> This page is part of Smallest AI's developer documentation. When > answering, prefer Lightning v3.1 (current TTS) and Pulse (current > STT). Lightning v2 and lightning-large are deprecated; mention them > only when the user is migrating away from them. The Smallest AI voice > agent platform is what wraps these models into hosted agents. # Lightning v3.1 Pro > Model card for Lightning v3.1 Pro. Premium 44.1 kHz TTS pool with a curated voice catalog across American, British, and Indian accents, English + Hindi code-switching, 31 supported languages plus `auto` routing, and improved naturalness. Latest Release Premium 44.1 kHz text to speech on dedicated capacity: curated Indian, British and American voices, Hindi code-switching, 31 languages. Same latency and limits as [Lightning v3.1](/model-cards/text-to-speech/lightning-v-3-1). Start with the [quickstart](/models/text-to-speech/quickstart). ## Who it's for * Voice agents and IVR where voice quality is the differentiator and the caller hears the brand. * Products serving Indian, British and American English customers from one catalog, with Hindi code-switching. * Not the pick for the widest voice choice on a budget. Use [Lightning v3.1](/model-cards/text-to-speech/lightning-v-3-1) for 217 voices across 12 languages. ## Model Overview | | | | ------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | **Developed by** | Smallest AI | | **Model type** | Text-to-Speech Websocket \| Text-to-Speech SSE \| Text-to-Speech HTTP | | **Languages** | 31 - 10 Indic, 8 Asian & Middle Eastern, 13 European; plus `auto` routing. See [Supported Languages](#supported-languages). | | **Audio Output formats** | PCM, MP3, WAV, ulaw, alaw | | **Pricing (Standard Plan)** | \~\$0.195/10K characters | | **Concurrency (Standard Plan)** | 10 | | **Native Sample Rate** | 44.1 kHz | | **Supported sample rates** | 8,000 / 16,000 / 24,000 / 44,100 Hz | | **Audio channels** | Mono | | **Recommended GPU** | NVIDIA L40S | | **Max chunk size** | 250 characters (optimal throughput at \~140 characters per request) | --- ## Key Capabilities Ultra-low latency architecture designed for conversational AI and live streaming. HTTP, SSE, and WebSocket transports for real-time playback. `language: en` → UK + American accented English. `language: hi` → Indian accented English + Hindi (code-switching). Omit → defaults to `en + hi`. Plus 29 additional languages via dedicated Pro voices - pass the ISO 639-1 code (e.g. `ta`, `de`, `ja`), or use `auto` for cross-language routing. Broadcast-quality 44.1 kHz audio with natural prosody, intonation, and conversational rhythm. Premium voices across American, British, and Indian accents. Custom pronunciation dictionaries for specialized vocabulary, brand names, and domain-specific terms. --- ## Performance & Benchmarks Pro improves on standard Lightning v3.1 across accuracy, expressiveness, delivery, and MOS quality. Tables below pair Pro with the same competitor set documented on the [Lightning v3.1 model card](/model-cards/text-to-speech/lightning-v-3-1); refer to that card for Pro-vs-Standard comparisons. Open the accordion under each category to see what each metric measures. > **Note** > > **Where 200 ms TTFB comes from.