Sold by: Lelapa AI
Deployed on AWS
Transcribe and interpret spoken language across African languages with Lelapa AI's Vulavula Multilingual ASR Model.
Overview
At Lelapa AI, we're dedicated to advancing language technology and expanding its accessibility through our specialized Automatic Speech Recognition (ASR) Model for Zulu and Sesotho languages. This innovative model is tailored to effectively transcribe and interpret spoken language, supporting a wide range of applications. By converting spoken words into text, it facilitates seamless communication and interaction, particularly in speech-to-text applications. Currently operational for Zulu and Sesotho, our ongoing efforts aim to extend support to additional South African and Sub-Saharan languages. This powerful tool enhances accessibility and usability, enabling more efficient communication and interaction in multilingual environments.
Highlights
- **Expansive Language Support:** Lelapa AI introduces the Vulavula ASR Model, engineered to transcribe and interpret spoken language across a wide array of African languages, significantly expanding access to advanced speech recognition tools beyond dominant global languages.
- **Precision and Speed:** With its cutting-edge technology, the Vulavula ASR Model excels in swiftly transcribing and interpreting spoken language with a higher degree of accuracy than conventional models, enhancing both user experience and data utility.
- **Adaptable Applications:** Designed to support diverse needs, Lelapa AI's model is perfect for a range of applications including speech-to-text conversion, voice search, and real-time language translation in multilingual African environments, making it a robust tool for developers and businesses targeting African markets.
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.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $30.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $21.00 |
Dimensions summary
You pay by the hour for the compute instance that runs the speech-to-text model. Both options use the same ml.m5.xlarge instance type and are billed per host hour. The difference is the delivery mode. Batch mode processes uploaded audio files in bulk after calls end. Real-time mode transcribes audio as it streams during a live call. You pick the mode that fits your workflow. Costs scale with how many host hours you run each instance, so usage grows with the volume of audio you process.
Top-of-mind questions for buyers
You pay for one running ml.m5.xlarge instance for one hour of use. This is a general-purpose AWS instance type with four virtual CPUs and 16 GiB of memory. Each hour that the instance runs to process audio counts as one host hour on your bill.
Charges accrue per host hour while the instance runs. A fully stopped instance does not accrue software host-hour charges. Batch mode runs the instance while processing uploaded files. Real-time mode runs the instance during live calls. When you stop the instance, host-hour metering stops.
Yes. Batch and real-time are billed independently, each per host hour on its own ml.m5.xlarge instance. If you run both at once, you pay host hours for each running instance separately. They appear as separate line items and add together on your invoice.
lelapa.ai
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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
This is the first version
Additional details
Inputs
- Summary
The input is of content type audio/wav
file_path = "input_audio.wav" with open(file_path, 'rb') as file: audio_data = file.read()
- Input MIME type
- audio/wav
https://github.com/Lelapa-AI/lelapa-aws/blob/main/lelapa_aws/input_audio.wav
https://github.com/Lelapa-AI/lelapa-aws/blob/main/lelapa_aws/input_audio.wav
Support
Vendor support
Lelapa AI Automatic Speech Recognition (ASR) Model support please contact us . support@lelapa.ai
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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