Automatic Speech Recognition
NeMo
Persian
speech
persian
farsi
fastconformer
ctc
streaming
on-device
shenava
shenava-1
visualears
rnnt
distillation
Instructions to use PersianML/Shenava-Rizeh-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use PersianML/Shenava-Rizeh-v1.0 with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("PersianML/Shenava-Rizeh-v1.0") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
| 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](https://huggingface.co/Reza2kn/Shenava-Koochik-v1.0) 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`](https://huggingface.co/Reza2kn/Shenava-Rizeh-v1.0) | |
| - PersianML mirror: [`PersianML/Shenava-Rizeh-v1.0`](https://huggingface.co/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 | |
| ```python | |
| 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](https://huggingface.co/Reza2kn/Shenava-Koochik-v1.0) · **⚖️ Rizeh 32M** · [🐣 Rizeh-Pizeh 6.9M](https://huggingface.co/Reza2kn/Shenava-Rizeh-Pizeh-v1.0) | |
| Apache-2.0. Accuracy varies with accent, noise, overlap, recording channel, and code-switching. | |