| # NeMo Voice Agent |
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| A fully open-source NVIDIA NeMo Voice Agent example demonstrating a simple way to combine NVIDIA NeMo STT/TTS service and HuggingFace LLM together into a conversational agent. Everything is open-source and deployed locally so you can have your own voice agent. Feel free to explore the code and see how different speech technologies can be integrated with LLMs to create a seamless conversation experience. |
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| As of now, we only support English input and output, but more languages will be supported in the future. |
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| ## 📋 Table of Contents |
| - [✨ Key Features](#-key-features) |
| - [💡 Upcoming Next](#-upcoming-next) |
| - [📅 Latest Updates](#-latest-updates) |
| - [🚀 Quick Start](#-quick-start) |
| - [📑 Supported Models and Features](#-supported-models-and-features) |
| - [🤖 LLM](#-llm) |
| - [🎤 ASR](#-asr) |
| - [💬 Speaker Diarization](#-speaker-diarization) |
| - [🔉 TTS](#-tts) |
| - [🔄 Turn-taking](#-turn-taking) |
| - [🔧 Tool Calling](#-tool-calling) |
| - [📝 Notes \& FAQ](#-notes--faq) |
| - [☁️ NVIDIA NIM Services](#️-nvidia-nim-services) |
| - [Acknowledgments](#acknowledgments) |
| - [Contributing](#contributing) |
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| ## ✨ Key Features |
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| - Open-source, local deployment, and flexible customization. |
| - Allow users to talk to most LLMs from HuggingFace with configurable prompts. |
| - Streaming speech recognition with low latency and end-of-utterance detection. |
| - Low latency TTS for fast audio response generation. |
| - Speaker diarization up to 4 speakers in different user turns. |
| - WebSocket server for easy deployment. |
| - Tool calling for LLMs to use external tools and adjust its own behavior. |
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| ## 💡 Upcoming Next |
| - Better tool calling support for LLMs. |
| - Better inference latency for Magpie TTS model. |
| - Evaluation tools and benchmarks for voice agents. |
| - Accuracy and robustness ASR model improvements. |
| - Combine ASR and speaker diarization model to handle overlapping speech. |
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| ## 📅 Latest Updates |
| - 2026-01-26: Added support for [NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) LLM model, and support for [magpie_tts_multilingual_357m](https://huggingface.co/nvidia/magpie_tts_multilingual_357m) TTS model. |
| - 2025-12-31: Added examples for [tool calling](#tool-calling), such as changing the speaking speed, switching between male/female voices and British/American accents, and getting the current weather of a city. Diarization model is updated to [nvidia/diar_streaming_sortformer_4spk-v2.1](https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2.1) with improved performance. |
| - 2025-11-14: Added support for joint ASR and EOU detection with [Parakeet-realtime-eou-120m](https://huggingface.co/nvidia/parakeet_realtime_eou_120m-v1) model. |
| - 2025-10-10: Added support for [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) TTS model. |
| - 2025-10-03: Add support for serving LLM with vLLM and auto-switch between vLLM and HuggingFace, add [nvidia/NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2) as default LLM. |
| - 2025-09-05: First release of NeMo Voice Agent. |
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| ## 🚀 Quick Start |
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| ### Hardware requirements |
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| - A computer with at least one GPU. At least 21GB VRAM is recommended for using 9B LLMs, and 13GB VRAM for 4B LLMs. |
| - A microphone connected to the computer. |
| - A speaker connected to the computer. |
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| ### Install dependencies |
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| First, install or update the npm and node.js to the latest version, for example: |
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| ```bash |
| sudo apt-get update |
| sudo apt-get install -y npm nodejs |
| ``` |
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| or: |
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| ```bash |
| curl -fsSL https://fnm.vercel.app/install | bash |
| . ~/.bashrc |
| fnm use --install-if-missing 20 |
| ``` |
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| Second, create a new conda environment with the dependencies: |
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| ```bash |
| conda env create -f environment.yaml |
| ``` |
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| Then you can activate the environment via `conda activate nemo-voice`. |
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| ### Configure the server |
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| If you want to just try the default server config, you can skip this step. |
