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sonic-speech/parakeet-tdt-0.6b-v3-int4
parakeet-tdt-0.6b-v3-int4 is a automatic speech recognition model from sonic-speech. Use it when you need speech turned into text. It is set up for mlx. The card lists the license as cc-by-4.0.
NVIDIA Parakeet TDT v3 with encoder-only INT4 quantization — the lite variant for memory-constrained devices (8 GB Macs). 61% smaller than BF16 with only +0.4pp WER on real-world speech.
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From the Hugging Face model README
NVIDIA Parakeet TDT v3 with encoder-only INT4 quantization — the lite variant for memory-constrained devices (8 GB Macs). 61% smaller than BF16 with only +0.4pp WER on real-world speech.
See also:
sonic-speech/parakeet-tdt-0.6b-v3 — Full BF16 referencesonic-speech/parakeet-tdt-0.6b-v3-int8 — Encoder INT8 (recommended)| Metric | BF16 | INT4 | Change |
|---|---|---|---|
| WER (LibriSpeech) | 0.82% | 0.82% | None |
| WER (TED-LIUM) | 15.1% | 15.5% | +0.4pp |
| RTFx | 73x | 98x | +34% |
| Peak Memory | 3,002 MB | 1,003 MB | -67% |
| Weight Size | 1,254 MB | 489 MB | -61% |
Benchmarked on Apple M3 Max (64 GB), macOS Sequoia 15.7.3, MLX 0.30.4.
The small WER increase on TED-LIUM (+0.4pp) appears on speakers with challenging acoustics. LibriSpeech (clean speech) shows no degradation.
Recommendation: Use INT8 for general use. Use INT4 when memory is the constraint (8 GB unified memory Macs).
Only the Conformer encoder (~85% of parameters) is quantized to INT4 (group_size=64). The decoder and joint network remain in BF16, preserving precision for token generation.
from parakeet import from_pretrained
import mlx.core as mx
model = from_pretrained("sonic-speech/parakeet-tdt-0.6b-v3-int4", dtype=mx.bfloat16)
result = model.transcribe("audio.wav")
Install: pip install parakeet-mlx
Quantized from sonic-speech/parakeet-tdt-0.6b-v3 using mlx.nn.quantize (encoder-only, 4-bit, group_size=64).
Part of the Sonic Speech model collection.