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Azeros

AZeroS (Auden Zero-instruction-tuned Speech-LLM) extends a frozen LLM to speech via Self-Generated Instruction-Free Tuning (SIFT). It keeps the LLM and audio encoders frozen and trains lightweight projection modules...

Sourcehuggingface.co/AudenAI/azeros/tree/e9392d32639e7079cb0e24f7373207ee658a8baf

KindOtherBase modelQwen/Qwen2.5-7B-InstructVersionRevision e9392d3Bgrade

Model source

Kind: Other
File size: 182 MB–4.59 GB
Base model: Qwen/Qwen2.5-7B-Instruct
Version: vRevision e9392d3
Updated: Oct 25, 2026
Downloads: 21
Source: Hugging Face

Source description

AZeroS

AZeroS (Auden Zero-instruction-tuned Speech-LLM) extends a frozen LLM to speech via Self-Generated Instruction-Free Tuning (SIFT). It keeps the LLM and audio encoders frozen and trains lightweight projection modules on speech–text pairs, achieving strong semantic and paralinguistic performance with modest training cost, generalizing well to unseen instructions.

🔗 Paper: https://arxiv.org/pdf/2601.06086
🔗 Code: https://github.com/AudenAI/Auden/tree/main/examples/azeros
🔗 Model: https://huggingface.co/AudenAI/azeros
🔗 Auden Repo: https://github.com/AudenAI/Auden

🔍 What Can This Model Do?

🎙️ Speech understanding (semantic content understanding and dialog)
😊 Paralinguistic analysis (emotion, age, gender, etc.)

Quick Start

import torch
from model import AZerosModel

device = "cuda" if torch.cuda.is_available() else "cpu"
model = AZerosModel.from_pretrained("AudenAI/azeros").to(device)

wav_files = ["speech1.wav", "speech2.wav"]
messages = [
    [
        {
            "role": "user",
            "content": f"{model.audio_token_wrapped} Please analyze speech content and paralinguistic information.",
        }
    ]
    for _ in wav_files
]

generate_config = {
    "max_new_tokens": 200,
    "num_beams": 1,
    "do_sample": False,
    "min_length": 1,
    "repetition_penalty": 1.0,
    "length_penalty": 1.0,
    "top_p": None,
    "top_k": None,
    "temperature": None,
}

outputs = model.generate(wav_files, messages, **generate_config)
print(outputs)

Auden Setup (Required)

This model relies on the Auden codebase for loading and inference:

git clone https://github.com/AudenAI/Auden.git
cd Auden
pip install -e .
cd examples/azeros

📌 Model Characteristics

Input: Raw audio waveform (16 kHz) or text
Output: Text responses regarding to the input
Backend LLM: Qwen2.5-7B-Instruct
Encoders: TTA and Auden-Voice
Architecture: Frozen LLM + frozen audio encoders + lightweight projection modules
Training paradigm: Self-Generated Instruction-Free Tuning (SIFT)

📊 Evaluation

VoiceBench

Model	Alpaca Eval	Comm Eval	Wild Voice	SD-QA	BBH	Adv Bench	IF Eval	OBQA	MMSU	Overall
Text Only Model
Qwen2.5	4.66	4.55	4.62	62.03	80.00	99.04	70.14	84.84	71.57	82.69
Qwen2.5 (TN)	4.61	4.53	4.56	63.84	56.30	98.85	66.11	74.07	64.51	77.52
Cascaded System
Whisper+GPT-4o	4.80	4.47	4.62	75.77	87.20	98.27	76.51	92.97	81.69	87.80
Whisper+Qwen2.5	4.64	4.33	4.21	58.50	52.85	98.27	63.99	78.24	69.00	76.05
End-to-end Speech-LLM
GPT-4o	4.78	4.49	4.58	75.50	84.10	98.65	76.02	89.23	80.25	86.75
Moshi	2.01	1.60	1.30	15.64	47.40	44.23	10.12	25.93	24.04	29.51
Phi-4-multimodal	3.81	3.82	3.56	39.78	61.80	100.00	45.35	65.93	42.19	64.32
GLM-4-Voice	3.97	3.42	3.18	36.98	52.80	88.08	25.92	53.41	39.75	56.48
Qwen2-Audio	3.42	3.29	2.76	31.65	53.00	99.04	26.35	48.35	36.14	53.77
DeSTA2.5	3.73	2.52	3.30	46.47	62.40	97.69	65.47	72.75	58.56	66.04
Qwen2.5-Omni	3.88	3.77	3.52	46.75	63.70	97.31	40.19	81.54	61.45	68.26
Qwen3-Omni-30B	4.74	4.54	4.58	76.90	80.40	99.30	77.80	89.70	68.10	85.49
AZeroS (ours)	4.44	4.18	3.91	60.22	56.30	98.65	61.29	72.09	59.01	73.13

