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---

license: apache-2.0
language:

- en
pipeline_tag: feature-extraction
tags:
- audio-retrieval
- embedding
- custom_code
- multimodal
base_model: mispeech/midashenglm-7b-0804-fp32

---

<h1 align="center">ALM2Vec-FT</h1>

<p align="center">
  <a href="https://arxiv.org/abs/xxxx.xxxxx"><img src="https://img.shields.io/badge/Paper-arXiv-b31b1b?logo=arxiv&logoColor=white" alt="Paper"></a>
  <a href="https://caml-labs.github.io/ALM2Vec"><img src="https://img.shields.io/badge/Project-Page-1f6feb?logo=googlechrome&logoColor=white" alt="Project Page"></a>
  <a href="https://github.com/caml-labs/ALM2Vec"><img src="https://img.shields.io/badge/Code-GitHub-181717?logo=github&logoColor=white" alt="GitHub"></a>
</p>

**ALM2Vec** is a universal audio embedding model for retrieval, derived from a pretrained large audio–language model (LALM). Instead of being optimized only for audio–caption matching like conventional contrastive dual-encoders, it transfers the audio understanding, instruction-following, and reasoning abilities of LALMs into a single unified embedding space that works across audio domains, task types, and user intents.

Its key feature is **instruction-aware retrieval**: a natural-language instruction guides the embedding, so the *same* audio can be encoded differently for different needs. This supports:

- **Instruction-aware retrieval** — focus the embedding on a specific aspect of the audio.
- **Text ↔ audio retrieval** — bidirectional matching between audio and text.
- **Audio question answering** — match an audio query plus a question against candidate answers.

ALM2Vec achieves competitive results on standard audio and speech retrieval benchmarks while adding these controllable retrieval capabilities. See the [project page](https://caml-labs.github.io/ALM2Vec/) for interactive demos.

This repository hosts the **finetune** checkpoint, built on [MiDashengLM](https://huggingface.co/mispeech/midashenglm-7b-0804-fp32).

Requirements: `transformers>=4.52`, `torch`, `safetensors`, and `torchaudio` for non-WAV audio. Requires a GPU (~31GB weights) and `trust_remote_code=True`.

## Example

```python
import torch
from transformers import AutoModel, AutoTokenizer

repo_id = "cara-ai/ALM2Vec-FT"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModel.from_pretrained(
    repo_id, trust_remote_code=True, torch_dtype=torch.float32
).cuda().eval()
model.set_tokenizer(tokenizer)

query_text = ["q1", "q2"]
query_audio = ["/path/to/audio1.wav", "https://example.com/audio2.wav"]

doc_text = ["d1", "d2"]

query_emb = model.encode_query(text=query_text, audio=query_audio, task="query")
doc_emb = model.encode_document(text=doc_text, task="document")
similarity = query_emb @ doc_emb.T
print(similarity)
```

## Results

**ALM2Vec-FT** is the checkpoint hosted in this repository; **ALM2Vec-PT** is the pretrain variant. In every table, **bold** marks the best score and <u>underline</u> the second best.

### Text–audio retrieval — AudioCaps

| Method | T→A R@1 | T→A R@5 | T→A R@10 | A→T R@1 | A→T R@5 | A→T R@10 |
| --- | --- | --- | --- | --- | --- | --- |
| LAION-CLAP | 36.1 | 71.8 | 83.9 | 46.8 | <u>82.9</u> | <u>90.7</u> |
| MS-CLAP | 15.4 | 47.2 | 64.5 | 32.0 | 66.0 | 79.2 |
| WavCaps-CLAP-PT | 39.7 | 74.5 | 86.1 | 51.7 | 82.3 | 90.6 |
| WavCaps-CLAP-FT | <u>42.2</u> | <u>76.5</u> | <u>87.1</u> | <u>54.6</u> | **85.2** | **92.4** |
| JINA-Embed.-v5 | 20.4 | 50.3 | 64.4 | 23.1 | 52.7 | 67.2 |
| **ALM2Vec-PT** | 40.0 | 74.5 | 85.9 | 43.8 | 74.3 | 86.5 |
| **ALM2Vec-FT** | **43.2** | **78.0** | **87.8** | **55.5** | 80.0 | 88.2 |

