Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Automatic Audio or Sound Classification Free trial
By:
Latest Version:
v1
Automatic classification of audio signals
Product Overview
Sensifai offers automatic audio recognition and tagging. For example, our basic software recognizes hundreds of different sound categories like musical instruments, natural sounds, and machinery audios. In Sagemaker platform, you can easily fine-tune this software to recognize a new set of audios and sounds by providing the required training dataset.
Key Data
Version
By
Categories
Type
Algorithm
Highlights
Sensifai's basic audio recognition system covers hundreds of concepts (this software is accessible through AWS marketplace). However, customers and users often deal with a new set of sounds. Therefore, we have designed an easy-to-use interface which automates the process of training an audio/sound recognition system.
You can use Sensifai's interface through Sagemaker to develop an audio/sound recognition system that covers your set of concepts for your own specific use-case. Provide a training dataset and create your own audio recognition system immediately.
If you do not have dataset for training or looking for pre-trained models for audio recognition or other domains of video/image analysis, you can check our ready to use API or contact us directly (sales@sensifai.com).
Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us
Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Algorithm Training$1/hr
running on ml.p3.8xlarge
Model Realtime Inference$3.60/hr
running on ml.p3.2xlarge
Model Batch Transform$3.60/hr
running on ml.p3.8xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Algorithm Training$14.688/host/hr
running on ml.p3.8xlarge
SageMaker Realtime Inference$3.825/host/hr
running on ml.p3.2xlarge
SageMaker Batch Transform$14.688/host/hr
running on ml.p3.8xlarge
About Free trial
Try this product for 2 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Algorithm Training
For algorithm training in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Algorithm/hr | |
|---|---|---|
ml.p2.xlarge | $0.10 | |
ml.p2.8xlarge | $0.70 | |
ml.p2.16xlarge | $1.00 | |
ml.p3.2xlarge | $0.40 | |
ml.p3.8xlarge Vendor Recommended | $1.00 | |
ml.p3.16xlarge | $2.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
See example notebook for example usage.
Channel specification
Fields marked with * are required
train
Input modes: File
Content types: audio/mp3, audio/wav
Compression types: None
validation
Input modes: File
Content types: audio/mp3, audio/wav
Compression types: None
Hyperparameters
Fields marked with * are required
data_type
0 is mp3 , 1 is wave and default is 1
Type: Integer
Tunable: No
num_gpus
data percentage for validation
Type: Integer
Tunable: No
num_classes
*Total number of classes
Type: Integer
Tunable: No
initial_learning_rate
Initial learning rate
Type: Continuous
Tunable: No
multilabel_flag
1 is multilabel, 0 is single label
Type: Integer
Tunable: No
lr_patience
Patience of LR scheduler
Type: Integer
Tunable: No
max_patience
Terminate training after validation loss become greater than train loss for this number of epochs
Type: Integer
Tunable: No
num_epochs
Total number of training epochs
Type: Integer
Tunable: No
weigghted_loss_flag
1 imeans weigghted_loss, 0 is not weighted
Type: Integer
Tunable: No
Additional Resources
End User License Agreement
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Support Information
AWS Infrastructure
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.
Learn MoreRefund Policy
You may cancel your subscription at any time; However, we will not refund payments made by you under the agreement for any reason whatsoever.
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