README.md · nvidia/stt_en_conformer_ctc_large at 1abfd6b8da3634a627d6362a7586f99d55bc740f

metadata

language:
  - en
library_name: nemo
datasets:
  - librispeech_asr
  - fisher_corpus
  - Switchboard-1
  - WSJ-0
  - WSJ-1
  - National Singapore Corpus Part 1
  - National Singapore Corpus Part 6
  - vctk
  - VoxPopuli (EN)
  - Europarl-ASR (EN)
  - Multilingual LibriSpeech (2000 hours)
  - mozilla-foundation/common_voice_7_0
thumbnail: null
tags:
  - automatic-speech-recognition
  - speech
  - audio
  - CTC
  - Conformer
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
license: cc-by-4.0
widget:
  - example_title: Librispeech sample 1
    src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
  - example_title: Librispeech sample 2
    src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
  - name: stt_en_conformer_ctc_large
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: LibriSpeech (clean)
          type: librispeech_asr
          config: clean
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 2.2
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: LibriSpeech (other)
          type: librispeech_asr
          config: other
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 4.3
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: Multilingual LibriSpeech
          type: facebook/multilingual_librispeech
          config: english
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 7.2
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: Mozilla Common Voice 7.0
          type: mozilla-foundation/common_voice_7_0
          config: en
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 8
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: Mozilla Common Voice 8.0
          type: mozilla-foundation/common_voice_8_0
          config: en
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 9.48
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: Wall Street Journal 92
          type: wsj_0
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 2
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: Wall Street Journal 93
          type: wsj_1
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 2.9
      - task:
          type: Automatic Speech Recognition
          name: automatic-speech-recognition
        dataset:
          name: National Singapore Corpus
          type: nsc_part_1
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 7

Model Overview

This is a "large" versions of Conformer-CTC (around 120M parameters) trained on NeMo ASRSet with around 16000 hours of english speech. The model transcribes speech in lower case english alphabet along with spaces and apostrophes.

NVIDIA Riva: Deployment

This model can be efficiently deployed with NVIDIA Riva on premises or with cloud providers. Additionally, with RIVA you get:

Streaming speech recognition mode
Boost specific words (e.g. brand and product names)
Conformer checkpoints trained on proprietary data

NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest Pytorch version.

pip install nemo_toolkit['all']

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 instantiate the model

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("nvidia/stt_en_conformer_ctc_large")

Transcribing using Python

First, let's get a sample

wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav

Then simply do:

asr_model.transcribe(['2086-149220-0033.wav'])

Transcribing many audio files

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \
 pretrained_name="nvidia/stt_en_conformer_ctc_large" \
 audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"

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.

Model Architecture

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: Conformer-CTC 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 a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:

Librispeech 960 hours of English speech
Fisher Corpus
Switchboard-1 Dataset
WSJ-0 and WSJ-1
National Speech Corpus (Part 1, Part 6)
VCTK
VoxPopuli (EN)
Europarl-ASR (EN)
Multilingual Librispeech (MLS EN) - 2,000 hours subset
Mozilla Common Voice (v7.0)

Note: older versions of the model may have trained on smaller set of datasets.

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	WSJ Eval92	WSJ Dev93	NSC Part 1	MLS Test	MLS Dev	MCV Test 6.1	Train Dataset
1.6.0	SentencePiece Unigram	128	4.3	2.2	2.0	2.9	7.0	7.2	6.5	8.0	NeMo ASRSET 2.0

You may use language models to improve the accuracy of the models. The WER(%) of the latest model with different language modeling techniques are reported in the follwoing table.

Language Modeling	Training Dataset	LS test-other	LS test-clean	Comment
N-gram LM	LS Train + LS LM Corpus	3.5	1.8	N=10, beam_width=128, n_gram_alpha=1.0, n_gram_beta=1.0
Neural Rescorer(Transformer)	LS Train + LS LM Corpus	3.4	1.7	N=10, beam_width=128
N-gram + Neural Rescorer(Transformer)	LS Train + LS LM Corpus	3.2	1.8	N=10, beam_width=128, n_gram_alpha=1.0, n_gram_beta=1.0

Limitations

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.

References

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

[2] Google Sentencepiece Tokenizer

[3] NVIDIA NeMo Toolkit