This collection contains large size versions of Conformer-CTC (around 120M parameters) trained on NeMo German ASRSet with over 2000 hours of German speech. The model transcribes speech in lower case German alphabet along with spaces and without punctuation. This model was trained with the English model as initialization.
Conformer-CTC model is a non-autoregressive variant of Conformer model  for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: Conformer-CTC Model.
The tokenizers for these models were built using the text transcripts of the train set with this script.
You may find more info on how to train and use language models for ASR models here: ASR Language Modeling
All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of over two thousand hours of cleaned German speech:
The performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.
The latest model obtains the following greedy scores on the following evaluation datasets -
|Version||Decoding||MCV7.0 dev||MCV7.0 Test||Comment|
|1.5.0_lm||4-gram LM||5.23||6.03||beam_width=256, n_gram_alpha=1, n_gram_beta=1|
|Version||Decoding||MLS dev||MLS test||Comment|
|1.5.0_lm||4-gram LM||3.53||4.23||beam_width=256, n_gram_alpha=0.5, n_gram_beta=0.5|
|Version||Decoding||Vox Populi dev||Vox Populi Test||Comment|
|1.5.0_lm||4-gram LM||11.69||9.82||beam_width=256, n_gram_alpha=0.5, n_gram_beta=0.5|
Note that these evaluation datasets have been filtered and preprocessed to only contain German alphabet characters and are removed of punctuation.
The model is available for use in the NeMo toolkit , and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_de_conformer_ctc_large")
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_de_conformer_ctc_large" \ audio_dir=""
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
This model provides transcribed speech as a string for a given audio sample.
Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.