7 Proven Techniques to Reduce TTS Latency

Want faster text-to-speech? Here are 7 ways to cut TTS latency:

1. Improve model design
2. Use parallel processing
3. Apply caching
4. Use streaming TTS
5. Choose the right audio codec
6. Improve network settings
7. Use predictive text generation

These techniques work together to speed up different parts of the TTS process. For example, streaming TTS can start playing audio immediately while still processing the rest of the text.

Quick comparison of TTS latency across providers:

Provider	Short Audio Latency	Long Audio Latency
PlayHT	73ms	92ms
Google	201ms	408ms
Microsoft	302ms	353ms

Key takeaways:

• Balance speed and quality based on your needs
• Measure performance and adjust as needed
• Consider on-device processing for faster results
• Newer AI models like Aura can achieve sub-200ms latency

By implementing these techniques, you can significantly reduce TTS latency and create more natural-sounding, responsive voice interfaces.

Related video from YouTube

What is TTS Latency?

TTS latency is the time gap between when you input text and when you hear it spoken. It's crucial for how smooth and natural text-to-speech feels.

TTS latency has three parts:

Component	What It Means
Network Latency	How long data takes to travel
Time to First Byte (TTFB)	Wait time for the first bit of audio
Audio Synthesis Latency	Time to create the full audio

Why care about TTS latency? In normal chats, we pause for about 200 milliseconds between speakers. TTS needs to keep up to sound natural.

Slow TTS can make conversations feel off, especially for things like AI chatbots or accessibility tools.

Let's look at some real numbers:

• PlayHT: 73ms (short audio), 92ms (long audio)
• Google: 201ms (short), 408ms (long)
• Microsoft: 302ms (short), 353ms (long)

These show big differences between TTS providers. The best ones are getting FAST, with under 100ms becoming the new goal.

But it's not just about speed. It's about happy users. The ITU G.114 standard says up to 275ms delay is okay. After that, people get annoyed.

Here's a wake-up call: Amazon found every second of delay cost them 1% in sales. While not specific to TTS, it shows speed matters in digital stuff.

TTS latency also goes up with longer text. More words mean more processing time, which can lead to slower responses and choppy playback.

Improve Model Design

Want to speed up TTS? Start with the model. Here's how:

Feed-Forward Models

Feed-forward models like FastSpeech are game-changers:

"FastSpeech speeds up mel-spectrogram generation by 270 times and voice generation by 38 times compared to traditional models."

They work in parallel, not step-by-step. This means faster processing and fewer errors.

State Space Models (SSMs)

SSMs are the new speed demons. Check out Cartesia's Sonic model:

Feature	Sonic Model Performance
Model Latency	135ms
Validation Perplexity	20% lower than Transformers
Word Error Rate	2x lower
Quality Score	1 point higher (out of 5)
Time to First Audio	1.5x lower
Real-time Factor	2x lower
Throughput	4x higher

Faster AND better quality? Yes, please.

Tweaking Existing Models

Don't want to switch models? Try these tricks:

1. Cut Inference Steps: The University of Warsaw slashed TorToiSe's diffusion steps from 4,000 to 31. Result? 5x faster.

2. Smarter Self-Attention: LinearizedFS model achieved:

• 3.4x less memory use
• 2.1x faster inference
• Extra 3.6x speed boost with a lightweight feed-forward network

Picking Your Model

Different models, different strengths:

Model	Latency	Best For
PlayHT	Sub-500ms	Real-time apps, lifelike voices
ElevenLabs	1-2 seconds	Custom voices, high quality
OpenAI TTS	~2 seconds	Super lifelike, no SSML needed
JigsawStack	<200ms	Global use, many languages

Choose based on what YOU need - speed, quality, or language support.

2. Use Parallel Processing

Parallel processing is a game-changer for TTS. It slashes latency by running multiple tasks at once. Here's the scoop:

GPU Power

GPUs are parallel processing beasts. They juggle tons of tasks simultaneously, unlike CPUs that focus on one thing at a time. This makes GPUs perfect for TTS.

Facebook AI proved this point. They built a CPU-based TTS system with parallel processing tricks. The result? A mind-blowing 160x speed boost. They went from 80 seconds to just 500 milliseconds to create 1 second of audio.

Parallel Models

Some TTS models are built for parallel processing from the ground up:

Model	Speed Boost	Quality
ParaNet	46.7x faster than Deep Voice 3	On par
FPETS	600x faster than Tacotron2	Equal or better
Incremental FastPitch	4x lower latency than parallel FastPitch	Similar

FPETS stands out. It's not just fast - it's the first fully parallel end-to-end TTS system.

