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
| # 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}" | |
| } | |