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xares-llm

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XARES-LLM

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# XARES-LLM

XARES-LLM trains a typical LALM using the audio encoder provided by the user. 
The system automatically downloads training data, trains the LALM then tests various downstream tasks, providing scores for each.

## Installation

```bash
uv venv 
uv pip install git+https://github.com/xiaomi-research/xares-llm
```

## Usage

You can either pass a .py file with your encoder defined inside, requiring the ending "Encoder":

```bash
python3 -m xares_llm.run example/dummy/dummyencoder.py 
```

Or pass the module directly:

```bash
python3 -m xares_llm.run example.dummy.dummyencoder.DummyEncoder
```

### Task 1 ( Classification )

```bash
python3 -m xares_llm.run example/dummy/dummyencoder.py task1 task1
```

### Task 2 ( Understanding )

```bash
python3 -m xares_llm.run example/dummy/dummyencoder.py task2 task2
```

For reprodution purposes **Please run the script on one GPU!**.

### Encoder example

Any Encoder needs to be derived from `torch.nn.Module`, accepting `(audio, audio_attention_mask)` as input and outputs `(features, feature_attention_mask)` and a set variable `output_dim` that represents the model's embedding size.
Masks use the huggingface format, 1 represents keep, 0 represents mask and `feature_attention_mask.size(-1) == features.size(1)`

```python
class DummyEncoder(torch.nn.Module):
    def __init__(self, **kwargs) -> None:
        super().__init__()
        self.output_dim = 256

def forward(self, audio, audio_attention_mask=None) -> tuple[torch.Tensor, torch.Tensor | None]:
        output = torch.randn(len(audio), 10, self.output_dim, device=audio.device)
        # Do something with attention mask or just return None, both are alright
        return output, audio_attention_mask
```

## Baseline results

### Task1

| Task | Dasheng-Base Score | Whisper-Base Score | 
| :--- | :---: | :---: |
| `eval_asvspoof2015` | 0.937 | 0.943 |
| `eval_cremad` | 0.621 | 0.516 |
| `eval_esc-50` | 0.755 | 0.635 |
| `eval_fluentspeechcommands` | 0.984 | 0.817 |
| `eval_freemusicarchive` | 0.429 | 0.579 |
| `eval_fsd50k` | 0.063 | 0.092 |
| `eval_fsdkaggle2018` | 0.415 | 0.552 |
| `eval_gtzan` | 0.323 | 0.697 |
| `eval_libricount` | 0.386 |0.409 |
| `eval_nsynth` | 0.675 | 0.638 |
| `eval_speechcommandsv1` | 0.655 | 0.694 |
| `eval_urbansound8k` | 0.829 | 0.737 |
| `eval_vocalsound` | 0.855 | 0.867 |
| `eval_voxceleb1` | 0.974 |0.762 |
| `eval_voxlingua33` | 0.311 | 0.835 |
| Overall | 0.614 | 0.652 |

### Task2

| Task | Dasheng-Base Score | Whisper-Base Score | 
| :--- | :---: | :---: |
| `eval_aishell-1` | 0.018 |  0.361 |
| `eval_clotho` | 0.207 | 0.358 |
| `eval_librispeech` | 0.103 | 0.385 | 
| `eval_mecat` | 0.60 | 0.624  | 
| `eval_songdescriber` | 0.41 | 0.448 |
| Overall | 0.27 | 0.40 |

### Single dataset training

Here we train on clotho and test on clotho.

```bash
python3 -m xares_llm.run example/dummy/dummyencoder.py clotho clotho
# Or for the Module interface
# python3 -m xares_llm.run example.dummy.dummyencoder.DummyEncoder clotho clotho

# Using Multiple GPU's with Accelerate:
# accelerate launch -m xares_llm.run example/dummy/dummyencoder.py clotho clotho
```

All available current datasets can be seen by running `python3 -m xares_llm.run -h`.

Datasets can also be passed with a custom .yaml:

For training, the format is:

```yaml
train_data:
  CustomDataName:
    prompt: My prompt
    data:
    - PATH_TO_MY_TARS{00..10}.tar
    key: DATAKEY
num_training_workers: 4
```

For evaluation:

```yaml
eval_custom:
  data:
    data:
    - PATH_TO_MY_TARS{00..10}.tar
    key: DATAKEY # Inside the json
    prompt: My prompt
  batch_size: 4
  num_workers: 0
  metric: Accuracy
```

### Modify downloaded dataset path

By default all data is downloaded and stored in `./xares_data` from the current directory.
During training the data is directly fetched and cached in this directory.
One can modify the data path with the environment variable `XARES_DATA_HOME`.

## Manual Downloading the data

The data is stored in `$XARES_DATA_HOME`, which defaults to `$PWD/xares_data`.

### Huggingface Cmdline tool (Recommended)

```bash
hf download mispeech/xares_llm_data --local-dir xares_data --repo-type dataset
hf download mispeech/MECAT-Caption --local-dir xares_data --repo-type dataset
```

### Provided script

```python
python3 -m xares_llm.download_data
```

The download location can also be changed:
```python
# XARES_DATA_HOME="NEW_LOCATION" python3 -m xares_llm.download_data
```

## Note on Precision and reprodution

By default the code uses `fp32` precision, which is slow but can be reproduced. Note that results might differ depending on your Graphics card. However, results on a single machine should be completely reproducible.

If one wants to speed up training (and no reproduction) use:

```bash
accelerate launch --mixed-precision='bf16' -m xares_llm.run task1
```

# Citation

```
@misc{dinkel2026interspeech2026audioencoder,
      title={The Interspeech 2026 Audio Encoder Capability Challenge for Large Audio Language Models}, 
      author={Heinrich Dinkel and Jiahao Zhou and Guanbo Wang and Yadong Niu and Junbo Zhang and Yufeng Hao and Ying Liu and Ke Li and Wenwu Wang and Zhiyong Wu and Jian Luan},
      year={2026},
      eprint={2603.22728},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2603.22728}, 
}
```

xares-llm

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