### Text–audio retrieval — Clotho

| Method | T→A R@1 | T→A R@5 | T→A R@10 | A→T R@1 | A→T R@5 | A→T R@10 |
| --- | --- | --- | --- | --- | --- | --- |
| LAION-CLAP | 16.1 | 38.3 | 51.1 | 22.7 | 48.5 | 60.8 |
| MS-CLAP | 15.6 | 38.9 | 51.4 | 22.1 | 48.9 | 62.0 |
| WavCaps-CLAP-PT | 19.5 | 45.2 | 58.2 | 23.4 | 50.9 | 63.4 |
| WavCaps-CLAP-FT | <u>19.7</u> | <u>45.7</u> | <u>59.4</u> | <u>26.9</u> | <u>52.6</u> | <u>64.9</u> |
| JINA-Embed.-v5 | 9.2 | 23.9 | 35.0 | 10.5 | 24.7 | 34.3 |
| **ALM2Vec-PT** | 19.2 | 43.4 | 55.7 | 17.9 | 39.4 | 52.2 |
| **ALM2Vec-FT** | **24.8** | **52.9** | **65.8** | **27.9** | **52.7** | **66.3** |


### Speech retrieval — LibriSQA


| Method         | T→S R@1 | T→S R@5 | T→S R@10 | S→T R@1 | S→T R@5 | S→T R@10 |
| -------------- | --------------- | -------- | -------- | --------------- | -------- | -------- |
| LAION-CLAP †   | 0.0             | 0.1      | 0.8      | 0.1             | 0.2      | 0.6      |
| Whisper+BGE    | 83.7            | 93.3     | 94.9     | 85.2            | 93.4     | 95.3     |
| CLSR           | **85.0**        | <u>93.4</u> | <u>95.0</u> | <u>85.5</u>        | <u>94.0</u> | <u>95.6</u> |
| **ALM2Vec-PT** | 43.7            | 64.5     | 72.8     | 11.2            | 24.9     | 34.1     |
| **ALM2Vec-FT** | <u>84.7</u>        | **94.1** | **95.8** | **86.0**        | **95.2** | **97.2** |


### Audio understanding — MMAU-mini (accuracy)


| Method             | Overall  | Music    | Sound    | Speech   |
| ------------------ | -------- | -------- | -------- | -------- |
| GPT-4o Audio ‡     | 60.8     | 63.2     | 64.6     | 56.3     |
| Gemini 2.5 Pro ‡   | <u>71.6</u> | <u>75.1</u> | 71.5     | 68.3     |
| Qwen2.5-Omni ‡     | 71.5     | 65.9     | <u>78.1</u> | <u>70.6</u> |
| Audio Flamingo 3 ‡ | **73.1** | **76.9** | 66.1     | **73.9** |
| **ALM2Vec-PT**     | 66.3     | 62.3     | **78.7** | 58.0     |
| **ALM2Vec-FT**     | 63.0     | 61.7     | 74.8     | 52.6     |


† LAION-CLAP is not trained for speech and effectively fails on LibriSQA; shown for reference.
‡ Generative large audio–language models, listed as reference upper bounds rather than directly comparable retrieval baselines.

## Citation

If you find this work useful, please consider citing:

```
@article{ALM2Vec2026,
  title={ALM2Vec: Learning Audio Embeddings for Universal
        Audio Retrieval with Large Audio-Language Models},
  author={TBD},
  journal={arXiv preprint arXiv:TBD},
  year={2026}
}
```

## Acknowledgement

ALM2Vec is built on [MiDashengLM](https://github.com/xiaomi-research/dasheng-lm) and further trained for universal audio retrieval. We thank MiDashengLM and its underlying [Dasheng](https://github.com/RicherMans/Dasheng) audio encoder for their open-source contributions.