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--- |
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language: en |
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license: mit |
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tags: |
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- audio |
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- captioning |
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- text |
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- audio-captioning |
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- automated-audio-captioning |
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task_categories: |
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- audio-captioning |
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--- |
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# CoNeTTE (ConvNext-Transformer with Task Embedding) for Automated Audio Captioning |
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<font color='red'>This model is currently in developement, and all the required files are not yet available.</font> |
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This model generate a short textual description of any audio file. |
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## Installation |
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```bash |
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pip install conette |
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``` |
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## Usage |
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```py |
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from conette import CoNeTTEConfig, CoNeTTEModel |
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config = CoNeTTEConfig.from_pretrained("Labbeti/conette") |
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model = CoNeTTEModel.from_pretrained("Labbeti/conette", config=config) |
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path = "/my/path/to/audio.wav" |
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outputs = model(path) |
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candidate = outputs["cands"][0] |
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print(candidate) |
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``` |
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The model can also accept several audio files at the same time (list[str]), or a list of pre-loaded audio files (list[Tensor]). IN this second case you also need to provide the sampling rate of this files: |
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```py |
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import torchaudio |
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path_1 = "/my/path/to/audio_1.wav" |
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path_2 = "/my/path/to/audio_2.wav" |
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audio_1, sr_1 = torchaudio.load(path_1) |
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audio_2, sr_2 = torchaudio.load(path_2) |
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outputs = model([audio_1, audio_2], sr=[sr_1, sr_2]) |
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candidates = outputs["cands"] |
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print(candidates) |
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``` |
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The model can also produces different captions using a Task Embedding input which indicates the dataset caption style. The default task is "clotho". |
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```py |
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outputs = model(path, task="clotho") |
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candidate = outputs["cands"][0] |
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print(candidate) |
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outputs = model(path, task="audiocaps") |
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candidate = outputs["cands"][0] |
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print(candidate) |
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``` |
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## Performance |
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| Dataset | SPIDEr (%) | SPIDEr-FL (%) | FENSE (%) | |
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| ------------- | ------------- | ------------- | ------------- | |
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| AudioCaps | 44.14 | 43.98 | 60.81 | |
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| Clotho | 30.97 | 30.87 | 51.72 | |
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This model checkpoint has been trained for the Clotho dataset, but it can also reach a good performance on AudioCaps with the "audiocaps" task. |
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## Citation |
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The preprint version of the paper describing CoNeTTE is available on arxiv: https://arxiv.org/pdf/2309.00454.pdf |
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``` |
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@misc{labbé2023conette, |
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title = {CoNeTTE: An efficient Audio Captioning system leveraging multiple datasets with Task Embedding}, |
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author = {Étienne Labbé and Thomas Pellegrini and Julien Pinquier}, |
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year = 2023, |
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journal = {arXiv preprint arXiv:2309.00454}, |
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url = {https://arxiv.org/pdf/2309.00454.pdf}, |
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eprint = {2309.00454}, |
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archiveprefix = {arXiv}, |
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primaryclass = {cs.SD} |
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} |
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``` |
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## Additional information |
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The encoder part of the architecture is based on a ConvNeXt model for audio classification, available here: https://huggingface.co/topel/ConvNeXt-Tiny-AT. |
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More precisely, the encoder weights used are named "convnext_tiny_465mAP_BL_AC_70kit.pth", available on Zenodo: https://zenodo.org/record/8020843. |
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It was created by [@Labbeti](https://hf.co/Labbeti). |
