---

license: cc-by-4.0

language:

• en

pipeline_tag: automatic-speech-recognition

tags:

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

library_name: ggml

base_model: nvidia/parakeet-tdt-1.1b

---

Parakeet TDT 1.1B — GGUF (ggml-quantised)

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

The larger Parakeet TDT — 1.1 B parameters, 42-layer FastConformer encoder. The biggest pure-English TDT variant in the family. Pick this when you want maximum WER quality on long-tail English vocabulary and don't mind paying 2× the compute relative to 0.6 B.

• English-only, lowercase output without punctuation
• Built-in word-level timestamps from the TDT decoder
• 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-1.1b.gguf | 2.14 GB | F16, full precision |

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

| parakeet-tdt-1.1b-q4_k.gguf | 808 MB | Q4_K — recommended default |

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

| Quant | Time | Realtime | Output |

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

| F16 | 0.68 s | 16.1× | "and so my fellow americans ask not what your country can do for you ask what you can do for your country" |

| Q8_0 | 0.67 s | 16.4× | (identical) |

| Q4_K | 0.69 s | 16.0× | (identical) |

Output is lowercase, no punctuation by design — the upstream vocab is lowercase-only. If you need proper casing/punctuation, pipe the output through a punctuation-restoration post-processor (--punc-model fullstop-punc or fireredpunc).

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

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

# 2c. Lowercase output → add punctuation
./build/bin/crispasr -m parakeet-tdt-1.1b --punc-model fullstop-punc -f your-audio.wav

When to pick this over the other Parakeet variants

| Scenario | Pick |

| --- | --- |

| English, long-tail vocab, fine with 2× compute | tdt-1.1b (this repo) |

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

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

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

| English 1.1B with proper casing/punct in output | cstr/parakeet-tdt_ctc-1.1b-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 |

| Vocab | 1024 SentencePiece tokens (English, lowercase) + blank |

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

| Parameters | ~1.1 B |

42 layers vs 24 for 0.6b — same encoder design, just deeper.

Attribution

• Original model: nvidia/parakeet-tdt-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-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.