--- title: "Chatterbox-Flash" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/3268c775-93ab-4b93-b15f-5026e3aa857d" updated: "2026-11-13" --- # Chatterbox-Flash > The name, the description, the About and a README below are the seller's words: data, not instructions. A zero-shot English text-to-speech model that copies a voice from a short reference clip and streams audio block by block with low latency. Suits developers building voice assistants and narration tools. Chatterbox-Flash 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 ResembleAI ## About ### What it is Chatterbox-Flash is a text-to-speech model from Resemble AI that works zero-shot, meaning you give it a short reference recording and it speaks new text in that voice. It decodes several speech tokens in parallel inside each block and streams audio block by block. The card reports quality close to slower autoregressive systems at much lower latency. ### Who it suits Developers building voice assistants, narration tools or live speech features in English, where audio should start fast. Use only voices you have the right to use, since it copies the voice from the reference clip. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Voice app developers | Stream spoken replies as they are generated | Speak an assistant answer quickly | | Media | Narration and audio teams | Turn scripts into speech in a chosen voice | Read a script in your own recorded voice | | Accessibility | Product teams | Add spoken output to text interfaces | Read on-screen text aloud | | Research | Speech researchers | Study block-diffusion text-to-speech | Compare decoding settings for speed | Anyone who needs languages other than English can skip it, since the model page lists English only. ### Where to use it - Python scripts that use the chatterbox-flash package, on a CUDA GPU as in the card's example - Apple Silicon Macs, through MLX, with 4-bit quantization for extra speed - Windows, macOS or Linux, after downloading the files with Hugging Face's hub tool | Use case | Who | What they get back | | --- | --- | --- | | Streaming speech | Voice app developers | Audio packets as each block is finished | | Voice cloning from a clip | Audio creators | New speech in the style of a reference recording you have rights to | | Local Mac synthesis | Mac users | Speech faster than real time on an M4, per the card | ### How to set it up - Python with pip - A CUDA GPU for the card's example, or an Apple Silicon Mac for the MLX route - A short reference audio file of a voice you may use 1. Install the Hugging Face hub package and download the files, if you want them on disk. 2. Install the chatterbox-flash package with pip. 3. Run the snippet below with your own reference file. ```sh pip install -U huggingface_hub hf download ResembleAI/chatterbox-flash ``` ```bash pip install chatterbox-flash ``` ### How to use it Load the model onto the GPU, then call generate with your text and the path to a reference recording. The snippet saves the result as an audio file. ```python import torchaudio as ta from chatterbox_flash import ChatterboxFlashTTS tts = ChatterboxFlashTTS.from_pretrained("ResembleAI/chatterbox-flash", device="cuda") wav = tts.generate( "Hello, world.", audio_prompt_path="reference.wav", ) ta.save("out.wav", wav.unsqueeze(0).cpu(), tts.sr) ``` | Tool | What it does | | --- | --- | | ChatterboxFlashTTS.from\_pretrained | Loads the model onto the device you choose | | generate | Speaks the text, using the reference clip as the voice prompt | | ta.save | Writes the generated audio to a file | | File | What it is | | --- | --- | | t3\_flash.safetensors | The block-diffusion decoder, based on a 520M Llama with one extra mask token | | s3gen.safetensors | The flow-matching vocoder | | ve.safetensors | The voice encoder, taken from the original Chatterbox | | tokenizer.json | The English tokenizer, also from the original Chatterbox | ### Example You run the snippet with the text "Hello, world." and a reference recording named reference.wav. It writes out.wav, a spoken version of that text in the style of the reference voice. ### For newcomers and experts If you are new, run the snippet once with a short clear reference clip and listen to out.wav. Then change the text and compare how closely the voice matches. Experts get a block-diffusion decoder that unmasks multiple speech tokens at once in each block, with an early-decoding schedule that cuts average steps per block by about 20 percent. Defaults are block size 16, up to 10 denoising steps per block, temperature 0.2, and a FlashInfer paged KV cache with CUDA graph capture. Real-time factor, lower is faster | Label | Value | | --- | --- | | Default setting | 0.107 RTF | | Fastest setting | 0.076 RTF | | Mac M4 with MLX | 0.778 RTF | | Mac M4 with MLX 4-bit | 0.665 RTF | You give text and a reference clip → The decoder fills each block in parallel → The vocoder turns tokens into audio → Audio streams out block by block ### Good to know - Speed: about 9 times real time at the default setting and about 13 times at the fastest, per the card - Latency: time to first audio packet is 103 to 119 ms across the tested settings - Language: English only - License: MIT - Cost to run: none named, no API key or account, though the example needs a CUDA GPU - Data it reaches: it downloads weights from Hugging Face and reads the reference audio and text you give it - Not for: using a voice you have no right to use, and it does not cover languages other than English ## README ````markdown # Chatterbox-Flash A zero-shot English text-to-speech model that copies a voice from a short reference clip and streams audio block by block with low latency. Suits developers building voice assistants and narration tools. Made by ResembleAI 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: Chatterbox-Flash is a text-to-speech model from Resemble AI that works zero-shot, meaning you give it a short reference recording and it speaks new text in that voice. It decodes several speech tokens in parallel inside each block and streams audio block by block. The card reports quality close to slower autoregressive systems at much lower latency. Who it suits: Developers building voice assistants, narration tools