This collection contains large size versions of Conformer-CTC (around 120M parameters) trained on ULCA & Europal with around ~2900 hours. The model transcribes speech in hindi characters along with spaces for hinglish speech.
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 tokenizers for these models were built using the text transcripts of the train set with this script.
The checkpoint of the language model used as the neural rescorer can be found here. You may find more info on how to train and use language models for ASR models here: ASR Language Modeling
All the models in this collection are trained on Hindi labelled dataset(~2900 hrs):
a. ULCA Hindi Corpus
b. Europal Dataset
The tokenizer for this model was built using text corpus provided with the train dataset.
We build a token set with the following script:
python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \ --manifest="train_manifest.json" \ --data_root="" \ --vocab_size=128 \ --tokenizer="spe" \ --spe_type="unigram" \ --spe_character_coverage=1.0 \ --log
Performance 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 and 6-Gram KenLM trained on AI4Bharat Corpus and Europal.
|Decoding||Version||Tokenizer||Vocabulary Size||MUCS 2021 Blind Test*||IITM 2020 Eval Set||IITM 2020 Dev Set||Common Voice 6 Test*||Common Voice 7 Test*||Common Voice 8 Test*|
|6-Gram KenLM**||1.10.0||SentencePiece Unigram||128||11.79%/3.35%||15.96%/6.39%||15.49%/6.25%||17.05%/5.43%||17.77%/6.23%||19.18%/7.1%|
*- Normalized and without special characters and punctuation.
**- KenLM with 128 beam size with n_gram_alpha=1.0, n_gram_beta=1.0.
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.EncDecCTCModelBPE.from_pretrained(model_name="stt_hi_conformer_ctc_large") output_transcript = asr_model.transcribe([filenames])
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_hi_conformer_ctc_large \ audio_dir="" \ dataset_manifest="" \ output_filename="" \ batch_size=32 \ cuda=0 \
python [NEMO_GIT_FOLDER]/examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py \ --asr_model="stt_hi_conformer_ctc_large" \ --test_manifest="" \ --output_path="" \ --model_stride=4
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.