Citrinet-512 model which has been trained on various Spanish corpus.
It utilizes a Google SentencePiece  tokenizer with vocabulary size 1024, and transcribes text in lower case Spanish alphabet alphabet characters.
Citrinet is a deep residual convolutional neural network architecture that is optimized for Automatic Speech Recognition tasks. There are many variants of the Citrinet family of models, which are further discussed in the paper .
We took an encoder from the English version of the CitriNet network trained on over 7,000 hours of English speech. Then we changed the model's decoder to output characters from the Spanish alphabet and fine-tuned this model using Spanish portions of the Mozilla CommonVoice (MCV)  and Multilingual LibriSpeech (MLS)  training sets. The NeMo toolkit  was used for training this model over several hundred epochs on multiple GPUs.
While finetuning this model, we used the following datasets:
While training this model, we used the following datasets:
The tokenizer for this model was built using the text that was in the training set.
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 performance of Automatic Speech Recognition models is measuring using Word Error Rate.
The model obtains the following scores on the following evaluation datasets -
MCV dev set
MCV test set
MLS dev set
MLS test set
Note that these scores above are not particularly indicative of the quality of transcriptions that models trained on ASR Set will achieve, but they are a useful proxy.
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_es_citrinet_512")
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_es_citrinet_512" \ 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 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.
 Google Sentencepiece Tokenizer
 Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition
 Multilingual LibriSpeech (MLS)