nano_parakeet 0.1.0

Pure-PyTorch Parakeet TDT inference — no NeMo required

nano-parakeet

Pure-PyTorch inference for NVIDIA Parakeet TDT — no NeMo required.

from nano_parakeet import from_pretrained
model = from_pretrained()
print(model.transcribe("audio.wav"))

Why?

The official NeMo inference stack pulls in ~180 packages — PyTorch Lightning, Hydra, OmegaConf, apex, distributed training scaffolding — none of which are needed at inference time. This makes it painful to integrate Parakeet into existing projects: version conflicts, long installs, and a 30-second cold-start on every process launch.

nano-parakeet reimplements the full inference pipeline in plain PyTorch. The only dependencies are things you probably already have:

	nano-parakeet	NeMo
Dependencies	5 (torch, numpy, soundfile, sentencepiece, huggingface-hub)	~180
Cold start	~3s (weights only)	~30s (framework init + CUDA kernel compile)
Warm RTF (Jetson AGX Orin)	93×	73×

Transcriptions are byte-identical to NeMo's output.

Install

pip install nano-parakeet

Requires Python 3.10+, PyTorch with CUDA, and ffmpeg.

Usage

Python API

from nano_parakeet import from_pretrained

model = from_pretrained()                    # downloads ~1.1GB on first run
text = model.transcribe("audio.wav")        # path, numpy array, or tensor
print(text)

CLI

nano-parakeet audio.wav
# or
python -m nano_parakeet audio.wav

Accepts OGG, WAV, M4A, or any format ffmpeg can read.

Benchmark

RTF > 1.0 = faster than real-time. 5 timed runs after a warm-up; best time reported.

Warm throughput

GPU	Audio	NeMo RTF	nano-parakeet RTF	Speedup
Jetson AGX Orin 64GB	12s	~73×	~92×	1.3×

Cold start (first inference, including framework load)

GPU	NeMo	nano-parakeet
Jetson AGX Orin 64GB	~30s	~3s

Run both yourself:

git clone https://github.com/andimarafioti/parakeet-stt
cd parakeet-stt
./benchmark.sh sample.wav

How It Works

The full pipeline in plain PyTorch — no NeMo at runtime:

Audio (16 kHz, mono)
  │
  ▼  pre-emphasis (α=0.97) → STFT (n_fft=512, hop=160, win=400)
     → Mel filterbank (128 bins) → log → per-feature normalisation
  │
  ▼  FastConformer Encoder  (24 layers, d_model=1024, 8 heads)
     └─ ConvSubsampling (3× stride-2 → 8× time reduction)
     └─ RelPositionalEncoding (Transformer-XL style)
     └─ 24 × FastConformerLayer:
           FF₁ (×0.5) → Self-Attn (rel-pos) → Conv (k=9) → FF₂ (×0.5) → LN
  │
  ▼  TDT Decoder
     └─ RNNT Prediction: Embed(8193, 640) + 2-layer LSTM(640)
     └─ Joint: Linear(1024→640) + Linear(640→640) → ReLU → Linear(640→8198)
     └─ TDT greedy decode (durations [0,1,2,3,4], blank_id=8192)
  │
  ▼  SentencePiece decode → text

Weights are loaded directly from the .nemo file (a ZIP archive) without importing any NeMo module.

Optimisations

	Encoder	Decoder	Effect
fp16 autocast	✓	✗	tensor cores for 1024→4096→1024 FFN matmuls × 24 layers
CUDA graph	✗	✓	~20 kernel launches per decode step → 1 graph replay

Jetson Setup

The PyPI wheel works on standard x86 CUDA machines. For Jetson (JetPack 6), PyTorch needs to be installed from NVIDIA's distribution first:

# Install CUDA-enabled PyTorch for JetPack 6
UV_SKIP_WHEEL_FILENAME_CHECK=1 uv pip install \
  https://developer.download.nvidia.com/compute/redist/jp/v61/pytorch/torch-2.5.0a0+872d972e41.nv24.08.17622132-cp310-cp310-linux_aarch64.whl

# Then install nano-parakeet (skipping torch since it's already installed)
pip install nano-parakeet --no-deps
pip install numpy soundfile sentencepiece huggingface-hub