# How to Configure VAD Parameters in Hugging Face Speech-to-Speech: Thresholds, Silence Detection, and Realtime Settings

> Learn to configure VAD parameters in Hugging Face Speech-to-Speech. Master thresholds, silence detection, and realtime settings for optimal performance. Get started today.

- Repository: [Hugging Face/speech-to-speech](https://github.com/huggingface/speech-to-speech)
- Tags: how-to-guide
- Published: 2026-07-08

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**Configure VAD parameters by instantiating `VADHandlerArguments` with your desired thresholds and timings, then pass it to `SpeechToSpeechPipeline`, or update settings dynamically via the OpenAI-compatible realtime API's `session.update` event.**

The Hugging Face `speech-to-speech` repository wraps the **Silero VAD** model inside `VADHandler` to detect voice activity in audio streams. All user-controllable VAD options are centralized in the `VADHandlerArguments` dataclass and applied through the handler's `setup()` method, giving you precise control over speech boundaries, silence detection, and realtime processing behavior.

## Understanding VADHandlerArguments

The `VADHandlerArguments` dataclass in [`src/speech_to_speech/arguments_classes/vad_arguments.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/arguments_classes/vad_arguments.py) exposes every tunable knob for voice activity detection. When you instantiate `SpeechToSpeechPipeline`, these arguments are forwarded to `VADHandler.setup()` in [`src/speech_to_speech/VAD/vad_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/VAD/vad_handler.py), which initializes the internal `VADIterator`.

### Speech Detection Thresholds

The `thresh` parameter (defined at `vad_arguments.py#L6-L10`) controls the confidence level required for the Silero model to consider audio as speech. It accepts float values between **0.0 and 1.0**, with a default of **0.6**. Higher values reduce false positives but may miss quiet speech, while lower values increase sensitivity to background noise.

### Speech and Silence Duration Controls

Several parameters govern the minimum and maximum durations for speech segments and silence gaps:

- **`min_silence_ms`** (default **64 ms**): Minimum continuous silence that triggers a segment split. Defined at `vad_arguments.py#L18-L22`.
- **`min_speech_ms`** (default **384 ms**): Minimum length for a valid speech utterance. Shorter fragments are held for merging or discarded. Defined at `vad_arguments.py#L24-L28`.
- **`min_speech_continuation_ms`** (default **192 ms**): Hysteresis threshold for reopening a speech turn in realtime mode. Must be less than or equal to `min_speech_ms`. Defined at `vad_arguments.py#L30-L34`.
- **`max_speech_ms`** (default **∞**): Hard limit on segment duration; forces a split when exceeded. Defined at `vad_arguments.py#L36-L40`.
- **`speech_pad_ms`** (default **500 ms** in arguments, overridable to **30 ms** in setup): Amount of audio prepended to each segment to capture speech onsets. Defined at `vad_arguments.py#L42-L46`.

### Realtime Processing and Enhancement Options

For streaming applications, the following settings control progressive transcription and audio preprocessing:

- **`enable_realtime_transcription`** (default **False**): Emits audio chunks while the user is still speaking. Defined at `vad_arguments.py#L54-L57`.
- **`realtime_processing_pause`** (default **0.5 s**): Base interval between progressive chunks, automatically scaled by speech length. Defined at `vad_arguments.py#L58-L62`.
- **`speculative_reopen_ms`** (default **1000 ms**) and **`unanswered_reopen_ms`** (default **7000 ms**): Duration that a soft-ended turn remains reopenable. Defined at `vad_arguments.py#L64-L74`.
- **`short_segment_merge_ms`** (default **0**): Window allowing tiny fragments to be stitched together. Useful when `min_silence_ms` is set very low. Defined at `vad_arguments.py#L76-L80`.
- **`audio_enhancement`** (default **False**): Enables DeepFilterNet noise reduction. Requires the optional `df` package. Defined at `vad_arguments.py#L48-L53`.

## Initializing VAD Parameters at Startup

Pass a configured `VADHandlerArguments` instance when constructing your pipeline. The `setup()` method receives these values and instantiates the `VADIterator` with the specified threshold, sampling rate, and silence duration:

```python
from speech_to_speech.pipeline import SpeechToSpeechPipeline
from speech_to_speech.arguments_classes.vad_arguments import VADHandlerArguments

vad_args = VADHandlerArguments(
    thresh=0.75,               # stricter detection

    min_silence_ms=100,        # longer silence before split

    min_speech_ms=250,         # accept shorter utterances

    speech_pad_ms=200,         # keep only 200 ms of pre-speech context

    audio_enhancement=True,    # enable noise reduction (requires df)

)

pipeline = SpeechToSpeechPipeline(
    vad_handler_args=vad_args,
    # … other arguments like model, tokenizer, etc.

)

```

Inside `VADHandler.setup()` (lines 59–77 of [`vad_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/vad_handler.py)), these arguments populate the `VADIterator`:

```python
self.iterator = VADIterator(
    self.model,
    threshold=thresh,
    sampling_rate=sample_rate,
    min_silence_duration_ms=min_silence_ms,
    speech_pad_ms=speech_pad_ms,
)

```

## Updating VAD Configuration at Runtime

When using the OpenAI-compatible realtime endpoint, clients can adjust VAD parameters without restarting the server. The handler watches for `session.update` events and applies changes via `_apply_runtime_turn_detection` (lines 45–74 of [`vad_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/vad_handler.py)).