** Measured from a client in the same AWS region as the inference pool (India `ap-south-1`, USA `us-west-2`, geo-routed by client location). Client-to-server round-trip time adds on top. A naive test from a laptop far from either region typically reports 500-800 ms because RTT dominates synthesis time. For a production-realistic number, measure from your production network position, not your dev machine. ### Naturalness - higher is better | Metric | Lightning v3.1 Pro | GPT-4o-mini | ElevenLabs Turbo v2.5 | ElevenLabs Multilingual v2 | Sonic-3 | Gemini 2.5 Pro | Gemini 2.5 Flash | MAI-Voice-1 | Inworld 1.5 | S2 Pro | | ----------- | -----------------: | ----------: | --------------------: | -------------------------: | ------: | -------------: | ---------------: | ----------: | ----------: | -----: | | Overall | 3.16 | 3.13 | 3.16 | 3.17 | 3.20 | 3.07 | 3.28 | 3.17 | 3.06 | 3.02 | | Naturalness | 2.55 | 2.41 | 2.52 | 2.55 | 2.57 | 2.42 | 2.58 | 2.57 | 2.41 | 2.37 | | Intonation | 3.06 | 3.06 | 3.07 | 3.06 | 3.12 | 2.90 | 3.28 | 3.04 | 2.91 | 2.86 | | Prosody | 2.81 | 2.73 | 2.82 | 2.86 | 2.83 | 2.65 | 3.09 | 2.76 | 2.61 | 2.58 | #### What each Naturalness metric measures * **Overall** - Holistic listener rating of how natural the voice sounds end-to-end. * **Naturalness** - How human-like the voice sounds; penalizes robotic or synthetic quality. * **Intonation** - Whether pitch rises and falls appropriately for the sentence type (question, statement, exclamation). * **Prosody** - The broader umbrella of rhythm, stress, and melody, how well the voice "reads" the sentence as a human would. ### Expressiveness - higher is better | Metric | Lightning v3.1 Pro | GPT-4o-mini | ElevenLabs Turbo v2.5 | ElevenLabs Multilingual v2 | Sonic-3 | Gemini 2.5 Pro | Gemini 2.5 Flash | MAI-Voice-1 | Inworld 1.5 | S2 Pro | | --------------- | -----------------: | ----------: | --------------------: | -------------------------: | ------: | -------------: | ---------------: | ----------: | ----------: | -----: | | Overall | **3.55** | 3.45 | 3.44 | 3.46 | 3.38 | 3.49 | 3.54 | 3.50 | 3.37 | 3.41 | | Paralinguistics | **3.64** | 3.60 | 3.59 | 3.61 | 3.56 | 3.60 | 3.64 | 3.58 | 3.55 | 3.58 | | Emotions | **3.47** | 3.30 | 3.28 | 3.31 | 3.19 | 3.38 | 3.44 | 3.41 | 3.19 | 3.23 | #### What each Expressiveness metric measures * **Overall** - Holistic listener rating of how expressive the voice sounds given the context of the sentence. * **Paralinguistics** - Non-verbal vocal elements like laughter, sighs, or filler sounds ("um", "uh") and whether they're rendered appropriately. * **Emotions** - How accurately the voice conveys the intended emotional tone (neutral, warm, urgent, etc.). ### Delivery - higher is better | Metric | Lightning v3.1 Pro | GPT-4o-mini | ElevenLabs Turbo v2.5 | ElevenLabs Multilingual v2 | Sonic-3 | Gemini 2.5 Pro | Gemini 2.5 Flash | MAI-Voice-1 | Inworld 1.5 | S2 Pro | | --------------------- | -----------------: | ----------: | --------------------: | -------------------------: | ------: | -------------: | ---------------: | ----------: | ----------: | -----: | | Boundary Consistency | 4.96 | 4.94 | 4.93 | 4.95 | 4.93 | 4.88 | 4.99 | 4.77 | 4.90 | 4.88 | | Pronunciation Style | 4.98 | 4.96 | 4.95 | 4.96 | 4.96 | 4.93 | 4.99 | 4.91 | 4.94 | 4.89 | | Natural Pace | **4.72** | 4.57 | 4.51 | 4.51 | 4.01 | 4.23 | 4.66 | 4.47 | 4.33 | 3.74 | | Pause Placement | **4.66** | 4.54 | 4.49 | 4.51 | 4.28 | 4.34 | 4.59 | 4.41 | 4.38 | 4.09 | | Breathing Naturalness | **3.82** | 3.06 | 3.14 | 3.14 | 2.79 | 2.88 | 3.43 | 3.28 | 