--- title: "Pipecat backchannel detector" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a06dbcca-30d4-4948-baa5-b39206354c9e" updated: "2026-11-13" --- # Pipecat backchannel detector > The name, the description, the About and a README below are the seller's words: data, not instructions. Listens to a user while a voice bot is talking and tells apart a quick "mm-hmm" from a real interruption, so the bot keeps going or stops. A tiny streaming audio model for voice agent builders. Pipecat backchannel detector 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 This is a small streaming audio model for voice agents. While the bot is speaking, it decides whether the user is only nodding along, as with a short "mm-hmm" or "right", or is taking the floor, as with "wait" or "no". The bot then keeps talking or stops. It works on the audio itself, so it decides ahead of the transcript. The model is tiny, at about 4 MB in full precision, and uses roughly 0.3 ms of CPU for each 40 ms chunk of audio. ### Who it suits It suits developers who build voice agents and want interruptions handled quickly, without a bot that either talks over people or stops at every small sound. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Voice agent developers | Add interruption handling to a spoken bot | Keep talking through an mm-hmm | | Customer support | Contact center teams | Let callers cut in naturally | Stop at a caller saying wait | | Voice products | Product teams | Make bot turn-taking feel natural | Tune the floor threshold | | Research | Dialogue researchers | Study backchannel detection in speech | Replay held-out conversations | If your bot does not speak, or you handle languages other than English, you can skip it. ### Where to use it It is meant to sit inside a streaming voice agent pipeline and run on a CPU. You fetch the files on Windows, macOS or Linux with Python. - A Pipecat style voice agent loop - An ONNX runtime on a CPU - English voice conversations | Use case | Who | What they get back | | --- | --- | --- | | Backchannel check | Voice agent developers | A probability that the user is only nodding | | Interruption check | Voice agent developers | A probability that the user is taking the floor | | Voice activity | Researchers | An auxiliary user voice activity signal | ### How to set it up You need Python with pip to fetch the files and an ONNX runtime to run the model in your voice agent. The repository holds a default fp32 model and an int8 model. - Python with pip - An ONNX runtime - A voice agent that can send 16 kHz audio chunks 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Load the fp32 ONNX file, or the int8 one for a smaller model. ```sh pip install -U huggingface_hub hf download pipecat-ai/backchannel-detector ``` ### How to use it You call the model once for every 40 ms chunk of the user's microphone audio, after echo cancellation. You also pass the bot's speaking state, and feed each cache output back in on the next call. | Tool | What it does | | --- | --- | | backchannel-fp32.onnx | The default streaming model | | backchannel-int8.onnx | An int8 quantized version | | model.safetensors | PyTorch weights for the same network | | model.json | Config, chunk size and state tensor names | The output is a set of probabilities over five labels, with backchannel meaning the continuer and assessment labels, and floor meaning the negative and floor-take labels. ### Example The bot is mid-sentence and the user says "mm-hmm". The model reports a high backchannel probability, so the bot keeps talking. If the user says "wait", the floor probability rises and the bot stops. ### For newcomers and experts If you are new, think of it as a listener that tells a voice bot when to stop talking. The first thing to try is to read its model.json to see the chunk size and states it needs. If you know the field, a causal convolution encoder feeds a GRU, with extra auxiliary outputs for voice activity and a voice activity projection. The card tests it on saved conversations that are played back in real time to the agent. User audio arrives in 40 ms chunks → The model reads it with bot state → It outputs intent probabilities → The bot keeps talking or stops ### Good to know The card reports results on two sets of held-out conversations, and a median time of about 280 to 300 ms to catch an interruption. | Measure | Open Yap | otoSpeech | | --- | --- | --- | | Interruptions caught within 700 ms | 82.9 % | 81.2 % | | Interruptions lost as backchannels | 2.6 % | 4.1 % | | Backchannels the bot talks through | 86.5 % | 75.2 % | - License: bsd-2-clause - Cost to run: none named, since the model runs on a CPU - Data it reaches: the audio chunks you feed it, and no training data is included - Not for: languages other than English, or people talking to a bot, since it was trained on human-to-human talk - Not for: real phone codecs, which are untested, or real echo cancellation residue ## README ````markdown # Pipecat backchannel detector Listens to a user while a voice bot is talking and tells apart a quick "mm-hmm" from a real interruption, so the bot keeps going or stops. A tiny streaming audio model for voice agent builders. 