Automatic Speech Recognition
NeMo
Persian
speech
persian
farsi
fastconformer
ctc
streaming
on-device
shenava
shenava-1
visualears
rnnt

🎙️ Shenava Koochik v1.0 · شنوا کوچیک

The 114M-parameter flagship of the Shenava-1 Persian ASR family. This is the FP32 NeMo source checkpoint for evaluation, fine-tuning, and export; use one of the deployment repositories below for browser, Core ML, sherpa-onnx, or tract applications.

✨ At a glance | معرفی سریع

English فارسی
🧠 Role 114M flagship teacher مدل معلم و پرچم‌دار ۱۱۴M
📦 Format FP32 NeMo source checkpoint اصلی FP32 و NeMo
🎧 Input 16 kHz mono Persian speech گفتار فارسی تک‌کانالهٔ ۱۶ کیلوهرتز
📝 Output Persian transcription رونویسی فارسی
🛠️ Best for Evaluation, fine-tuning, and export ارزیابی، آموزش تکمیلی و تبدیل

🧠 Model

  • Architecture: FastConformer Hybrid RNNT/CTC; the CTC head is used by the published deployment exports.
  • Audio: mono, 16 kHz Persian speech.
  • Encoder: d_model=512, 17 layers, 8x subsampling (about 80 ms per encoder step).
  • Contexts: [70,13], [70,6], [70,1], and [70,0].
  • Tokenizer: ve_tok_v4, SentencePiece BPE-1024 plus CTC blank.
  • Output: Persian text. Numbers are emitted in spoken form; apply Persian inverse text normalization when digits are wanted.

The two .nemo filenames are compatibility aliases with identical content and SHA-256:

f7b5124a9fd2d50c15bf070abdc0f80ec2449d64948c5657adb79a7e91dd1d16

📊 Published evaluation

Decoded with context [70,13] and the double-benchmark ITN/Persian-digit normalization convention.

Set WER CER
visualears-golden-6669 7.49% 2.30%
FLEURS-fa 10.64% 3.79%

🚀 Load with NeMo

from nemo.collections.asr.models import ASRModel

model = ASRModel.restore_from("shenava-koochik-v1.0.nemo")
text = model.transcribe(["speech.wav"])[0].text
print(text)

🧩 Deployment variants

🇮🇷 خلاصهٔ فارسی

این مخزن نسخهٔ اصلی FP32 و NeMo مدل ۱۱۴ میلیون‌پارامتری «شنوا کوچیک» است. برای اجرای مرورگر، Core ML، sherpa-onnx یا tract از مخزن مخصوص همان قالب استفاده کنید. ورودی گفتار فارسی تک‌کانالهٔ ۱۶ کیلوهرتز و خروجی متن فارسی است.

🌌 Explore Shenava-1

🧠 Koochik 114M · ⚖️ Rizeh 32M · 🐣 Rizeh-Pizeh 6.9M · 🌐 Browser · 🍎 Apple · 🦀 Rust

Apache-2.0. ASR quality can vary with accent, noise, overlap, recording channel, and code-switching; review meaning-critical transcripts before relying on them.

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