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| Edit the `server/server_configs/default.yaml` file to configure the server as needed, for example: |
| - Changing the LLM and system prompt you want to use in `llm.model` and `llm.system_prompt`, by either putting a local path to a text file or the whole prompt string. See `server/example_prompts/` for examples to start with. |
| - Configure the LLM parameters, such as temperature, max tokens, etc. You may also need to change the HuggingFace or vLLM server parameters, depending on the LLM you are using. Please refer to the LLM's model page for details on the recommended parameters. |
| - If you know whether you want to use vLLM or HuggingFace, you can set `llm.type` to `vllm` or `hf` to force using vLLM or HuggingFace, respectively. Otherwise, it will automatically switch between the two based on the model's support. Please also remember to update the parameters of the chosen backend as well, by referring to the LLM's model page. |
| - Distribute different components to different GPUs if you have more than one. |
| - Adjust VAD parameters for sensitivity and end-of-turn detection timeout. |
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| **If you want to access the server from a different machine, you need to change the `baseUrl` in `client/src/app.ts` to the actual ip address of the server machine.** |
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| ### Start the server |
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| Open a terminal and run the server via: |
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| ```bash |
| NEMO_PATH=??? # Use your local NeMo path with the latest main branch from: https://github.com/NVIDIA-NeMo/NeMo |
| export PYTHONPATH=$NEMO_PATH:$PYTHONPATH |
| # export HF_TOKEN="hf_..." # Use your own HuggingFace API token if needed, as some models may require. |
| # export HF_HUB_CACHE="/path/to/your/huggingface/cache" # change where HF cache is stored if you don't want to use the default cache |
| # export SERVER_CONFIG_PATH="/path/to/your/server/config.yaml" # change to the server config you want to use, otherwise it will use the default config in `server/server_configs/default.yaml` |
| python ./server/server.py |
| ``` |
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| ### Launch the client |
| In another terminal on the server machine, start the client via: |
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| ```bash |
| cd client |
| npm install |
| npm run dev |
| ``` |
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| There should be a message in terminal showing the address and port of the client. |
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| ### Connect to the client via browser |
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| Open the client via browser: `http://[YOUR MACHINE IP ADDRESS]:5173/` (or whatever address and port is shown in the terminal where the client was launched). |
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| You can mute/unmute your microphone via the "Mute" button, and reset the LLM context history and speaker cache by clicking the "Reset" button. |
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| **If using chrome browser, you need to add `http://[YOUR MACHINE IP ADDRESS]:5173/` to the allow list via `chrome://flags/#unsafely-treat-insecure-origin-as-secure`.** You may also need to restart the browser for the changes to take effect. |
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| If you want to use a different port for client connection, you can modify `client/vite.config.js` to change the `port` variable. |
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| ## 📑 Supported Models and Features |
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| ### 🤖 LLM |
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| Most LLMs from HuggingFace are supported. A few examples are: |
| - [nvidia/NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2) (default) |
| - Please use `server/server_configs/llm_configs/nemotron_nano_v2.yaml` as the server config. |
| - Tool calling is enabled for this model. |
| - [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16) |
| - Please use `server/server_configs/llm_configs/nemotron_nano_v3.yaml` as the server config. It needs more than 60GB VRAM to host the model, thus the config by default is set to use tensor parallelism of 2. Expect additional 5GB for kv-cache and other components in the voice agent. To better monitor the vllm status, `start_vllm_on_init` is set to `false`, so that you can manually start the vllm server in another terminal via: |
| ```bash |
| vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 \ |
| --trust-remote-code --max-num-seqs 1 --gpu-memory-utilization 0.8 --max-model-len 8192 \ |
| --tensor-parallel-size 2 --enable-auto-tool-choice --tool-call-parser qwen3_coder --enable-prefix-caching \ |
| --reasoning-parser-plugin server/parsers/nano_v3_reasoning_parser.py --reasoning-parser nano_v3 |
| ``` |
| - If you have a GPU with FP8 support, the VRAM requirement is reduced to about 30GB. You can switch to [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8) by modifying the llm config accordingly. |
