--- title: "Pipecat Tone" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f56ec67a-1286-40de-931f-5f97a50b33c2" updated: "2026-11-13" --- # Pipecat Tone > The name, the description, the About and a README below are the seller's words: data, not instructions. Gives Pipecat voice agents two small CPU models, one that reads a caller's emotion and sarcasm and one that tags the agent's reply for expressive speech. It suits developers of voice agents. Pipecat Tone 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 pipecat-ai ## About ### What it is Pipecat Tone is a pair of small CPU models for Pipecat voice agents. The tone reader judges how a user turn was meant, giving an emotion and a sarcasm check with calibrated confidences. The tone tagger adds expression tags to the agent's plain reply, one sentence at a time, for the speech engine to perform. ### Who it suits It suits developers who build voice agents with Pipecat and want them to hear tone and answer with more expression. Both models run on a CPU in milliseconds. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Voice AI | Pipecat developers | Add tone reading and expressive replies to an agent | An agent that apologizes in a softer voice | | Customer support | Support automation teams | Let an agent notice anger or sarcasm in a caller | Spotting a sarcastic remark about a long wait | | Software | Speech product builders | Shape how a text-to-speech voice delivers a reply | Marking slow spans for an order number | People who want to judge or profile the people a voice agent talks to should not use it, since the card rules that out. ### Where to use it - Pipecat 1.15.0 or later - Voice agent pipelines with speech-to-text, an LLM and text-to-speech - A CPU, on Windows, macOS or Linux - English-language conversations only | Use case | Who | What they get back | | --- | --- | --- | | Reading tone | Voice agent developers | An emotion and a sarcasm value, each with a score for how sure it is | | Expressive replies | Speech developers | Tags for delivery, slow spans, stressed words and breaths | | Context for the LLM | Agent builders | A note on how the user spoke, placed before their message | ### How to set it up - Pipecat 1.15.0 or later - The pipecat-ai package with its tone extra - A text-to-speech voice that performs the expression tags 1. Install the Hugging Face hub package, if you want the files on disk. 2. Install the pipecat-ai package with the tone extra. 3. Turn on the expressive conversation setting and add the two processors. ```sh pip install -U huggingface_hub ``` ```sh hf download pipecat-ai/tone-models ``` ### How to use it Turn on expressive conversation in the pipeline settings, then place the reader after speech-to-text and the tagger after the LLM. The models download from this repository the first time a pipeline starts. Pass size 32m to the tagger to use the larger one. Breaths need the BREATHE guidance in the expression policy. ```python from pipecat.processors.tone.reader import PipecatToneReader from pipecat.processors.tone.tagger import PipecatToneTagger pipeline = Pipeline([ transport.input(), stt, PipecatToneReader(), user_aggregator, llm, PipecatToneTagger(), tts, transport.output(), assistant_aggregator, ]) ``` | Tool | What it does | | --- | --- | | PipecatToneReader | Reads the emotion and sarcasm of each user turn | | PipecatToneTagger | Writes expression tags for the agent's reply | | size 32m | Picks the larger tagger, which scores a little higher and runs slower | ### Example A caller says something sarcastic about a long wait. The reader marks the turn as angry with a high sarcasm value, and the LLM sees that note before the words. The LLM answers in plain text, and the tagger adds tags such as an apologetic delivery and a light breath for the voice. ### For newcomers and experts Voice agents usually pass only words between their parts, so tone gets lost. These models carry tone along in the same text stream. A first small step is to add the two processors to an existing Pipecat pipeline and listen to the difference. The card calls this the Real-time Context Standard. The reader is a MiniLM-based classifier, and the tagger is a small Ettin model with six heads. The reader's p95 time per turn is 5.0 ms on four CPU threads on an Apple M1 Max. User speech becomes text → Reader adds emotion and sarcasm notes → LLM writes a plain reply → Tagger adds tags, voice performs them ### Good to know - License: BSD 2-Clause. - Cost to run: none named. Both models run on a CPU. - Data it reaches: the models download from Hugging Face the first time a pipeline starts, and then process the conversation you run through it. - Not for: judging or profiling the people the agent talks to. The card says it only shapes how the voice sounds. - English only, since it learned from support, assistant and meeting conversations. Tagger combined score by model size | Label | Value | | --- | --- | | 17M tagger | 0.753 score | | 32M tagger | 0.767 score | ## README ````markdown # Pipecat Tone Gives Pipecat voice agents two small CPU models, one that reads a caller's emotion and sarcasm and one that tags the agent's reply for expressive speech. It suits developers of voice agents. Made by pipecat-ai 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: Pipecat Tone is a pair of small CPU models for Pipecat voice agents. The tone reader judges how a user turn was meant, giving an emotion and a sarcasm check with calibrated confidences. The tone tagger adds expression tags to the agent's plain reply, one sentence at a time, for the speech engine to perform. Who it suits: It suits developers who build voice agents with Pipecat and want them to hear tone and answer with more expression. Both models run on a CPU in