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Zero shot audio classification pipeline using `ClapModel`. This pipeline predicts the class of an audio when you
provide an audio and a set of `candidate_labels`.
The default `hypothesis_template` is : `"This is a sound of {}."`. Make sure you update it for your usage.
Example:
```python
>>> from transformers import pipeline
>>> from datasets import load_dataset
>>> dataset = load_dataset("ashraq/esc50")
>>> audio = next(iter(dataset["train"]["audio"]))["array"]
>>> classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-unfused")
>>> classifier(audio, candidate_labels=["Sound of a dog", "Sound of vacuum cleaner"])
[{'score': 0.9996, 'label': 'Sound of a dog'}, {'score': 0.0004, 'label': 'Sound of vacuum cleaner'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial). This audio
classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"zero-shot-audio-classification"`. See the list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=zero-shot-audio-classification).
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Assign labels to the audio(s) passed as inputs.
Args:
audios (`str`, `list[str]`, `np.array` or `list[np.array]`):
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- A string containing a http link pointing to an audio
- A string containing a local path to an audio
- An audio loaded in numpy
candidate_labels (`list[str]`):
The candidate labels for this audio. They will be formatted using *hypothesis_template*.
hypothesis_template (`str`, *optional*, defaults to `"This is a sound of {}"`):
The format used in conjunction with *candidate_labels* to attempt the audio classification by
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Return:
A list of dictionaries containing one entry per proposed label. Each dictionary contains the
following keys:
- **label** (`str`) -- One of the suggested *candidate_labels*.
- **score** (`float`) -- The score attributed by the model to that label. It is a value between
0 and 1, computed as the `softmax` of `logits_per_audio`.
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