Transforming Spoken Text to Written Text: A Comprehensive Guide

September 12, 2024

In the ever-evolving landscape of artificial intelligence, transforming spoken text into written text has become a vital tool. This blog will delve into a model designed specifically for converting raw automatic speech recognition (ASR) text outputs into a well-formatted written format, complete with the functionality to handle out-of-vocabulary words through external vocabulary. Catch up as we explore how to use this model effectively!

Understanding the Model’s Purpose

Imagine you are listening to a friend who is sharing detailed information with you. However, their way of speaking is raw, lacking structure—dates are not in numeric format, numbers are spoken out loud, and specific words are used without adequate context. This model acts like a personal assistant, refining that conversation, converting it into a structured format that’s easy to understand.

How to Use the Model

By leveraging PyTorch, the model can take observed inputs and produce formatted outputs seamlessly. Let’s break it down step by step:

1. Installation and Set Up

First, ensure you have the necessary libraries installed like PyTorch and model handling.
Clone the model from the source repository to your local machine.

2. Initialize the Tokenizer and Model

You will need to initialize both the tokenizer and model. This is where the magic begins to unfold!

python
tokenizer = model_handling.init_tokenizer()
model = EncoderDecoderSpokenNorm.from_pretrained('nguyenvulebinh/spoken-norm', cache_dir=model_handling.cache_dir)

3. Format Input Text

Here, you will be able to format text with or without bias phrases:

python
# Format input text with bias phrases
outputs = model.generate(**inputs, output_attentions=True, num_beams=1, num_return_sequences=1)

This command generates a formatted output that reflects your conversational context, making it substantial.

4. Handling Different Inputs

Now, let’s compare two scenarios; one with bias phrases and the other without. Bias phrases are specific words like ‘covid’ or ‘delta’ that may require special attention during the transformation process.

python
# Without bias phrases
outputs = model.generate(
    input_ids=torch.tensor(input_ids),
    attention_mask=torch.tensor(attention_mask),
    bias_input_ids=None,
    bias_attention_mask=None,
    output_attentions=True, 
    num_beams=1, 
    num_return_sequences=1
)

Troubleshooting Ideas

Even the best models encounter hurdles. Here are some troubleshooting tips should you run into any issues:

Ensure library versions are compatible: Version mismatches can create conflicts. Make sure your versions are up-to-date.
Check for adequate input formats: Ensure that the inputs are clean and appropriately formatted as specified.
Memory allocation issues: If you experience memory errors, check if your GPU settings are correct.

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

Conclusion

Transforming spoken text to written text not only enhances clarity but also paves the way for more intelligent interactions with AI systems. 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.