| - Tool calling is enabled for this model. |
| - [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
| - Please use `server/server_configs/llm_configs/qwen2.5-7B.yaml` as the server config. |
| - [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
| - Please use `server/server_configs/llm_configs/qwen3-8B.yaml` as the server config. |
| - Please use `server/server_configs/llm_configs/qwen3-8B_think.yaml` if you want to enable thinking mode. |
| - [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) |
| - Please use `server/server_configs/llm_configs/llama3.1-8B-instruct.yaml` as the server config. |
| - Note that you need to get access to the model first, and specify `export HF_TOKEN="hf_..."` when launching the server. |
| - [nvidia/Llama-3.1-Nemotron-Nano-8B-v1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1) |
| - [nvidia/Nemotron-Mini-4B-Instruct](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct) |
| |
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| Please refer to the homepage of each model to configure the model parameters: |
| - If `llm.type=hf`, please set `llm.generation_kwargs` and `llm.apply_chat_template_kwargs` in the server config as needed. |
| - If `llm.type=vllm`, please set `llm.vllm_server_params` and `llm.vllm_generation_params`in the server config as needed. |
| - If `llm.type=auto`, the server will first try to use vLLM, and if it fails, it will try to use HuggingFace. In this case, you need to make sure parameters for both backends are set properly. |
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| You can change the `llm.system_prompt` in `server/server_configs/default.yaml` to configure the behavior of the LLM, by either putting a local path to a text file or the whole prompt string. See `server/example_prompts/` for examples to start with. |
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| #### Thinking/reasoning Mode for LLMs |
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| A lot of LLMs support thinking/reasoning mode, which is useful for complex tasks, but it will create a long latency for the final answer. By default, we turn off the thinking/reasoning mode for all models for best latency. |
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| Different models may have different ways to support thinking/reasoning mode, please refer to the model's homepage for details on their thinking/reasoning mode support. Meanwhile, in many cases, they support enabling thinking/reasoning can be achieved by adding `/think` or `/no_think` to the end of the system prompt, and the thinking/reasoning content is wrapped by the tokens `["<think>", "</think>"]`. Some models may also support enabling thinking/reasoning by setting `llm.apply_chat_template_kwargs.enable_thinking=true/false` in the server config when `llm.type=hf`. |
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| If thinking/reasoning mode is enabled (e.g., in `server/server_configs/llm_configs/qwen3-8B_think.yaml`), the voice agent server will print out the thinking/reasoning content so that you can see the process of the LLM thinking and still have a smooth conversation experience. The thinking/reasoning content will not go through the TTS process, so you will only hear the final answer, and this is achieved by specifying the pair of thinking tokens `tts.think_tokens=["<think>", "</think>"]` in the server config. |
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| For vLLM server, if you specify `--reasoning_parser` in `vllm_server_params`, the thinking/reasoning content will be filtered out and does not show up in the output. |
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| ### 🎤 ASR |
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| We use [cache-aware streaming FastConformer](https://arxiv.org/abs/2312.17279) to transcribe the user's speech into text. While new models will be released soon, we use the existing English models for now: |
| - [nvidia/parakeet_realtime_eou_120m-v1](https://huggingface.co/nvidia/parakeet_realtime_eou_120m-v1) (default) |
| - This model supports EOU prediction and optimized for lowest latency, but does not support punctuation and capitalization. |
| - [nvidia/nemotron-speech-streaming-en-0.6b](https://huggingface.co/nvidia/nemotron-speech-streaming-en-0.6b) |
| - This model has better ASR accuracy and supports punctuation and capitalization, but does not predict EOU. |
| - [stt_en_fastconformer_hybrid_large_streaming_80ms](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_fastconformer_hybrid_large_streaming_80ms) |
| - [nvidia/stt_en_fastconformer_hybrid_large_streaming_multi](https://huggingface.co/nvidia/stt_en_fastconformer_hybrid_large_streaming_multi) |
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| ### 💬 Speaker Diarization |
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| Speaker diarization aims to distinguish different speakers in the input speech audio. We use [streaming Sortformer](http://arxiv.org/abs/2507.18446) to detect the speaker for each user turn. |
