Automatic Speech Recognition
Transformers
Safetensors
Chinese
English
qwen3_asr
taiwan-mandarin
traditional-chinese
code-switching
qwen3-asr
speech
Instructions to use JacobLinCool/TEA-ASR-1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JacobLinCool/TEA-ASR-1-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JacobLinCool/TEA-ASR-1-mini")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("JacobLinCool/TEA-ASR-1-mini") model = AutoModelForMultimodalLM.from_pretrained("JacobLinCool/TEA-ASR-1-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
model card: fresh-eval numbers + protocol
Browse files
README.md
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@@ -34,8 +34,6 @@ A companion **TEA-ASR-1** shares the identical recipe β see [`JacobLinCool/TEA
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- π **Code-switch robust** β handles natural zh-en mixing instead of translating Mandarin into English.
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- π§© **Drop-in Qwen3-ASR compatible** β same loading and inference API as the base model; nothing else to install
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or call.
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- π« **No runtime OpenCC, no string post-processing** β Traditional-Chinese output is baked into the model's own
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tokenizer; inference is a pure model forward + decode.
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- πͺΆ **Lightweight adaptation** β a small decoder LoRA on a frozen audio encoder, trained on a few hours of public
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audio, then merged for deployment.
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- π **Code-switch robust** β handles natural zh-en mixing instead of translating Mandarin into English.
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| 35 |
- π§© **Drop-in Qwen3-ASR compatible** β same loading and inference API as the base model; nothing else to install
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or call.
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- πͺΆ **Lightweight adaptation** β a small decoder LoRA on a frozen audio encoder, trained on a few hours of public
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audio, then merged for deployment.
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