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STT En Fast Conformer-CTC Large LibriSpeech

Logo for STT En Fast Conformer-CTC Large LibriSpeech
Description
Fast Conformer-CTC-Large model for English Automatic Speech Recognition, Trained with NeMo on LibriSpeech dataset
Publisher
NVIDIA
Latest Version
1.0.0
Modified
April 25, 2023
Size
439.9 MB

Model Overview

This collection contains the large size version of Fast Conformer-CTC (around 115M parameters) trained on LibriSpeech dataset. The model transcribes speech in lower case english alphabet along with spaces and apostrophes.

Model Architecture

Fast Conformer-CTC model is a non-autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: Fast-Conformer Model.

Training

The NeMo toolkit [3] 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 can be found here. You may find more info on how to train and use language models for ASR models here: ASR Language Modeling

Datasets

All the models in this collection are trained on LibriSpeech dataset which contains around 1000 hours of English speech.

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.

Version Tokenizer Vocabulary Size LS test-other LS test-clean
1.0.0 SentencePiece Unigram [2] 128 6.0 2.5

You may use language models to improve the accuracy of the models.

How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_en_fastconformer_ctc_large_ls")

Transcribing text with this model

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \
 pretrained_name="stt_en_fastconformer_ctc_large_ls" \
 audio_dir=""

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.

Limitations

Since all models are trained on just LibriSpeech dataset, 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.

References

[1] Conformer: Convolution-augmented Transformer for Speech Recognition

[2] Google Sentencepiece Tokenizer

[3] NVIDIA NeMo Toolkit

Licence

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.