Canary 1B v2

May 9, 2026 · View on GitHub

NVIDIA's nvidia/canary-1b-v2 ported to transcribe.cpp. A 978M-parameter multitask AED with a 32-layer FastConformer encoder and an 8-layer Transformer decoder, covering 25 European languages.

What it's for

Offline multilingual speech-to-text and translation across 25 European languages: Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Russian, and Ukrainian.

The model takes a 16 kHz mono WAV and produces a transcript. Supports:

ASR for any of the 25 supported languages (with explicit language hint).
Translation between supported language pairs (per the upstream model card).

This is the broadest-coverage canary variant. The other multilingual variants (180m-flash, 1b-flash) cover only English/German/Spanish/French.

Not a streaming model. Word and segment timestamps from the upstream model are not exposed in the v1 port.

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

Licensed CC-BY-4.0. Ported from upstream commit 87bc526, pinned 2026-05-08.

Download

Quantization	Download	Size	WER (LibriSpeech test-clean)
F32	canary-1b-v2-F32.gguf	3.7 GB	1.92%
F16	canary-1b-v2-F16.gguf	1.8 GB	1.92%
Q8_0	canary-1b-v2-Q8_0.gguf	1.1 GB	1.91%
Q6_K	canary-1b-v2-Q6_K.gguf	889 MB	1.94%
Q5_K_M	canary-1b-v2-Q5_K_M.gguf	798 MB	1.93%
Q4_K_M	canary-1b-v2-Q4_K_M.gguf	701 MB	1.91%

WER is measured on the full LibriSpeech test-clean split (2620 utterances) with greedy decoding and no external LM. F32 reference baseline: 1.92%. NVIDIA's self-reported number on the upstream model card is 2.18%; our F32 port comes in slightly under upstream (Δ −0.26pp) and is likely down to scoring differences.

Quick Start

cmake -B build
cmake --build build

# ASR (any supported language)
build/bin/transcribe-cli \
  -m models/canary-1b-v2/canary-1b-v2-Q8_0.gguf \
  -l en \
  samples/jfk.wav

# ASR (German)
build/bin/transcribe-cli \
  -m models/canary-1b-v2/canary-1b-v2-Q8_0.gguf \
  -l de \
  samples/german.wav

# Translation (English audio → German text)
build/bin/transcribe-cli \
  -m models/canary-1b-v2/canary-1b-v2-Q8_0.gguf \
  --task translate \
  -l en --target-language de \
  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

CLI flags specific to canary:

--pnc / --no-pnc — punctuation & capitalization. Note: canary-1b-v2 ignores --no-pnc and emits PNC-on output regardless. This matches upstream NeMo behavior on this checkpoint.
-l <code> — source language code (one of the 25 supported BCP-47 codes).
--task translate + --target-language <code> — switch to translation mode.

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).

Apple M4 Max

Backend	Sample	Q8_0	Q4_K_M
Metal	jfk (11.0s)	124.3 ms (88.5×)	121.6 ms (90.5×)
Metal	dots (35.3s)	430.7 ms (82.0×)	406.1 ms (87.0×)
CPU	jfk (11.0s)	555.0 ms (19.8×)	453.5 ms (24.3×)
CPU	dots (35.3s)	1.96 s (18.0×)	1.66 s (21.3×)

macOS 26.4.1, transcribe.cpp 19b3b87.

AMD Ryzen 7 PRO 4750U

Backend	Sample	Q8_0	Q4_K_M
Vulkan	jfk (11.0s)	829.9 ms (13.3×)	748.8 ms (14.7×)
Vulkan	dots (35.3s)	2.70 s (13.1×)	2.46 s (14.4×)
CPU	jfk (11.0s)	1.55 s (7.1×)	1.16 s (9.5×)
CPU	dots (35.3s)	5.74 s (6.2×)	4.70 s (7.5×)

Fedora Linux 43, transcribe.cpp 4d44530. Vulkan device: AMD Radeon Graphics (RADV RENOIR).

Benchmark reproduction:

uv run scripts/bench/run.py \
  --models canary-1b-v2 \
  --quants q8_0,q4_k_m \
  --samples jfk,dots \
  --backends metal,cpu,vulkan \
  --iters 3 --warmup 1 \
  --name canary-1b-v2-publication

Numerical Validation

transcribe.cpp is validated tensor-by-tensor against NeMo on samples/jfk.wav. All checkpointed tensors fall within family tolerance and the F32 transcript matches the NeMo reference. Last validated at commit db53eda.

Field	Value
Reference	NeMo, `nvidia/canary-1b-v2`
Dump script	`scripts/dump_reference_canary_nemo.py`
Manifest	`tests/golden/canary/canary-1b-v2.manifest.json`
Tolerances	`tests/tolerances/canary.json`
Command	`uv run scripts/validate.py all --family canary --variant canary-1b-v2`

For the full porting writeup, see docs/porting/families/canary.md.

Reproduction

Convert

uv run --project scripts/envs/canary \
  scripts/convert-canary.py nvidia/canary-1b-v2 --repo-id nvidia/canary-1b-v2

Quantize

uv run scripts/quantize-all.py models/canary-1b-v2/canary-1b-v2-F32.gguf

Validate

uv run scripts/validate.py all --family canary --variant canary-1b-v2