Welcome to the world of automatic speech recognition (ASR) with TeleSpeech-ASR! In this guide, we’ll walk you through the steps to set up and use this powerful tool, ensuring you can transform audio signals into text with ease. So, get ready to dive into the essentials of ASR configuration!

Getting Started with TeleSpeech-ASR

TeleSpeech-ASR is a versatile automatic speech recognition toolkit. It’s built on advanced architectures and supported by frameworks like Fairseq and Wenet. Here’s how to set it up in your local environment:

Installation Steps

1. Clone the Repository

   First, you’ll need to clone the TeleSpeech-ASR repository from GitHub:

   $ git clone https://github.com/Tele-AI/TeleSpeech-ASR

2. Navigate into the Directory

   Change to the cloned directory:

   $ cd TeleSpeech-ASR

3. Install Requirements

   Now, install the necessary packages:

   $ pip install -r requirements.txt

Understanding the Model Structures

TeleSpeech-ASR offers several pretrained models to cater to different needs, similar to how a toolbox has various tools for specific tasks. Let’s break it down:

• Pre-trained Models:

  Think of pre-trained models as ready-to-use power tools designed for a specific function:

  • TeleSpeech-ASR1.0-base
  • TeleSpeech-ASR1.0-large
  • TeleSpeech-ASR1.0-large-kespeech
• Fine-tuning:

  If you need to customize these models further (much like sanding a piece of wood to suit your needs), you can use the available datasets to fine-tune them.

Running the Training Script

Once the models are set up, you can start training with the following command:

$ bash run_scripts/run_d2v_finetune.sh

Troubleshooting

Here are some troubleshooting tips you might find useful:

• Ensure you have the correct Python version (3.8) and PyTorch version (1.13.0) installed.
• If encountering package-related issues, try creating a new virtual environment and reinstalling the requirements.
• Check paths in your scripts to ensure they are correctly pointing to your datasets and models.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Examples of Model Performance

Performance metrics, akin to measuring the accuracy of a crafted product, are crucial for evaluating your ASR systems:

• Aishell-1 dataset: Exhibits a Character Error Rate (CER) of 4.7% for pre-trained base models.
• WenetSpeech dataset: Shows a competitive CER of 14.3% with pre-trained large models.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.