Mirror Reza2kn/Shenava-Koochik-Lite-v1.0 at revision b20f2223ba15b8f4df13a95885710c4eee1a2c0a

LICENSE

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README.md

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+---
+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](https://huggingface.co/Reza2kn/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.
+
+- Canonical repository: [`Reza2kn/Shenava-Koochik-Lite-v1.0`](https://huggingface.co/Reza2kn/Shenava-Koochik-Lite-v1.0)
+- PersianML mirror: [`PersianML/Shenava-Koochik-Lite-v1.0`](https://huggingface.co/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
+
+```python
+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.
+
+## 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.
+
+Apache-2.0. Compression method: [LITEASR](https://arxiv.org/abs/2502.20583).

koochik_lite099_enc.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d6ea7af0b1428160d3d74b242fb1f0bd95a4f548bc4b43775dc20ff1766c884b
+size 341748739

koochik_lite099_kmap.json

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+{"layers.0.feed_forward1.linear1": 208, "layers.0.feed_forward1.linear2": 352, "layers.0.self_attn.linear_q": 208, "layers.0.self_attn.linear_k": 160, "layers.0.self_attn.linear_v": 160, "layers.0.self_attn.linear_out": 176, "layers.0.self_attn.linear_pos": 32, "layers.0.feed_forward2.linear1": 256, "layers.0.feed_forward2.linear2": 400, "layers.1.feed_forward1.linear1": 256, "layers.1.feed_forward1.linear2": 336, "layers.1.self_attn.linear_q": 208, "layers.1.self_attn.linear_k": 160, "layers.1.self_attn.linear_v": 240, "layers.1.self_attn.linear_pos": 48, "layers.1.feed_forward2.linear1": 288, "layers.2.feed_forward1.linear1": 288, "layers.2.feed_forward1.linear2": 304, "layers.2.self_attn.linear_q": 240, "layers.2.self_attn.linear_k": 192, "layers.2.self_attn.linear_v": 240, "layers.2.self_attn.linear_pos": 32, "layers.2.feed_forward2.linear1": 288, "layers.3.feed_forward1.linear1": 304, "layers.3.feed_forward1.linear2": 368, "layers.3.self_attn.linear_q": 240, "layers.3.self_attn.linear_k": 176, "layers.3.self_attn.linear_pos": 32, "layers.3.feed_forward2.linear1": 304, "layers.4.feed_forward1.linear1": 288, "layers.4.feed_forward1.linear2": 64, "layers.4.self_attn.linear_q": 240, "layers.4.self_attn.linear_k": 192, "layers.4.self_attn.linear_out": 240, "layers.4.self_attn.linear_pos": 32, "layers.4.feed_forward2.linear1": 288, "layers.5.feed_forward1.linear1": 288, "layers.5.feed_forward1.linear2": 304, "layers.5.self_attn.linear_q": 224, "layers.5.self_attn.linear_k": 176, "layers.5.self_attn.linear_out": 208, "layers.5.self_attn.linear_pos": 32, "layers.5.feed_forward2.linear1": 272, "layers.6.feed_forward1.linear1": 272, "layers.6.feed_forward1.linear2": 320, "layers.6.self_attn.linear_q": 224, "layers.6.self_attn.linear_k": 176, "layers.6.self_attn.linear_v": 240, "layers.6.self_attn.linear_out": 240, "layers.6.self_attn.linear_pos": 32, "layers.6.feed_forward2.linear1": 256, "layers.7.feed_forward1.linear1": 272, "layers.7.feed_forward1.linear2": 240, "layers.7.self_attn.linear_q": 208, "layers.7.self_attn.linear_k": 160, "layers.7.self_attn.linear_out": 240, "layers.7.self_attn.linear_pos": 32, "layers.7.feed_forward2.linear1": 256, "layers.8.feed_forward1.linear1": 272, "layers.8.feed_forward1.linear2": 96, "layers.8.self_attn.linear_q": 224, "layers.8.self_attn.linear_k": 176, "layers.8.self_attn.linear_v": 240, "layers.8.self_attn.linear_out": 240, "layers.8.self_attn.linear_pos": 32, "layers.8.feed_forward2.linear1": 272, "layers.9.feed_forward1.linear1": 288, "layers.9.feed_forward1.linear2": 384, "layers.9.self_attn.linear_q": 224, "layers.9.self_attn.linear_k": 192, "layers.9.self_attn.linear_pos": 32, "layers.9.feed_forward2.linear1": 272, "layers.10.feed_forward1.linear1": 288, "layers.10.feed_forward1.linear2": 336, "layers.10.self_attn.linear_q": 224, "layers.10.self_attn.linear_k": 208, "layers.10.self_attn.linear_pos": 32, "layers.10.feed_forward2.linear1": 272, "layers.11.feed_forward1.linear1": 304, "layers.11.feed_forward1.linear2": 304, "layers.11.self_attn.linear_k": 224, "layers.11.self_attn.linear_pos": 32, "layers.11.feed_forward2.linear1": 288, "layers.12.feed_forward1.linear1": 336, "layers.12.feed_forward1.linear2": 368, "layers.12.self_attn.linear_k": 240, "layers.12.self_attn.linear_pos": 32, "layers.12.feed_forward2.linear1": 320, "layers.12.feed_forward2.linear2": 400, "layers.13.feed_forward1.linear1": 352, "layers.13.feed_forward1.linear2": 144, "layers.13.self_attn.linear_q": 240, "layers.13.self_attn.linear_k": 224, "layers.13.self_attn.linear_pos": 32, "layers.13.feed_forward2.linear1": 320, "layers.13.feed_forward2.linear2": 336, "layers.14.feed_forward1.linear1": 320, "layers.14.feed_forward1.linear2": 16, "layers.14.self_attn.linear_q": 240, "layers.14.self_attn.linear_k": 224, "layers.14.self_attn.linear_pos": 48, "layers.14.feed_forward2.linear1": 320, "layers.14.feed_forward2.linear2": 400, "layers.15.feed_forward1.linear1": 304, "layers.15.feed_forward1.linear2": 16, "layers.15.self_attn.linear_q": 240, "layers.15.self_attn.linear_k": 224, "layers.15.self_attn.linear_pos": 48, "layers.15.feed_forward2.linear1": 288, "layers.15.feed_forward2.linear2": 320, "layers.16.feed_forward1.linear1": 288, "layers.16.self_attn.linear_q": 240, "layers.16.self_attn.linear_k": 224, "layers.16.self_attn.linear_pos": 32, "layers.16.feed_forward2.linear1": 320, "layers.16.feed_forward2.linear2": 352}

