Automatic Speech Recognition
NeMo
Core ML
PyTorch
speech
audio
Transducer
TDT
FastConformer
Conformer
NeMo
hf-asr-leaderboard
Instructions to use FluidInference/parakeet-tdt-0.6b-v3-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use FluidInference/parakeet-tdt-0.6b-v3-coreml with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("FluidInference/parakeet-tdt-0.6b-v3-coreml") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Add Encoder_v2.mlmodelc: int8-linear re-quantization fixing token corruption (FluidAudio#760)
#4
by alexwengg - opened
int8 per-channel linear quantization of the fp16 encoder export (568M, vs 425M 6-bit LUT original). The original Encoder.mlmodelc's 6-bit LUT palettization noise corrupts tokens under specific right-context (e.g. Ukrainian 'фоновий процес' -> 'фоновиц' on the issue #760 repro); the int8-linear rebuild decodes all repro cuts identically to fp32 and parakeet-mlx. Original file left untouched for older library versions; FluidAudio PR #872 adds Swift-side preference for Encoder_v2 with fallback. config.json now records actual weight formats per encoder file.
alexwengg changed pull request status to open
alexwengg changed pull request status to merged