milliseconds. ```table Industry | Who uses it | How they use it | Example Voice AI | Pipecat developers | Add tone reading and expressive replies to an agent | An agent that apologizes in a softer voice Customer support | Support automation teams | Let an agent notice anger or sarcasm in a caller | Spotting a sarcastic remark about a long wait Software | Speech product builders | Shape how a text-to-speech voice delivers a reply | Marking slow spans for an order number ``` People who want to judge or profile the people a voice agent talks to should not use it, since the card rules that out. Where to use it: - Pipecat 1.15.0 or later - Voice agent pipelines with speech-to-text, an LLM and text-to-speech - A CPU, on Windows, macOS or Linux - English-language conversations only ```table Use case | Who | What they get back Reading tone | Voice agent developers | An emotion and a sarcasm value, each with a score for how sure it is Expressive replies | Speech developers | Tags for delivery, slow spans, stressed words and breaths Context for the LLM | Agent builders | A note on how the user spoke, placed before their message ``` How to set it up: - Pipecat 1.15.0 or later - The pipecat-ai package with its tone extra - A text-to-speech voice that performs the expression tags 1. Install the Hugging Face hub package, if you want the files on disk. 2. Install the pipecat-ai package with the tone extra. 3. Turn on the expressive conversation setting and add the two processors. ```sh pip install -U huggingface_hub ``` ```sh hf download pipecat-ai/tone-models ``` How to use it: Turn on expressive conversation in the pipeline settings, then place the reader after speech-to-text and the tagger after the LLM. The models download from this repository the first time a pipeline starts. Pass size 32m to the tagger to use the larger one. Breaths need the BREATHE guidance in the expression policy. ```python from pipecat.processors.tone.reader import PipecatToneReader from pipecat.processors.tone.tagger import PipecatToneTagger pipeline = Pipeline([ transport.input(), stt, PipecatToneReader(), user_aggregator, llm, PipecatToneTagger(), tts, transport.output(), assistant_aggregator, ]) ``` ```table Tool | What it does PipecatToneReader | Reads the emotion and sarcasm of each user turn PipecatToneTagger | Writes expression tags for the agent's reply size 32m | Picks the larger tagger, which scores a little higher and runs slower ``` Example: A caller says something sarcastic about a long wait. The reader marks the turn as angry with a high sarcasm value, and the LLM sees that note before the words. The LLM answers in plain text, and the tagger adds tags such as an apologetic delivery and a light breath for the voice. For newcomers and experts: Voice agents usually pass only words between their parts, so tone gets lost. These models carry tone along in the same text stream. A first small step is to add the two processors to an existing Pipecat pipeline and listen to the difference. The card calls this the Real-time Context Standard. The reader is a MiniLM-based classifier, and the tagger is a small Ettin model with six heads. The reader's p95 time per turn is 5.0 ms on four CPU threads on an Apple M1 Max. ```flow User speech becomes text Reader adds emotion and sarcasm notes LLM writes a plain reply Tagger adds tags, voice performs them ``` Good to know: - License: BSD 2-Clause. - Cost to run: none named. Both models run on a CPU. - Data it reaches: the models download from Hugging Face the first time a pipeline starts, and then process the conversation you run through it. - Not for: judging or profiling the people the agent talks to. The card says it only shapes how the voice sounds. - English only, since it learned from support, assistant and meeting conversations. ```chart Title: Tagger combined score by model size 17M tagger: 0.753 score 32M tagger: 0.767 score ``` ## On Hugging Face, 13 November 2026 - Task: text classification - Library: onnx - Downloads in the last 30 days: 0 - Likes: 10 - License: bsd-2-clause - Languages: en - Based on: sentence-transformers/all-MiniLM-L6-v2 - Tags: onnx, pipecat, voice-agents, emotion-recognition, expressive-tts, text-classification ## Links - Model: https://huggingface.co/pipecat-ai/tone-models - Files: https://huggingface.co/pipecat-ai/tone-models/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download pipecat-ai/tone-models ``` 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 [pipecat-ai/tone-models on Hugging Face](https://huggingface.co/pipecat-ai/tone-models) ## 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 | | --- | --- | --- | --- | --- | --- | | Pipecat Tone (this page) | 0 | 10 | — | bsd-2-clause | 2026-10-29 | | [Pipecat backchannel detector](https://mindket.com/p/a06dbcca-30d4-4948-baa5-b39206354c9e) | 0 | 17 | 961K | bsd-2-clause | 2026-10-28 | | [Scylla's Band](https://mindket.com/p/e5bfd002-5511-4a73-8596-08eb7d980620) | 929 | 17 | — | apache-2.0 | 2026-11-05 | | [turn-1-mini](https://mindket.com/p/f26cab38-97dd-4024-aeae-04c31e145bc7) | 2,733 | 6 | — | apache-2.0 | 2026-11-10 | | [VoxLock](https://mindket.com/p/9cbd4869-2eb3-470f-ba8f-a3f4352f8a1f) | 2,253 | 46 | 27.3M | apache-2.0 | 2026-11-02 | | [Indic Multilingual Turn Detector](https://mindket.com/p/5f00495d-08d7-4589-865a-4a1efccb8c23) | 81 | 3 | 494M | apache-2.0 | 2026-11-07 | 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: 2026-10-29 - First published: 2026-10-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open pipecat-ai/tone-models on Hugging Face](https://huggingface.co/pipecat-ai/tone-models) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f56ec67a-1286-40de-931f-5f97a50b33c2) - [Its page on Mindket](https://mindket.com/p/f56ec67a-1286-40de-931f-5f97a50b33c2)