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--- |
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license: mit |
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tags: |
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- small audio-language model |
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- ALM |
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- audio |
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- music |
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- sound events |
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- audio reasoning |
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- audio captioning |
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- audio question answering |
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- zero-shot |
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- audio-text |
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--- |
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# Mellow: a small audio language model for reasoning |
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[[`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)] |
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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. |
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## Index |
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* [Setup](#setup) |
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* [Usage](#usage) |
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* [Examples](#example) |
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* [ReasonAQA](#reasonaqa) |
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* [Limitation](#limitation) |
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## Setup |
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1. Install the required dependencies: `pip install -r requirements.txt`. For [conda](https://www.anaconda.com), run the following: |
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```shell |
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cd Mellow && \ |
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conda create -n mellow python=3.10.14 && \ |
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conda activate mellow && \ |
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pip install -r requirements.txt |
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``` |
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2. To test the setup is complete, run: |
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```shell |
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python example.py |
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``` |
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## Usage |
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The MellowWrapper class allows easy interaction with the model. To use the wrapper, inputs required are: |
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- `config`: The option supported is "v0" |
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- `model`: The option supported is "v0" and "v0_s". The "v0_s" is trained on more audios i.e. scaled version of "v0" |
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- `examples`: List of examples. Each example is a list containing three entries: audiopath1, audiopath2, prompt |
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Supported functions: |
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- `generate`: Produces text response for the given audio inputs and text prompt |
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## Example |
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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. |
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```python |
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import torch |
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from pathlib import Path |
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import os |
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from mellow import MellowWrapper |
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# setup cuda and device |
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cuda = torch.cuda.is_available() |
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device = 0 if cuda else "cpu" |
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# setup mellow |
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mellow = MellowWrapper( |
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config="v0", |
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model = "v0", |
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device=device, |
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use_cuda=cuda, |
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) |
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# pick up audio file paths |
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parent_path = Path(os.path.realpath(__file__)).parent |
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path1 = os.path.join(parent_path, "resource", "1.wav") |
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path2 = os.path.join(parent_path, "resource", "2.wav") |
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# list of filepaths and prompts |
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examples = [ |
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[path1, path2, "what can you infer about the surroundings from the audio?"], |
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[path1, path2, "is there a cat in the audio? answer yes or no"], |
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[path1, path2, "caption the audio."] |
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[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"], |
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[path1, path2, "explain the difference between the two audios in detail."], |
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[path1, path2, "what is the primary sound event present in the clip? a) dog barking b) chirping birds c) car engine d) clapping"], |
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] |
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# generate response |
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response = mellow.generate(examples=examples, max_len=300, top_p=0.8, temperature=1.0) |
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print(f"\noutput: {response}") |
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``` |
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## ReasonAQA |
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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. |
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- 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 |
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- The audio files can be downloaded from their respective hosting website: [Clotho](https://zenodo.org/records/4783391) and [AudioCaps](https://github.com/cdjkim/audiocaps) |
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--- |
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The format of the dataset is a JSON file of a list of dicts, in the following format: |
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```json |
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[ |
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{ |
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"taskname": "audiocaps", |
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"filepath1": "AudioCapsLarger/test/Y6BJ455B1aAs.wav", |
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"filepath2": "AudioCapsLarger/test/YZsf2YvJfCKw.wav", |
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"caption1": "A rocket flies by followed by a loud explosion and fire crackling as a truck engine runs idle", |
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"caption2": "Water trickling followed by a toilet flushing then liquid draining through a pipe", |
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"input": "explain the difference in few words", |
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"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).", |
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"subtype": "ACD-1.json" |
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}, |
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... |
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] |
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``` |
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The field of the JSON dict are: |
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- `taskname`: indicates the dataset. The two options are "audiocaps" or "clothov21" |
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- `filepath1`: the first audio file path |
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- `filepath2`: the second audio file path. This is empty for all tasks except for the audio difference explanation task |
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- `caption1`: the ground truth caption for the first audio |
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- `caption2`: the ground truth caption for the second audio. This is empty for all tasks except for the audio difference explanation task |
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- `input`: the input question or prompt to the model |
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- `answer`: the answer or response for the given input |
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- `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. |
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## Limitation |
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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. |
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## Citation |
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``` |
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@misc{mellow, |
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title={Mellow: a small audio language model for reasoning}, |
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author={Soham Deshmukh and Satvik Dixit and Rita Singh and Bhiksha Raj}, |
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year={2025}, |
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eprint={2503.08540}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.SD}, |
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url={https://arxiv.org/abs/2503.08540}, |
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} |
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``` |
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