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)# 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
File size: 301 Bytes
0381a28 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"do_ceptral_normalize": true,
"feature_extractor_type": "Speech2TextFeatureExtractor",
"feature_size": 80,
"normalize_means": true,
"normalize_vars": true,
"num_mel_bins": 80,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": true,
"sampling_rate": 16000
}
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