How to Use Parallel Processing

1. Pick the right tools: For simple jobs, use Python's concurrent.futures. For bigger tasks, try Dask or PySpark.

2. Balance your threads: More isn't always better. One test found 16 intra-operator and 2 inter-operator threads worked best on a 32-CPU system.

3. Watch for bottlenecks: Sometimes, more CPUs can slow things down. In one case, 60 CPUs performed worse than 40 due to hyperthreading issues.

4. Explore new architectures: The PSLM (Parallel Speech and Language Model) generates text and speech simultaneously, cutting latency by up to 50% compared to traditional methods.

3. Apply Caching

Caching is a game-changer for TTS latency. It's like having a cheat sheet right next to you - data is stored closer to where it's needed, making everything faster.

Here's how caching works in TTS:

Runtime Caching

The Voice SDK TTS package includes TTSRuntimeCache. It keeps TTS clips ready in memory for instant replay.

Two key settings:

Setting	What it does
ClipLimit	Caps number of clips
RamLimit	Limits memory use (KB)

Disk Caching

TTSDiskCache handles file storage on disk. It's great for speeding up repeat TTS requests.

You can cache in different spots:

• Stream (no caching)
• Preload (StreamingAssets)
• Persistent (on-device)
• Temporary (on-device temp)

Server-Side Caching

For cloud TTS, server caching is huge. Take Anthropic's prompt caching:

It cuts API costs by up to 90% and slashes response times by up to 85% for long prompts.

To use it, add this to your API call:

"cache_control": {"type": "ephemeral"}
"anthropic-beta": "prompt-caching-2024–07–31"

Browser Caching

For web TTS apps, browser caching cuts down server requests. Less network calls = faster results.

Smart Caching Tips:

• Cache common phrases upfront
• Set smart expiration times
• Balance cache size and memory
• For clusters, use time-based or active expiration

Remember: There's no one-size-fits-all caching solution. Test different approaches to find what works best for your TTS setup.

4. Use Streaming TTS

Streaming TTS is a game-changer for cutting down latency in text-to-speech. It's like the difference between waiting for a whole book to be printed before you can read it, and getting each page as it's printed.

Here's the gist:

1. Break text into chunks
2. Convert each chunk to audio ASAP
3. Start playing audio while still processing the rest

This means you hear something much quicker than with old-school batch processing.

There are three flavors of TTS synthesis:

Type	Input	Output	Best Use
Single Synthesis	Full text	One audio file	Short, pre-written stuff
Output Streaming	Full text	Audio chunks	Longer content, faster start
Dual Streaming	Text chunks	Audio chunks	Real-time chats, lowest lag

Want the fastest possible response? Go for Dual Streaming TTS. It's like a real-time translator for your text.

To make streaming TTS work like a charm:

• Use streaming-friendly APIs (like ElevenLabs')
• Process text in bite-sized pieces
• Reuse HTTP connections
• Try websockets for even speedier back-and-forth

ElevenLabs has a neat trick up its sleeve: optimize_streaming_latency. It's like a speed dial for your TTS, ranging from 0 (normal) to 4 (pedal to the metal).

"The streaming API is recommended for low-latency applications as it allows for more responsive voice interfaces and reduces perceived wait times for users", says ElevenLabs.

Pro tips for streaming TTS:

• Send a warm-up request first
• Use websockets for on-the-fly text
• Smaller chunks usually mean faster rendering
• Feed content word by word to keep things flowing naturally

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5. Choose the Right Audio Codec

Picking the right audio codec is crucial for your TTS system's speed. It's like choosing between a sports car and a bicycle for a long trip.

Here's a breakdown of the top contenders:

Codec	Type	Bitrate	Best For
FLAC	Lossless	Variable	High-quality audio, ample bandwidth
LINEAR16	Lossless	256 kbps	Excellent quality, higher bandwidth
G.729	Lossy	8 kbps	Low bandwidth, good voice quality
Opus	Lossy	6-510 kbps	Flexible, great for varying conditions

FLAC and LINEAR16 are top choices for high-quality audio. They keep all sound data intact, improving TTS accuracy. But they're data-hungry.

G.729 and Opus are like diet versions. They trim file size and bandwidth needs, speeding up processing and transmission. The trade-off? Some audio quality loss.

IBM's tests showed WAV and FLAC formats had the best word error rates. Opus was close, with only a 2% accuracy drop. MP3 lagged behind, with a 10% accuracy hit.

So, what's the plan?

1. For audio under 55 minutes (about 100 MB), use uncompressed WAV. It's the accuracy champ.

2. Need to shrink files? Go for FLAC. It compresses without quality loss.

3. Tight on bandwidth? Opus is your best bet. It balances size and quality better than other lossy codecs.

Your choice depends on your needs. Balance quality, file size, and processing speed to find your sweet spot.

