The best audio file formats for speech-to-text: A guide

The accuracy of Speech-to-Text (STT) systems is strongly influenced by the quality of the audio input. Choosing the right audio file format is essential, as it directly impacts how accurately the system can interpret and transcribe spoken words. In this blog post, we'll explore the best audio and video formats for Speech-to-Text, focusing on sound quality, file size, and compatibility with STT software, as well as discussing the potential pitfalls of post-processing.

Why audio format is crucial for speech-to-text

Audio format directly impacts speech-to-text accuracy because different formats preserve varying levels of audio detail that AI models need to recognize speech patterns. Poor format choices can reduce transcription accuracy by 15-30% compared to optimal formats.

1. Sound Quality: High-quality audio captures clear speech signals, making it easier for the STT system to recognize words accurately. Poor audio quality, on the other hand, can lead to errors in transcription.
2. File Size and Processing: Larger, uncompressed audio files retain more detail but require more storage. Compressed files are easier to handle but might sacrifice some accuracy.
3. Compatibility: Not all Speech-to-Text systems support every audio format. Choosing a widely supported format ensures smooth processing and avoids conversion steps that could degrade audio quality.

Key considerations for selecting audio formats

When choosing an audio format for Speech-to-Text applications, consider the following:

• Sample Rate: A higher sample rate captures more audio detail. For Speech-to-Text applications, 16 kHz is generally sufficient because it effectively captures the frequency range of human speech. While higher sample rates may be beneficial for other audio applications, such as music or animal sounds, they don't provide additional value for transcribing human speech and only increase file size.
• Bit Depth: Higher bit depth provides better dynamic range. A minimum of 16-bit is recommended for Speech-to-Text applications.
• Compression: Lossless formats retain all audio details but result in larger files, while lossy formats reduce file size at the cost of some quality. The choice depends on the specific application's need for quality versus efficiency.

Best audio formats for speech-to-text

Let's dive into some of the most commonly used audio formats for Speech-to-Text and evaluate their suitability.

1. WAV (Waveform Audio File Format)

• Sample Rate: Up to 192 kHz
• Bit Depth: Up to 32-bit
• Compression: Uncompressed
• Suitability: Excellent

WAV is the industry standard for professional audio recording because it's uncompressed and preserves all audio details. This makes it ideal for speech-to-text applications where accuracy is paramount.

Key advantages:

• Maximum accuracy: Uncompressed format retains all speech details
• Professional compatibility: Supported by all major speech-to-text providers
• Flexible specifications: Supports high sample rates and bit depths

The trade-off is larger file sizes, but WAV delivers the best transcription results for applications like legal or medical documentation.

2. FLAC (Free Lossless Audio Codec)

• Sample Rate: Up to 655.35 kHz
• Bit Depth: Up to 32-bit
• Compression: Lossless
• Suitability: Excellent

FLAC offers lossless compression, meaning it reduces file size without any loss of audio quality. This makes it a strong candidate for Speech-to-Text applications where both quality and file size are important considerations. FLAC is especially useful when dealing with longer recordings, as it maintains the high fidelity of WAV files while being more manageable in size.

3. MP3 (MPEG Audio Layer-3)

• Sample Rate: Typically 44.1 kHz
• Bit Depth: 16-bit (effectively)
• Compression: Lossy
• Suitability: Good

MP3 is a ubiquitous audio format known for its efficient compression and decent sound quality. While it is a lossy format, meaning some audio data is discarded to reduce file size, MP3 files can still deliver good quality at higher bit rates (128 kbps and above). MP3 is a practical choice for general Speech-to-Text applications where file size is a concern, and extreme accuracy is not as critical.

4. AAC (Advanced Audio Coding)

• Sample Rate: Up to 96 kHz
• Bit Depth: 16-bit (effectively)
• Compression: Lossy
• Suitability: Good to Excellent

AAC is a more advanced lossy compression format than MP3, providing better sound quality at similar bit rates. It is widely used in streaming and digital broadcasting. AAC's efficiency makes it a good choice for Speech-to-Text applications, especially in environments where bandwidth or storage space is limited.

