Speeding Up Speech-to-Text with Batch APIs: A Guide to Reducing Costs

In this article

1. Why Switch from Real-Time to Batch APIs?
2. Specific Benefits of Batch Processing
3. Implementing Batch APIs with AIVISION
4. Step 1: Prepare and Segment Data
5. Step 2: Send Batch Requests
6. Step 3: Process Results and Synchronize
7. Comparison Table: Real-Time vs. Batch API
8. Tips to Optimize Costs and Performance
9. Conclusion

In the digital age, businesses face a massive accumulation of audio files from meetings, customer support calls, and educational content. Processing these files manually or using real-time APIs for each individual file creates bottlenecks and drives up operational costs. Batch speech to text offers a crucial solution, allowing for large-scale audio processing with high efficiency. This guide explains how to optimize STT costs by leveraging Batch APIs, reducing system load and shortening completion times.

Why Switch from Real-Time to Batch APIs?

When dealing with a few short audio files, using a real-time API via WebSocket is a sensible choice due to immediate response times. However, when the data scale grows to hundreds or thousands of hours of recordings, this model reveals significant limitations.

First, regarding infrastructure, every real-time connection consumes server resources (memory, CPU) to maintain the session. If you send 10,000 audio files simultaneously, the system must open 10,000 parallel connections, leading to potential overload and latency.

Second, regarding cost and response time. For data that does not require immediate results (offline data), waiting for each connection to complete is inefficient. Batch APIs operate on a "submit batch - wait for processing - receive results" mechanism. You can push an entire list of files to the system, and the server optimizes data reading and runs the speech recognition model continuously, uninterrupted by individual network requests.

Specific Benefits of Batch Processing

Implementing batch speech to text delivers practical value:

• Reduced Overall Latency (Throughput): The system processes data continuously, optimizing I/O (Input/Output) to accelerate the completion of the entire job batch.
• System Stability: Fewer simultaneous connections help servers run more smoothly, especially during peak hours.
• Optimized GPU Resources: AI models can be loaded into memory and process data batches efficiently, avoiding the constant loading/unloading of models.
• Simplified Code: Instead of writing complex logic to manage thousands of WebSocket connections, you only need a loop to send REST requests and a polling or callback mechanism to receive results.

Implementing Batch APIs with AIVISION

AIVISION, with experience deploying speech AI for hundreds of enterprises in Vietnam and internationally, provides flexible APIs supporting both streaming and file transcription. To start optimizing STT costs, you need to understand the basic steps when working with large-scale data.

Step 1: Prepare and Segment Data

Before sending files to the API, check the audio quality. Although AIVISION handles Vietnamese and English code-switching well, as well as speech in medical or business environments, standardizing formats (e.g., MP3, WAV, M4A) and bitrates helps speed up encoding/decoding.

Practical Tip: If you have a 5-hour recording, consider splitting it into 5-10 minute segments if the system allows. This makes progress management easier and enables parallel processing if needed, while reducing the risk of losing the entire file if an error occurs in a small segment.

Step 2: Send Batch Requests

With AIVISION, you can use the REST API to submit audio files. While the platform supports streaming via WebSocket for real-time needs, for large-scale audio processing, you should group requests or use queue mechanisms if supported.

Here is the basic logic flow:

1. Create a list of files to process.
2. Send each file (or group of files) to AIVISION's REST endpoint.
3. Save the ID of each request (Job ID) for tracking.
4. Perform polling (status checks) or wait for a callback (if available) to receive the text results.

Step 3: Process Results and Synchronize

Upon receiving results, you will get the text accompanied by word timestamps (time markers for each word) if requested. This is valuable data for building content search applications, sentiment analysis, or detailed meeting minutes.

Important Note on Accuracy: AIVISION maintains a Vietnamese speech corpus of 690,517 hours, helping the model achieve high accuracy. According to internal measurements, the average Word Error Rate (WER) across Vietnamese test sets is 10.53%, with particularly strong performance for standard read speech (FLEURS-vi: 3.60%) and natural multi-speaker speech (VIVOS: 4.71%). Even in actual business meetings, accuracy remains at a level useful for commercial applications.

Comparison Table: Real-Time vs. Batch API

To better visualize the differences, refer to the comparison table below:

Criteria	Real-Time (Streaming)	Batch (File Transcription)
Method	WebSocket	REST API
Response Speed	Immediate (Real-time)	Wait for completion (Offline)
Suitable Applications	Live calls, Chatbots, Live subtitles	Meeting recordings, Podcasts, Historical data, Medical data
System Resources	High (Maintains continuous connection)	Low (Background processing, batch)
Complexity	High (Manages state, buffer)	Lower (Send and receive)
Scalability	Limited by concurrent connections	Flexibly scales via queue

Tips to Optimize Costs and Performance

To ensure batch speech to text operates most effectively, here are some recommendations from the technical team:

1. Check File Formats: Use efficient compressed formats like MP3 or OGG instead of uncompressed WAV to reduce upload bandwidth, unless you absolutely need raw audio quality.
2. Error Handling: Always have a mechanism to catch errors. If a file has a format error or is too large, the system should skip it and continue processing other files, then log the error for review.
3. Leverage Word Timestamps: When processing in batches, enable the word timestamps feature. This data allows you to quickly search for keywords in video/audio without listening to the entire file, saving time for editing staff.
4. Monitor Performance: Record the average processing time for each batch. This helps you estimate completion times for large future projects and adjust your data submission strategy.

AIVISION not only provides technology but also partners with businesses to deploy speech AI solutions tailored to industry specifics. With multilingual processing capabilities (24 languages) and deep Vietnamese support, the platform meets needs ranging from basic applications to complex ones in healthcare or finance.

Conclusion

Transitioning to batch speech to text is not just a technical choice but a necessary STT cost optimization strategy for any business with large volumes of audio data. By processing audio in batches, you free up resources, accelerate data processing, and ensure system stability.

If you are looking for an accurate, stable, and easy-to-integrate Vietnamese Speech-to-Text solution, explore AIVISION's services. We are ready to support you in deploying the most suitable voice AI applications for your workflows.

Contact the technical team for detailed advice on integrating the API into your system, or Start free to experience AIVISION's Vietnamese speech recognition quality. To learn more about the latest AI technologies, visit our Blog.

Frequently asked questions

How does a Batch API differ from a Real-Time API?

Batch APIs are designed to process existing audio files (offline) in bulk, optimizing throughput and reducing system load. In contrast, Real-Time APIs (typically via WebSocket) are for live audio data that requires immediate response, such as in calls or chatbots.

What audio formats does AIVISION support for Batch APIs?

AIVISION supports common formats like MP3, WAV, M4A, and others. You should use efficient compressed formats to reduce upload bandwidth when processing thousands of files.

What is the accuracy of AIVISION's Vietnamese Speech-to-Text?

According to internal measurements on data not used for training, the average Word Error Rate (WER) is 10.53%. This figure is significantly lower for standard read speech (3.60% on the FLEURS-vi test set) and natural speech (4.71% on VIVOS), making it suitable for practical commercial applications.

How long can a single audio file be for processing?

AIVISION supports processing long audio files. However, to optimize performance and better manage errors, technical experts often recommend splitting very long files (hours long) into smaller segments (5-10 minutes) when processing in batches.

How do I start using AIVISION's Batch API?

You can register for a free account at [Start free](/signup) to receive an API key and begin integration. If you need in-depth technical support for processing large data volumes, please [Contact](/en/contact) the AIVISION team.

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