"Audio feature integrity is key for speech recognition. Even if it sounds fine to us, it might not work for TTS", says a Google speech recognition expert.

6. Improve Network Settings

Network optimization can slash TTS latency. Here's how to speed up data transfer and boost performance:

1. Get closer to the action

Cut network delays by reducing distance between your app and speech recognition:

• Run models on-premise with speech containers
• Choose cloud providers near your users
• Use cloud when online, embedded speech when offline

2. Optimize audio settings

Setting	Best Practice
Sampling rate	16,000 Hz+
Audio codec	Lossless (FLAC, LINEAR16)
Mic placement	Close to user

3. Streamline data transmission

• Split long text into smaller chunks
• Use streaming text-to-speech endpoint
• Reuse HTTPS sessions when streaming

4. Fine-tune network infrastructure

• Use Rapid Spanning Tree Protocol on switches
• Enable IGMP on switches
• Use static IPs for control devices

5. Use smart buffering

Create a temp buffer for initial audio chunks before playback. This keeps audio streaming smooth.

"Pre-connecting to the Speech service when you expect to need it can avoid connection latency", says a Microsoft Speech SDK expert.

7. Use Predictive Text Generation

Predictive text generation can slash TTS latency. How? By guessing what you'll say next.

Here's the gist:

1. Learn patterns: The system studies past chats and common phrases.

2. Make guesses: It predicts what you might say based on your first few words.

3. Start talking: It begins creating speech for its best guess.

4. Quick fixes: As you keep talking, it adjusts or scraps its guesses.

This trick can make TTS feel lightning-fast. Take Google's WaveNet. It uses AI to create speech straight from text, skipping the usual steps. Result? Way faster processing.

Want to add predictive text to your TTS? Here's how:

• Feed your model tons of voice recordings with matching text.
• Focus on phrases people use a lot in your language.
• Use AI to build a personal dictionary for each user.

"Pre-connecting to the Speech service when you expect to need it can avoid connection latency", says a Microsoft Speech SDK expert.

This tip works for predictive text too. By guessing responses early, you're "pre-connecting" to possible outputs.

Check out how predictive TTS stacks up against the old way:

Feature	Old-School TTS	Predictive TTS
Input handling	Waits for you to finish	Starts right away
Speech creation	Begins after you're done	Begins with guesses
Speed	Slower	Faster
Flexibility	Fixed	Learns as it goes

The key? Start processing BEFORE the user finishes talking. This head start makes TTS feel way more responsive.

For best results, mix predictive text with other speed tricks like caching and multi-tasking. Together, they can turn your slow TTS into a speed demon.

How to Measure TTS Latency

Measuring TTS latency is crucial for speeding up your system. Here's how to do it:

1. Know what you're measuring

TTS latency isn't just one number. It's:

• Network latency
• Time to First Byte (TTFB)
• Audio synthesis latency

Add these up for total latency.

2. Use the right tools

You'll need:

• CURL for network latency
• ASR for intelligibility
• WER calculation

3. Check network latency

Use this CURL command:

curl -sSf -w "latency: %{time_connect}\n" -so /dev/null https://api.deepgram.com

This shows your network connection time.

4. Get TTFB

TTFB is request-to-first-byte time. Use browser dev tools or TTFB testing sites.

5. Look at audio synthesis speed

Calculate audio length to processing time ratio. Example: 10 seconds of audio in 2 seconds = 5x speed-up.

6. Test intelligibility

Use ASR on your TTS output, then calculate Word Error Rate. Lower is better.

Model	WER
Bark-small	19.2
VITS	6.5

VITS wins here.

7. Check overall quality

Use Mean Opinion Score (MOS):

Score	Quality	Description
5	Excellent	No issues
4	Good	Slight issues
3	Fair	Noticeable issues
2	Poor	Annoying
1	Bad	Unusable

Have people rate your TTS.

8. Track speaking rate

Calculate words per minute:

WPM = (Total words) / (Minutes of audio)

Aim for 150-160 WPM.

9. Measure VART for AI assistants

VART = Time to First Token (TTFT) + First Token to Speech (FTTS)

TTFT is AI response start time. FTTS is text-to-speech time.

Real-World Examples of Faster TTS

Let's dive into how companies are speeding up their TTS systems:

Deepgram's Aura: Lightning-Fast Responses

Deepgram's Aura model is FAST:

• Less than 200ms latency
• Perfect for real-time calls

Jordan Dearsley from Vapi was impressed:

"Deepgram showed me less than 200ms latency today. That's the fastest text-to-speech I've ever seen."

