Parakeet Tdt 0.6b v2 Mlx 8bit

Loading requires quantizing the module tree before loading weights; parakeet_mlx.from_pretrained alone will not load this checkpoint:

import json, mlx.nn as nn
from parakeet_mlx.utils import from_config

model = from_config(json.load(open("config.json")))
nn.quantize(model, group_size=64, bits=8)
model.load_weights("model.safetensors")

mel_filters.npy is the precomputed slaney mel filterbank (librosa.filters.mel(sr=16000, n_fft=512, n_mels=128, fmin=0, fmax=8000, norm="slaney")), shipped so inference environments can stub librosa out entirely.

Quantized for Spoke local dictation.

--- license: cc-by-4.0 base_model: nvidia/parakeet-tdt-0.6b-v2 tags: - mlx - automatic-speech-recognition --- # Parakeet TDT 0.6b v2, MLX 8-bit 8-bit MLX quantization of [nvidia/parakeet-tdt-0.6b-v2](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2), built from [mlx-community/parakeet-tdt-0.6b-v2](https://huggingface.co/mlx-community/parakeet-tdt-0.6b-v2) via `mlx.nn.quantize(bits=8, group_size=64)` (nn.Linear and nn.Embedding only; Conv1d, BatchNorm, and the TDT prediction LSTM stay bf16). English-only. Benchmarks (LibriSpeech test-clean, 250 samples, M4): WER 1.91% (bf16: 1.87%), RTF 0.024, peak MLX memory 1.26 GB. Loading requires quantizing the module tree before loading weights; `parakeet_mlx.from_pretrained` alone will not load this checkpoint: ```python import json, mlx.nn as nn from parakeet_mlx.utils import from_config model = from_config(json.load(open("config.json"))) nn.quantize(model, group_size=64, bits=8) model.load_weights("model.safetensors") ``` `mel_filters.npy` is the precomputed slaney mel filterbank (`librosa.filters.mel(sr=16000, n_fft=512, n_mels=128, fmin=0, fmax=8000, norm="slaney")`), shipped so inference environments can stub librosa out entirely. Quantized for [Spoke](https://spoke.so) local dictation.