Send a JSON message over the websocket to modify the threshold or silence duration:

```python

# Assuming you have a websocket session `session`

session.send({
    "type": "session.update",
    "session": {
        "audio": {
            "input": {
                "turn_detection": {
                    "type": "server_vad",
                    "threshold": 0.8,               # raise confidence

                    "silence_duration_ms": 120       # lengthen silence window

                }
            }
        }
    }
})

```

The handler updates `self.iterator.threshold` and `self.iterator.min_silence_samples` immediately upon receiving the event.

## Handling Edge Cases: Short Segments and Audio Enhancement

### Merging Short Speech Segments

When `min_silence_ms` is set aggressively low, you may generate many tiny fragments. Enable stitching by setting `short_segment_merge_ms` to a positive value:

```python
vad_args = VADHandlerArguments(
    short_segment_merge_ms=150,   # allow fragments within 150 ms to be merged

)

pipeline = SpeechToSpeechPipeline(vad_handler_args=vad_args)

```

With this setting, the handler invokes `_hold_short_segment` (lines 107–115) and `_merge_pending_short_segment` (lines 83–106) to combine fragments before discarding them.

### Enabling Progressive Realtime Transcription

For low-latency streaming, enable progressive audio release:

```python
vad_args = VADHandlerArguments(
    enable_realtime_transcription=True,
    realtime_processing_pause=0.3,   # emit chunks every ~300 ms (scaled automatically)

)

pipeline = SpeechToSpeechPipeline(vad_handler_args=vad_args)

```

When enabled, the handler enters the `_process_realtime` branch (beginning at line 69 of [`vad_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/vad_handler.py)), yielding `VADAudio` objects in `"progressive"` mode while the user is still speaking.

## Summary

- **Configuration Source**: All VAD defaults live in `VADHandlerArguments` ([`src/speech_to_speech/arguments_classes/vad_arguments.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/arguments_classes/vad_arguments.py)), while runtime logic resides in `VADHandler` ([`src/speech_to_speech/VAD/vad_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/VAD/vad_handler.py)).
- **Threshold Control**: Adjust `thresh` (0.0–1.0) to balance between false positives and missed speech.
- **Segment Boundaries**: Use `min_silence_ms`, `min_speech_ms`, and `speech_pad_ms` to define how audio is split and padded.
- **Realtime Updates**: Modify `threshold` and `silence_duration_ms` on-the-fly via the OpenAI-compatible API's `session.update` event, processed by `_apply_runtime_turn_detection`.
- **Advanced Features**: Enable `audio_enhancement` for noise reduction, `short_segment_merge_ms` for fragment stitching, and `enable_realtime_transcription` for progressive streaming.

## Frequently Asked Questions

### What is the default VAD threshold in the speech-to-speech pipeline?

The default `thresh` value is **0.6**, defined in [`src/speech_to_speech/arguments_classes/vad_arguments.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/arguments_classes/vad_arguments.py). This means the Silero model must output a probability of at least 60% for the audio to be classified as speech. You can override this at initialization or update it at runtime via the realtime API.

### How do I enable realtime transcription with custom VAD timing?

Set `enable_realtime_transcription=True` in your `VADHandlerArguments` and adjust `realtime_processing_pause` to control the base interval between chunks. For example, setting `realtime_processing_pause=0.3` yields emissions approximately every 300 milliseconds, scaled automatically by the handler based on speech length.

### Can I change VAD settings without restarting the server?

Yes. When using the OpenAI-compatible realtime endpoint, send a `session.update` event with the `turn_detection` payload. The `VADHandler._apply_runtime_turn_detection` method (lines 45–74 of [`vad_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/vad_handler.py)) updates `self.iterator.threshold` and `self.iterator.min_silence_samples` immediately, allowing dynamic adjustment without pipeline restart.

### What is the difference between `min_speech_ms` and `min_speech_continuation_ms`?

`min_speech_ms` (default 384 ms) defines the absolute minimum duration for a speech segment to be emitted as valid. `min_speech_continuation_ms` (default 192 ms) is a hysteresis value used specifically in realtime mode to determine whether a soft-ended turn should be reopened when new audio arrives. The continuation threshold must be less than or equal to the minimum speech duration.