--- title: "Indic Tiny TTS: Hindi" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/689a3aa1-dbbe-4eb0-a816-383030be1806" updated: "2026-11-13" --- # Indic Tiny TTS: Hindi > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads Hindi text in Devanagari script aloud with a female or a male voice, small enough to run on a CPU without a GPU. Made for developers adding Hindi speech to apps. Indic Tiny TTS: Hindi is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by sulabhkatiyar ## About ### What it is Indic Tiny TTS: Hindi is a small text-to-speech model for Hindi. It reads Devanagari script in either a female or a male voice and has 22.5 million parameters. It runs with PyTorch or ONNX Runtime and needs no GPU. ### Who it suits It suits developers who want Hindi speech on ordinary hardware, and researchers who compare small speech models by error rate. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Read Hindi text aloud on devices without a GPU | Speaking a news sentence inside an app | | Media | Content creators | Voice Hindi narration in a female or a male voice | Recording narration for a short video | | Education | Language learning builders | Add spoken Hindi to lessons | Playing practice sentences to a learner | | Research | Speech researchers | Compare small models by character error rate | Reading the card's benchmark tables | You can skip it if you need any language other than Hindi, since the model speaks Hindi only. ### Where to use it - Python, with PyTorch or ONNX Runtime - CPU-only machines, including an Apple M1 laptop and an x86 CPU - An NVIDIA T4 GPU, which the card also measures - Windows, macOS or Linux, once the files are downloaded | Use case | Who | What they get back | | --- | --- | --- | | Hindi voice over | Creators | Spoken audio from Hindi text in one of two voices | | App speech output | Developers | Fast speech on a CPU without a GPU | | Model comparison | Researchers | Error rates against other open Hindi models | ### How to set it up - Python, to run the Hugging Face download tool - The huggingface\_hub package - PyTorch or ONNX Runtime, to run the model 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Run the model with PyTorch or ONNX Runtime, following the full card. ```sh pip install -U huggingface_hub hf download sulabhkatiyar/indic-tiny-tts-hindi ``` ### How to use it You give the model Hindi text written in Devanagari script and choose the female or the male voice. The part of the card shown here has no run command, so follow the run steps in the full card. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | PyTorch | Runs the model, on a CPU or a GPU | | ONNX Runtime | Another way to run the model on a CPU | ### Example You pass one Hindi news sentence in Devanagari script and pick the male voice. The model returns spoken audio of that sentence, and on a single Apple M1 CPU thread it speaks about 38 times faster than real time. ### For newcomers and experts If you are new, download the files with the two commands above and open the card's Listen section to hear its samples. Then try one short Hindi sentence of your own. If you know the field, it was trained on the 8-hour-per-voice Hindi subset of AI4Bharat Rasa at 24 kHz. The card measures character error rate on news sentences and held-out sentences. The trade-off is one language and two voices. You give Hindi text in Devanagari script → You choose the female or the male voice → The model synthesizes the speech → You get audio back ### Good to know Character error rate on news sentences, lower is better | Label | Value | | --- | --- | | This model | 1.64% | | Kokoro | 1.56% | | Indic Parler TTS | 2.23% | | Coqui XTTS | 2.37% | | MMS Hindi | 3.83% | | SYSPIN VITS Hindi | 5.58% | Speed, times faster than real time | Label | Value | | --- | --- | | Apple M1 CPU, 1 thread | 38 x real time | | Intel Xeon CPU, 1 thread | 7 x real time | | NVIDIA T4 GPU | 62 x real time | - Languages: Hindi only, with one female and one male voice - Collection: one of 12 tiny open models for 11 Indian languages and Indian English - Training data: 8 hours per voice of Hindi female and male speech from AI4Bharat Rasa - License: CC BY 4.0 - Cost to run: none named, and no GPU is needed - Data it reaches: the Hindi text you give it, the audio it makes, and the files you download from Hugging Face - Not for: languages other than Hindi ## README ````markdown # Indic Tiny TTS: Hindi Reads Hindi text in Devanagari script aloud with a female or a male voice, small enough to run on a CPU without a GPU. Made for developers adding Hindi speech to apps. Made by sulabhkatiyar on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Indic Tiny TTS: Hindi is a small text-to-speech model for Hindi. It reads Devanagari script in either a female or a male voice and has 22.5 million parameters. It runs with PyTorch or ONNX Runtime and needs no GPU. Who it suits: It suits developers who want Hindi speech on ordinary hardware, and researchers who compare small speech models by error rate. ```table Industry | Who uses it | How they use it | Example Software | App developers | Read Hindi text aloud on devices without a GPU | Speaking a news