Stt Rw Conformer Transducer Large

🚀 NVIDIA Conformer-Transducer Large (Kinyarwanda)

This model focuses on transcribing speech into the lowercase Latin alphabet, including spaces and apostrophes. It is trained on approximately 2000 hours of Kinyarwanda speech data. It's a non - autoregressive "large" variant of Conformer, boasting around 120 million parameters. For comprehensive architecture details, refer to the model architecture section and the NeMo documentation.

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🚀 Quick Start

✨ Features

• Transcribes speech into lowercase Latin alphabet (including space and apostrophe).
• Trained on around 2000 hours of Kinyarwanda speech data.
• Non - autoregressive "large" variant of Conformer with about 120 million parameters.

📦 Installation

To train, fine - tune or play with the model, you need to install NVIDIA NeMo. We recommend installing it after installing the latest PyTorch version.

pip install nemo_toolkit['all']

💻 Usage Examples

Basic Usage

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.

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

Advanced Usage

Transcribing using Python

output = asr_model.transcribe(['sample.wav'])
print(output[0].text)

Transcribing many audio files

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

📚 Documentation

Model Architecture

Conformer - Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding. You may find more info on the detail of this model here: Conformer - Transducer 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 vocabulary we use contains 28 characters:

[' ', "'", 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']

Rare symbols with diacritics were replaced during preprocessing.

The tokenizers for these models were built using the text transcripts of the train set with this script. For vocabulary of size 1024 we restrict maximum subtoken length to 4 symbols to avoid populating vocabulary with specific frequent words from the dataset. This does not affect the model performance and potentially helps to adapt to other domain without retraining tokenizer.

Full config can be found inside the .nemo files.

Datasets

All the models in this collection are trained on MCV - 9.0 Kinyarwanda dataset, which contains around 2000 hours training, 32 hours of development and 32 hours of testing speech audios.

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.

🔧 Technical Details

• Input: This model accepts 16 kHz mono - channel Audio (wav files) as input.
• Output: This model provides transcribed speech as a string for a given audio sample.

📄 License

This model is licensed under cc - by - 4.0.

Limitations

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.

Deployment with NVIDIA Riva

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.

References

• [1] Conformer: Convolution - augmented Transformer for Speech Recognition
• [2] Google Sentencepiece Tokenizer
• [3] NVIDIA NeMo Toolkit