Parakeet TDT+CTC 110M — GGUF (ggml-quantised)
GGUF / ggml conversions of nvidia/parakeet-tdt_ctc-110m for use with the crispasr CLI from CrispStrobe/CrispASR.
The smallest Parakeet variant: 110 M parameters, 17-layer FastConformer encoder with both TDT and CTC heads. Designed for low-RAM hosts and high-throughput batch transcription.
- English-only, mixed-case + punctuation output
- Hybrid TDT+CTC head — runtime defaults to CTC decode (the TDT path needs a 2-LSTM predictor which this model doesn't have; single-LSTM predictor + CTC head is a deliberate trade for size)
- ~45× realtime on M1 Metal with Q4_K — fastest in the family
- CC-BY-4.0 licence
This repo provides three quantisations, all converted from the same .nemo checkpoint via the convert-parakeet-to-gguf.py script and quantised with crispasr-quantize.
Files
| File | Size | Notes |
|---|---|---|
parakeet-tdt_ctc-110m.gguf | 230 MB | F16, full precision |
parakeet-tdt_ctc-110m-q8_0.gguf | 139 MB | Q8_0, near-lossless |
parakeet-tdt_ctc-110m-q4_k.gguf | 91 MB | Q4_K — recommended default |
Smoke test on samples/jfk.wav (11 s clip, M1 Metal):
| Quant | Time | Realtime | Output |
|---|---|---|---|
| F16 | 0.24 s | 45.7× | "And so, my fellow Americans, askk not what your country can do for you. Ask what you can do for your country." |
| Q8_0 | 0.25 s | 44.6× | (identical) |
| Q4_K | 0.25 s | 43.8× | (identical) |
Note: the model emits "askk" instead of "ask" on this clip — a small-model artifact, not a runtime bug. Larger Parakeet variants (v2 / tdt-1.1b / tdt_ctc-1.1b) get it right.
Quick Start
# 1. Build the runtime
git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc) --target crispasr
# 2a. Auto-download via the registry key
./build/bin/crispasr -m parakeet-tdt_ctc-110m --auto-download -f your-audio.wav
# 2b. Or explicit download + load
hf download cstr/parakeet-tdt_ctc-110m-GGUF \
parakeet-tdt_ctc-110m-q4_k.gguf --local-dir .
./build/bin/crispasr -m parakeet-tdt_ctc-110m-q4_k.gguf -f your-audio.wav
The runtime detects pred_layers=1 + has_ctc=True at load time and automatically flips to CTC decode — no flag needed. See parakeet_init_from_file in src/parakeet.cpp.
When to pick this over the other Parakeet variants
| Scenario | Pick |
|---|---|
| English, tightest RAM (mobile / edge / embedded) | 110m (this repo) |
| English, best WER, ~600 M params | cstr/parakeet-tdt-0.6b-v2-GGUF |
| Multilingual (25 EU languages) | cstr/parakeet-tdt-0.6b-v3-GGUF |
| English, long-tail vocab | cstr/parakeet-tdt-1.1b-GGUF |
Model architecture
| Component | Details |
|---|---|
| Encoder | 17-layer FastConformer, d=512, 8 heads, head_dim=64, FFN=2048, conv kernel=9 |
| Subsampling | Conv2d dw_striding stack, 8× temporal (100 → 12.5 fps) |
| Predictor | 1-layer LSTM, hidden 640 (smaller than the standard 2-LSTM) |
| Joint head | enc(512 → 640) + pred(640 → 640) → ReLU → linear(640 → 1029) — TDT, 5 durations |
| CTC head | linear(512 → 1025) — used by default at runtime |
| Vocab | 1024 SentencePiece tokens (English) + blank |
| Audio | 16 kHz mono, 80 mel bins, n_fft=512, hop=160, win=400 |
| Parameters | ~110 M |
Why the runtime auto-flips to CTC
Hybrid TDT+CTC models normally let the user choose between the two decoders. But this checkpoint ships with pred_layers=1 — a single LSTM rather than the usual two — which is too shallow for the TDT prediction network to produce useful joint scores. Upstream's recommended decode is CTC for this checkpoint. The C++ runtime detects this at load (pred_layers < 2 && has_ctc) and sets decode_ctc=true automatically. You can opt back into TDT (not recommended) by removing that auto-flip in parakeet_init_from_file.
Attribution
- Original model:
nvidia/parakeet-tdt_ctc-110m(CC-BY-4.0). NVIDIA NeMo team. - GGUF conversion + ggml runtime:
CrispStrobe/CrispASR.
License
CC-BY-4.0, inherited from the base model.
Provenance and EU AI Act Art. 53 note
- Upstream model: nvidia/parakeet-tdt_ctc-110m — published by
nvidia. - Upstream licence:
cc-by-4.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.