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thoddnn/parakeet-tdt-0.6b-v3-mlx-4bit
parakeet-tdt-0.6b-v3-mlx-4bit is a automatic speech recognition model from thoddnn. 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.
This is a stability mirror of animaslabs/parakeet-tdt-0.6b-v3-mlx-4bit at source revision 65247a0a9e735426eba06056a9535f7e67dcbbb9. The model weights and configuration are unchanged. The original conversion and its at…
Downloads · 30 days
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64% of all-time downloads
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.safetensors636 MB · 100%
How the weights are stored.
U32544M · 87%
From the Hugging Face model README
This is a stability mirror of
animaslabs/parakeet-tdt-0.6b-v3-mlx-4bit
at source revision 65247a0a9e735426eba06056a9535f7e67dcbbb9.
The model weights and configuration are unchanged. The original conversion and
its attribution are preserved below.
This model was converted to MLX format, 4-bit quantized from nvidia/parakeet-tdt-0.6b-v3 using the scripts in this github repo. Please refer to original model card for more details on the model.
Quantized models require calling mlx.nn.quantize() before loading weights.
import json
import mlx.nn as nn
from huggingface_hub import hf_hub_download
from parakeet_mlx.utils import from_config
# Download and load config
config_path = hf_hub_download("thoddnn/parakeet-tdt-0.6b-v3-mlx-4bit", "config.json")
with open(config_path) as f:
config = json.load(f)
# Build model and apply quantization structure
model = from_config(config)
nn.quantize(
model,
bits=config["quantization"]["bits"],
group_size=config["quantization"]["group_size"],
)
# Load quantized weights
weights_path = hf_hub_download("thoddnn/parakeet-tdt-0.6b-v3-mlx-4bit", "model.safetensors")
model.load_weights(weights_path)
# Transcribe
result = model.transcribe("audio.wav")
print(result.text)