Automatic Speech Recognition
Transformers
Safetensors
English
joint_aed_ctc_speech-encoder-decoder
custom_code
Eval Results (legacy)
Instructions to use BUT-FIT/DeCRED-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/DeCRED-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/DeCRED-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/DeCRED-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -130,7 +130,7 @@ It achieves Word Error Rates (WERs) comparable to `openai/whisper-medium` across
|
|
| 130 |
|
| 131 |
Architecture details, training hyperparameters, and a description of the proposed technique will be added soon.
|
| 132 |
|
| 133 |
-
*Disclaimer: The model currently
|
| 134 |
|
| 135 |
The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
|
| 136 |
class to transcribe audio files of arbitrary length.
|
|
|
|
| 130 |
|
| 131 |
Architecture details, training hyperparameters, and a description of the proposed technique will be added soon.
|
| 132 |
|
| 133 |
+
*Disclaimer: The model currently produce insertions on utterances containing silence only, as it was previously not trained on such data. The fix will be added soon.*
|
| 134 |
|
| 135 |
The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
|
| 136 |
class to transcribe audio files of arbitrary length.
|