---
license: mit
tags:
- small audio-language model
- ALM
- audio
- music
- sound events
- audio reasoning
- audio captioning
- audio question answering
- zero-shot
- audio-text
---
# Mellow: a small audio language model for reasoning
[[`Paper`](https://arxiv.org/abs/2503.08540)] [[`GitHub`](https://github.com/soham97/Mellow)] [[`Checkpoint`](https://huggingface.co/soham97/Mellow)] [[`Zenodo`](https://zenodo.org/records/15036628)] [[`Demo`](https://tinyurl.com/mellowredirect)]
Mellow is a small Audio-Language Model that takes in two audios and a text prompt as input and produces free-form text as output. It is a 167M parameter model and trained on ~155 hours of audio (AudioCaps and Clotho), and achieves SoTA performance on different tasks with 50x fewer parameters.
## Index
* [Setup](#setup)
* [Usage](#usage)
* [Examples](#example)
* [ReasonAQA](#reasonaqa)
* [Limitation](#limitation)
## Setup
1. Install the required dependencies: `pip install -r requirements.txt`. For [conda](https://www.anaconda.com), run the following:
```shell
cd Mellow && \
conda create -n mellow python=3.10.14 && \
conda activate mellow && \
pip install -r requirements.txt
```
2. To test the setup is complete, run:
```shell
python example.py
```
## Usage
The MellowWrapper class allows easy interaction with the model. To use the wrapper, inputs required are:
- `config`: The option supported is "v0"
- `model`: The option supported is "v0"
- `examples`: List of examples. Each example is a list containing three entries: audiopath1, audiopath2, prompt
Supported functions:
- `generate`: Produces text response for the given audio inputs and text prompt
## Example
Mellow supports open-ended questions-answering and can produce response based on the user's prompt. Below, we provide some example questions for testing Mellow on different tasks.
```python
import torch
from pathlib import Path
import os
from mellow import MellowWrapper
# setup cuda and device
cuda = torch.cuda.is_available()
device = 0 if cuda else "cpu"
# setup mellow
mellow = MellowWrapper(
config="v0",
model = "v0",
device=device,
use_cuda=cuda,
)
# pick up audio file paths
parent_path = Path(os.path.realpath(__file__)).parent
path1 = os.path.join(parent_path, "resource", "1.wav")
path2 = os.path.join(parent_path, "resource", "2.wav")
# list of filepaths and prompts
examples = [
[path1, path2, "what can you infer about the surroundings from the audio?"],
[path1, path2, "is there a cat in the audio? answer yes or no"],
[path1, path2, "caption the audio."]
[path1, path2, "Based on the audio, what can be said about the hypothesis - \"A farmer is giving a tour of his ranch while chickens roam nearby\"? a) It is definitely true b) It is definitely false c) It is plausible d) I cannot determine"],
[path1, path2, "explain the difference between the two audios in detail."],
[path1, path2, "what is the primary sound event present in the clip? a) dog barking b) chirping birds c) car engine d) clapping"],
]
# generate response
response = mellow.generate(examples=examples, max_len=300, top_p=0.8, temperature=1.0)
print(f"\noutput: {response}")
```
## ReasonAQA
The composition of the ReasonAQA dataset is shown in Table. The training set is restricted to AudioCaps and Clotho audio files and the testing is performed on 6 tasks - Audio Entailment, Audio Difference, ClothoAQA, Clotho MCQ, Clotho Detail, AudioCaps MCQ and AudioCaps Detail.
- The ReasonAQA JSONs can be downloaded from: [Zenodo](https://zenodo.org/records/15036628). The zip file contain three files including train.json, val.json and test.json
- The audio files can be downloaded from their respective hosting website: [Clotho](https://zenodo.org/records/4783391) and [AudioCaps](https://github.com/cdjkim/audiocaps)
---
The format of the dataset is a JSON file of a list of dicts, in the following format:
```json
[
{
"taskname": "audiocaps",
"filepath1": "AudioCapsLarger/test/Y6BJ455B1aAs.wav",
"filepath2": "AudioCapsLarger/test/YZsf2YvJfCKw.wav",
"caption1": "A rocket flies by followed by a loud explosion and fire crackling as a truck engine runs idle",
"caption2": "Water trickling followed by a toilet flushing then liquid draining through a pipe",
"input": "explain the difference in few words",
"answer": "Audio 1 features a sudden, intense sonic event (rocket explosion) with high-frequency crackling (fire) and a steady, low-frequency hum (truck engine), whereas Audio 2 consists of gentle, mid-frequency water sounds (trickling, flushing, and draining).",
"subtype": "ACD-1.json"
},
...
]
```
The field of the JSON dict are:
- `taskname`: indicates the dataset. The two options are "audiocaps" or "clothov21"
- `filepath1`: the first audio file path
- `filepath2`: the second audio file path. This is empty for all tasks except for the audio difference explanation task
- `caption1`: the ground truth caption for the first audio
- `caption2`: the ground truth caption for the second audio. This is empty for all tasks except for the audio difference explanation task
- `input`: the input question or prompt to the model
- `answer`: the answer or response for the given input
- `subtype`: the type of question or prompt. The type matches the first column in the reasonaqa image above. The options are - "ACD-1.json", "CLE.json", "AudioCaps.json", and more.
## Limitation
With Mellow, we aim to showcase that small audio-language models can engage in reasoning. As a research prototype, Mellow has not been trained at scale on publicly available audio datasets, resulting in a limited understanding of audio concepts. Therefore, we advise caution when considering its use in production settings. Ultimately, we hope this work inspires researchers to explore small audio-language models for multitask capabilities, complementing ongoing research on general-purpose audio assistants.
## Citation
```
@misc{mellow,
title={Mellow: a small audio language model for reasoning},
author={Soham Deshmukh and Satvik Dixit and Rita Singh and Bhiksha Raj},
year={2025},
eprint={2503.08540},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2503.08540},
}
```