Automatic Speech Recognition
Transformers
Safetensors
Japanese
qwen3_asr
japanese
qwen3-asr
lora-merged
noise-robustness
Instructions to use Junlaii/Bro-ASR-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Junlaii/Bro-ASR-1.7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Junlaii/Bro-ASR-1.7B")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Junlaii/Bro-ASR-1.7B") model = AutoModelForMultimodalLM.from_pretrained("Junlaii/Bro-ASR-1.7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4ff9f129204eb9e4ada46baadd3f8c1143e673026b618d60090abe5801dd93d
- Size of remote file:
- 11.4 MB
- SHA256:
- fe1fad59be22a41ee293363fcf95fdedbc7c93f3b49270b1d2e18bd1399a7a05
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.