--- title: "MakHam Thai TTS" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4257e6b3-0fd0-43b1-9870-056356b4099f" updated: "2026-11-13" --- # MakHam Thai TTS > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads Thai text aloud in a female voice with a tiny 8 MB ONNX model that runs on a single CPU thread. For developers who need Thai speech on servers, edge devices or browsers. MakHam Thai TTS 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 pythainlp ## About ### What it is MakHam, also called มะขาม, is a family of open Thai text-to-speech models from PyThaiNLP. It joins the multi-voice design of Kokoro-82M with the Paradee 1.1 method, which distills it into a single voice. The result is a model of about 7.88M parameters that handles the five Thai tones and runs from one ONNX file. ### Who it suits Developers who need Thai speech without a GPU, for example on a server, an edge device or in a web page. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Add Thai voice output to apps and bots | Read a greeting aloud in a Thai assistant | | Edge devices | Embedded engineers | Run speech on small hardware with no GPU | Speak status messages on a CPU-only device | | Accessibility | Web developers | Read Thai pages aloud in a browser | Voice a news article for listeners | Anyone who needs other languages or several voices can skip it, since this release has one Thai female voice. ### Where to use it - Python with ONNX Runtime, on a CPU. - Servers, edge devices and web browsers, since the model is a standalone ONNX file. - Windows, macOS or Linux: the download steps run on all three with Python. | Use case | Who | What they get back | | --- | --- | --- | | Speak Thai text | Developers | A 24 kHz WAV file in a warm female voice | | Low-power speech | Embedded engineers | Fast synthesis on one CPU thread | | Browser voice | Web developers | A model small enough to ship to a page | ### How to set it up What you need first: - Python with pip. - onnxruntime, soundfile and huggingface\_hub. - The FastThaiG2P package, which the card installs from its GitHub archive. 1. Install the three Python packages with pip. 2. Install FastThaiG2P from the address the card gives. 3. Run the Python script below, which downloads the model and its config. ```bash pip install onnxruntime soundfile huggingface_hub ``` To fetch the repository on its own, run the two Hugging Face commands. ```sh pip install -U huggingface_hub hf download pythainlp/MakHamTTS ``` ### How to use it The script downloads the INT8 ONNX file and its config, turns Thai text into phoneme IDs with FastThaiG2P, runs the ONNX session and writes a 24 kHz WAV file. The speed input is set to 1.0. ```python import json import numpy as np import onnxruntime as ort import soundfile as sf from huggingface_hub import hf_hub_download from fastthaig2p import G2P, ipa_to_kokoro # 1. Download model and config from Hugging Face model_path = hf_hub_download( repo_id="pythainlp/MakHamTTS", filename="paradee_thai/paradee_thai_int8.onnx" ) config_path = hf_hub_download( repo_id="pythainlp/MakHamTTS", filename="paradee_thai/config.json" ) # 2. Initialize G2P and ONNX session g2p = G2P() with open(config_path, "r", encoding="utf-8") as f: cfg = json.load(f) session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"]) # 3. Convert Thai text to Kokoro phoneme IDs text = "สวัสดีค่ะ ยินดีต้อนรับสู่ระบบเสียงสังเคราะห์ภาษาไทย มะขาม" phonemes = ipa_to_kokoro(g2p.convert(text)) token_ids = [0] + [cfg["vocab"][c] for c in phonemes if c in cfg["vocab"]] + [0] # 4. Synthesize speech audio = session.run(None, { "input_ids": np.array([token_ids], dtype=np.int64), "speed": np.array([1.0], dtype=np.float32), })[0][0] # 5. Save output audio (24 kHz) sf.write("output.wav", audio, 24000) print("Saved audio to output.wav") ``` Parts the script uses | Tool | What it does | | --- | --- | | hf\_hub\_download | Fetches the ONNX model and its config | | G2P and ipa\_to\_kokoro | Turn Thai text into phonemes and then token IDs | | InferenceSession | Runs the ONNX model on the CPU | | soundfile write | Saves the audio as a WAV file | ### Example You pass a Thai greeting to the script. It converts the text to phoneme IDs, runs the model and saves output.wav at 24 