Update model card, results, figures, and Apache 2.0 license

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NOTICE

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+Zipper-LoRA
+Copyright (c) 2026 Zipper-LoRA contributors
+
+Licensed under the Apache License, Version 2.0.

README.md

@@ -1,62 +1,230 @@
 ---
-license: mit
+license: apache-2.0
+language:
+- en
+- fr
+- ru
+- th
+- de
+- es
+- it
+- pt
+- ar
+- vi
+- ja
+- ko
+pipeline_tag: automatic-speech-recognition
+tags:
+- multilingual
+- speech-llm
+- lora
+- zipper-lora
+- arxiv:2603.17558
 ---
 
+<div align="center">
+
 # Zipper-LoRA
 
-<p>
-  <a href="https://arxiv.org/abs/2603.17558"><img src="https://img.shields.io/badge/arXiv-2603.17558-b31c1b.svg" alt="arXiv"></a>
-  <a href="https://github.com/YuCeong-May/Zipper-LoRA"><img src="https://img.shields.io/badge/GitHub-Zipper--LoRA-green.svg" alt="GitHub"></a>
-  <a href="https://huggingface.co/YuCeong-May/Zipper-LoRA"><img src="https://img.shields.io/badge/🤗-Zipper--LoRA-yellowbadge.svg" alt="HuggingFace"></a>
-  <img src="https://img.shields.io/badge/License-MIT-blue.svg" alt="License">
+### Dynamic Parameter Decoupling for Speech-LLM Based Multilingual Speech Recognition
+
+**IEEE Transactions on Audio, Speech and Language Processing · 2026**
+
+[![Paper](https://img.shields.io/badge/Paper-IEEE_Xplore-00629B?logo=ieee&logoColor=white)](https://ieeexplore.ieee.org/document/11636226)
+[![arXiv](https://img.shields.io/badge/arXiv-2603.17558-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2603.17558)
+[![Code](https://img.shields.io/badge/Code-GitHub-181717?logo=github&logoColor=white)](https://github.com/YuCeong-May/Zipper-LoRA)
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE)
+
+[Getting Started](#getting-started) · [Checkpoints](#checkpoints) · [Method](#method) · [Results](#results) · [Visualizations](#visualizations) · [Citation](#citation)
+
+</div>
+
+## Overview
+
+Zipper-LoRA is a language-conditioned adaptation framework for multilingual speech recognition. It combines shared and language-specific LoRA parameters at the rank level, balancing cross-lingual knowledge transfer with language specialization under imbalanced supervision.
+
+The framework provides three variants—**Static**, **Hard**, and **Soft**—and an **Initial-B warm start** within a two-stage training strategy. Experiments cover 12 languages with both chunked and full-context speech encoders.
+
+- **Multilingual recognition:** Soft + Initial-B achieves the lowest error rate across all 12 languages in the main non-chunked comparison.
+- **Low-resource adaptation:** Up to **40.7% relative error reduction** over Vanilla-LoRA with 1 hour of supervision per language.
+- **Parameter efficiency:** Hard and Soft use **38.7% fewer encoder PEFT parameters** than Independent-LoRA.
+
+## Getting Started
+
+```bash
+git clone https://github.com/YuCeong-May/Zipper-LoRA.git
+cd Zipper-LoRA
+python -m pip install -e './inference[inference]'
+```
+
+Requires Python 3.10 or later. For NVIDIA GPUs, install a PyTorch build compatible with your CUDA environment before installing the package.
+
+| Resource | Description |
+| --- | --- |
+| [Usage guide](https://github.com/YuCeong-May/Zipper-LoRA/blob/main/inference/README.md) | Installation, checkpoint inference, adapter API, and LID embedding export |
+| [Model checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts) | Released models on Hugging Face |
+| [Data splits](https://github.com/YuCeong-May/Zipper-LoRA/tree/main/data/utt-id) | Utterance lists for the experimental training and evaluation subsets |
+| [Paper](https://ieeexplore.ieee.org/document/11636226) | Method, experimental setup, and complete results |
+
+## Checkpoints
+
+Browse the [checkpoint collection](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts) or select a run below. The main comparison uses the 1-hour low-resource setting.
+
+| Method | Chunked encoder | Non-chunked encoder |
+| --- | --- | --- |
+| Zipper-LoRA-Soft + Initial-B | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-zipper-lora-soft-init-B) | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-zipper-lora-soft-init-B-non-chunked) |
+| Zipper-LoRA-Soft | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-zipper-lora-soft) | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-zipper-lora-soft-non-chunked) |
+| Zipper-LoRA-Hard | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-zipper-lora-hard) | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-zipper-lora-hard-non-chunked) |
+| Zipper-LoRA-Static | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-zipper-lora-static) | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-zipper-lora-static--non-chunked) |
+| Independent-LoRA | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-independent-lora) | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-independent-lora-non-chunked) |
+| Vanilla-LoRA | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-vanilla-lora) | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-vanilla-lora-non-chunked) |
+| FlyLoRA | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-flylora) | [Checkpoints](https://huggingface.co/YuCeong-May/Zipper-LoRA/tree/main/ckpts/qwen3-1.7b-whisper-12x1000h-lite1h-flylora-audio-non-chunked) |
+
+Run directories contain saved training steps. Select one global-step directory and follow the [usage guide](https://github.com/YuCeong-May/Zipper-LoRA/blob/main/inference/README.md) for checkpoint loading and ASR. The guide describes the required model code, tokenizer, and configuration files.
+
+## Method
+
+### Speech-LLM Backbone
+
+<p align="center">
+  <img src="https://huggingface.co/YuCeong-May/Zipper-LoRA/resolve/main/asserts/speech-llm-backbone.png" alt="Speech-LLM backbone" width="800">
+</p>
+
+The Speech-LLM backbone combines **Whisper Large-v3**, a gated modality projector, and **Qwen3-1.7B**. The encoder and projector reduce the speech sequence by a total factor of 8, producing speech embeddings at 12.5 Hz.
+
+### LoRA Adaptation
+
+<p align="center">
+  <img src="https://huggingface.co/YuCeong-May/Zipper-LoRA/resolve/main/asserts/peft-baselines.png" alt="LoRA adaptation architectures" width="900">
 </p>
 
