Automatic Speech Recognition
Transformers
qwen3-asr
latent-reasoning
test-time-compute
parameter-efficient
Instructions to use voidful/latentASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/latentASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/latentASR")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("voidful/latentASR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 633 Bytes
262fa3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | [project]
name = "latentASR"
version = "0.1.0"
description = "Continuous latent test-time scaling for frozen Qwen3-ASR backbones."
readme = "README.md"
requires-python = ">=3.10"
license = { text = "Apache-2.0" }
authors = [
{ name = "LatentASR contributors" }
]
dependencies = [
"torch>=2.6",
"torchaudio>=2.6",
"transformers>=4.57",
"datasets>=3.6",
"huggingface-hub>=0.34",
"qwen-asr>=0.0.6",
"qwen-omni-utils>=0.0.8",
"jiwer>=4.0",
"peft>=0.18",
"numpy>=1.24",
"tqdm>=4.66"
]
[tool.setuptools]
py-modules = [
"config",
"data",
"eval",
"losses",
"model",
"peft_utils",
"train",
"utils"
]
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