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younghan-meta/Parakeet-TDT-ExecuTorch-CUDA-Windows
Parakeet-TDT-ExecuTorch-CUDA-Windows is a automatic speech recognition model from younghan-meta. Use it when you need speech turned into text. The card lists the license as apache-2.0.
Pre-exported ExecuTorch .pte file for Parakeet TDT 0.6B with CUDA-Windows backend (NVIDIA GPU) in fp32 precision (no quantization).
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.ptd2.5 GB · 99%
From the Hugging Face model README
Pre-exported ExecuTorch .pte file
for Parakeet TDT 0.6B with
CUDA-Windows backend (NVIDIA GPU) in fp32 precision (no quantization).
Fast speech-to-text with word-level timestamps and GPU acceleration on Windows.
For the quantized variant (3.2× smaller, 2.5× faster prefill), see Parakeet-TDT-ExecuTorch-CUDA-Windows-Quantized.
git clone https://github.com/pytorch/executorch/ ~/executorch
cd ~/executorch && pip install .
Build on Windows (PowerShell):
cmake --workflow --preset llm-release-cuda
Push-Location examples/models/parakeet
cmake --workflow --preset parakeet-cuda
Pop-Location
pip install huggingface_hub
huggingface-cli download younghan-meta/Parakeet-TDT-ExecuTorch-CUDA-Windows --local-dir parakeet_cuda_windows
Windows (PowerShell):
.\cmake-out\examples\models\parakeet\Release\parakeet_runner.exe `
--model_path parakeet_cuda_windows\model.pte `
--data_path parakeet_cuda_windows\aoti_cuda_blob.ptd `
--audio_path C:\path\to\audio.wav `
--tokenizer_path parakeet_cuda_windows\tokenizer.model
Optional flags:
--timestamps segment — timestamp granularity: none|token|word|segment|all (default: segment)pip install "nemo_toolkit[asr]"
python examples/models/parakeet/export_parakeet_tdt.py \
--backend cuda-windows \
--output-dir ./parakeet_cuda_windows
Cross-compilation requires x86_64-w64-mingw32-g++ on PATH and WINDOWS_CUDA_HOME
pointing to the extracted Windows CUDA package. See the
Parakeet README
for detailed setup steps.
| Metric | Value |
|---|---|
| Prefill throughput | 1,301 tok/s |
| Decode throughput | 1,325 tok/s |
| Model load time | 4.1 s |
| Time to first token | 202 ms |
| Total inference | 270 ms |
| Real-time factor | 74× real-time |
| Model size | 2,445 MB |