This collection contains small size versions of Conformer-CTC (around 13M parameters) trained on LibriSpeech dataset. The model transcribes speech in lower case english alphabet along with spaces and apostrophes.
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.
All the models in this collection are trained on LibriSpeech dataset which contains around 1000 hours of English speech.
The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
|Version||Tokenizer||Vocabulary Size||LS test-other||LS test-clean|
|1.0.0||SentencePiece Unigram ||128||8.8||3.4|
You may use language models to improve the accuracy of the models. The WER(%) of the latest model with different language modeling techniques are reported in the follwoing table.
|Language Modeling||Training Dataset||LS test-other||LS test-clean||Comment|
|N-gram LM||LS Train + LS LM Corpus||6.5||2.6||N=10, beam_width=128, n_gram_alpha=2.0, n_gram_beta=1.0|
|Neural Rescorer(Transformer)||LS Train + LS LM Corpus||6.6||2.4||N=10, beam_width=128|
|N-gram + Neural Rescorer(Transformer)||LS Train + LS LM Corpus||5.8||2.4||N=10, beam_width=128, n_gram_alpha=2.0, n_gram_beta=1.0|
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_en_conformer_ctc_small_ls")
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_en_conformer_ctc_small_ls" \ audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
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 all models are trained on just LibriSpeech dataset, 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.