load_koochik_lite.py

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+#!/usr/bin/env python3
+"""Load Shenava-Koochik-Lite-v1.0 — a LITEASR-compressed (-21.6% encoder) Koochik.
+
+Needs the base model Reza2kn/Shenava-Koochik-v1.0 (.nemo) plus this repo's
+`koochik_lite099_enc.pt` (compressed encoder state_dict) and `koochik_lite099_kmap.json`
+(per-layer low-rank sizes k). LITEASR replaces each targeted encoder Linear with a
+rank-k Sequential(Linear(D_in->k, bias=False), Linear(k->D_out, bias=True)); this loader
+rebuilds that skeleton from the kmap, then loads the compressed weights.
+
+    from huggingface_hub import hf_hub_download, snapshot_download
+    base = hf_hub_download("Reza2kn/Shenava-Koochik-v1.0", "shenava-koochik-v1.0.nemo")
+    repo = snapshot_download("Reza2kn/Shenava-Koochik-Lite-v1.0")
+    from load_koochik_lite import load_koochik_lite
+    m = load_koochik_lite(base, f"{repo}/koochik_lite099_enc.pt", f"{repo}/koochik_lite099_kmap.json")
+    print(m.transcribe(["clip.wav"])[0].text)
+"""
+import json, torch, torch.nn as nn
+import nemo.collections.asr as A
+
+
+def load_koochik_lite(base_nemo, enc_sd_path, kmap_path, device="cuda", ctc=True):
+    m = A.models.ASRModel.restore_from(base_nemo, map_location=device).eval()
+    if ctc:
+        try:
+            m.change_decoding_strategy(decoder_type="ctc")
+        except Exception:
+            pass
+
+    def parent(root, name):
+        parts = name.split("."); p = root
+        for x in parts[:-1]:
+            p = getattr(p, x)
+        return p, parts[-1]
+
+    for n, k in json.load(open(kmap_path)).items():
+        par, at = parent(m.encoder, n)
+        mod = getattr(par, at)
+        Dout, Din = mod.weight.shape
+        setattr(par, at, nn.Sequential(nn.Linear(Din, k, bias=False), nn.Linear(k, Dout, bias=True)))
+    m.encoder.load_state_dict(torch.load(enc_sd_path, map_location=device), strict=True)
+    return m.to(device).eval()
+
+
+if __name__ == "__main__":
+    import sys
+    m = load_koochik_lite(sys.argv[1], sys.argv[2], sys.argv[3], device="cuda" if torch.cuda.is_available() else "cpu")
+    for r in m.transcribe(sys.argv[4:], batch_size=8):
+        print(r.text if hasattr(r, "text") else r)