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Parakeet Tdt 0.6b v2 GGUF

GGUF / ggml conversions of nvidia/parakeet-tdt-0.6b-v2 for use with the crispasr CLI from CrispStrobe/CrispASR.

TypeOtherModèle de basenvidia/parakeet-tdt-0.6b-v2VersionRevision 8878172BClasse

Source du modèle

Type
Other
Taille du fichier
379 MB–1,15 GB
Modèle de base
nvidia/parakeet-tdt-0.6b-v2
Version
vRevision 8878172
Mis à jour
26 sept. 2026
Téléchargements
1 k
Source
Hugging Face

Description de la source

Parakeet TDT 0.6B v2 — GGUF (ggml-quantised)

GGUF / ggml conversions of nvidia/parakeet-tdt-0.6b-v2 for use with the crispasr CLI from CrispStrobe/CrispASR.

Parakeet TDT 0.6B v2 is NVIDIA's English-only 600 M-parameter ASR model — the original Open ASR Leaderboard topper before v3 spread capacity across 25 European languages. On plain English, v2 is often stronger than v3 since it didn't have to share encoder capacity with 24 other languages.

  • English-only, mixed-case + punctuation output
  • Built-in word-level timestamps from the TDT (Token-and-Duration Transducer) decoder — no separate CTC alignment model required
  • CC-BY-4.0 licence (friendlier than most ASR models)

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

FileSizeNotes
parakeet-tdt-0.6b-v2.gguf1.24 GBF16, full precision
parakeet-tdt-0.6b-v2-q8_0.gguf735 MBQ8_0, near-lossless
parakeet-tdt-0.6b-v2-q4_k.gguf468 MBQ4_K — recommended default

All three precisions produce the same text on samples/jfk.wav:

And so, my fellow Americans, ask not what your country can do for you, ask what you can do for your country.

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-v2 --auto-download -f your-audio.wav

# 2b. Or explicit download + load
hf download cstr/parakeet-tdt-0.6b-v2-GGUF \
    parakeet-tdt-0.6b-v2-q4_k.gguf --local-dir .
./build/bin/crispasr -m parakeet-tdt-0.6b-v2-q4_k.gguf -f your-audio.wav

When to pick v2 over v3

ScenarioPick
English only, want best WERv2 (this repo)
Multilingual, 25 EU languagesv3 — cstr/parakeet-tdt-0.6b-v3-GGUF
Tight RAM, Englishsmaller hybrid — cstr/parakeet-tdt_ctc-110m-GGUF
Long-tail English vocab, willing to pay 2x computelarger — cstr/parakeet-tdt-1.1b-GGUF

Model architecture

ComponentDetails
Encoder24-layer FastConformer, d=1024, 8 heads, head_dim=128, FFN=4096, conv kernel=9
SubsamplingConv2d dw_striding stack, 8× temporal (50 → 12.5 fps)
Predictor2-layer LSTM, hidden 640
Joint headenc(1024 → 640) + pred(640 → 640) → ReLU → linear(640 → 1029)
Vocab1024 SentencePiece tokens (English, mixed case + punctuation)
Audio16 kHz mono, 128 mel bins, n_fft=512, hop=160, win=400
Parameters~600 M

Same FastConformer encoder + TDT decoder as v3 — just trained on English-only data with an English-only BPE.

How this was made

  1. The .nemo checkpoint was unpacked, NeMo state-dict keys were remapped to ggml-friendly names, and weights were written to GGUF F16 (matmul tensors) + F32 (norms / biases / mel filterbank).
  2. Quantised variants are produced by crispasr-quantize (the same llama.cpp-style quantiser used for the other GGUF releases).
  3. Inference uses src/parakeet.{h,cpp}: FastConformer encoder runs as a single ggml graph (BN folded out), LSTM predictor + joint head run as CPU F32 loops, TDT greedy decode alternates "advance encoder frame" / "emit token + advance predictor" using the duration head's argmax.

Attribution

License

CC-BY-4.0, inherited from the base model. Use of these GGUF files must comply with the CC-BY-4.0 license including attribution.

Sources

1 source
Hugging FacePrincipalVersion 8878172f3c45ba231fac8cfd4aff2542b13bc875
huggingface.co/cstr/parakeet-tdt-0.6b-v2-GGUF/tree/8878172f3c45ba231fac8cfd4aff2542b13bc875

Vérifié 6 sept.

Artefacts du modèle

3 artefacts

parakeet-tdt-0.6b-v2-q4_k.gguf

gguf · 379 MB · SHA-256 287f8a46b64e…8518 · Hugging Face

Télécharger

parakeet-tdt-0.6b-v2-q8_0.gguf

gguf · 634 MB · SHA-256 9da7c8a51077…806d · Hugging Face

Télécharger

parakeet-tdt-0.6b-v2.gguf

gguf · 1,15 GB · SHA-256 c82b001dcb0a…d7f7 · Hugging Face

Télécharger

Extraits de sources

2 extraits

--- 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-0.6b-v2 --- # Parakeet TDT 0.6B v2 — GGUF (ggml-quantised) GGUF / ggml conversions of [`nvidia/parakeet-tdt-0.6b-v2`](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) for use with the `crispasr` CLI from **[CrispStrobe/CrispASR](https://github.com/CrispStrobe/CrispASR)**. Parakeet TDT 0.6B v2 is NVIDIA's English-only 600 M-parameter ASR model — the original Open ASR Leaderboard topper before v3 spread capacity across 25 European languages. On plain English, v2 is often stronger than v3 since it didn't have to share encoder capacity with 24 other languages. - **English-only**, mixed-case + punctuation output - **Built-in word-level timestamps** from the TDT (Token-and-Duration Transducer) decoder — no separate CTC alignment model required - **CC-BY-4.0** licence (friendlier than most ASR models) 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-0.6b-v2.gguf` | 1.24 GB | F16, full precision | | `parakeet-tdt-0.6b-v2-q8_0.gguf` | 735 MB | Q8_0, near-lossless | | `parakeet-tdt-0.6b-v2-q4_k.gguf` | 468 MB | **Q4_K — recommended default** | All three precisions produce the same text on `samples/jfk.wav`: > And so, my fellow Americans, ask not what your country can do for you, ask what you can do for your country. ## Quick Start ```bash # 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-v2 --auto-download -f your-audio.wav # 2b. Or explicit download + load hf download cstr/parakeet-tdt-0.6b-v2-GGUF \ parakeet-tdt-0.6b-v2-q4_k.gguf --local-dir . ./build/bin/crispasr -m parakeet-tdt-0.6b-v2-q4_k.gguf -f your-audio.wav ``` ## When to pick v2 over v3 | Scenario | Pick | | --- | --- | | English only, want best WER | **v2** (this repo) | | Multilingual, 25 EU languages | v3 — [`cstr/parakeet-tdt-0.6b-v3-GGUF`](https://huggingface.c...

Source context: 1030 downloads · 0 likes · Pipeline automatic-speech-recognition · Library ggml · Repo cstr/parakeet-tdt-0.6b-v2-GGUF