-## 📝 Abstract
+Vanilla-LoRA shares adaptation parameters across languages; Independent-LoRA maintains separate language-specific adapters; FlyLoRA uses sparse rank activation. Adaptation uses rank 32 and LoRA alpha 64.
+
+### Zipper-LoRA Architecture
+
+<p align="center">
+  <img src="https://huggingface.co/YuCeong-May/Zipper-LoRA/resolve/main/asserts/Zipper-LoRA.png" alt="Zipper-LoRA architecture" width="900">
+</p>
 
-Speech Large Language Models (Speech-LLMs) have emerged as a powerful approach for automatic speech recognition (ASR) by aligning speech encoders with large language models. However, adapting these systems to multilingual settings with imbalanced data distributions remains challenging.
+Zipper-LoRA composes shared and language-specific updates at the **LoRA rank level**. All three variants share a down-projection matrix A; Static uses a fixed split of the up-projection ranks, Hard selects shared or language-specific columns with a binary router, and Soft continuously mixes the two banks using Whisper language-ID embeddings. **Initial-B** provides a warm start within the two-stage training strategy.
 
-In such scenarios, a **stability-plasticity dilemma** often arises:
-- Fully shared Parameter-Efficient Fine-Tuning (PEFT) can cause **negative inter-lingual interference** for under-represented languages
-- Fully language-specific tuning limits the **cross-lingual beneficial knowledge transfer** needed for low-resource tasks
+## Results
 
-To address this, we propose **Zipper-LoRA**, a novel rank-level decoupling framework with three variants (Static, Hard, and Soft) that dynamically synthesizes LoRA updates from shared and language-specific subspaces.
+The evaluation covers **12 target languages**. Eight languages have 500–1,000 hours of supervision each; Arabic, Japanese, Korean, and Portuguese have **1 hour each**. Results below are from the paper.
 
-### Key Features
+### Multilingual Speech Recognition
 
-- **Language-Conditioned Router**: Dynamically controls the contribution of each subspace at the LoRA rank level
-- **Fine-grained Sharing**: Enables sharing where languages are compatible, strict decoupling when conflicts occur
-- **Two-Stage Training**: With Initial-B warm start for accelerated convergence
-- **Robust Performance**: Works across both chunked and non-chunked encoder configurations
+**Non-chunked encoder · WER/CER (%) ↓**
 
-### Results
+Soft + Initial-B achieves the lowest error rate across all 12 languages among the methods in this comparison (Table V). Japanese, Korean, and Thai use CER; other languages use WER. English is the average of LibriSpeech Clean and Other.
 
