espnet2_librispeech_100_conformer_word

Introduction
The ESPnet2_LibriSpeech_100_Conformer_Word model is designed for Automatic Speech Recognition (ASR). It utilizes the ESPnet library and is trained on the LibriSpeech 100 dataset. The model is referenced in arXiv paper 1804.00015.

Architecture
The model employs a Conformer architecture, which effectively integrates convolutional neural networks with self-attention mechanisms to enhance ASR performance on audio data.

Training
The training process for this model involves using the LibriSpeech 100 dataset, which provides a diverse range of audio samples. The dataset is processed through the ESPnet framework, leveraging the Conformer architecture to optimize word recognition accuracy.

Guide: Running Locally
To run the ESPnet2_LibriSpeech_100_Conformer_Word model locally, follow these steps:

1. Clone the repository from Hugging Face.
2. Install necessary dependencies, ensuring compatibility with the ESPnet library.
3. Load the pre-trained model and configure it for inference on your audio data.
4. Execute the ASR task, processing audio files through the model for transcription.

For optimal performance, it is recommended to utilize cloud GPUs such as those provided by AWS, Google Cloud, or Azure, which can handle the computational demands of ASR tasks.

License
This model is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0), allowing for sharing and adaptation with appropriate credit.