5. M4A (MPEG-4 Audio)

• Sample Rate: Up to 96 kHz
• Bit Depth: 16-bit (effectively)
• Compression: Typically lossy (can be lossless)
• Suitability: Good

M4A is often used for audio files encoded with AAC or Apple Lossless (ALAC). When encoded with AAC, it offers similar benefits to AAC in terms of quality and compression. M4A is a viable option, particularly when working with mobile devices or cloud-based transcription services.

Best video formats for speech-to-text

When dealing with video files for transcription, the format you choose is important. Video formats are containers that hold both video and audio streams, and the underlying codec used for compression and encoding plays a significant role in quality and file size.

MP4 is one of the best options due to its widespread compatibility and efficient compression. It typically uses AAC for audio, providing clear sound without creating overly large files, making it ideal for most transcription needs.

MOV is another excellent choice, especially for high-quality audio and video, often used in professional settings. However, MOV files tend to be larger, which could be a drawback for longer recordings.

AVI and MKV formats are versatile, supporting various codecs that can influence the audio quality and file size. AVI offers good quality but often at the cost of larger files, while MKV is flexible and supports multiple audio tracks, though it may not be as widely supported.

Finally, WMV is suitable for Windows environments, offering good compression, but its compatibility with transcription tools outside the Windows ecosystem can be limited.

In choosing the best video format, focus on those that offer high audio quality and compatibility with your transcription software. The codec used provides clear and accurate sound for the best transcription results.

Format compatibility across speech-to-text providers

While lossless formats like WAV and FLAC are technically superior for accuracy, their practical use depends on what your speech-to-text provider supports.

Provider compatibility overview:

• Major providers: AssemblyAI, Google Cloud Speech-to-Text, and AWS Transcribe support common formats (WAV, FLAC, MP3)
• Encoding variations: Sample rate, bit depth, and codec requirements can vary between APIs
• Implementation risk: Wrong specifications can force quality-degrading conversions

Always check your provider's documentation before building audio processing pipelines. At AssemblyAI, we've designed our API for developer flexibility, supporting a wide range of formats right out of the box.

Audio preprocessing: When it helps and when it hurts

The idea of "cleaning up" audio before feeding it into a speech recognition engine seems logical, but the reality is more nuanced. Let's explore how post-processing affects STT accuracy, including common practices like converting file formats and removing background noise.

Converting file formats: A misguided solution

A common misconception is that converting an audio file to a different format might improve its suitability for STT processing. For example, some might believe that converting a compressed MP3 file to an uncompressed WAV file will enhance the audio quality and thus improve transcription accuracy. However, this approach is misguided.

Why doesn't conversion help?

• No Gain in Quality: When you convert a lossy format like MP3 to a lossless format like WAV, the conversion doesn't magically restore lost data. The audio quality remains exactly the same as the original MP3 file.
• Potential Artifacts: Converting between formats, especially multiple times, can introduce unwanted artifacts or degradation when lossy file formats are involved, further complicating the STT process. It's best to work with the highest-quality original recording possible, rather than relying on conversions.

Removing background noise: Proceed with caution

Another common post-processing step is noise reduction. Intuitively, it makes sense to remove background noise to make the speech signal clearer for the STT system. However, this process can sometimes backfire.

Why can noise reduction worsen results?

• Speech Signal Distortion: Advanced noise reduction algorithms work by identifying and filtering out non-speech sounds, but in doing so, they might inadvertently distort the speech signal itself. These distortions can confuse STT algorithms, leading to errors in transcription.
• Loss of Contextual Clues: Background noise, when not overpowering, often contains contextual information that STT models can use to better understand the audio. Removing this noise can sometimes strip away these contextual clues, reducing the overall accuracy.

