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README.md
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[![arXiv](https://img.shields.io/badge/arXiv-2306.01533-brightgreen.svg?style=flat-square)](https://arxiv.org/abs/2306.01533)
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language:
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- en
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---
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[![arXiv](https://img.shields.io/badge/arXiv-2306.01533-brightgreen.svg?style=flat-square)](https://arxiv.org/abs/2306.01533)
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# Usage
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```python
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import torch
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from transformers import AutoModel, PreTrainedTokenizerFast
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import torchaudio
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = AutoModel.from_pretrained(
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"wsntxxn/cnn14rnn-tempgru-audiocaps-captioning",
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trust_remote_code=True
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).to(device)
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tokenizer = PreTrainedTokenizerFast.from_pretrained(
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"wsntxxn/audiocaps-simple-tokenizer"
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)
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wav, sr = torchaudio.load("/path/to/file.wav")
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wav = torchaudio.functional.resample(wav, sr, model.config.sample_rate)
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if wav.size(0) > 1:
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wav = wav.mean(0).unsqueeze(0)
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with torch.no_grad():
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word_idxs = model(
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audio=wav,
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audio_length=[wav.size(1)],
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)
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caption = tokenizer.decode(word_idxs[0], skip_special_tokens=True)
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print(caption)
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```
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This will make the description as specific as possible.
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You can also manually assign a temporal tag to control the specificity of temporal relationship description:
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```python
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with torch.no_grad():
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word_idxs = model(
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audio=wav,
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audio_length=[wav.size(1)],
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temporal_tag=[2], # desribe "sequential" if there are sequential events, otherwise use the most complex relationship
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)
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```
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The temporal tag is defined as:
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|Temporal Tag|Definition|
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|----:|-----:|
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|0|Only 1 Event|
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|1|Simultaneous Events|
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|2|Sequential Events|
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|3|More Complex Events|
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# Citation
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If you find the model useful, please cite this paper:
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```BibTeX
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@inproceedings{xie2023enhance,
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author = {Zeyu Xie and Xuenan Xu and Mengyue Wu and Kai Yu},
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title = {Enhance Temporal Relations in Audio Captioning with Sound Event Detection},
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year = 2023,
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booktitle = {Proc. INTERSPEECH},
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pages = {4179--4183},
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}
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```
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