Sold by: Appen (fka Figure Eight)Â
Deployed on AWS
This model is trained to recognize if there is any sort of background noise
Overview
This model is trained to recognize if there is any sort of background noise (be it a dog barking, street sounds, static, airplane noise, or anything other than the main speaker speaking) when there is a single speaker in the audio snippet. Fundamentally, it classifies an audio recording as noisy or not noisy and can detect background noise from both female and male speaker. We’ve tested this model on .wav audio, with 44100 sample rates, 16 bits per sample, and 2 channels with an average file size of 2MB.
Highlights
- Identify background noise - anything from a dog barking to street sounds to static - when there is a single speaker in the audio snippet
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.c5.xlarge Inference (Batch) Recommended | Model inference on the ml.c5.xlarge instance type, batch mode | $0.25 |
ml.c5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.c5.xlarge instance type, real-time mode | $0.25 |
Vendor refund policy
We do not currently support refunds, but you can cancel at any time.
Legal
Vendor terms and conditions
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
For CPU: We recommend using a ml.c5.xlarge (CPU) instance type. Our tests on these took 6.1 seconds prediction time for average payloads of 2.5 MB when invoked from a desktop.
Additional details
Inputs
- Summary
Example for the /invocations endpoint:
Input (application/json): Audio file of the recording [wav, base64 encoded].
Payload: {"instances": [{"audio": {"b64": "BASE_64_ENCODED_WAV_FILE_CONTENTS"}}]}
Output (application/json): Classification into not noisy (clean) or noisy
Content: {"labels": ["clean", ... ], "predictions": [ { "label": "clean", "scores": [0.3, ...]}]}
- Input MIME type
- json
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
If you need any support, have questions, or suggestions, please email ml-support@figure-eight.comÂ
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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