NVIDIA Parakeet TDT 0.6B v3 (GGUF) - 25 European Languages

This repository contains the highly optimized GGUF version of NVIDIA's flagship parakeet-tdt-0.6b-v3 multilingual Automatic Speech Recognition (ASR) model.

This model was converted directly from the official NeMo PyTorch checkpoints and is designed to be run locally with zero Python dependencies using the lightweight C++ C-API (parakeet.cpp) and the ggml execution engine.

CRITICAL EXECUTION REQUIREMENT: This model utilizes NVIDIA's advanced Token-and-Duration Transducer (TDT) architecture. To run this model in parakeet-cli, you MUST specify the --decoder tdt flag. It will crash or hallucinate if you use the standard CTC decoder.

Supported Languages (Automatic Detection)

One of the most powerful features of this model is automatic language detection. You do not need to prompt it with a specific language; it will dynamically detect and transcribe any of the following 25 languages:

• English (en)
• French (fr)
• Spanish (es)
• German (de)
• Italian (it)
• Portuguese (pt)
• Russian (ru)
• Ukrainian (uk)
• Polish (pl)
• Dutch (nl)
• Greek (el)
• Swedish (sv)
• Finnish (fi)
• Danish (da)
• Romanian (ro)
• Bulgarian (bg)
• Croatian (hr)
• Czech (cs)
• Estonian (et)
• Hungarian (hu)
• Latvian (lv)
• Lithuanian (lt)
• Maltese (mt)
• Slovak (sk)
• Slovenian (sl)

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Local Performance & Hardware Requirements

Based on local testing with ggml (CUDA offloading), here are the exact hardware requirements for this 600-million parameter model in its unquantized f32 (Float32) state:

• Model File Size: ~2.3 GB
• System RAM Peak (CPU Memory): ~2.8 GB (for memory mapping)
• VRAM Peak (GPU Memory): ~2.6 GB

Low VRAM Footprint: Because of GGML optimizations, this massive multilingual model runs comfortably on consumer GPUs, leaving over 13GB+ free on a standard 16GB RTX card while transcribing at blazing speeds.

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Technical Details: What is TDT?

Unlike standard CTC (Connectionist Temporal Classification) or RNNT decoders, the Token-and-Duration Transducer (TDT) is a novel architecture invented by NVIDIA.

Instead of just predicting the next word, TDT models predict two things simultaneously:

1. The text token itself.
2. The exact duration (in milliseconds) that the token spans in the audio.

By doing both at once, it allows the model to "skip" forward in the audio timeline, making transcription significantly faster and more accurate across varying speech tempos.

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Licensing and Copyright Notice

The original model architecture and weights were created and published by NVIDIA.

Both the source models and these converted GGUF files are released under the permissive Creative Commons Attribution 4.0 International (CC-BY-4.0).

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💻 Integrates perfectly with RenderCaption

This model was explicitly converted and optimized to be run inside RenderCaption—our custom desktop transcription software.

What is RenderCaption? RenderCaption is a fully offline, high-speed transcription application. It is built using Rust and Tauri, meaning it is incredibly lightweight, fast, and 100% private (no audio is ever sent to the cloud). Instead of writing python code or using terminal commands, you can simply load this model into the RenderCaption desktop app and transcribe audio instantly with a beautiful user interface.

Check out the RenderCaption Desktop App on GitHub Here!

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Usage Instructions

To run this model, you need the parakeet-cli C++ execution engine.

1. Go to the parakeet.cpp GitHub Repository.
2. Follow their build instructions to compile the parakeet-cli executable for your specific operating system (Windows/Linux/macOS).
3. Once compiled, open your terminal and run the model using the following command:

Transcribing Audio (Auto-Language Detection)

parakeet-cli transcribe --model parakeet-tdt-0.6b-v3.f32.gguf --input audio.wav --decoder tdt