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| As of now, we only support detecting 1 speaker per user turn, but different turns come from different speakers, with a maximum of 4 speakers in the whole conversation. |
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| Currently supported models are: |
| - [nvidia/diar_streaming_sortformer_4spk-v2.1](https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2.1) (default) |
| - [nvidia/diar_streaming_sortformer_4spk-v2](https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2) |
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| Please note that in some circumstances, the diarization model might not work well in noisy environments, or it may confuse the speakers. In this case, you can disable the diarization by setting `diar.enabled` to `false` in `server/server_configs/default.yaml`. |
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| ### 🔉 TTS |
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| Here are the supported TTS models: |
| - [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) is a lightweight TTS model. This model is the default speech generation backend. |
| - Please use `server/server_configs/tts_configs/kokoro_82M.yaml` as the server config. |
| - [FastPitch-HiFiGAN](https://huggingface.co/nvidia/tts_en_fastpitch) is an NVIDIA-NeMo TTS model. It only supports English output. |
| - Please use `server/server_configs/tts_configs/nemo_fastpitch-hifigan.yaml` as the server config. |
| - [magpie_tts_multilingual_357m](https://huggingface.co/nvidia/magpie_tts_multilingual_357m) is a multilingual TTS model. |
| - Please use `server/server_configs/tts_configs/magpie_tts_multilingual_357m.yaml` as the server config. |
| We will support more TTS models in the future. |
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| ### 🔄 Turn-taking |
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| As the new turn-taking prediction model is not yet released, we use the VAD-based turn-taking prediction for now. You can set the `vad.stop_secs` to the desired value in `server/server_configs/default.yaml` to control the amount of silence needed to indicate the end of a user's turn. |
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| Additionally, the voice agent supports ignoring back-channel phrases while the bot is talking, which means phrases such as "uh-huh", "yeah", "okay" will not interrupt the bot while it's talking. To control the backchannel phrases to be used, you can set the `turn_taking.backchannel_phrases` in the server config to the desired list of phrases or a file path to a yaml file containing the list of phrases. By default, it will use the phrases in `server/backchannel_phrases.yaml`. Setting it to `null` will disable detecting backchannel phrases, and that the VAD will interrupt the bot immediately when the user starts speaking. |
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| ### 🔧 Tool Calling |
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| We support tool calling for LLMs to use external tools (e.g., getting the current weather of a city) or adjust its own behavior (e.g., changing the speaking speed). Some example queries to try with the default server config: |
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| 1. Getting the current weather of a city: |
| - "What's the weather in New York city?" |
| - "What's the weather in Paris?" |
| - "What's the weather in Paris, Texas, USA?" |
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| 2. Changing the speaking speed of the voice agent: |
| - "Can you speak faster?" |
| - "Can you speak slower?" |
| - "Reset to the original speaking speed." |
| - "Speak twice as fast." |
| - "Speak half as slow." |
| |
| 3. Switching between British and American accents, and changing the gender of the voice: |
| - "Speak in British accent." |
| - "Switch to a male voice." |
| - "Switch to a female voice." |
| - "Reset to the original language and voice." |
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| Currently, tool calling is only supported for vLLM server and specific LLM models: |
| - [nvidia/NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2) (default) |
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| More LLMs can be supported by referring to their documentation on how to enable tool calling in vLLM. Note that the system prompt may need to be tuned accordingly. |
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| More tools will be added later. However, if you cannot wait to hack and add your own tools, please read the following section. |
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| #### Adding new tools |
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| Additional tools can be added in two ways: |
| - Adding a new [direct function](https://docs.pipecat.ai/guides/learn/function-calling#using-direct-functions-shorthand) such as the `get_city_weather` function in `nemo/agents/voice_agent/pipecat/utils/tool_calling/basic_tools.py`. |
| - Adding new tools to adjust the behavior of each of the STT/TTS/Diar/LLM/TurnTaking components, by adding the `ToolCallingMixin` to the component and implementing the `setup_tool_calling` method as the `KokoroTTSService` class in `nemo/agents/voice_agent/pipecat/services/nemo/tts.py`. |
