This collection contains large size version of Conformer-Transducer (around 120M parameters) trained on Mozilla Common Voice 10.0 Kabyle train set with around 131 hours of Kabyle speech from 141620 utterances and 145 unique speakers. The model transcribes speech in lower case latin 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 NeMo toolkit  was used for training the models for few epochs. These models are trained with this example script and this base config.
Mozilla Common Voice (v10.0) Kabyle dataset
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=dev_manifest.json,train_manifest.json \ --vocab_size=1024 \ --data_root="" \ --tokenizer="spe" \ --spe_type=bpe \ --spe_character_coverage=1.0 \ --spe_max_sentencepiece_length=4 --log
The performance of Automatic Speech Recognition models is measured using Word Error Rate(WER) and Character Error Rate(CER).
The model obtains the following scores on the Mozilla Common Voice test set: 18.86 % WER, 6.74 % CER
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(model_name="stt_kab_conformer_transducer_large")
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_kab_conformer_transducer_large" \ audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
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.
 Conformer: Convolution-augmented Transformer for Speech Recognition
 Google Sentencepiece Tokenizer