This collection contains large size versions of Conformer-CTC (around 120M parameters) trained on ParlaSpeech-HR v1.0 dataset.
It utilizes a Google SentencePiece  tokenizer with vocabulary size 128, and transcribes text in lower case Croatian alphabet characters.
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 NeMo toolkit  was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.
The tokenizers for these models were built using the text transcripts of the train set with this script, and can be found inside the
The vocabulary we use contains 27 characters:
[' ', 'a', 'b', 'c', 'č', 'ć', 'd', 'đ', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 'š', 't', 'u', 'v', 'z', 'ž']
Full config can be found inside the
All the models in this collection are trained on ParlaSpeech-HR v1.0 Croatian dataset, which contains around 1665 hours of training data after data cleaning, 2.2 hours of developement and 2.3 hours of test data.
python [NEMO_GIT_DIR]/scripts/tokenizers/process_asr_text_tokenizer.py \ --manifest=train_manifest.json,dev_manifest.json \ --data_root="[OUTPUT_DIR_FOR_TOKENIZER]" \ --tokenizer="spe" \ --spe_type="unigram" \ --vocab_size=128 \ --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||Dev WER||Test WER||Train Dataset|
|1.11.0||SentencePiece Unigram||128||4.43||4.70||ParlaSpeech-HR v1.0|
You may use language models (LMs) and beam search to improve the accuracy of the models.
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_hr_conformer_ctc_large")
python [NEMO_GIT_DIR]/examples/asr/transcribe_speech.py \ pretrained_name="stt_hr_conformer_ctc_large" \ audio_dir="[AUDIO_DIR]"
This model accepts 16 kHz single-channel audio as input.
This model provides transcribed speech as a string for a given audio sample.
Since the model is trained just on ParlaSpeech-HR v1.0 dataset, the performance of this model might degrade for speech which includes 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