Lightning v3.1

Lightning v3.1 is a high-fidelity, low-latency text-to-speech model delivering natural, expressive, and realistic speech at 44 kHz. Optimized for real-time applications with ultra-low latency and voice cloning support, it delivers broadcast-quality audio with genuinely conversational characteristics. Now with 15 languages, automatic language detection, and code-switching.

Model Overview

Key Capabilities

Performance & Benchmarks

Audio Generation Evaluation

Full-sentence audio generation. The entire text is synthesized in a single pass and then evaluated.

Agent Call Evaluation

Chunk-by-chunk audio generation. Simulates real-world voice agent behavior where text is streamed and synthesized incrementally, as it happens during live calls.

Want to reproduce these results? See the TTS evaluation script to measure TTFB and synthesis quality in your own environment.

Supported Languages

Voice Catalog

English (US) — Best Voices

Hindi / English — Best Voices

Spanish — Best Voices

Other Indian Languages — Best Voices

Voice Cloning

API Reference

Endpoints

Request Parameters

Technical Specifications

Audio Output

Text Formatting Guidelines

Number & Date Handling

Best Practices

Code-Switching

Lightning v3.1 supports real-time intra-session language switching via two mutually exclusive language groups. Each group shares a unified phoneme space, enabling seamless mid-utterance transitions between member languages without session re-initialization. Cross-group switching is not supported within a single session.

Language Groups

Indic Group. Optimized for South Asian language pairs with English as the bridging language.

Global Group. Optimized for European language pairs with English and Hindi as bridging languages.

Intra-group switching is unrestricted. Any language within the same group can be interleaved at the token level. Cross-group switching (e.g., Tamil from Indic + French from Global) is architecturally unsupported and will produce undefined behavior.

Routing Examples

// Indic group — Hindi <-> Tamil interleaving
"Valid: all languages within Indic group"
// Global group — Spanish <-> French interleaving
"Valid: all languages within Global group"
// Cross-group — Tamil (Indic) + French (Global)
"Invalid: cross-group switching unsupported"

Voice Cloning

Reference Audio

• Environment. Record in a quiet room with no background noise, hiss, or rumble. Ambient sound is captured in the clone and cannot be removed after the fact.
• Speaking style. Speak naturally in your normal conversational voice. The model captures timbre, accent, emotional tone, rhythm, and pacing automatically. Do not exaggerate unless a specific tone is intended.
• Audio length. Provide 5 to 15 seconds of clean, continuous speech.

Multi-Lingual Cloning

• Language matching. For best results, record reference audio in the same language as your intended output. Cross-lingual cloning is supported (e.g., English reference used for Spanish output), but a language-matched reference produces higher fidelity.
• Accent retention. When synthesizing in a different language than the reference, the original accent is preserved. A clone from a South Indian English speaker will retain that accent in Hindi or Tamil output. This is by design: the clone reproduces your voice, including accent characteristics. For accent-neutral output in a specific language, provide reference audio from a native speaker of that language.
• Script encoding. Input text must use native script for each language (Devanagari for Hindi/Marathi/Gujarati, respective Brahmic scripts for Dravidian languages, Latin for European languages). Transliterated input degrades synthesis quality.
• Group constraint. Cloned voices follow the same language group routing rules. A session initialized in the Indic group cannot switch to Global-exclusive languages, regardless of the voice’s source language.

For detailed recording examples and expressive cloning techniques, see Voice Cloning Best Practices.

Text Formatting

• Chunk boundaries. Segment input at natural prosodic boundaries (. ! ? ,). Maximum chunk size is 250 characters; optimal throughput at 140 characters per request.
• Script integrity. Avoid transliteration. Use native script for each language. Mixed-script input within a single language token produces unpredictable phoneme mappings.
• Numeric normalization. Use standard formats (DD/MM/YYYY, HH:MM). Phone numbers default to 3-4-3 digit grouping.
• Lexicon overrides. Use pronunciation dictionaries for domain-specific terms, brand names, and acronyms where default grapheme-to-phoneme conversion is insufficient.

For comprehensive text formatting rules (numeric handling, date/time, symbols, chunking logic), see TTS Best Practices.

Use Cases

Direct Use

• Voice assistants and conversational AI
• Interactive chatbots with voice output
• Real-time narration and live streaming
• Accessibility tools and screen readers
• Gaming (dynamic character voices)
• Customer service automation

Downstream Use

• Multi-turn conversational agents
• Audio content generation pipelines
• Telephony and IVR systems
• Podcast and audiobook generation

Limitations & Safety

Known Limitations

• Mixed-language text (transliteration) may produce suboptimal results. Hindi text should be in Devanagari script (e.g., “namaste” in Devanagari), not Latin. English text should be in Latin script, not Devanagari. Each language should use its native script.

Safety & Compliance

• Voice cloning requires explicit consent
• No retention of synthesized audio
• No storage of personal voice data beyond cloning scope
• Usage monitoring for policy compliance

For compliance documentation (GDPR, SOC2, HIPAA), contact support@smallest.ai.