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Parakeet De Med GGUF

GGUF / ggml conversions of johannhartmann/parakeetdemed for use with the parakeet-main CLI from CrispStrobe/CrispASR@parakeet.

TipoOtherModelo basejohannhartmann/parakeet_de_medVersiónRevision e7cf898Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
399 MB–1,17 GB
Modelo base
johannhartmann/parakeet_de_med
Versión
vRevision e7cf898
Actualizado
26 sept 2026
Descargas
147
Fuente
Hugging Face

Descripción de la fuente

Parakeet-DE-Med — GGUF (ggml-quantised)

GGUF / ggml conversions of johannhartmann/parakeet_de_med for use with the parakeet-main CLI from CrispStrobe/CrispASR@parakeet.

parakeet_de_med is Johann Hartmann's PEFT decoder+joint fine-tune of nvidia/parakeet-tdt-0.6b-v3 specialised for German medical documentation (Arztbriefe). On the German medical test set it scores 3.28% WER vs the base model's 11.73% — a 72% relative reduction.

The fine-tune freezes the encoder and trains only the TDT decoder + joint head (18.1M out of 627M parameters, 2.89%). This means:

  • The architecture is identical to parakeet-tdt-0.6b-v3 (24-layer FastConformer encoder, 2-layer LSTM predictor, 8198-class TDT joint head)
  • The same GGUF converter, runtime, and CLI work as-is
  • The frozen encoder still uses the base model's auto-language detection — for clean German speech this works well, for accented or noisy audio you may want to fall back to a different runtime (see comparison table)

Files

FileSizeNotes
parakeet_de_med.gguf1.26 GBF16, full precision
parakeet_de_med-q8_0.gguf711 MBQ8_0, near-lossless
parakeet_de_med-q5_0.gguf516 MBQ5_0
parakeet_de_med-q4_k.gguf467 MBQ4_K — recommended default

All quantisations produce the same text on the German verification clip:

Leider zu spät. Leider zu spät.

Quick start

# 1. Build the runtime
git clone -b parakeet https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc) --target parakeet-main

# 2. Download a quantisation
huggingface-cli download cstr/parakeet_de_med-GGUF \
    parakeet_de_med-q4_k.gguf --local-dir .

# 3. Transcribe German audio
./build/bin/parakeet-main \
    -m parakeet_de_med-q4_k.gguf \
    -f german_audio.wav -t 8

The runtime is the same parakeet-main binary used for the base parakeet-tdt-0.6b-v3. All the usual flags work: -vad-model for Silero VAD slicing, -ck N for fixed chunking, -ml N for max chars per line, -osrt/-ovtt/-ot for subtitle output, -v for per-token timestamps via the TDT duration head.

Word-level timestamps

Like the base parakeet model, this fine-tune emits TDT durations as part of decoding, so word-level timestamps come for free at one encoder frame = 80 ms granularity. No separate forced alignment model needed:

$ ./build/bin/parakeet-main -m parakeet_de_med-q4_k.gguf -f german.wav -t 8 -v
[ 0.32s →  0.64s]  Der
[ 0.64s →  1.04s]  Patient
[ 1.04s →  1.32s]  klagt
[ 1.32s →  1.92s]  über
...

Which runtime should I use?

For German speech specifically:

Use caseRight tool
German medical documentationparakeet_de_med-q4_k.gguf ← this repo
General German ASR with explicit language controlcanary-1b-v2-q4_k.gguf (-sl de -tl de)
German → English translationcanary-1b-v2-q4_k.gguf (-sl de -tl en)
General multilingual ASR (auto-detect)parakeet-tdt-0.6b-v3-q4_k.gguf
Lowest English WERcohere-transcribe-q4_k.gguf

Architecture (inherited from base)

ComponentDetails
Encoder24-layer FastConformer (frozen), d=1024, 8 heads, head_dim=128, FFN=4096, conv kernel=9
SubsamplingConv2d dw_striding stack, 8× temporal (100 → 12.5 fps)
Predictor2-layer LSTM, hidden 640, embed 8193 × 640 (fine-tuned)
Joint headenc(1024 → 640) + pred(640 → 640) → ReLU → linear(640 → 8198) (fine-tuned)
Vocab8192 SentencePiece tokens (multilingual, but generation biased toward German medical)
Audio16 kHz mono, 128 mel bins, n_fft=512, hop=160, win=400
Parameters627M total, 18.1M trained (2.89%)

Attribution

Related

License

CC-BY-4.0, inherited from both the base model and the fine-tune. Use of these GGUF files must comply with the CC-BY-4.0 license including attribution to NVIDIA NeMo team and Johann Hartmann.

