torch-vggish/README.md

VGGish Embedding Operator (Pytorch)

Authors: Jael Gu

Overview

This operator uses reads the waveform of an audio file and then applies VGGish to extract features. The original VGGish model is built on top of Tensorflow.[1] This operator converts VGGish into Pytorch. It generates a set of vectors given an input. Each vector represents features of a non-overlapping clip with a fixed length of 0.96s and each clip is composed of 64 mel bands and 96 frames. The model is pre-trained with a large scale of audio dataset AudioSet. As suggested, this model is suitable to extract features at high level or warm up a larger model.

Interface

__call__(self, datas: List[NamedTuple('data', [('audio', 'ndarray'), ('sample_rate', 'int')])])

Args:

• datas:
  • a named tuple including audio data in numpy.ndarray and sample rate in integer

Returns:

The Operator returns a tuple Tuple[('embs', numpy.ndarray)] containing following fields:

• vec:
  • embeddings of the audio
  • data type: numpy.ndarray
  • shape: (num_clips, 128)

Requirements

You can get the required python package by requirements.txt.

How it works

The towhee/torch-vggish Operator implements the function of audio embedding, which can be added to a towhee pipeline. For example, it is the key operator of the pipeline audio-embedding-vggish.

Reference

[1]. https://github.com/tensorflow/models/tree/master/research/audioset/vggish [2]. https://tfhub.dev/google/vggish/1

More Resources

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At its heart, a transformer model is a bridge between disparate linguistic structures, employing sophisticated neural network configurations to decode and manipulate human language input. An example of a transformer model is GPT-3, which ingests human language and generates text output.

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