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: This is a small streaming audio model for voice agents. While the bot is speaking, it decides whether the user is only nodding along, as with a short "mm-hmm" or "right", or is taking the floor, as with "wait" or "no". The bot then keeps talking or stops. It works on the audio itself, so it decides ahead of the transcript. The model is tiny, at about 4 MB in full precision, and uses roughly 0.3 ms of CPU for each 40 ms chunk of audio. Who it suits: It suits developers who build voice agents and want interruptions handled quickly, without a bot that either talks over people or stops at every small sound. ```table Industry | Who uses it | How they use it | Example Software | Voice agent developers | Add interruption handling to a spoken bot | Keep talking through an mm-hmm Customer support | Contact center teams | Let callers cut in naturally | Stop at a caller saying wait Voice products | Product teams | Make bot turn-taking feel natural | Tune the floor threshold Research | Dialogue researchers | Study backchannel detection in speech | Replay held-out conversations ``` If your bot does not speak, or you handle languages other than English, you can skip it. Where to use it: It is meant to sit inside a streaming voice agent pipeline and run on a CPU. You fetch the files on Windows, macOS or Linux with Python. - A Pipecat style voice agent loop - An ONNX runtime on a CPU - English voice conversations ```table Use case | Who | What they get back Backchannel check | Voice agent developers | A probability that the user is only nodding Interruption check | Voice agent developers | A probability that the user is taking the floor Voice activity | Researchers | An auxiliary user voice activity signal ``` How to set it up: You need Python with pip to fetch the files and an ONNX runtime to run the model in your voice agent. The repository holds a default fp32 model and an int8 model. - Python with pip - An ONNX runtime - A voice agent that can send 16 kHz audio chunks 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Load the fp32 ONNX file, or the int8 one for a smaller model. ```sh pip install -U huggingface_hub hf download pipecat-ai/backchannel-detector ``` How to use it: You call the model once for every 40 ms chunk of the user's microphone audio, after echo cancellation. You also pass the bot's speaking state, and feed each cache output back in on the next call. ```table Tool | What it does backchannel-fp32.onnx | The default streaming model backchannel-int8.onnx | An int8 quantized version model.safetensors | PyTorch weights for the same network model.json | Config, chunk size and state tensor names ``` The output is a set of probabilities over five labels, with backchannel meaning the continuer and assessment labels, and floor meaning the negative and floor-take labels. Example: The bot is mid-sentence and the user says "mm-hmm". The model reports a high backchannel probability, so the bot keeps talking. If the user says "wait", the floor probability rises and the bot stops. For newcomers and experts: If you are new, think of it as a listener that tells a voice bot when to stop talking. The first thing to try is to read its model.json to see the chunk size and states it needs. If you know the field, a causal convolution encoder feeds a GRU, with extra auxiliary outputs for voice activity and a voice activity projection. The card tests it on saved conversations that are played back in real time to the agent. ```flow User audio arrives in 40 ms chunks The model reads it with bot state It outputs intent probabilities The bot keeps talking or stops ``` Good to know: The card reports results on two sets of held-out conversations, and a median time of about 280 to 300 ms to catch an interruption. ```table Measure | Open Yap | otoSpeech Interruptions caught within 700 ms | 82.9 % | 81.2 % Interruptions lost as backchannels | 2.6 % | 4.1 % Backchannels the bot talks through | 86.5 % | 75.2 % ``` - License: bsd-2-clause - Cost to run: none named, since the model runs on a CPU - Data it reaches: the audio chunks you feed it, and no training data is included - Not for: languages other than English, or people talking to a bot, since it was trained on human-to-human talk - Not for: real phone codecs, which are untested, or real echo cancellation residue ## On Hugging Face, 13 November 2026 - Task: audio classification - Library: onnx - Parameters: 961K - Weights: about 4 MB in F32; about 1 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 0 - Likes: 17 - License: bsd-2-clause - Languages: en - Tags: onnx, safetensors, pipecat, voice-agents, turn-taking, backchannel, streaming, audio-classification ## Links - Model: https://huggingface.co/pipecat-ai/backchannel-detector - Files: https://huggingface.co/pipecat-ai/backchannel-detector/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download pipecat-ai/backchannel-detector ``` 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/backchannel-detector on Hugging Face](https://huggingface.co/pipecat-ai/backchannel-detector) ## 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 backchannel detector (this page) | 0 | 17 | 961K | bsd-2-clause | 2026-10-28 | | [turn-1-mini](https://mindket.com/p/f26cab38-97dd-4024-aeae-04c31e145bc7) | 2,733 | 6 | — | apache-2.0 | 2026-11-10 | | [Pipecat Tone](https://mindket.com/p/f56ec67a-1286-40de-931f-5f97a50b33c2) | 0 | 10 | — | bsd-2-clause | 2026-10-29 | | [Maya 2 Native](https://mindket.com/p/1c8868e5-58de-48eb-9c9b-a9de50e1f0f7) | 5,774 | 170 | 2.5B | apache-2.0 | 2026-11-03 | | [Backchannel Cue 32M](https://mindket.com/p/aa99a1e5-7344-483a-8c16-31f4f4116b9b) | 114 | 27 | — | mit | 2026-11-02 | | [Anuvani-LID](https://mindket.com/p/4ae1946d-0be8-40b6-8a36-b11c5c25d65c) | 276 | 7 | 116M | apache-2.0 | 2026-10-19 | 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: 2026-10-28 - First published: 2026-10-28 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open pipecat-ai/backchannel-detector on Hugging Face](https://huggingface.co/pipecat-ai/backchannel-detector) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a06dbcca-30d4-4948-baa5-b39206354c9e) - [Its page on Mindket](https://mindket.com/p/a06dbcca-30d4-4948-baa5-b39206354c9e)