-Experiments on a 12-language mixed-resource setting show that Zipper-LoRA consistently outperforms both fully shared and independent baselines, particularly in **extremely low-resource scenarios**.
+| Dataset | Language | Vanilla-LoRA | FlyLoRA | Independent-LoRA | Zipper-LoRA-Soft + Initial-B |
+| --- | --- | ---: | ---: | ---: | ---: |
+| Common Voice | Arabic | 53.08 | 55.01 | 41.01 | **36.94** |
+| Common Voice | Japanese | 46.28 | 46.86 | 37.53 | **34.04** |
+| Common Voice | Korean | 36.03 | 35.99 | 24.53 | **21.35** |
+| Common Voice | Portuguese | 29.30 | 30.16 | 23.82 | **23.22** |
+| Common Voice | Thai | 1.48 | 1.49 | 1.36 | **1.32** |
+| LibriSpeech | English (Avg.) | 4.15 | 4.19 | 3.98 | **3.96** |
+| MLS | German | 8.52 | 9.04 | 8.36 | **8.20** |
+| MLS | Spanish | 5.27 | 5.79 | 5.05 | **4.84** |
+| MLS | French | 8.26 | 8.13 | 8.09 | **7.50** |
+| MLS | Italian | 11.86 | 12.36 | 11.66 | **11.09** |
+| MSR86k | Russian | 9.05 | 9.23 | 9.02 | **8.79** |
+| MSR86k | Vietnamese | 6.31 | 6.51 | 6.13 | **5.94** |
 
----
+<p align="center">
+  <img src="https://huggingface.co/YuCeong-May/Zipper-LoRA/resolve/main/asserts/target-language-radar.png" alt="Performance across 12 target languages" width="700">
+</p>
+
+The radar plot displays 100 minus the WER/CER percentage, so **higher is better**. The original paper figure is reproduced as supplied; use the table above for exact numerical results.
+
+### Low-Resource Adaptation
+
+With 1 hour of supervision per language, the non-chunked Soft + Initial-B model reduces error relative to Vanilla-LoRA by **30.4% on Arabic**, **26.4% on Japanese**, **40.7% on Korean**, and **20.8% on Portuguese**. These relative reductions are calculated from the table above.
+
+| Encoder setting | Method | Arabic WER | Japanese CER | Korean CER | Portuguese WER |
+| --- | --- | ---: | ---: | ---: | ---: |
+| Chunked | Vanilla-LoRA | 62.32 | 48.93 | 38.37 | 33.49 |
+| Chunked | Soft + Initial-B | **38.64** | **35.21** | **21.87** | **24.29** |
+| Non-chunked | Vanilla-LoRA | 53.08 | 46.28 | 36.03 | 29.30 |
+| Non-chunked | Soft + Initial-B | **36.94** | **34.04** | **21.35** | **23.22** |
+
+Selected results from Table VII. All Zipper-LoRA variants improve over Vanilla-LoRA on these four languages in both encoder settings; chunked Static has the best Portuguese result among the full set of variants (23.26%).
+
+<p align="center">
+  <img src="https://huggingface.co/YuCeong-May/Zipper-LoRA/resolve/main/asserts/low-resource-comparison.png" alt="Relative improvements from extremely low-resource to low-resource adaptation" width="700">
+</p>
 
-## 📋 TODO
+Bars show relative performance change against Vanilla-LoRA: positive values indicate improvement. The 1-hour condition uses Common Voice for all four languages. In the nominal 30-hour condition, Arabic and Japanese use 30 hours of Common Voice, Korean uses the available 6.51 hours, and Portuguese uses MLS (the original figure labels this subset as 33 hours). Zipper-LoRA retains gains as supervision increases.
 
-- [x] Paper
-- [x] Data
-- [ ] Code (will be released after paper accepted)
-- [x] Model Weights (coming soon)
+### Encoder Efficiency
 
+The paper profiles encoder computation with **30-second audio, batch size 1, bf16, and a forward-backward pass**. Relative speed is normalized to the non-chunked encoder; these are encoder profiling results.
 
