Audio Embedding with data2vec

author: David Wang

Description

This operator extracts features for audio with data2vec. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture.

Code Example

Generate embeddings for the audio "test.wav".

Write a pipeline with explicit inputs/outputs name specifications:

from towhee import pipe, ops, DataCollection

p = (
    pipe.input('path')
        .map('path', 'frame', ops.audio_decode.ffmpeg(sample_rate=16000))
        .map('frame', 'vecs', ops.audio_embedding.data2vec(model_name='facebook/data2vec-audio-base-960h'))
        .output('path', 'vecs')
)

DataCollection(p('test.wav')).show()

Factory Constructor

Create the operator via the following factory method

data2vec(model_name='facebook/data2vec-audio-base')

Parameters:

​ model_name: str

The model name in string. The default value is "facebook/data2vec-audio-base-960h".

Supported model name:

• facebook/data2vec-audio-base-960h
• facebook/data2vec-audio-large-960h
• facebook/data2vec-audio-base
• facebook/data2vec-audio-base-100h
• facebook/data2vec-audio-base-10m
• facebook/data2vec-audio-large
• facebook/data2vec-audio-large-100h
• facebook/data2vec-audio-large-10m

Interface

An audio embedding operator generates vectors in numpy.ndarray given an audio file path or towhee audio frames.

Parameters:

​ data: List[towhee.types.audio_frame.AudioFrame]

​ Input audio data is a list of towhee audio frames. The input data should represent for an audio longer than 0.9s.

Returns: numpy.ndarray

​ The audio embedding extracted by model.

More Resources

• What is a Transformer Model? An Engineer's Guide: A transformer model is a neural network architecture. It's proficient in converting a particular type of input into a distinct output. Its core strength lies in its ability to handle inputs and outputs of different sequence length. It does this through encoding the input into a matrix with predefined dimensions and then combining that with another attention matrix to decode. This transformation unfolds through a sequence of collaborative layers, which deconstruct words into their corresponding numerical representations. 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.
• Exploring Multimodal Embeddings with FiftyOne and Milvus - Zilliz blog: This post explored how multimodal embeddings work with Voxel51 and Milvus.
• How to Get the Right Vector Embeddings - Zilliz blog: A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
• What is BERT (Bidirectional Encoder Representations from Transformers)? - Zilliz blog: Learn what Bidirectional Encoder Representations from Transformers (BERT) is and how it uses pre-training and fine-tuning to achieve its remarkable performance.
• Transforming Text: The Rise of Sentence Transformers in NLP - Zilliz blog: Everything you need to know about the Transformers model, exploring its architecture, implementation, and limitations
• What Are Vector Embeddings?: Learn the definition of vector embeddings, how to create vector embeddings, and more.
• Vector Database Use Case: Audio Similarity Search - Zilliz: Building agile and reliable audio similarity search with Zilliz vector database (fully managed Milvus).
• Enhancing Information Retrieval with Sparse Embeddings | Zilliz Learn - Zilliz blog: Explore the inner workings, advantages, and practical applications of learned sparse embeddings with the Milvus vector database
• An Introduction to Vector Embeddings: What They Are and How to Use Them - Zilliz blog: In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.