Sold by: Waltsoft Inc.
Deployed on AWS
Wav2Vec2 - Facebook Self-Supervised Speech Recognition on SageMaker
Overview
Deploy Facebook Wav2Vec2 on Amazon SageMaker for fast, private English speech-to-text. Trained on 960 hours of LibriSpeech, Wav2Vec2-base delivers production-quality ASR at 95MB with no GPU required. Alternative to Whisper for English-only pipelines requiring low latency and high throughput. Apache-2.0 licensed.
Highlights
- 95MB Facebook self-supervised ASR - no GPU required
- Trained on 960h LibriSpeech - production-quality English transcription
- Drop-in alternative to Whisper for English-only low-latency pipelines
Details
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Latest version
Deployed on AWS
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Pricing
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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 (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
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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.
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Version release notes
Initial release
Additional details
Inputs
- Summary
Wav2Vec2 - Facebook Self-Supervised Speech Recognition on SageMaker
- Input MIME type
- application/json
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
Support
Vendor support
Contact support@waltsoft.net for deployment assistance.
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