AIRBench

Model	Gender	Emotion	Age	LID	Entity	Intent	Avg	Chat
Cascaded System
Whisper+GPT-4o	21.90	59.50	41.10	96.80	69.80	87.70	62.80	7.54
Whisper+Qwen2.5	28.36	50.80	36.40	88.00	73.60	82.70	59.98	7.34
End-to-end Speech-LLM
GPT-4o	*	49.10	*	76.00	61.60	85.80	*	7.53
Gemini2.5-pro	90.70	60.70	34.10	99.10	68.50	92.20	74.22	8.52
SALMONN	35.50	29.90	48.70	28.10	51.70	36.70	38.43	6.16
GLM-4-Voice	23.91	22.95	18.70	25.40	27.90	21.10	23.33	5.53
Qwen2-Audio	64.71	48.15	23.10	77.80	87.00	84.70	64.24	7.20
DeSTA2.5	84.24	64.30	65.60	97.30	65.20	83.70	76.72	7.57
Qwen2.5-Omni	89.76	54.85	44.80	89.70	79.70	88.60	74.57	6.97
Qwen3-Omni-30B	91.11	62.20	36.90	97.70	80.40	90.70	76.50	7.85
AZeroS (ours)	86.75	71.45	61.30	84.80	73.60	85.60	77.25	8.28

An additional prompt is added to ensure steady output of choices: “Please make your choice among A/B/C/D and do not output other texts.”

⚠️ Limitations

Trained on public datasets; performance may degrade on out-of-domain audio.
Not designed for safety-critical applications.

Citation

If you use AZeroS in your research, please cite:

@article{shao2026azeros,
  title={AZEROS: Extending LLM to Speech with Self-Generated Instruction-Free Tuning},
  author={Shao, Yiwen and Liu, Wei and Li, Jiahong and Wang, Tianzi and Wei, Kun and Yu, Meng and Yu, Dong},
  journal={arXiv preprint arXiv:2601.06086},
  year={2026}
}

Sources

1 source

Hugging FacePrimaryVersion e9392d32639e7079cb0e24f7373207ee658a8baf

huggingface.co/AudenAI/azeros/tree/e9392d32639e7079cb0e24f7373207ee658a8baf

Verified Oct 25

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Source excerpts

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ReadmeAug 7huggingface.co/AudenAI/azeros

--- license: apache-2.0 datasets: - wenetspeech - gigaspeech - common_voice - iemocap - crema-d - meld - ravdess - tess - dailytalk - aishell-1 - emotiontalk - cs-dialogue - voxceleb2 language: - en - zh base_model: - Qwen/Qwen2.5-7B-Instruct pipeline_tag: audio-text-to-text tags: - speech - speech-llm - audio - instruction-free - paralinguistic --- # AZeroS **AZeroS** (Auden Zero-instruction-tuned Speech-LLM) extends a frozen LLM to speech via **Self-Generated Instruction-Free Tuning (SIFT)**. It keeps the LLM and audio encoders frozen and trains lightweight projection modules on speech–text pairs, achieving strong semantic and paralinguistic performance with modest training cost, generalizing well to unseen instructions. 🔗 **Paper**: https://arxiv.org/pdf/2601.06086 🔗 **Code**: https://github.com/AudenAI/Auden/tree/main/examples/azeros 🔗 **Model**: https://huggingface.co/AudenAI/azeros 🔗 **Auden Repo**: https://github.com/AudenAI/Auden ## 🔍 What Can This Model Do? - 🎙️ **Speech understanding** (semantic content understanding and dialog) - 😊 **Paralinguistic analysis** (emotion, age, gender, etc.) ## Quick Start ```python import torch from model import AZerosModel device = "cuda" if torch.cuda.is_available() else "cpu" model = AZerosModel.from_pretrained("AudenAI/azeros").to(device) wav_files = ["speech1.wav", "speech2.wav"] messages = [ [ { "role": "user", "content": f"{model.audio_token_wrapped} Please analyze speech content and paralinguistic information.", } ] for _ in wav_files ] generate_config = { "max_new_tokens": 200, "num_beams": 1, "do_sample": False, "min_length": 1, "repetition_penalty": 1.0, "length_penalty": 1.0, "top_p": None, "top_k": None, "temperature": None, } outputs = model.generate(wav_files, messages, **generate_config) print(outputs) ``` ## Auden Setup (Required) This model relies on the Auden codebase for loading and inference: ```bash git clone https://github.com/AudenAI/Auden.git cd Auden pip install -e . cd examples/azeros ``` ## 📌 Model Characteristics - Input: Raw audio waveform (16 kHz) or text - Output: Text responses regarding to the input - Backend LLM: Qwen2.5-7B-Instruct - Encoders: [TTA](https://huggingface.co/AudenAI/auden-encoder-tta-m10) and [Auden-Voice](https://huggingface.co/AudenAI/auden-encoder-voice) - Architecture: Frozen LLM + frozen audio encode...

NoteOct 25huggingface.co/AudenAI/azeros

Source context: 21 downloads · 2 likes · Pipeline audio-text-to-text · Repo AudenAI/azeros

NoteAug 7huggingface.co/AudenAI/azeros

Source context: 6 downloads · 2 likes · Pipeline audio-text-to-text · Repo AudenAI/azeros