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| The tools are then registered to the LLM via the `register_direct_tools_to_llm` function in `nemo/agents/voice_agent/pipecat/utils/tool_calling/mixins.py`, as shown in the example in `examples/voice_agent/server/server.py`. |
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| More details on tool calling with Pipecat can be found in the [Pipecat documentation](https://docs.pipecat.ai/guides/learn/function-calling). |
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| #### Notes on tool calling issues |
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| We notice that sometimes the LLM cannot do anything that's not related to the provided tools, or it might not actually use the tools even though it says it's using them. To alleviate this issue, we insert additional instructions to the system prompt to regulate its behavior (e.g., in `server/server_configs/llm_configs/nemotron_nano_v2.yaml`). |
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| Sometimes, after answering a question related to the tools, the LLM might refuse to answer questions that are not related to the tools, or vice versa. This phenomenon can be called "commitment bias" or "tunnel vision". To alleviate this issue, we can insert additional instructions to the system prompt and explicitly asking the LLM to use or not use the tools in the user's query. |
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| ## 📝 Notes & FAQ |
| - Only one connection to the server is supported at a time, a new connection will disconnect the previous one, but the context will be preserved. |
| - If directly loading from HuggingFace and got I/O errors, you can set `llm.model=<local_path>`, where the model is downloaded using a command like `huggingface-cli download Qwen/Qwen2.5-7B-Instruct --local-dir <local_path>`. Same for TTS models. |
| - The current ASR and diarization models are not noise-robust, you might need to use a noise-cancelling microphone or a quiet environment. But we will release better models soon. |
| - The diarization model works best with speakers that have much more different voices from each other, while it might not work well on some accents due to the limited training data. |
| - If you see errors like `SyntaxError: Unexpected reserved word` when running `npm run dev`, please update the Node.js version. |
| - If you see the error `Error connecting: Cannot read properties of undefined (reading 'enumerateDevices')`, it usually means the browser is not allowed to access the microphone. Please check the browser settings and add `http://[YOUR MACHINE IP ADDRESS]:5173/` to the allow list, e.g., via `chrome://flags/#unsafely-treat-insecure-origin-as-secure` for chrome browser. |
| - If you see something like `node:internal/errors:496` when running `npm run dev`, remove the `client/node_modules` folder and run `npm install` again, then run `npm run dev` again. |
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| ## ☁️ NVIDIA NIM Services |
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| NVIDIA also provides a variety of [NIM](https://developer.nvidia.com/nim?sortBy=developer_learning_library%2Fsort%2Ffeatured_in.nim%3Adesc%2Ctitle%3Aasc&hitsPerPage=12) services for better ASR, TTS and LLM performance with more efficient deployment on either cloud or local servers. |
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| You can also modify the `server/server.py` to use NVIDIA NIM services for better LLM, ASR and TTS performance, by referring to these Pipecat services: |
| - [NVIDIA NIM LLM Service](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/nim/llm.py) |
| - [NVIDIA Riva ASR Service](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/riva/stt.py) |
| - [NVIDIA Riva TTS Service](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/riva/tts.py) |
| - Please refer to this [NVIDIA ACE Controller example](https://github.com/NVIDIA/ace-controller/blob/main/examples/speech-to-speech/bot.py#L63) for more details on how to use NVIDIA NIM services in the voice agent. |
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| For details of available NVIDIA NIM services, please refer to: |
| - [NVIDIA NIM LLM Service](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html) |
| - [NVIDIA Riva ASR NIM Service](https://docs.nvidia.com/nim/riva/asr/latest/overview.html) |
| - [NVIDIA Riva TTS NIM Service](https://docs.nvidia.com/nim/riva/tts/latest/overview.html) |
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| ## Acknowledgments |
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| - This example uses the [Pipecat](https://github.com/pipecat-ai/pipecat) orchestrator framework. |
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| ## Contributing |
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| We welcome contributions to this project. Please feel free to submit a pull request or open an issue. |
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