Conformer-Transducer model which was pre-trained in a self supervised manner  and then finetuned on Spanish labelled speech data. The model was pretrained on a subset of the unlabeled Voxpopuli  dataset. Only the English, German, Spanish and French unlabelled subsets were used, which amounted to 100,000 hours. Then the model was finetuned 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]. This amounted to 1,340 hours of Spanish training data.
The model utilizes a Google SentencePiece  tokenizer with vocabulary size 128, and transcribes text in lower case Spanish alphabet characters.
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 NeMo toolkit  was used for training this model over several hundred epochs on multiple GPUs.
While pre-training this model (in a self-supervised manner), we used the following datasets:
While finetuning 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/process_asr_text_tokenizer.py \ --manifest="train_manifest.json" \ --data_root="<OUTPUT DIRECTORY FOR TOKENIZER>" \ --vocab_size=128 \ --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 greedy scores on the following evaluation 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_es_conformer_transducer_large")
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_es_conformer_transducer_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.
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.