README.md · parthiv11/stt_hi_conformer_ctc_large

metadata

model-index:
  - name: parthiv11/stt_hi_conformer_ctc_large_v2
    results:
      - task:
          type: automatic-speech-recognition
        dataset:
          name: MUCS 2021 Blind Test
          type: MUCS
          config: other
          split: test
          args:
            language: hi
        metrics:
          - type: wer
            value: 9.37
            name: WER
language:
  - hi
metrics:
  - wer
library_name: nemo
pipeline_tag: automatic-speech-recognition

Model Overview

This model 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.

Model Architecture

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.

Training

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.

The checkpoint of the language model used as the neural rescorer.

Datasets

All the models in this collection are trained on Hindi labelled dataset (~2900 hrs):

ULCA Hindi Corpus
Europal Dataset

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
Greedy	1.10.0	SentencePiece Unigram	128	9.37%/2.74%	12.93%/5.60%	12.63%/5.49%	13.16%/4.5%	13.5%/5.2%	14.37%/5.95%
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.

How to Use this Model

Can also be used from NGC, intrution here.
Follow colab to use it directly

Input

This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Licence (Credit goes to Nvidia)

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.