Automatic Speech Recognition
Transformers
Safetensors
joint_aed_ctc_speech-encoder-decoder
custom_code
Instructions to use BUT-FIT/ED-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/ED-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/ED-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/ED-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update train.sh
Verified signature892de3d -
Update train.sh
Verified signaturec3066e9 -
Create train.sh
Verified signaturec81ad45 -
Upload JointCTCAttentionEncoderDecoder
Verified signature1b9475a -
Upload tokenizer
Verified signature4d69b96 -
Upload feature extractor
Verified signature0381a28 -
Upload config
Verified signaturee10cd03 -
initial commit
Verified signaturee7dd4af