---

license: cc-by-4.0

language:

• en

pipeline_tag: automatic-speech-recognition

tags:

• audio
• speech-recognition
• transcription
• ggml
• gguf
• parakeet
• tdt
• ctc
• hybrid
• fastconformer
• english

library_name: ggml

base_model: nvidia/parakeet-tdt_ctc-1.1b

---

Parakeet TDT+CTC 1.1B — GGUF (ggml-quantised)

GGUF / ggml conversions of nvidia/parakeet-tdt_ctc-1.1b for use with the crispasr CLI from CrispStrobe/CrispASR.

The largest hybrid Parakeet — 1.1 B parameters, 42-layer FastConformer encoder with both TDT and CTC heads. The hybrid head gives you two decode strategies on the same encoder: native TDT word timestamps (default), or CTC if you need shallow-fusion biasing.

• English, mixed-case + punctuation output (vocab includes uppercase + punctuation tokens, unlike the pure parakeet-tdt-1.1b)
• Hybrid TDT+CTC — default decode is TDT; pass --parakeet-decoder ctc for the CTC head
• 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-1.1b.gguf | 2.15 GB | F16, full precision |

| parakeet-tdt_ctc-1.1b-q8_0.gguf | 1.27 GB | Q8_0, near-lossless |

| parakeet-tdt_ctc-1.1b-q4_k.gguf | 810 MB | Q4_K — recommended default |

Smoke test on samples/jfk.wav (11 s clip, M1 Metal):

| Quant | Time | Realtime | Output |

| --- | ---: | ---: | --- |

| F16 | 0.74 s | 14.8× | "And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country." |

| Q8_0 | 2.12 s | 5.2× | (identical) |

| Q4_K | 2.67 s | 4.1× | (identical) |

> Note: this checkpoint's Q4_K/Q8_0 run slower than the pure parakeet-tdt-1.1b quants on M1 (CTC + TDT both wired in, plus a per-tensor q4_0 fallback on the joint head). F16 is the fastest precision here.

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-1.1b --auto-download -f your-audio.wav

# 2b. Or explicit download + load
hf download cstr/parakeet-tdt_ctc-1.1b-GGUF \
    parakeet-tdt_ctc-1.1b-q4_k.gguf --local-dir .
./build/bin/crispasr -m parakeet-tdt_ctc-1.1b-q4_k.gguf -f your-audio.wav

# 2c. Switch to the CTC head (e.g. when adding hotword biasing)
./build/bin/crispasr -m parakeet-tdt_ctc-1.1b --parakeet-decoder ctc -f your-audio.wav

When to pick this over the other Parakeet variants

| Scenario | Pick |

| --- | --- |

| English 1.1B with proper casing + punctuation in output | tdt_ctc-1.1b (this repo) |

| English 1.1B, lowercase output, faster Q4_K/Q8_0 | cstr/parakeet-tdt-1.1b-GGUF |

| English, best WER per FLOP | cstr/parakeet-tdt-0.6b-v2-GGUF |

| Multilingual (25 EU languages) | cstr/parakeet-tdt-0.6b-v3-GGUF |

| Tight RAM | cstr/parakeet-tdt_ctc-110m-GGUF |

Model architecture

| Component | Details |

| --- | --- |

| Encoder | 42-layer FastConformer, d=1024, 8 heads, head_dim=128, FFN=4096, conv kernel=9 |

| Subsampling | Conv2d dw_striding stack, 8× temporal (100 → 12.5 fps) |

| Predictor | 2-layer LSTM, hidden 640 |

| Joint head | enc(1024 → 640) + pred(640 → 640) → ReLU → linear(640 → 1029) — TDT, 5 durations |

| CTC head | linear(1024 → 1025) |

| Vocab | 1024 SentencePiece tokens (English, mixed case + punctuation) + blank |

| Audio | 16 kHz mono, 80 mel bins, n_fft=512, hop=160, win=400 |

| Parameters | ~1.1 B |

Same 42-layer encoder as parakeet-tdt-1.1b, but with an added CTC head and a mixed-case + punctuated vocab.

Attribution

• Original model: nvidia/parakeet-tdt_ctc-1.1b (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-1.1b — 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.