---
language:
- en
datasets:
- mozilla-foundation/common_voice_13_0
- facebook/voxpopuli
- LIUM/tedlium
- librispeech_asr
- fisher_corpus
- WSJ-0
metrics:
- wer
pipeline_tag: automatic-speech-recognition
model-index:
- name: tbd
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: LibriSpeech (clean)
      type: librispeech_asr
      config: clean
      split: test
      args:
        language: en
    metrics:
    - type: wer
      value: 3.5
      name: Test WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: LibriSpeech (other)
      type: librispeech_asr
      config: other
      split: test
      args:
        language: en
    metrics:
    - type: wer
      value: 8.1
      name: Test WER
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: tedlium-v3
      type: LIUM/tedlium
      config: release1
      split: test
      args:
        language: en
    metrics:
    - type: wer
      value: 5.4
      name: Test WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Vox Populi
      type: facebook/voxpopuli
      config: en
      split: test
      args:
        language: en
    metrics:
    - type: wer
      value: 8.3
      name: Test WER
  - task:
      type: Automatic Speech Recognition
      name: automatic-speech-recognition
    dataset:
      name: Mozilla Common Voice 13.0
      type: mozilla-foundation/common_voice_13_0
      config: en
      split: test
      args:
        language: en
    metrics:
    - type: wer
      value: 16.3
      name: Test WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: FLEURS
      type: google/fleurs
      split: test
      args:
        language: en_us
    metrics:
    - type: wer
      value: 9.6
      name: Test WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Switchboard
      type: unk
      split: eval2000
      args:
        language: en
    metrics:
    - type: wer
      value: 9.2
      name: Test WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Wall Street Journal
      type: unk
      split: eval92
      args:
        language: en
    metrics:
    - type: wer
      value: 2.6
      name: Test WER
---
# DeCRED-base
This is a  **40M encoder-decoder Ebranchformer model** trained with an decoder-centric regularization technique on 6,000 hours of open-source normalised English data. 

Architecture details, training hyperparameters, and a description of the proposed technique will be added soon.

*Disclaimer: The model currently hallucinates on segments containing silence only, as it was previously not trained on such data. The fix will be added soon.*

The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
class to transcribe audio files of arbitrary length.

```python
from transformers import pipeline

model_id = "BUT-FIT/DeCRED-small"
pipe = pipeline("automatic-speech-recognition", model=model_id, feature_extractor=model_id, trust_remote_code=True)
# In newer versions of transformers (>4.31.0), there is a bug in the pipeline inference type.
# The warning can be ignored.
pipe.type = "seq2seq"

# Run beam search decoding with joint CTC-attention scorer
result_beam = pipe("audio.wav")

# Run greedy decoding without joint CTC-attention scorer
pipe.model.generation_config.ctc_weight = 0.0
pipe.model.generation_config.num_beams = 1

result_greedy = pipe("audio.wav")

```