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
Usage
Examples
ReasonAQA
Limitation

Setup

Install the required dependencies: pip install -r requirements.txt. For conda, run the following:

shell

cd Mellow && \
conda create -n mellow python=3.10.14 && \
conda activate mellow && \
pip install -r requirements.txt

To test the setup is complete, run:

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" and "v0_s". The "v0_s" is trained on more audios i.e. scaled version of "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. 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 and 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}, 
}

soham97/mellow

Mellow: a small audio language model for reasoning