This collection contains large size versions of Conformer-Transducer (around 120M parameters) trained on NeMo ASRSet with around 500 hours of Italian speech.
It utilizes a Google SentencePiece  tokenizer with vocabulary size 1024, and transcribes speech in lowercase Italian alphabet along with spaces and apostrophes.
Conformer-Transducer model is an autoregressive variant of Conformer model  for Automatic Speech Recognition which uses Transducer loss/decoding. You may find more info on the detail of this model here: Conformer-Transducer 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 a composite dataset (NeMo ASRSET) comprising of 487 hours of Italian speech:
The tokenizer for this model was built using text corpus provided with the train dataset.
We build a Google Sentencepiece Tokenizer  with the following script :
python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \ --manifest="train_manifest.json" \ --data_root="<OUTPUT DIRECTORY FOR TOKENIZER>" \ --vocab_size=1024 \ --tokenizer="spe" \ --spe_type="unigram" \ --spe_character_coverage=1.0 \ --no_lower_case \ --log
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||MCV 11.0 Dev||MCV 11.0 Test||MLS Dev||MLS Test||VoxPopuli Dev||VoxPopuli Test||Train Dataset|
|1.13.0||SentencePiece Unigram||1024||4.80||5.24||14.62||12.18||12.00||15.15||NeMo ASRSET It 2.0|
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.EncDecRNNTBPEModel.from_pretrained("stt_it_conformer_transducer_large")
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_it_conformer_transducer_large" \ audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
This model accepts 16 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.