Instructions to use pszemraj/parakeet-tdt-0.6b-v3-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use pszemraj/parakeet-tdt-0.6b-v3-gguf with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("pszemraj/parakeet-tdt-0.6b-v3-gguf") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Quantized via the updated
crispasr-quantizetool. This CLI provides superior performance over the oldercohere-quantizetool due to better tensor handling and model-agnostic alignment logic. Empirical tests confirm higher quality compared to versions using the legacy tool, specifics are in the manifest.json
quantized versons of nvidia/parakeet-tdt-0.6b-v3 in gguf format via CrispASR (ggml backend)
- made for use with parakit, a daemon-style local dictation tool I made
- Empirical tests comparing the Q8, the F16, and the original Nemo file (all in this repo) on a ~60s dictation show essentially no degradation in both Q8 and F16
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Model tree for pszemraj/parakeet-tdt-0.6b-v3-gguf
Base model
nvidia/parakeet-tdt-0.6b-v3