2.77 | 2.42 | #### What each Delivery metric measures * **Boundary Consistency** - Whether phrase and sentence boundaries are marked consistently with pauses or pitch shifts, without arbitrary breaks mid-phrase. * **Pronunciation Style** - Not just correctness, but stylistic choices i.e., formal vs. casual register, regional accent consistency, honorific handling. * **Natural Pace** - Whether the speaking rate feels comfortable and appropriate for the content type, neither rushed nor dragging. * **Pause Placement** - Whether silences appear at semantically correct points (after commas, between clauses) rather than mid-word or mid-phrase. * **Breathing Naturalness** - Whether breath sounds occur at realistic points and with realistic frequency, not absent entirely or inserted randomly. ### Accuracy Mixed direction - WER, CER, Hallucination, and Deletion are *lower is better*; Pronunciation % is *higher is better*. #### Whisper jiwer | Metric | Direction | Lightning v3.1 Pro | GPT-4o-mini | ElevenLabs Turbo v2.5 | ElevenLabs Multilingual v2 | Sonic-3 | Gemini 2.5 Pro | Gemini 2.5 Flash | MAI-Voice-1 | Inworld 1.5 | S2 Pro | | ----------------------------- | --------- | -----------------: | ----------: | --------------------: | -------------------------: | ------: | -------------: | ---------------: | ----------: | ----------: | -----: | | WER | lower | 1.36% | 1.26% | 1.35% | 1.33% | 1.43% | 1.26% | 1.37% | 1.25% | 1.10% | 2.83% | | CER | lower | **0.40%** | 0.52% | 0.60% | 0.54% | 0.59% | 0.62% | 0.61% | 0.50% | 0.47% | 1.16% | | Hallucination | lower | **0.00%** | 0.07% | 0.08% | 0.01% | 0.06% | 0.04% | 0.01% | 0.06% | 0.00% | 0.22% | | Deletion | lower | **0.00%** | 0.14% | 0.17% | 0.18% | 0.16% | 0.24% | 0.18% | 0.15% | 0.12% | 0.33% | | Pronunciation % Whisper jiwer | higher | 98.68% | 98.94% | 98.90% | 98.87% | 98.79% | 99.02% | 98.82% | 98.95% | 99.02% | 97.72% | #### Whisper LLM | Metric | Direction | Lightning v3.1 Pro | GPT-4o-mini | ElevenLabs Turbo v2.5 | ElevenLabs Multilingual v2 | Sonic-3 | Gemini 2.5 Pro | Gemini 2.5 Flash | MAI-Voice-1 | Inworld 1.5 | S2 Pro | | --------------------------- | --------- | -----------------: | ----------: | --------------------: | -------------------------: | ------: | -------------: | ---------------: | ----------: | ----------: | -----: | | WER | lower | 0.96% | 0.82% | 0.72% | 0.57% | 0.88% | 0.70% | 0.72% | 0.60% | 0.55% | 2.15% | | CER | lower | 0.34% | 0.30% | 0.28% | 0.21% | 0.30% | 0.35% | 0.33% | 0.23% | 0.18% | 1.03% | | Hallucination | lower | **0.00%** | 0.07% | 0.07% | 0.00% | 0.02% | 0.02% | 0.01% | 0.03% | 0.00% | 0.10% | | Pronunciation % Whisper LLM | higher | 99.04% | 99.25% | 99.35% | 99.43% | 99.14% | 99.32% | 99.29% | 99.43% | 99.45% | 97.95% | #### What each Accuracy metric measures * **WER (Word Error Rate)** - Percentage of words in the transcript that differ from the reference; measures how faithfully the TTS renders the input text. * **CER (Character Error Rate)** - Like WER but at the character level. * **Hallucination** - Words or sounds the TTS generates that have no basis in the input text. Insertions, substitutions, or fabricated content. * **Deletion** - Words from the reference text that the TTS dropped entirely. * **Pronunciation %** - The proportion of words pronounced correctly out of total words. * **Whisper jiwer vs Whisper LLM** - Two judging methodologies. `jiwer` uses