sentence inside an app Media | Content creators | Voice Hindi narration in a female or a male voice | Recording narration for a short video Education | Language learning builders | Add spoken Hindi to lessons | Playing practice sentences to a learner Research | Speech researchers | Compare small models by character error rate | Reading the card's benchmark tables ``` You can skip it if you need any language other than Hindi, since the model speaks Hindi only. Where to use it: - Python, with PyTorch or ONNX Runtime - CPU-only machines, including an Apple M1 laptop and an x86 CPU - An NVIDIA T4 GPU, which the card also measures - Windows, macOS or Linux, once the files are downloaded ```table Use case | Who | What they get back Hindi voice over | Creators | Spoken audio from Hindi text in one of two voices App speech output | Developers | Fast speech on a CPU without a GPU Model comparison | Researchers | Error rates against other open Hindi models ``` How to set it up: - Python, to run the Hugging Face download tool - The huggingface_hub package - PyTorch or ONNX Runtime, to run the model 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Run the model with PyTorch or ONNX Runtime, following the full card. ```sh pip install -U huggingface_hub hf download sulabhkatiyar/indic-tiny-tts-hindi ``` How to use it: You give the model Hindi text written in Devanagari script and choose the female or the male voice. The part of the card shown here has no run command, so follow the run steps in the full card. ```table Tool | What it does hf download | Fetches the model files to your computer PyTorch | Runs the model, on a CPU or a GPU ONNX Runtime | Another way to run the model on a CPU ``` Example: You pass one Hindi news sentence in Devanagari script and pick the male voice. The model returns spoken audio of that sentence, and on a single Apple M1 CPU thread it speaks about 38 times faster than real time. For newcomers and experts: If you are new, download the files with the two commands above and open the card's Listen section to hear its samples. Then try one short Hindi sentence of your own. If you know the field, it was trained on the 8-hour-per-voice Hindi subset of AI4Bharat Rasa at 24 kHz. The card measures character error rate on news sentences and held-out sentences. The trade-off is one language and two voices. ```flow You give Hindi text in Devanagari script You choose the female or the male voice The model synthesizes the speech You get audio back ``` Good to know: ```chart Title: Character error rate on news sentences, lower is better This model: 1.64% Kokoro: 1.56% Indic Parler TTS: 2.23% Coqui XTTS: 2.37% MMS Hindi: 3.83% SYSPIN VITS Hindi: 5.58% ``` ```chart Title: Speed, times faster than real time Apple M1 CPU, 1 thread: 38 x real time Intel Xeon CPU, 1 thread: 7 x real time NVIDIA T4 GPU: 62 x real time ``` - Languages: Hindi only, with one female and one male voice - Collection: one of 12 tiny open models for 11 Indian languages and Indian English - Training data: 8 hours per voice of Hindi female and male speech from AI4Bharat Rasa - License: CC BY 4.0 - Cost to run: none named, and no GPU is needed - Data it reaches: the Hindi text you give it, the audio it makes, and the files you download from Hugging Face - Not for: languages other than Hindi ## On Hugging Face, 13 November 2026 - Task: text to speech - Parameters: 8.9M - Weights: about 36 MB in F32; about 4 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 508 - Likes: 2 - License: cc-by-4.0 - Languages: hi - Tags: onnx, safetensors, text-to-speech, hindi, speech-synthesis, on-device ## Links - Model: https://huggingface.co/sulabhkatiyar/indic-tiny-tts-hindi - Files: https://huggingface.co/sulabhkatiyar/indic-tiny-tts-hindi/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download sulabhkatiyar/indic-tiny-tts-hindi ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [sulabhkatiyar/indic-tiny-tts-hindi on Hugging Face](https://huggingface.co/sulabhkatiyar/indic-tiny-tts-hindi) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Indic Tiny TTS: Hindi (this page) | 508 | 2 | 8.9M | cc-by-4.0 | 2026-11-04 | | [Supertonic 3](https://mindket.com/p/910af7e2-128f-445f-bcb1-310264314e6d) | 92,359 | 1,043 | — | openrail | 2026-05-18 | | [ZeroTTS](https://mindket.com/p/97fb68f6-db3d-4745-9202-2956cafdf46f) | 8,091 | 54 | — | mit | 2026-10-31 | | [Klein-TTS-X1.5-Pro](https://mindket.com/p/c2bfb923-e753-414e-9acd-851872b8e3e6) | 258 | 10 | 13.1M | apache-2.0 | 2026-11-03 | | [Maya 2 Native](https://mindket.com/p/1c8868e5-58de-48eb-9c9b-a9de50e1f0f7) | 5,774 | 170 | 2.5B | apache-2.0 | 2026-11-03 | | [Confucius4-TTS](https://mindket.com/p/ffed57c0-84d9-4cec-92f5-8dfa0074545a) | 2,190 | 46 | — | apache-2.0 | 2026-11-04 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-04 - First published: 2026-10-18 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open sulabhkatiyar/indic-tiny-tts-hindi on Hugging Face](https://huggingface.co/sulabhkatiyar/indic-tiny-tts-hindi) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/689a3aa1-dbbe-4eb0-a816-383030be1806) - [Its page on Mindket](https://mindket.com/p/689a3aa1-dbbe-4eb0-a816-383030be1806)