with NeMo
from nemo.collections.asr.models import ASRModel
model = ASRModel.restore_from("shenava-rizeh-v1.0.nemo")
print(model.transcribe(["speech.wav"])[0].text)
Choose Rizeh when Koochik’s accuracy/size trade-off is too heavy but the 6.9M Rizeh-Pizeh model is too small for the required accuracy.
🇮🇷 خلاصهٔ فارسی
«شنوا ریزه» مدل میانی ۳۲ میلیونپارامتری خانوادهٔ Shenava-1 است. این مخزن checkpoint اصلی FP32 و NeMo را نگه میدارد و برای ارزیابی، fine-tune یا تبدیل به قالبهای اجرایی مناسب است.
🌌 Explore Shenava-1
🧠 Koochik 114M · ⚖️ Rizeh 32M · 🐣 Rizeh-Pizeh 6.9M
Apache-2.0. Accuracy varies with accent, noise, overlap, recording channel, and code-switching.