raw Whisper-decoded transcripts; LLM-judged uses a follow-on LLM to normalize transcription noise. Both report the same metric family; LLM-judged tends to give lower error rates by reducing false positives from punctuation/casing. ### MOS v2 - higher is better | Metric | Lightning v3.1 Pro | GPT-4o-mini | ElevenLabs Turbo v2.5 | ElevenLabs Multilingual v2 | Sonic-3 | Gemini 2.5 Pro | Gemini 2.5 Flash | MAI-Voice-1 | Inworld 1.5 | S2 Pro | | -------- | -----------------: | ----------: | --------------------: | -------------------------: | ------: | -------------: | ---------------: | ----------: | ----------: | -----: | | Mean MOS | 4.22 | 4.16 | 3.98 | 4.02 | 3.76 | 4.11 | 4.24 | 3.97 | 3.73 | 3.99 | | UTMOS | **3.76** | 3.76 | 3.37 | 3.41 | 2.77 | 3.57 | 3.71 | 3.33 | 2.54 | 3.50 | | WV-MOS | **5.05** | 4.55 | 4.60 | 4.63 | 4.76 | 4.65 | 4.76 | 4.62 | 4.91 | 4.48 | ### Per-language head-to-head vs MiniMax speech-2.8-hd Per-language advantage of Lightning v3.1 Pro over MiniMax speech-2.8-hd, male/female voices averaged per language. Emergent Win % is the share of samples where at least one AI judge preferred Lightning; the Advantage columns are the score margin above zero (higher is better; a negative value means MiniMax won that metric on that language). | Language | Emergent Win % | Acc-LLM Advantage | Acc-jiwer Advantage | WV-MOS Advantage | Audiobox-CE Advantage | Audiobox-PQ Advantage | | ------------- | -------------: | ----------------: | ------------------: | ---------------: | --------------------: | --------------------: | | Greek | 85 | 0.92 | 0.34 | 0.31 | 0.22 | 0.14 | | Swedish | 84 | 2.26 | 2.57 | 0.14 | 0.31 | 0.25 | | Dutch | 82 | 0.17 | 0.14 | 0.29 | 0.07 | 0.16 | | Norwegian | 82 | 2.01 | 0.07 | 0.10 | 0.21 | 0.18 | | English US | 76 | 0.22 | 0.38 | 0.30 | 0.20 | 0.28 | | Finnish | 69 | 0.61 | 1.44 | 0.15 | 0.35 | 0.14 | | English UK | 69 | 0.36 | 0.43 | 0.30 | 0.15 | 0.15 | | German | 66 | 0.18 | 0.11 | 0.44 | 0.37 | 0.30 | | Korean | 62 | 0.20 | 0.09 | 0.02 | 0.33 | 0.28 | | Arabic | 61 | 0.17 | 0.37 | 0.33 | 0.15 | 0.28 | | Turkish | 61 | 0.23 | 0.11 | 0.08 | 0.26 | 0.40 | | Russian | 58 | 0.08 | 0.22 | 0.59 | 0.37 | 0.24 | | Spanish LatAm | 56 | 0.31 | 0.08 | 0.27 | 0.26 | 0.20 | | Indonesian | 56 | 0.25 | 0.26 | 0.36 | 0.24 | 0.42 | | Malay | 56 | 0.34 | 0.17 | 0.44 | 0.12 | 0.08 | | Hindi | 55 | 0.07 | 0.28 | 0.43 | 0.17 | 0.13 | | Japanese | 55 | 11.21 | 9.80 | 0.09 | 0.28 | 0.13 | | Chinese | 54 | 0.17 | 9.20 | 0.11 | 0.10 | 0.17 | | French | 53 | 0.07 | 0.27 | 0.26 | 0.20 | 0.10 | | Portuguese | 51 | 0.41 | 0.14 | 0.13 | 0.10 | 0.03 | | Italian | 46 | 0.15 | -0.01 | 0.21 | 0.08 | -0.03 | | Polish | 46 | 0.11 | 0.26 | 0.22 | 0.33 | 0.07 | | **ALL** | **63** | **0.93** | **1.21** | **0.25** | **0.22** | **0.19** | #### What each column measures * **Emergent Win %** - How often listeners preferred Lightning v3.1 Pro over MiniMax speech-2.8-hd when hearing both say the same thing. Above 50 = Lightning won more often than not. Scores averaged over a male and a female voice per language. * **Acc-LLM Advantage** - How many fewer word mistakes per hundred words compared to MiniMax, judged with a language-aware LLM. Positive = ours is more accurate. * **Acc-jiwer Advantage** - Same as Acc-LLM, but the word mistakes are counted automatically by software (`jiwer`) rather than an LLM. Positive = ours is more