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mlx-community/
NemotronLabs-VoiceChat-11B-bf16

Name: NemotronLabs-VoiceChat-11B-bf16
Availability: InStock

nvidia/NVIDIA-NemotronLabs-VoiceChat-11B

finetune

openmdw-1.1

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NVIDIA NemotronLabs VoiceChat

NemotronLabs VoiceChat is a full-duplex speech model that listens, transcribes, responds with text, and synthesizes aligned speech on a continuous audio timeline. This MLX implementation supports online inference through the /v1/realtime WebSocket endpoint and a stateful Python API, plus offline WAV inference.

Input audio is 16 kHz mono. Generated audio is 22,050 Hz mono using the model's built-in Aria voice.

Supported Models

| Model | Precision | |---|---|---| | mlx-community/NemotronLabs-VoiceChat-11B-bf16 | BF16 | | mlx-community/NemotronLabs-VoiceChat-11B-8bit | 8-bit | | mlx-community/NemotronLabs-VoiceChat-11B-4bit | 4-bit |

Install

pip install -U mlx-vlm

Realtime WebSocket API

Start the normal mlx-vlm server:

mlx_vlm.server --host 127.0.0.1 --port 8080

VoiceChat clients connect to:

ws://127.0.0.1:8080/v1/realtime

The server sends session.created after accepting the connection. Configure the session before sending audio:

json

{
  "type": "session.update",
  "session": {
    "model": "mlx-community/NemotronLabs-VoiceChat-11B-8bit",
    "system_prompt": "Be concise and answer in one sentence.",
    "seed": 0
  }
}

After session.updated, send any number of base64-encoded PCM16 chunks. Chunk boundaries may be arbitrary; the server advances the model on complete 1,280 sample (80 ms) frames.

json

{
  "type": "input_audio_buffer.append",
  "audio": "<base64 PCM16>",
  "sample_rate": 16000
}

Commit the input when the session is finished:

json

{
  "type": "input_audio_buffer.commit",
  "pad_partial": true
}

The server emits aligned events as inference advances:

Event	Contents
`response.text.delta`	Assistant token, incremental text, and cumulative text
`conversation.item.input_audio_transcription.delta`	Incremental and cumulative user transcript
`response.function.delta`	Raw function-channel token and text delta
`response.audio.delta`	Base64 PCM16 audio at 22,050 Hz plus raw audio codes
`response.done`	The committed session has finished
`response.cancelled`	The session was cancelled
`error`	Request or inference error details

Send session.cancel or response.cancel to stop without committing. A client may also send session.ping and receive session.pong.

Microphone and speakers

The live microphone example also requires PortAudio through sounddevice:

pip install -U sounddevice

For a live microphone and speaker loop:

python examples/nemotron_voicechat_microphone.py \
  mlx-community/NemotronLabs-VoiceChat-11B-8bit

Use --list-devices, --input-device, and --output-device to select audio devices. Headphones are strongly recommended: the example keeps listening while the assistant speaks and does not perform acoustic echo cancellation.

Python API

Load the model and processor through the standard mlx_vlm.load interface, then create a model-specific VoiceChat session.

Online inference

The stateful API accepts floating-point mono PCM with arbitrary chunk boundaries. Text fields on events are cumulative; delta contains the incremental update.

python

import numpy as np
from mlx_audio.audio_io import write as write_audio
from mlx_audio.stt.utils import load_audio

from mlx_vlm import load

model, processor = load(
    "mlx-community/NemotronLabs-VoiceChat-11B-8bit"
)
voicechat = model.create_session(processor)
stream = voicechat.create_streaming_session(
    system_prompt="Be concise and answer in one sentence.",
    seed=0,
)

input_audio = load_audio("input.wav", sr=16_000).squeeze()
audio_chunks = []

def handle(events):
    for event in events:
        if event.kind == "assistant_text_delta":
            print(event.delta or "", end="", flush=True)
        elif event.kind == "user_transcript_delta":
            print(f"\n[user] {event.text}")
        elif event.kind == "function_delta":
            print(f"\n[function] {event.delta}")
        elif event.kind == "audio":
            audio_chunks.append(np.asarray(event.samples, dtype=np.float32))

for offset in range(0, input_audio.shape[0], 1_280):
    handle(
        stream.push_audio(
            input_audio[offset : offset + 1_280],
            sample_rate=16_000,
        )
    )
handle(stream.flush(pad_partial=True))

response_audio = (
    np.concatenate(audio_chunks)
    if audio_chunks
    else np.zeros(0, dtype=np.float32)
)
write_audio("response.wav", response_audio, 22_050)

Online sessions keep persistent log-mel, FastConformer, Nemotron-H, EAR-TTS, and codec state by default. This bounded per-frame path is the recommended mode for interactive applications. Whether it keeps pace with wall clock depends on the model precision and host hardware.

For diagnostic comparisons, language and perception caches can be disabled independently:

python

stream = voicechat.create_streaming_session(
    use_language_cache=False,
    use_perception_cache=False,
)

Offline inference

python

result = voicechat.generate(
    "input.wav",
    system_prompt="Be concise and answer in one sentence.",
    extra_decoding_seconds=3,
    seed=0,
)

print(result.user_transcript)
print(result.text)
# result.audio is mono float PCM at result.sample_rate (22,050 Hz).

The offline path uses full-history Nemotron-H inference by default.

Notes

The model is continuously duplex: silence and overlapping speech remain part of its timeline. The server does not use VAD to gate input.
/v1/realtime currently allows one active VoiceChat WebSocket at a time.
Arbitrary voice cloning is not supported; inference uses the checkpoint's built-in Aria voice.
Remote Hugging Face model code is not executed.