Google VGGish is a pre-trained deep neural network model designed for audio classification tasks. It converts raw audio waveforms into embeddings, which are compact representations that capture the essential features of the audio, enabling efficient and effective analysis and classification.
Howdy Network Rank#111
Top 5*
Generative AI Tools
*Survey of over 20,000+ Howdy Professionals
About Google VGGish
Google VGGish was developed by Google in 2017. It was created to provide a robust and efficient model for audio classification tasks, leveraging deep learning techniques to extract meaningful features from raw audio data. The model aimed to improve the accuracy and efficiency of audio analysis in various applications.
Strengths of Google VGGish include its pre-trained nature, efficient feature extraction, and high accuracy in audio classification tasks. Weaknesses involve potential limitations in handling diverse audio contexts and the need for fine-tuning for specific applications. Competitors include OpenAI's CLIP and Facebook's Wav2Vec.
Hire Google VGGish Experts
Work with Howdy to gain access to the top 1% of LatAM Talent.
Share your Needs
Talk requirements with a Howdy Expert.
Choose Talent
We'll provide a list of the best candidates.
Recruit Risk Free
No hidden fees, no upfront costs, start working within 24 hrs.
How to hire a Google VGGish expert
A Google VGGish expert must have skills in Python programming, deep learning frameworks such as TensorFlow or PyTorch, audio signal processing, and experience with neural network architectures. Proficiency in handling and preprocessing audio data is also essential.
Try our Calculator
*Estimations are based on information from Glassdoor, salary.com and live Howdy data.
USA
$ 97K
$ 127K
$ 54K
$ 73K
$ 224K
Employer Cost
$ 127K
Employer Cost
Howdy savings:
$ 97K
Benefits + Taxes + Fees
Salary
The Best of the Best Optimized for Your Budget
Thanks to our Cost Calculator, you can estimate how much you're saving when hiring top LatAm talent with no middlemen or hidden fees.
