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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("voidful/latentASR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run_eval_all_datasets.sh from voidful/latentASR: direct link, hf CLI and curl.
- Browser
- Download file 165 Bytes
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https://huggingface.co/voidful/latentASR/resolve/main/run_eval_all_datasets.sh
- Command line
-
hf download hf://voidful/latentASR/run_eval_all_datasets.sh
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curl -L -o run_eval_all_datasets.sh https://huggingface.co/voidful/latentASR/resolve/main/run_eval_all_datasets.sh
165 Bytes
| set -euo pipefail | |
| SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| exec "${SCRIPT_DIR}/experiments/run_legacy_generalization.sh" "$@" | |