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_8_0 - MLCommons/peoples_speech thumbnail: null tags: - automatic-speech-recognition - speech - audio - Transducer - 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_transducer_xlarge 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: 1.62 - 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: 3.01 - 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: 5.32 - 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: 5.13 - 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: 6.46 - 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: 1.17 - 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: 2.05 - 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: 5.7
NVIDIA Conformer-Transducer X-Large (en-US)
This model transcribes speech in lower case English alphabet along with spaces and apostrophes.
It is an "extra-large" versions of Conformer-Transducer (around 600M parameters) model.
See the model architecture section and NeMo documentation for complete architecture details.
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'] ''' ''' (if it causes an error): pip install nemo_toolkit[all]
How to Use this Model
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.
Automatically instantiate the model
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
Transcribing using Python
First, let's get a sample
Then simply do:
Transcribing many audio files
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_en_conformer_transducer_xlarge" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
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.
Conformer-Transducer model is an autoregressive variant of Conformer model  for Automatic Speech Recognition which uses Transducer loss/decoding instead of CTC Loss. You may find more info on the detail of this model here: Conformer-Transducer 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.
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)
- VoxPopuli (EN)
- Europarl-ASR (EN)
- Multilingual Librispeech (MLS EN) - 2,000 hrs subset
- Mozilla Common Voice (v8.0)
- People's Speech - 12,000 hrs subset
Note: older versions of the model may have trained on smaller set of datasets.
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 8.0||Train Dataset|
|1.10.0||SentencePiece Unigram||1024||3.01||1.62||1.17||2.05||5.70||5.32||4.59||6.46||NeMo ASRSET 3.0|
Since this model was trained on publicly 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.
NVIDIA Riva: Deployment
NVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides:
- World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
- Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
- Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support
Although this model isn’t supported yet by Riva, the list of supported models is here.
Check out Riva live demo.
 Conformer: Convolution-augmented Transformer for Speech Recognition  Google Sentencepiece Tokenizer  NVIDIA NeMo Toolkit
License to use this model is covered by the CC-BY-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-4.0 license.