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: 675 Bytes
262fa3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"project": "latentASR",
"model_repo": "voidful/latentASR",
"base_model": "Qwen/Qwen3-ASR-0.6B",
"checkpoint_file": "checkpoints/latentASR_adapter.pth",
"source_checkpoint": "activation_500_epoch10.pth",
"checkpoint_sha256": "f0ce39fa5e6952fced6992508f3e2b32ea8467442b545678781a0f04e64f2430",
"train_mode": "latent",
"n_latent": 4,
"train_max_samples": 500,
"adapter_parameters": 5251077,
"value_forced_neg_prob": 0.3,
"latent_use_bounded_delta": true,
"latent_use_injection_gate": true,
"latent_use_embedding_anchor": true,
"deployed_halt_threshold": 0.0,
"paper": "Listen, Think, Transcribe: Continuous Latent Test-Time Scaling for ASR"
}
|