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: 783 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 | #!/usr/bin/env bash
# Shared shell helpers for LatentASR experiment runners.
# This file is sourced by scripts under experiments/.
latent_asr_repo_root() {
local script_dir="$1"
cd "${script_dir}/.." && pwd
}
latent_asr_python_bin() {
local requested="${1:-}"
if [[ -n "${requested}" ]]; then
printf '%s\n' "${requested}"
elif [[ -x "/user_data/miniconda3/envs/py311/bin/python" ]]; then
printf '%s\n' "/user_data/miniconda3/envs/py311/bin/python"
else
printf '%s\n' "python"
fi
}
latent_asr_require_file() {
local path="$1"
local label="$2"
if [[ ! -f "${path}" ]]; then
echo "[error] ${label} not found: ${path}" >&2
exit 1
fi
}
latent_asr_print_kv() {
local key="$1"
local value="$2"
printf '%-16s: %s\n' "${key}" "${value}"
}
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