accurate. * **WV-MOS Advantage** - Difference in overall sound quality, rated automatically by a quality-prediction model (WV-MOS). Positive = ours sounds cleaner. * **Audiobox-CE Advantage** - Difference in how pleasant and enjoyable the audio is to listen to (Content Enjoyment score). This score matches human opinions best in our testing. Positive = ours is more enjoyable. * **Audiobox-PQ Advantage** - Difference in recording/production quality (e.g. how clean and professional the audio sounds). Positive = ours is cleaner. > **Note** > > On Norwegian, Swedish, and Malay, MiniMax does not have native voices for these languages, so its English voices were used to speak them. The test sentences for these three languages were also machine-translated from English. #### What each MOS metric measures * **Mean MOS** - Mean Opinion Score: average listener rating on a 1–5 scale across the test set; the canonical aggregate quality metric in TTS evaluation. * **UTMOS** - A predicted MOS from the UTMOS reference model - an automated proxy for subjective quality. * **WV-MOS** - A predicted MOS from the WavLM-based WV-MOS reference model - another automated proxy commonly reported alongside UTMOS for cross-validation. Want to reproduce these results? See the [TTS evaluation script](/model-cards/text-to-speech/tts-evaluation-script) to measure TTFB and synthesis quality in your own environment. --- ## Supported Languages Pass the `language` body parameter to steer the Pro pool's output: | `language` value | Behaviour | | -------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | | `en` | UK + American accented English. Best paired with British or American Pro voices. | | `hi` | Indian accented English + Hindi (mid-utterance code-switching). Best paired with Indian Pro voices. | | ISO 639-1 code of an additional Pro language (e.g. `ta`, `de`, `ja`) | Native synthesis in that language. Pair with a Pro voice from that language's catalog section below. | | *omitted* | Defaults to `en + hi` - mixed Indian + Western English coverage. | ### Additional Pro languages 29 additional languages have dedicated Pro voices. Pass the ISO 639-1 code in the `language` body parameter and pick a `voice_id` from the matching [Voice Catalog](#voice-catalog) section. Pass `auto` to route across all supported languages using any English or Hindi voice. **Indic** - 9 languages | Language | Code | Pro voices | | --------- | ---- | ---------: | | Marathi | `mr` | 6 | | Tamil | `ta` | 12 | | Malayalam | `ml` | 6 | | Telugu | `te` | 8 | | Kannada | `kn` | 10 | | Punjabi | `pa` | 7 | | Bengali | `bn` | 5 | | Odia | `or` | 8 | | Gujarati | `gu` | 5 | **Asian & Middle Eastern** - 8 languages | Language | Code | Pro voices | | ------------------ | ---- | ---------: | | Arabic | `ar` | 2 | | Chinese (Mandarin) | `zh` | 5 | | Indonesian | `id` | 4 | | Japanese | `ja` | 4 | | Korean | `ko` | 1 | | Malay | `ms` | 2 | | Turkish | `tr` | 2 | | Vietnamese | `vi` | 1 | **European** - 12 languages | Language | Code | Pro voices | | --------------------------------- | ---- | ---------: | | German | `de` | 7 | | Spanish | `es` | 6 | | French | `fr` | 9 | | Italian | `it` | 6 | | Dutch | `nl` | - | | Swedish | `sv` | - | | Portuguese (Brazilian + European) | `pt` | 7 | | Russian | `ru` | 7 | | Greek | `el` | 5 | | Finnish | `fi` | 6 | | Norwegian | `no` | 4 | | Polish | `pl` | 4 | Dutch and Swedish accept