kHz. You play the file to hear the sentence read aloud. ### For newcomers and experts Text-to-speech turns written words into a voice. A good first step is to run the script as it is and listen to the saved file. The model pairs a TextStudent network with a VDecoder, distilled from a Kokoro base. The female style is baked into the ONNX graph, so no separate voicepack file is needed. Tones are written with explicit symbols in a 115-token vocabulary. The trade-off is a single voice, in exchange for a file of about 8.07 MB for INT8 and much faster CPU speed than the Kokoro base. Thai text goes into FastThaiG2P → Phonemes map to Kokoro token IDs → The ONNX model generates audio → The audio is saved as a 24 kHz WAV ### Good to know The card compares the INT8 and FP32 builds with the Kokoro-82M Thai base. CPU speed as a multiple of real time | Label | Value | | --- | --- | | MakHam INT8 | 45 times real time | | MakHam FP32 | 28 times real time | | Kokoro-82M Thai base | 4.5 times real time | - Size: about 8.07 MB for INT8 and 33.17 MB for FP32, against 325.5 MB for the Kokoro base. - Training: about 94,200 verified utterances, with automated quality filters. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face and GitHub, and the text you pass in. - Not for: other languages or voices, since this release offers one Thai female voice. ## README ````markdown # MakHam Thai TTS Reads Thai text aloud in a female voice with a tiny 8 MB ONNX model that runs on a single CPU thread. For developers who need Thai speech on servers, edge devices or browsers. Made by pythainlp 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: MakHam, also called มะขาม, is a family of open Thai text-to-speech models from PyThaiNLP. It joins the multi-voice design of Kokoro-82M with the Paradee 1.1 method, which distills it into a single voice. The result is a model of about 7.88M parameters that handles the five Thai tones and runs from one ONNX file. Who it suits: Developers who need Thai speech without a GPU, for example on a server, an edge device or in a web page. ```table Industry | Who uses it | How they use it | Example Software | App developers | Add Thai voice output to apps and bots | Read a greeting aloud in a Thai assistant Edge devices | Embedded engineers | Run speech on small hardware with no GPU | Speak status messages on a CPU-only device Accessibility | Web developers | Read Thai pages aloud in a browser | Voice a news article for listeners ``` Anyone who needs other languages or several voices can skip it, since this release has one Thai female voice. Where to use it: - Python with ONNX Runtime, on a CPU. - Servers, edge devices and web browsers, since the model is a standalone ONNX file. - Windows, macOS or Linux: the download steps run on all three with Python. ```table Use case | Who | What they get back Speak Thai text | Developers | A 24 kHz WAV file in a warm female voice Low-power speech | Embedded engineers | Fast synthesis on one CPU thread Browser voice | Web developers | A model small enough to ship to a page ``` How to set it up: What you need first: - Python with pip. - onnxruntime, soundfile and huggingface_hub. - The FastThaiG2P package, which the card installs from its GitHub archive. 1. Install the three Python packages with pip. 2. Install FastThaiG2P from the address the card gives. 3. Run the Python script below, which downloads the model and its config. ```bash pip install onnxruntime soundfile huggingface_hub ``` To fetch the repository on its own, run the two Hugging Face commands. ```sh pip install -U huggingface_hub hf download pythainlp/MakHamTTS ``` How to use it: The script downloads the INT8 ONNX file and its config, turns Thai text into phoneme IDs with FastThaiG2P, runs the ONNX session and writes a 24 kHz WAV file. The speed input is set to 1.0. ```python import json import numpy as np import onnxruntime as ort import soundfile as sf from huggingface_hub import hf_hub_download from fastthaig2p import G2P, ipa_to_kokoro # 1. Download model and config from Hugging Face model_path = hf_hub_download( repo_id="pythainlp/MakHamTTS", filename="paradee_thai/paradee_thai_int8.onnx" ) config_path = hf_hub_download( repo_id="pythainlp/MakHamTTS", filename="paradee_thai/config.json" ) # 