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Parakeet RNN-T 0.6B

NVIDIA's nvidia/parakeet-rnnt-0.6b ported to transcribe.cpp. A 0.6B-parameter FastConformer-Large encoder with a classic RNN-T transducer decoder (predictor + joint, no duration head).

What it's for

Offline English speech-to-text with greedy RNN-T decoding. Output is lowercase, no punctuation (per the upstream model card). Token- and word-level timestamps are available. Not a streaming model; does not translate.

The encoder shape matches parakeet-tdt-0.6b-v2 (24 layers, 1024-d) but the head is plain RNN-T rather than TDT — the joint network emits exactly vocab + 1 logits with no duration extras, and decoding has no frame-skip choice. Per-frame iterative decode means RNN-T runs slower than the CTC variant at the same encoder size.

See NVIDIA's model card for training data, intended use, and upstream evaluation methodology.

Licensed CC-BY-4.0. Ported from upstream commit c0c1f09, pinned 2026-05-10.

Download

Quantization Download Size WER (LibriSpeech test-clean)
F32 parakeet-rnnt-0.6b-F32.gguf 2.47 GB 1.62%
F16 parakeet-rnnt-0.6b-F16.gguf 1.24 GB 1.62%
Q8_0 parakeet-rnnt-0.6b-Q8_0.gguf 730 MB 1.62%
Q6_K parakeet-rnnt-0.6b-Q6_K.gguf 601 MB 1.62%
Q5_K_M parakeet-rnnt-0.6b-Q5_K_M.gguf 540 MB 1.62%
Q4_K_M parakeet-rnnt-0.6b-Q4_K_M.gguf 476 MB 1.59%

WER is measured on the full LibriSpeech test-clean split (2620 utterances) with greedy RNN-T decoding and no external LM. F32 reference baseline: 1.62%. NVIDIA's self-reported number on the same split is 1.63% (from the HF model card).

Quick Start

cmake -B build
cmake --build build

build/bin/transcribe-cli \
  -m models/parakeet-rnnt-0.6b/parakeet-rnnt-0.6b-Q8_0.gguf \
  samples/jfk.wav

If your audio is not already 16 kHz mono WAV, convert it first:

ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav

Performance

Cells are wall-clock latency (mean over 3 iterations after 1 warmup), with speedup over realtime in parentheses. Units: ms below 1 s, s above (2 decimal places). Cells gated on Tctl < 55°C per backend.

Apple M4 Max

Backend Sample Q8_0 Q4_K_M
Metal jfk (11.0s) 64 ms (173×) 65 ms (170×)
Metal dots (35.3s) 178 ms (198×) 181 ms (196×)
CPU jfk (11.0s) 360 ms (31×) 302 ms (36×)
CPU dots (35.3s) 1.22 s (29×) 1.03 s (34×)

macOS 26.4.1, transcribe.cpp 12f1076.

AMD Ryzen 7 4750U Pro

Backend Sample Q8_0 Q4_K_M
Vulkan jfk (11.0s) 742 ms (15×) 754 ms (15×)
Vulkan dots (35.3s) 2.55 s (14×) 2.59 s (14×)
CPU jfk (11.0s) 1.24 s (9×) 1.07 s (10×)
CPU dots (35.3s) 4.71 s (7×) 4.14 s (9×)

Fedora 43, transcribe.cpp 12f1076. Vulkan device: AMD Radeon Graphics (RADV RENOIR).

Benchmark reproduction:

uv run scripts/bench/run.py \
  --models parakeet-rnnt-0.6b \
  --quants q8_0,q4_k_m \
  --samples jfk,dots \
  --backends metal,cpu,vulkan \
  --iters 3 --warmup 1 \
  --name parakeet-rnnt-0.6b-publication

Numerical Validation

transcribe.cpp is validated tensor-by-tensor against NeMo on samples/jfk.wav via scripts/validate.py, sharing the parakeet family tolerance file. The family-level forward map at reports/porting/parakeet/forward-map.md documents the per-stage divergence sources (fp64 STFT, mel amplification, attenuation through the encoder).

Field Value
Reference NeMo, nvidia/parakeet-rnnt-0.6b
Dump script scripts/dump_reference_parakeet_nemo.py
Manifest tests/golden/parakeet/parakeet-rnnt-0.6b.manifest.json
Command uv run scripts/validate.py all --family parakeet --variant parakeet-rnnt-0.6b

Reproduction

Convert

uv run --project scripts/envs/parakeet \
  scripts/convert-parakeet.py nvidia/parakeet-rnnt-0.6b

Quantize

build/bin/transcribe-quantize \
  models/parakeet-rnnt-0.6b/parakeet-rnnt-0.6b-F32.gguf \
  models/parakeet-rnnt-0.6b/parakeet-rnnt-0.6b-Q8_0.gguf \
  --quant Q8_0

Validate

uv run scripts/validate.py all --family parakeet --variant parakeet-rnnt-0.6b