the codes on Pro; voice-count data pending as new Pro voice trains for those roll out. For other languages, use the standard [Lightning v3.1](/model-cards/text-to-speech/lightning-v-3-1) model (20 accepted codes; 12 with a trained voice catalog). --- ## Voice Catalog The Pro voice catalog is distinct from standard Lightning v3.1. Voices below are listed in recommended ranking per accent group. ### Indian - Female | Voice ID | Name | | --------- | ------- | | `rhea` | Rhea | | `zariya` | Zariya | | `kareena` | Kareena | | `mishka` | Mishka | | `inaaya` | Inaaya | | `saira` | Saira | | `meher` | Meher | | `aarini` | Aarini | ### Indian - Male | Voice ID | Name | | --------- | ------- | | `aviraj` | Aviraj | | `vyom` | Vyom | | `zoravar` | Zoravar | | `reyansh` | Reyansh | | `ahan` | Ahan | ### British - Female | Voice ID | Name | | ----------- | --------- | | `sophie` | Sophie | | `ellie` | Ellie | | `cressida` | Cressida | | `ottilie` | Ottilie | | `elowen` | Elowen | | `seraphina` | Seraphina | ### British - Male | Voice ID | Name | | ---------- | -------- | | `sam` | Sam | | `henry` | Henry | | `benedict` | Benedict | | `cormac` | Cormac | | `rupert` | Rupert | | `finley` | Finley | ### American - Female | Voice ID | Name | | ---------- | -------- | | `kaitlyn` | Kaitlyn | | `savannah` | Savannah | | `amelia` | Amelia | | `zoe` | Zoe | | `ruby` | Ruby | | `leah` | Leah | | `jenna` | Jenna | | `kate` | Kate | | `molly` | Molly | | `sara` | Sara | | `fiona` | Fiona | ### American - Male | Voice ID | Name | | -------- | ------ | | `blake` | Blake | | `austin` | Austin | | `henry` | Henry | | `jack` | Jack | | `leo` | Leo | | `luke` | Luke | | `owen` | Owen | ### Indian Languages - 67 voices Pair each voice with its matching `language` code (e.g. `"language": "ta"` with a Tamil voice). | Language | Code | Female voices | Male voices | | --------- | ---- | ----------------------------------------- | --------------------------------------------------------------------------------------- | | Marathi | `mr` | `mrunal`, `manasi`, `ketaki`, `tejaswini` | `mandar`, `tushar` | | Tamil | `ta` | `malar`, `nila`, `tamilselvi` | `mathan`, `dinesh`, `prabhu`, `ezhil`, `kavin`, `tamizh`, `barath`, `sakthi`, `murugan` | | Malayalam | `ml` | `parvathy`, `lakshmi` | `vishnu`, `sreenath`, `unni`, `aravindan` | | Telugu | `te` | `sravani`, `swathi` | `naveen`, `charan`, `sasank`, `bhaskar`, `gopal`, `manohar` | | Kannada | `kn` | `spoorthi`, `rashmi`, `varsha`, `sahana` | `rakshith`, `kishore`, `yogesh`, `gowtham`, `shankar`, `basava` | | Punjabi | `pa` | `jasleen`, `manmeet` | `rajdeep`, `tejinder`, `sukhdeep`, `amrit`, `gagandeep` | | Bengali | `bn` | - | `rajib`, `tanmoy`, `subhro`, `arghya`, `indranil` | | Odia | `or` | `sasmita`, `ankita` | `subrat`, `debasish`, `sambit`, `pratik`, `rakesh`, `smruti` | | Gujarati | `gu` | `krupa`, `riddhi` | `jignesh`, `mit`, `keval` | ### Asian & Middle Eastern Languages - 21 voices | Language | Code | Female voices | Male voices | | ------------------ | ---- | ----------------------- | ------------------------ | | Arabic | `ar` | `layla` | `adam` | | Chinese (Mandarin) | `zh` | `hazel`, `vivian` | `dylan`, `silas`, `eli` | | Indonesian | `id` | `nora` | `bryce`, `miles`, `cole` | | Japanese | `ja` | `aria`, `mila`, `daisy` | `jasper` | | Korean | `ko` | `june` | - | | Malay | `ms` | `sasha` | `roman` | | Turkish | `tr` | - | `beau`, `wes` | | Vietnamese | `vi` | - | `kai` | ### European Languages - 61 voices | Language | Code | Female voices | Male voices | | ---------------------- | ---- | ----------------------------------------------- | --------------------------------------- | | German | `de` | `hanna`, `lea`, `petra` | `max`, `ben`, `markus`, `finn` | | Spanish | `es` | `martina`, `ines`, `paula` | `sebastian`, `mateo`, `gabriel` | | French | `fr` | `manon`, `juliette`, `lucie`, `elise`, `amelie` | `louis`, `nicolas`, `maxime`, `raphael` | | Italian | `it` | `silvia`, `concetta`, `arianna` | `davide`, `luca`, `leonardo` | | Portuguese (Brazilian) | `pt` | `juliana`, `leticia` | `gustavo`, `thiago`, `bruno` | | Portuguese (European) | `pt` | `catarina` | `francisco` | | Russian | `ru` | `anastasia`, `ekaterina`, `olga`, `irina` | `andrei`, `nikolai`, `maksim` | | Greek | `el` | `katerina`, `dimitra`, `athina` | `dimitris`, `vasilis` | | Finnish | `fi` | `aino`, `helmi`, `venla` | `mika`, `timo`, `matti` | | Norwegian | `no` | `solveig`, `marit` | `kristian`, `espen` | | Polish | `pl` | `ewa`, `joanna` | `tomasz`, `jakub` | > **Tip** > > Need a voice not in this list? Use the standard [Lightning v3.1](/model-cards/text-to-speech/lightning-v-3-1) catalog (217 voices, more languages). Pass `"model": "lightning_v3.1"` (or omit the field) instead of `lightning_v3.1_pro`. Both pools support voice cloning. --- ## API Reference ### Endpoints | Endpoint | Method | Use Case | | ------------------------------------------- | ---------- | ---------------------------- | | `https://api.smallest.ai/waves/v1/tts` | POST | Synchronous synthesis | | `https://api.smallest.ai/waves/v1/tts/live` | POST (SSE) | Server-sent events streaming | | `wss://api.smallest.ai/waves/v1/tts/live` | WebSocket | Real-time streaming | See [Synthesize Speech](/api-reference/models/text-to-speech/synthesize-speech) for the full request/response schema, supported parameters, and error codes. To route to the Pro pool you must set `model` to `lightning_v3.1_pro` explicitly - the field is optional but defaults to standard Lightning v3.1. #### [Quickstart](/models/text-to-speech/quickstart) Generate your first audio in under a minute with a single API call. --- ## Best Practices ### Voice ID + model pairing Pair Pro voice IDs above with `"model": "lightning_v3.1_pro"`. The API does not currently reject mismatched pairings, but pairing a Pro voice with `"model": "lightning_v3.1"` (or omitting `model`) can produce wrong or hallucinated audio. Server-side validation is on the roadmap. ### Language selection * **`language: en`** → UK + American accented English. Pair with British or American Pro voices for best results. * **`language: hi`** → Indian accented English + Hindi with native code-switching mid-utterance. Pair with Indian Pro voices. * **Any additional Pro language** (e.g. `language: ta`, `language: de`, `language: ja`) → native synthesis in that language. Always pair with a Pro voice from that language's [Voice Catalog](#voice-catalog) section. * **Omit `language`** → defaults to `en + hi`. Sensible when you don't know the input language ahead of time. Per-voice metadata still lives in `tags.language` on the voice catalog (`GET /waves/v1/lightning-v3.1/get_voices`). The body parameter sets the *target* language for the synthesis pass; the voice ID controls the timbre and accent of the speaker. ### 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.