-## 📄 Citation## 📄 Citation
+| Encoder setting | Peak GPU memory | Extra peak GPU memory | Extra memory reduction | Relative speed |
+| --- | ---: | ---: | ---: | ---: |
+| Non-chunked | 6.314 GB | 4.947 GB | — | 1.00× |
+| 8-second chunks | 4.382 GB | 3.011 GB | 39.14% | 1.41× |
+| 4-second chunks | 4.010 GB | 2.638 GB | 46.68% | 1.53× |
+| 2-second chunks | 3.645 GB | 2.273 GB | 54.05% | 1.56× |
+| 1-second chunks | 3.580 GB | 2.208 GB | 55.36% | 1.56× |
 
-If you find this work helpful, please cite:
+The experiments use 8-second chunks during decoding. “Extra peak” measures additional GPU memory attributed to encoder computation under the profiling setup.
+
+### Adapter Parameter Footprint
+
+| Method | Encoder PEFT parameters | PEFT / total encoder parameters |
+| --- | ---: | ---: |
+| Vanilla-LoRA | 23.60 M | 3.16% |
+| FlyLoRA | 23.60 M | 3.16% |
+| Independent-LoRA | 283.13 M | 28.14% |
+| Zipper-LoRA-Static | 88.47 M | 10.90% |
+| Zipper-LoRA-Hard | 173.53 M | 19.36% |
+| Zipper-LoRA-Soft | 173.53 M | 19.36% |
+
+Hard and Soft use **38.7% fewer encoder PEFT parameters** than Independent-LoRA (173.53 M vs. 283.13 M). These counts cover the encoder side, not the complete Speech-LLM.
+
+## Visualizations
+
+### Language-ID Similarity
+
+<p align="center">
+  <img src="https://huggingface.co/YuCeong-May/Zipper-LoRA/resolve/main/asserts/lid-similarity.png" alt="Whisper language-ID embedding cosine similarity" width="700">
+</p>
+
+The cosine-similarity heatmap shows the structure of Whisper LID embeddings used as the routing anchor. Japanese/Korean and Thai/Vietnamese each have a cosine similarity of 0.41 in the paper's analysis.
+
+### Encoder Representations
+
+<p align="center">
+  <img src="https://huggingface.co/YuCeong-May/Zipper-LoRA/resolve/main/asserts/encoder-tsne.png" alt="Encoder representation t-SNE comparison" width="900">
+</p>
+
+Each point represents an utterance embedding, colored by language. Compared with Vanilla-LoRA, Zipper-LoRA-Soft yields more compact language clusters for **10 of 12 languages**. The average per-language covariance determinant in the **2-D t-SNE coordinates** decreases from 392.67 to 109.12 (72.21%); Arabic and Italian are the exceptions. This visualization complements the recognition results.
+
+All figures and reported measurements in these sections come from the paper; relative error and parameter reductions are calculated from its reported values.
+
+## Code and Data
+
+The [GitHub repository](https://github.com/YuCeong-May/Zipper-LoRA) provides the Python package, ASR example, Whisper LID embedding export tool, and experimental utterance lists. See the [usage guide](https://github.com/YuCeong-May/Zipper-LoRA/blob/main/inference/README.md) for installation and execution.
+
+## License
+
+The code is released under the [Apache License 2.0](LICENSE). Refer to the respective source datasets and base models for their terms of use.
+
+## Citation
+
+If you find this work useful, please cite our paper:
 
 ```bibtex
 @article{ZipperLoRA2026,
-  title={Dynamic Parameter Decoupling for Speech-LLM based Multilingual Speech Recognition},
   author={Mei, Yuxiang and Qiu, Delai and Liu, Shengping and Liang, Jiaen and Long, Yanhua},
-  journal={arXiv preprint arXiv:2603.17558},
-  year={2026}
+  journal={IEEE Transactions on Audio, Speech and Language Processing},
+  title={Zipper-LoRA: Dynamic Parameter Decoupling for Speech-LLM Based Multilingual Speech Recognition},
+  year={2026},
+  volume={34},
+  pages={3956--3969},
+  doi={10.1109/TASLPRO.2026.3719331}
 }
 ```
-
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-
-<p align="center">
-  <img src="https://img.shields.io/badge/Status-Under_Construction-yellow" alt="Status">
-</p>