Parakeet De Med GGUF
GGUF / ggml conversions of johannhartmann/parakeetdemed for use with the parakeet-main CLI from CrispStrobe/CrispASR@parakeet.
Modellquelle
- Art
- Other
- Dateigröße
- 399 MB–1,17 GB
- Basismodell
- johannhartmann/parakeet_de_med
- Version
- vRevision e7cf898
- Aktualisiert
- 26. Sept. 2026
- Downloads
- 147
- Quelle
- Hugging Face
Quellenbeschreibung
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
| 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
# 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 case | Right tool |
|---|---|
| German medical documentation | parakeet_de_med-q4_k.gguf ← this repo |
| General German ASR with explicit language control | canary-1b-v2-q4_k.gguf (-sl de -tl de) |
| German → English translation | canary-1b-v2-q4_k.gguf (-sl de -tl en) |
| General multilingual ASR (auto-detect) | parakeet-tdt-0.6b-v3-q4_k.gguf |
| Lowest English WER | cohere-transcribe-q4_k.gguf |
Architecture (inherited from base)
| Component | Details |
|---|---|
| Encoder | 24-layer FastConformer (frozen), 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, embed 8193 × 640 (fine-tuned) |
| Joint head | enc(1024 → 640) + pred(640 → 640) → ReLU → linear(640 → 8198) (fine-tuned) |
| Vocab | 8192 SentencePiece tokens (multilingual, but generation biased toward German medical) |
| Audio | 16 kHz mono, 128 mel bins, n_fft=512, hop=160, win=400 |
| Parameters | 627M total, 18.1M trained (2.89%) |
Attribution
- Base model:
nvidia/parakeet-tdt-0.6b-v3(CC-BY-4.0). NVIDIA NeMo team. - Fine-tune:
johannhartmann/parakeet_de_med(CC-BY-4.0). Johann Hartmann. Trained on 976 German medical documentation samples for 5 epochs with PEFT decoder+joint strategy. - GGUF conversion + ggml runtime:
CrispStrobe/CrispASR@parakeet.
Related
- C++ runtime: CrispStrobe/CrispASR@parakeet
- Base multilingual model (auto-detect):
cstr/parakeet-tdt-0.6b-v3-GGUF - Encoder–decoder companion (canary, with explicit language control + speech translation):
cstr/canary-1b-v2-GGUF - Cohere Transcribe (lowest English WER):
cstr/cohere-transcribe-03-2026-GGUF
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.
Quellen
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Quellen
1 QuelleVerifiziert 6. Sept.
Modellartefakte
4 Artefakte
Modellartefakte
4 ArtefakteQuellenauszüge
2 Auszüge
Quellenauszüge
2 Auszüge--- 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