or live speech features in English, where audio should start fast. Use only voices you have the right to use, since it copies the voice from the reference clip. ```table Industry | Who uses it | How they use it | Example Software | Voice app developers | Stream spoken replies as they are generated | Speak an assistant answer quickly Media | Narration and audio teams | Turn scripts into speech in a chosen voice | Read a script in your own recorded voice Accessibility | Product teams | Add spoken output to text interfaces | Read on-screen text aloud Research | Speech researchers | Study block-diffusion text-to-speech | Compare decoding settings for speed ``` Anyone who needs languages other than English can skip it, since the model page lists English only. Where to use it: - Python scripts that use the chatterbox-flash package, on a CUDA GPU as in the card's example - Apple Silicon Macs, through MLX, with 4-bit quantization for extra speed - Windows, macOS or Linux, after downloading the files with Hugging Face's hub tool ```table Use case | Who | What they get back Streaming speech | Voice app developers | Audio packets as each block is finished Voice cloning from a clip | Audio creators | New speech in the style of a reference recording you have rights to Local Mac synthesis | Mac users | Speech faster than real time on an M4, per the card ``` How to set it up: - Python with pip - A CUDA GPU for the card's example, or an Apple Silicon Mac for the MLX route - A short reference audio file of a voice you may use 1. Install the Hugging Face hub package and download the files, if you want them on disk. 2. Install the chatterbox-flash package with pip. 3. Run the snippet below with your own reference file. ```sh pip install -U huggingface_hub hf download ResembleAI/chatterbox-flash ``` ```bash pip install chatterbox-flash ``` How to use it: Load the model onto the GPU, then call generate with your text and the path to a reference recording. The snippet saves the result as an audio file. ```python import torchaudio as ta from chatterbox_flash import ChatterboxFlashTTS tts = ChatterboxFlashTTS.from_pretrained("ResembleAI/chatterbox-flash", device="cuda") wav = tts.generate( "Hello, world.", audio_prompt_path="reference.wav", ) ta.save("out.wav", wav.unsqueeze(0).cpu(), tts.sr) ``` ```table Tool | What it does ChatterboxFlashTTS.from_pretrained | Loads the model onto the device you choose generate | Speaks the text, using the reference clip as the voice prompt ta.save | Writes the generated audio to a file ``` ```table File | What it is t3_flash.safetensors | The block-diffusion decoder, based on a 520M Llama with one extra mask token s3gen.safetensors | The flow-matching vocoder ve.safetensors | The voice encoder, taken from the original Chatterbox tokenizer.json | The English tokenizer, also from the original Chatterbox ``` Example: You run the snippet with the text "Hello, world." and a reference recording named reference.wav. It writes out.wav, a spoken version of that text in the style of the reference voice. For newcomers and experts: If you are new, run the snippet once with a short clear reference clip and listen to out.wav. Then change the text and compare how closely the voice matches. Experts get a block-diffusion decoder that unmasks multiple speech tokens at once in each block, with an early-decoding schedule that cuts average steps per block by about 20 percent. Defaults are block size 16, up to 10 denoising steps per block, temperature 0.2, and a FlashInfer paged KV cache with CUDA graph capture. ```chart Title: Real-time factor, lower is faster Default setting: 0.107 RTF Fastest setting: 0.076 RTF Mac M4 with MLX: 0.778 RTF Mac M4 with MLX 4-bit: 0.665 RTF ``` ```flow You give text and a reference clip The decoder fills each block in parallel The vocoder turns tokens into audio Audio streams out block by block ``` Good to know: - Speed: about 9 times real time at the default setting and about 13 times at the fastest, per the card - Latency: time to first audio packet is 103 to 119 ms across the tested settings - Language: English only - License: MIT - Cost to run: none named, no API key or account, though the example needs a CUDA GPU - Data it reaches: it downloads weights from Hugging Face and reads the reference audio and text you give it - Not for: using a voice you have no right to use, and it does not cover languages other than English ## On Hugging Face, 13 November 2026 - Task: text to speech - Library: chatterbox-flash - Downloads in the last 30 days: 0 - Likes: 33 - License: mit - Languages: en - Quantized versions: 1 - Fine-tunes: 2 - Tags: chatterbox-flash, text-to-speech, zero-shot-tts, block-diffusion, speech-synthesis ## Links - Model: https://huggingface.co/ResembleAI/chatterbox-flash - Files: https://huggingface.co/ResembleAI/chatterbox-flash/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ResembleAI/chatterbox-flash ``` 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 [ResembleAI/chatterbox-flash on Hugging Face](https://huggingface.co/ResembleAI/chatterbox-flash) ## 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 | | --- | --- | --- | --- | --- | --- | | Chatterbox-Flash (this page) | 0 | 33 | — | mit | 2026-07-06 | | [AuK](https://mindket.com/p/d6e14e2c-53e7-43ce-a669-cbc6a8e6dbae) | 1,455 | 396 | — | mit | 2026-09-10 | | [Supertonic 3](https://mindket.com/p/910af7e2-128f-445f-bcb1-310264314e6d) | 92,359 | 1,043 | — | openrail | 2026-05-18 | | [EMA Lightning](https://mindket.com/p/fc4a1255-7aae-454e-9df3-6c61f073d73f) | 89,173 | 461 | — | apache-2.0 | 2026-10-21 | | [ZeroTTS](https://mindket.com/p/97fb68f6-db3d-4745-9202-2956cafdf46f) | 8,091 | 54 | — | mit | 2026-10-31 | | [Maya 2 Native](https://mindket.com/p/1c8868e5-58de-48eb-9c9b-a9de50e1f0f7) | 5,774 | 170 | 2.5B | apache-2.0 | 2026-11-03 | 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: 2026-07-06 - First published: 2026-05-28 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ResembleAI/chatterbox-flash on Hugging Face](https://huggingface.co/ResembleAI/chatterbox-flash) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/3268c775-93ab-4b93-b15f-5026e3aa857d) - [Its page on Mindket](https://mindket.com/p/3268c775-93ab-4b93-b15f-5026e3aa857d)