2. Initialize G2P and ONNX session g2p = G2P() with open(config_path, "r", encoding="utf-8") as f: cfg = json.load(f) session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"]) # 3. Convert Thai text to Kokoro phoneme IDs text = "สวัสดีค่ะ ยินดีต้อนรับสู่ระบบเสียงสังเคราะห์ภาษาไทย มะขาม" phonemes = ipa_to_kokoro(g2p.convert(text)) token_ids = [0] + [cfg["vocab"][c] for c in phonemes if c in cfg["vocab"]] + [0] # 4. Synthesize speech audio = session.run(None, { "input_ids": np.array([token_ids], dtype=np.int64), "speed": np.array([1.0], dtype=np.float32), })[0][0] # 5. Save output audio (24 kHz) sf.write("output.wav", audio, 24000) print("Saved audio to output.wav") ``` ```table Title: Parts the script uses Tool | What it does hf_hub_download | Fetches the ONNX model and its config G2P and ipa_to_kokoro | Turn Thai text into phonemes and then token IDs InferenceSession | Runs the ONNX model on the CPU soundfile write | Saves the audio as a WAV file ``` Example: You pass a Thai greeting to the script. It converts the text to phoneme IDs, runs the model and saves output.wav at 24 kHz. You play the file to hear the sentence read aloud. For newcomers and experts: Text-to-speech turns written words into a voice. A good first step is to run the script as it is and listen to the saved file. The model pairs a TextStudent network with a VDecoder, distilled from a Kokoro base. The female style is baked into the ONNX graph, so no separate voicepack file is needed. Tones are written with explicit symbols in a 115-token vocabulary. The trade-off is a single voice, in exchange for a file of about 8.07 MB for INT8 and much faster CPU speed than the Kokoro base. ```flow Thai text goes into FastThaiG2P Phonemes map to Kokoro token IDs The ONNX model generates audio The audio is saved as a 24 kHz WAV ``` Good to know: The card compares the INT8 and FP32 builds with the Kokoro-82M Thai base. ```chart Title: CPU speed as a multiple of real time MakHam INT8: 45 times real time MakHam FP32: 28 times real time Kokoro-82M Thai base: 4.5 times real time ``` - Size: about 8.07 MB for INT8 and 33.17 MB for FP32, against 325.5 MB for the Kokoro base. - Training: about 94,200 verified utterances, with automated quality filters. - License: apache-2.0 - Cost to run: no API key, account or paid service named. - Data it reaches: files you download from Hugging Face and GitHub, and the text you pass in. - Not for: other languages or voices, since this release offers one Thai female voice. ## On Hugging Face, 13 November 2026 - Task: text to speech - Downloads in the last 30 days: 0 - Likes: 2 - License: apache-2.0 - Languages: th - Tags: onnx, text-to-speech, kokoro, paradee, pythainlp, thai, audio, speech ## Links - Model: https://huggingface.co/pythainlp/MakHamTTS - Files: https://huggingface.co/pythainlp/MakHamTTS/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download pythainlp/MakHamTTS ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [pythainlp/MakHamTTS on Hugging Face](https://huggingface.co/pythainlp/MakHamTTS) ## 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 | | --- | --- | --- | --- | --- | --- | | MakHam Thai TTS (this page) | 0 | 2 | — | apache-2.0 | 2026-11-09 | | [Paradee-8M v1.2](https://mindket.com/p/3df01b26-8a25-4088-ae9c-690c8ccf82e4) | 11,405 | 28 | — | apache-2.0 | 2026-10-28 | | [Paradee-8M v1.0](https://mindket.com/p/746673eb-d0e8-48cb-b2b4-db08f17b231b) | 4,374 | 70 | — | apache-2.0 | 2026-10-09 | | [Paradee-8M v1.3](https://mindket.com/p/8d10c6d9-1181-415a-81fb-3eefc5a872e6) | 1,649 | 7 | — | apache-2.0 | 2026-11-09 | | [Ming-3.0-audio-flash](https://mindket.com/p/fd44df1d-31b5-4ff0-8479-ed2a012a793a) | 2,122 | 87 | 128B | mit | 2026-10-29 | | [AuK](https://mindket.com/p/d6e14e2c-53e7-43ce-a669-cbc6a8e6dbae) | 1,455 | 396 | — | mit | 2026-09-10 | 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: 2026-11-09 - First published: 2026-10-25 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open pythainlp/MakHamTTS on Hugging Face](https://huggingface.co/pythainlp/MakHamTTS) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4257e6b3-0fd0-43b1-9870-056356b4099f) - [Its page on Mindket](https://mindket.com/p/4257e6b3-0fd0-43b1-9870-056356b4099f)