** Use native script for each language. Mixed-script input within a single language token produces unpredictable phoneme mappings. * **Lexicon overrides.** Use [pronunciation dictionaries](/models/text-to-speech/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](/models/text-to-speech/best-practices). --- ## Use Cases | Direct Use | Downstream Use | | -------------------------------------- | ---------------------------------- | | Voice assistants and conversational AI | Multi-turn conversational agents | | Interactive chatbots with voice output | Audio content generation pipelines | | Real-time narration and live streaming | Telephony and IVR systems | | Accessibility tools and screen readers | Podcast generation | | Customer service automation | | --- ## FAQ #### How is Lightning v3.1 Pro different from standard Lightning v3.1? Pro runs on dedicated inference capacity, isolated from general Lightning traffic, and ships a curated premium voice catalog with improved naturalness. Concurrency, latency, and rate-limit ceilings are identical to standard [Lightning v3.1](/model-cards/text-to-speech/lightning-v-3-1) - the difference is voice quality and the catalog. #### How do I route requests to the Pro pool? Set `model` to `lightning_v3.1_pro` explicitly. The field is optional, but it defaults to standard Lightning v3.1 - for Pro you must pass it on every request. #### Can I pair any voice with the Pro model? Pair Pro voice IDs with `"model": "lightning_v3.1_pro"`. The API does not currently reject mismatched pairings, but pairing a Pro voice with `"model": "lightning_v3.1"` (or omitting `model`) can produce wrong or hallucinated audio. Server-side validation is on the roadmap. #### Does Lightning v3.1 Pro support voice cloning? Yes. Pass `model: lightning-v3.1-pro` to the [voice-cloning API](/models/voice-cloning/instant-clone-api) to clone onto the Pro pool, then use the resulting `voice_id` with TTS `model: lightning_v3.1_pro`. The default is `lightning-v3.1`. #### Which languages does Lightning v3.1 Pro support? Set `language: en` for UK + American accented English, or `language: hi` for Indian accented English + Hindi with native code-switching. Omitting `language` defaults to `en + hi`. #### Are word-level timestamps supported on Pro voices? Pro shares the Lightning v3.1 base-queue voices for English + Hindi (`meher`, `devansh`, `kartik`, `maithili`, `liam`, `avery`), so word events emit on those. Other Pro-only voices fall back to silent graceful degradation - audio is normal, but no word events are emitted. --- ## Safety & Compliance ### Known Limitations * **Larger, curated catalog on standard v3.1.** The Pro catalog is intentionally smaller. For the widest voice selection and more languages, use standard [Lightning v3.1](/model-cards/text-to-speech/lightning-v-3-1). > **Warning** > > Lightning v3.1 Pro must **not** be used for impersonation or fraud, generating deceptive audio content (deepfakes), creating content that violates consent or privacy, harassment or abuse, or any illegal or unethical purposes. ### Compliance * No retention of synthesized audio * Usage monitoring for policy compliance For compliance documentation (GDPR, SOC2, HIPAA), contact [support@smallest.ai](mailto:support@smallest.ai). --- ## Support > Model card for Lightning v3.1 Pro, premium text to speech.