Stt En Fastconformer Ctc Xlarge

🚀 NVIDIA FastConformer-CTC XLarge (en)

This model is an "extra large" version of the FastConformer CTC model (around 600M parameters), which can transcribe speech into lower - case English alphabet. It offers high - quality automatic speech recognition capabilities and is trained on a large - scale English speech dataset.

🚀 Quick Start

📦 Installation

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']

💻 Usage Examples

🔍 Basic Usage

Automatically instantiate the model:

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCTBPEModel.from_pretrained(model_name="nvidia/stt_en_fastconformer_ctc_xlarge")

🔍 Advanced Usage

Transcribing using Python

First, get a sample:

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

Then simply do:

output = asr_model.transcribe(['2086-149220-0033.wav'])
print(output[0].text)

Transcribing many audio files

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

✨ Features

• Model Architecture: FastConformer [1] is an optimized version of the Conformer model with 8x depthwise - separable convolutional downsampling. The model is trained using CTC loss. You may find more information on the details of FastConformer here: [Fast - Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast - conformer).
• Training: The NeMo toolkit [3] was used for training the models for over several hundred epochs. These models are trained with this example script and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast - conformer_ctc_bpe.yaml). The tokenizers for these models were built using the text transcripts of the train set with this script.

📚 Documentation

📊 Input

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

📤 Output

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

📈 Datasets

The model in this collection is trained on a composite dataset (NeMo ASRSet En) comprising 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 hrs subset
• Mozilla Common Voice (v7.0)
• People's Speech - 12,000 hrs subset

📊 Performance

The performance of Automatic Speech Recognition models is measured using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.

The following table summarizes the performance of the available models in this collection with the Transducer decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

🔧 Technical Details

Model Index

Tags

• automatic - speech - recognition
• speech
• audio
• CTC
• FastConformer
• Transformer
• pytorch
• NeMo
• hf - asr - leaderboard

Datasets

• librispeech_asr
• fisher_corpus
• Switchboard - 1
• WSJ - 0
• WSJ - 1
• National - Singapore - Corpus - Part - 1
• National - Singapore - Corpus - Part - 6
• vctk
• VoxPopuli
• Europarl - ASR
• Multilingual - LibriSpeech
• mozilla - foundation/common_voice_8_0
• MLCommons/peoples_speech

⚠️ 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.

🚀 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.

📄 License

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.

📚 References

[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition

[2] Google Sentencepiece Tokenizer

[3] NVIDIA NeMo Toolkit