Conformer-CTC Large model, which was initially pre-trained on the English NeMo ASRSet with around 16000 hours of english speech and then finetuned on a combination of Spanish and English labeled speech data.
It can transcribe audio samples into English or Spanish. The language is detected automatically.
We started with the english Conformer-CTC large model and finetuned it using labelled Spanish portions of the Mozilla CommonVoice (MCV7.0) , Multilingual LibriSpeech (MLS) , and Voxpopuli  training sets, as well as a subset of the Spanish Fisher dataset [5, 6], plus English Librispeech . This amounted to 1,340 hours of Spanish training data and 960 hours of English data.
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 NeMo toolkit  was used for training this model over several hundred epochs on multiple GPUs using bfloat16.
While finetuning this model, we used the following datasets:
The performance of Automatic Speech Recognition models is measured using Word Error Rate (WER).
The model obtains the following greedy scores on the following evaluation datasets:
The model was not trained on the above datasets.
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_enes_conformer_ctc_large")
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_enes_conformer_ctc_large" \ audio_dir=""
This model accepts 16000 Hz Mono-channel Audio (wav files) as input.
This model provides transcribed speech as a string for a given audio sample. The output string may contain English or Spanish characters, depending on the detected language in the audio sample.
Since this model was trained on publicly 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.
Finally, the model may not peform well on true "code-switching" samples, where each sample contains more than one language. This is because the model was trained only on samples where each sample had only one language. We will address this in the future releases.