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Deployed on AWS
Parakeet-tdt-0.6b-v2 is a 600-million-parameter automatic speech recognition (ASR) model designed for high-quality English transcription, featuring support for punctuation, capitalization, and accurate timestamp prediction.
Overview
Parakeet-tdt-0.6b-v2 is a 600-million-parameter automatic speech recognition (ASR) model designed for high-quality English transcription, featuring support for punctuation, capitalization, and accurate timestamp prediction.
This XL variant of the FastConformer architecture integrates the TDT decoder and is trained with full attention, enabling efficient transcription of audio segments up to 24 minutes in a single pass.
Key Features
Accurate word-level timestamp predictions Automatic punctuation and capitalization Robust performance on spoken numbers, and song lyrics transcription
This model is ready for commercial/non-commercial use.
Highlights
- Architecture Type: FastConformer-TDT
- Network Architecture: This model was developed based on FastConformer encoder architecture and TDT decoder. This model has 600 million model parameters.
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.g5.48xlarge Inference (Batch) Recommended | Model inference on the ml.g5.48xlarge instance type, batch mode | $1.00 |
ml.g6e.12xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $1.00 |
ml.g5.12xlarge Inference (Batch) | Model inference on the ml.g5.12xlarge instance type, batch mode | $1.00 |
ml.g5.24xlarge Inference (Batch) | Model inference on the ml.g5.24xlarge instance type, batch mode | $1.00 |
ml.g6e.2xlarge Inference (Real-Time) | Model inference on the ml.g6e.2xlarge instance type, real-time mode | $1.00 |
ml.g6e.4xlarge Inference (Real-Time) | Model inference on the ml.g6e.4xlarge instance type, real-time mode | $1.00 |
ml.g6e.8xlarge Inference (Real-Time) | Model inference on the ml.g6e.8xlarge instance type, real-time mode | $1.00 |
ml.g6e.16xlarge Inference (Real-Time) | Model inference on the ml.g6e.16xlarge instance type, real-time mode | $1.00 |
ml.g6e.24xlarge Inference (Real-Time) | Model inference on the ml.g6e.24xlarge instance type, real-time mode | $1.00 |
ml.g6e.48xlarge Inference (Real-Time) | Model inference on the ml.g6e.48xlarge instance type, real-time mode | $1.00 |
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No refund
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
version 2.0
Additional details
Inputs
- Summary
Accepts mono 16 kHz WAV/FLAC audio via multipart/form-data, raw audio (audio/*), or JSON with base64. Routing: default auto (HTTP unless payload >4 MB → gRPC). You can force routing with the SageMaker header X-Amzn-SageMaker-Custom-Attributes set to /invocations/http or /invocations/grpc. Optional flag enable_word_time_offsets returns per-word timestamps (gRPC payload shape).
- Input MIME type
- multipart/form-data
b'------WebKitFormBoundary3b7e81b48a7846d3a273a0c36f7f53a1\r\n'
b'Content-Disposition: form-data; name="file"; filename="test.wav"\r\n'
b'Content-Type: audio/wav\r\n\r\n'
b'RIFF$\x03\x00\x00WAVEfmt \x10\x00\x00\x00\x01\x00\x01\x00\x80>\x00\x00\x00}\x00\x00\x02\x00\x10\x00data`\x03\x00\x00...'
b'\r\n------WebKitFormBoundary3b7e81b48a7846d3a273a0c36f7f53a1\r\n'
b'Content-Disposition: form-data; name="language_code"\r\n\r\n'
b'en-US\r\n'
b'------WebKitFormBoundary3b7e81b48a7846d3a273a0c36f7f53a1--'
Notes:
The container enforces client_max_size = 50 MB per request.
Auto-routing rule: if len(audio_bytes) > 4 MB, the request is handled via gRPC-style path internally.
No sample data for Batch job, can use same as above
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
file / audio | Audio payload when using multipart/form-data (field name file or audio). | WAV or FLAC, mono 16 kHz, PCM 16-bit; request size ≤ 50 M | Yes |
audio_base64 | Audio payload when using application/json. | Base64-encoded WAV/FLAC (mono 16 kHz, PCM 16-bit) | Yes |
language_code | Language hint (English supported). | en-US | No |
enable_word_time_offsets | Include per-word timestamps and confidence (structured output—gRPC route shape). | "true" / "false" (or boolean if JSON) | No |
Support
Vendor support
Free support via NVIDIA NIM Developer Forum:
AWS infrastructure support
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