Stt En Fastconformer Transducer Large

🚀 NVIDIA FastConformer-Transducer Large (en)

This model is designed to transcribe speech into lower - case English text. It's a "large" version of the FastConformer Transducer model with around 114M parameters. It offers high - quality automatic speech recognition capabilities.

🚀 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 the latest Pytorch version.

pip install nemo_toolkit['all']

Usage

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(model_name="nvidia/stt_en_fastconformer_transducer_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:

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_transducer_large" 
 audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"

✨ Features

• High - Performance Architecture: FastConformer is an optimized version of the Conformer model with 8x depthwise - separable convolutional downsampling. It's trained in a multitask setup with a Transducer decoder loss.
• Multidomain Training: Trained on a large composite dataset (NeMo ASRSet En) that includes multiple English speech datasets, enabling better performance in general audio transcription.

📦 Installation

To work with this model, you need to install NVIDIA NeMo. After installing the latest Pytorch version, run the following command:

pip install nemo_toolkit['all']

💻 Usage Examples

Basic Usage

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

Advanced Usage

Transcribing multiple audio files:

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

📚 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.

Model Architecture

FastConformer [1] is an optimized version of the Conformer model with 8x depthwise - separable convolutional downsampling. The model is trained in a multitask setup with a Transducer decoder 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 model are trained with this example script and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast - conformer_transducer_bpe.yaml).

The tokenizers for these models were built using the text transcripts of the train set with this script.

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 measuring 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 tables 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.

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.

🔧 Technical Details

FastConformer is based on an optimized architecture with 8x depthwise - separable convolutional downsampling. The multitask training with a Transducer decoder loss enhances its performance. The model is trained on a large and diverse dataset, which contributes to its better generalization ability.

📄 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