Fuentes

1 fuente
Hugging FacePrincipalVersión e7cf8988cd2479830252b8af165842ea7b8fb454
huggingface.co/cstr/parakeet_de_med-GGUF/tree/e7cf8988cd2479830252b8af165842ea7b8fb454

Verificado 6 sept

Artefactos del modelo

4 artefactos

parakeet_de_med-q4_k.gguf

gguf · 399 MB · SHA-256 23cc05addca4…d5ac · Hugging Face

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parakeet_de_med-q5_0.gguf

gguf · 448 MB · SHA-256 c3e6a42dba12…be51 · Hugging Face

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parakeet_de_med-q8_0.gguf

gguf · 643 MB · SHA-256 59e0691bf70d…83f2 · Hugging Face

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parakeet_de_med.gguf

gguf · 1,17 GB · SHA-256 84ae94142285…b740 · Hugging Face

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Extractos de fuentes

2 extractos

--- license: cc-by-4.0 language: - de pipeline_tag: automatic-speech-recognition tags: - audio - speech-recognition - transcription - ggml - gguf - parakeet - tdt - fastconformer - german - medical library_name: ggml base_model: johannhartmann/parakeet_de_med --- # Parakeet-DE-Med — GGUF (ggml-quantised) GGUF / ggml conversions of [`johannhartmann/parakeet_de_med`](https://huggingface.co/johannhartmann/parakeet_de_med) for use with the `parakeet-main` CLI from **[CrispStrobe/CrispASR@parakeet](https://github.com/CrispStrobe/CrispASR/tree/parakeet)**. `parakeet_de_med` is Johann Hartmann's PEFT decoder+joint fine-tune of [`nvidia/parakeet-tdt-0.6b-v3`](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) specialised for **German medical documentation** (Arztbriefe). On the German medical test set it scores **3.28% WER** vs the base model's 11.73% — a 72% relative reduction. The fine-tune freezes the encoder and trains only the TDT decoder + joint head (18.1M out of 627M parameters, 2.89%). This means: - The architecture is identical to `parakeet-tdt-0.6b-v3` (24-layer FastConformer encoder, 2-layer LSTM predictor, 8198-class TDT joint head) - The same GGUF converter, runtime, and CLI work as-is - The frozen encoder still uses the base model's auto-language detection — for clean German speech this works well, for accented or noisy audio you may want to fall back to a different runtime (see [comparison table](#which-runtime-should-i-use)) ## Files | File | Size | Notes | | --- | ---: | --- | | `parakeet_de_med.gguf` | 1.26 GB | F16, full precision | | `parakeet_de_med-q8_0.gguf` | 711 MB | Q8_0, near-lossless | | `parakeet_de_med-q5_0.gguf` | 516 MB | Q5_0 | | `parakeet_de_med-q4_k.gguf` | 467 MB | **Q4_K — recommended default** | All quantisations produce the same text on the German verification clip: > Leider zu spät. Leider zu spät. ## Quick start ```bash # 1. Build the runtime git clone -b parakeet https://github.com/CrispStrobe/CrispASR cd CrispASR cmake -B build -DCMAKE_BUILD_TYPE=Release cmake --build build -j$(nproc) --target parakeet-main # 2. Download a quantisation huggingface-cli download cstr/parakeet_de_med-GGUF \ parakeet_de_med-q4_k.gguf --local-dir . # 3. Transcribe German audio ./build/bin/parakeet-main \ -m parakeet_de_med-q4_k.gguf \ -f german_audio.wav -t 8 ``` The runtime is the same `parakeet-main` binary used for the bas...

Source context: 147 downloads · 1 likes · Pipeline automatic-speech-recognition · Library ggml · Repo cstr/parakeet_de_med-GGUF