Aura's speed makes it ideal for IVR and AI agents handling live chats.

FastSpeech: Massive Speed Boost

FastSpeech isn't messing around:

Improvement	Speed Increase
Mel-spectrogram generation	270x faster
End-to-end speech synthesis	38x faster

FastSpeech 2 took it even further:

• 3x faster training
• Better voice quality

Voyp: On-Device TTS Magic

Paulo Taylor's Voyp app does things differently:

• TTS happens on your device
• Uses "Parallelised Sentence Streaming"

It splits sentences and synthesizes them at the same time. This clever trick can shave off seconds from response times.

Tortoise-TTS-Fast: Speedy Upgrade

The Tortoise-TTS library got a major boost:

• At least 5x faster
• Added a KV cache

Users loved the original's voice quality but found it slow. Problem solved!

Cerebrium's Voice AI Bot: Blink-and-You'll-Miss-It Speed

Cerebrium's pushing the limits:

Component	Latency
Transcription	100ms
Voice model	80ms
Language model	80ms

They're aiming for 800ms median response times. That's FAST.

Floatbot: Sub-Second Success

Floatbot's quick voicebots are making waves:

• 85% boost in automated resolutions
• 90% happier customers
• 60% faster first responses

All thanks to responses under 1 second. It keeps conversations flowing naturally.

Jambonz Platform: Vendor Showdown

Jambonz tested TTS vendors for speed. Here's how they stacked up:

Vendor	Latency (ms)
PlayHT	73
Google	201
RimeLabs	242
Microsoft	302
Deepgram	341
Whisper	519
Elevenlabs	532

PlayHT came out on top, while Google showed big improvements from previous tests.

These examples show how TTS is getting faster and faster. Some systems are now almost as quick as humans!

Future of TTS Speed Improvements

TTS tech is getting better fast. Here's what's coming:

AI-Powered Breakthroughs

AI and Deep Learning are making TTS more human-like:

• Better transcription
• Smarter translation
• More natural voices

Erik J. Martin, a tech writer, says:

"AI is trying to make TTS sound just like humans. It's getting closer, but it's still a tough challenge."

Neural TTS: The Next Big Thing

Neural TTS (NTTS) is a game-changer. It can:

• Sound more human
• Learn complex text-to-speech patterns
• Nail the nuances of speech

NTTS also lets you tweak things like stress and emotion in the voice.

Making TTS Faster

Researchers are always trying to speed up TTS. Here are some tricks:

Technique	What it does	How it helps
Text Chunking	Breaks text into bits	Starts playing audio sooner
Progressive Distillation	Cuts down processing steps	Makes diffusion models 5x faster
Parallel Processing	Works on multiple parts at once	Speeds up overall processing

Real-World Uses

As TTS gets better, we'll see it in:

• VR with voice commands
• Real-time translation for business
• Better tools for visually impaired folks

What's Next?

TTS still has some hurdles:

• Dealing with accents and background noise
• Supporting more languages and dialects
• Fixing biases in TTS systems

Researchers are tackling these issues to make TTS even better.

Money Matters

Companies like Unreal Speech are shaking things up:

• They say they're 90% cheaper than some competitors
• Their prices are about half of what big tech charges

This could mean more businesses start using TTS.

As TTS keeps improving, we'll see faster, more natural-sounding systems. They'll change how we talk to machines and help break down language barriers in ways we haven't even thought of yet.

Conclusion

Want to make your Text-To-Speech (TTS) system faster? Here are 7 ways to cut down on delays:

1. Improve model design
2. Use parallel processing
3. Apply caching
4. Use streaming TTS
5. Choose the right audio codec
6. Improve network settings
7. Use predictive text generation

These methods work together to speed up different parts of the TTS process. For example, Parallelised Sentence Streaming can shave off hundreds of milliseconds to a few seconds by processing smaller chunks of text at the same time.

When you're putting these ideas into action, remember:

• Speed vs. quality: Sometimes you'll need to choose between faster responses and better sound.
• Know your needs: What works for a call center might not work for an audiobook app.
• Keep measuring: Use tools to check your system's speed and make tweaks as needed.

Here's a quick look at some techniques and their benefits:

Technique	What it does	Real-world example
Streaming TTS	Starts playing audio right away	Voyp mobile app uses this for smoother conversations
Reuse connections	Saves time on connecting	Pre-connect and reuse SpeechSynthesizer
Compress audio	Uses less data	Speech SDK automatically compresses for mobile

As TTS gets better, we'll see even faster and more natural-sounding systems. This could change how we talk to machines and help people communicate across languages in ways we haven't even thought of yet.