When post-processing helps

This isn't to say that all post-processing is detrimental. In fact, certain practices can be beneficial if done correctly:

• Volume Normalization: Ensuring consistent audio levels can help STT systems process the entire recording more uniformly, reducing errors caused by sudden volume changes.
• Trimming Silence or Filtering Speech: Removing long periods of silence can make the transcription process more efficient. Similarly, using a feature like AssemblyAI's Speech Threshold allows you to only transcribe files that contain a minimum percentage of spoken audio, which can save costs by filtering out silent or music-only files.
• Enhancing Speech Quality: If done carefully, some audio enhancement techniques, like boosting certain frequency ranges or clarifying speech intelligibility, can help improve transcription accuracy, but these should be applied with a clear understanding of their impact on the speech signal.

In summary, converting audio formats does not recover lost data and can introduce artifacts that degrade performance. Similarly, aggressive noise reduction can distort the speech signal and remove contextual cues, potentially worsening results. The best practice is to focus on capturing high-quality recordings from the start and use minimal, targeted post-processing.

Real-time and streaming considerations

Real-time transcription has different format requirements than pre-recorded files. The industry standard uses WebSocket connections with raw, uncompressed audio data.

Real-time format specifications:

• Data format: 16-bit PCM (raw, uncompressed)
• Connection type: WebSocket for instant data transmission
• Latency benefit: No compression/decompression delays

Some APIs also support streaming compressed formats like FLAC to reduce bandwidth usage. However, this introduces a trade-off, as both the client and the server need to perform additional processing to encode and decode the stream, which can add latency.

A robust streaming API, like AssemblyAI's Universal-Streaming model, is built to handle these complexities. It delivers immutable transcripts in ~300ms and uses intelligent endpointing to determine the most logical points to break up speech, allowing you to build responsive, real-time Voice AI features without getting bogged down in the low-level details.

Choosing the right format for your use case

Choose your audio format based on these three decision criteria:

• For maximum accuracy: If your top priority is getting the most accurate transcript possible, always use a lossless format like WAV or FLAC. For English audio, pairing a lossless format with a high-accuracy model like AssemblyAI's Slam-1 is ideal for use cases like legal transcription, medical dictation, or generating high-quality training data for your own AI models.
• For web and mobile apps: When you're dealing with user-generated content or need to balance quality with file size, a high-bitrate lossy format is often the most practical choice. MP3 (at 128 kbps or higher) or M4A (using AAC) provide good quality without creating massive files that are slow to upload and expensive to store.
• For archiving large volumes of audio: If you need to store thousands of hours of audio while preserving quality, FLAC is the clear winner. It offers the same lossless quality as WAV but with a significantly smaller file size, saving you on storage costs.

Ultimately, the best way to know for sure is to test. Different audio sources, recording environments, and speakers can affect performance. By understanding these trade-offs, you can make an informed choice that aligns with your product goals. If you're ready to see how different formats perform with our industry-leading AI models, you can try our API for free.

Frequently asked questions about audio formats for speech-to-text

What is the best audio format for transcription?

WAV and FLAC deliver the highest accuracy because they preserve all original audio data. For smaller file sizes, use MP3 at 128kbps or higher.

Is WAV or MP3 better for speech-to-text?

WAV is better for transcription accuracy because it is uncompressed and contains more audio detail for an AI model to analyze. MP3 is better for minimizing file size, but this comes at the cost of audio quality, which can reduce accuracy, especially in noisy environments.

Should I convert a lossy file like MP3 to a lossless format like WAV?

No, converting from MP3 to WAV doesn't restore lost audio data—you'll get a larger file with identical quality. Always work with the highest-quality original recording available.

What audio specifications are most important for accuracy?

For human speech, a 16 kHz sample rate and 16-bit depth are optimal. These settings effectively capture the full frequency range of the human voice without adding unnecessary file size, providing the best balance of quality and efficiency for speech-to-text transcription.