# How to Configure DeepFilterNet for VAD Audio Enhancement Without NumPy Conflicts

> Configure DeepFilterNet for VAD audio enhancement. Avoid NumPy conflicts by installing the df package and managing NumPy versions. Enable audio enhancement with a simple flag.

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

---

**To configure DeepFilterNet for VAD audio enhancement without NumPy conflicts, install the `df` package and maintain NumPy within the repository's specified range (`>=1.26.0,<2.4.4` on macOS or `>=1.26.0` on other platforms), then enable the feature by setting `audio_enhancement=True` in your `VADHandlerArguments`.**

The `huggingface/speech-to-speech` repository supports optional audio enhancement via DeepFilterNet within its Voice Activity Detection (VAD) pipeline. When you configure DeepFilterNet for VAD audio enhancement alongside Pocket TTS, both components rely on NumPy, requiring careful version management to prevent dependency conflicts.

## Understanding the DeepFilterNet Integration

DeepFilterNet serves as an optional backend for audio enhancement in the VAD handler. According to the source code 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), the system implements a defensive import pattern that allows the pipeline to function whether or not the enhancement library is present.

### Optional Import Guard in the VAD Handler

Between lines 42 and 50 of [`vad_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/vad_handler.py), the handler attempts to import the `df` package (DeepFilterNet) within a try-except block. If the import fails, the handler logs a warning and continues without enhancement capabilities. This design ensures that missing optional dependencies never break the core speech-to-speech pipeline.

### NumPy Version Constraints

The repository manages potential conflicts through strict version pinning in [`pyproject.toml`](https://github.com/huggingface/speech-to-speech/blob/main/pyproject.toml) (lines 31-34). For Darwin (macOS) systems, NumPy is constrained to `>=1.26.0,<2.4.4`, while Linux and Windows platforms use `>=1.26.0`. These ranges satisfy both Pocket TTS and DeepFilterNet requirements simultaneously.

## Step-by-Step Configuration Guide

Follow these steps to enable DeepFilterNet enhancement while maintaining NumPy compatibility with Pocket TTS.

### 1. Install DeepFilterNet

Install the `df` package to enable the enhancement backend:

```bash
pip install df

```

This command installs DeepFilterNet along with its own NumPy dependencies.

### 2. Verify NumPy Compatibility

Ensure your NumPy installation falls within the repository's supported range. For macOS systems, use the upper bound constraint:

```bash
pip install "numpy>=1.26.0,<2.4.4"

```

For Linux or Windows environments, the lower bound is sufficient:

```bash
pip install "numpy>=1.26.0"

```

### 3. Enable Audio Enhancement in VAD Arguments

Import `VADHandlerArguments` from `speech_to_speech.arguments_classes.vad_arguments` and set the enhancement flag:

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

vad_args = VADHandlerArguments(
    audio_enhancement=True,  # Activates DeepFilterNet

    thresh=0.6,
    sample_rate=16000
)

```

### 4. Initialize the Pipeline with Pocket TTS

Configure Pocket TTS arguments without modifying NumPy requirements. The `PocketTTSHandlerArguments` class in [`src/speech_to_speech/arguments_classes/pocket_tts_arguments.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/arguments_classes/pocket_tts_arguments.py) operates independently of DeepFilterNet:

```python
from speech_to_speech.arguments_classes.pocket_tts_arguments import PocketTTSHandlerArguments
from speech_to_speech.s2s_pipeline import SpeechToSpeechPipeline

tts_args = PocketTTSHandlerArguments(
    pocket_tts_device="cpu",
    pocket_tts_voice="jean",
    pocket_tts_sample_rate=16000
)

pipeline = SpeechToSpeechPipeline(
    vad_args=vad_args,
    tts_args=tts_args
)

```

## Complete Implementation Example

The following example demonstrates the full integration of VAD with DeepFilterNet enhancement alongside Pocket TTS, ensuring all NumPy dependencies remain compatible:

```python
from speech_to_speech.arguments_classes.vad_arguments import VADHandlerArguments
from speech_to_speech.arguments_classes.pocket_tts_arguments import PocketTTSHandlerArguments
from speech_to_speech.s2s_pipeline import SpeechToSpeechPipeline

# Configure VAD with DeepFilterNet audio enhancement

vad_args = VADHandlerArguments(
    audio_enhancement=True,   # Enables DeepFilterNet processing

    thresh=0.6,
    sample_rate=16000
)

# Configure Pocket TTS with standard NumPy compatibility

tts_args = PocketTTSHandlerArguments(
    pocket_tts_device="cpu",
    pocket_tts_voice="jean",
    pocket_tts_sample_rate=16000
)

# Build and execute the pipeline

pipeline = SpeechToSpeechPipeline(
    vad_args=vad_args,
    tts_args=tts_args,
    # Additional components (STT, LLM) configured here

)

pipeline.run()

```

## Key Source Files and Architecture

Understanding the file structure helps diagnose configuration issues:

- **[`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)**: Defines `VADHandlerArguments` including the `audio_enhancement` boolean flag that controls DeepFilterNet activation.

- **[`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)**: Contains the import logic (lines 42-50) that conditionally loads the `df` module and initializes the enhancement model only when available.

- **[`src/speech_to_speech/arguments_classes/pocket_tts_arguments.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/arguments_classes/pocket_tts_arguments.py)**: Specifies arguments for Pocket TTS without DeepFilterNet dependencies.

- **[`src/speech_to_speech/TTS/pocket_tts_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/src/speech_to_speech/TTS/pocket_tts_handler.py)**: Implements the Pocket TTS handler using NumPy independently of the VAD enhancement chain.

- **[`pyproject.toml`](https://github.com/huggingface/speech-to-speech/blob/main/pyproject.toml)**: Declares NumPy version constraints (`>=1.26.0,<2.4.4` for Darwin, `>=1.26.0` for others) that prevent version conflicts between components.

## Summary

- **DeepFilterNet is optional**: The VAD handler in [`vad_handler.py`](https://github.com/huggingface/speech-to-speech/blob/main/vad_handler.py) gracefully degrades to standard VAD if the `df` package is missing.
- **Version alignment is critical**: Keep NumPy within the ranges specified in [`pyproject.toml`](https://github.com/huggingface/speech-to-speech/blob/main/pyproject.toml) (`>=1.26.0,<2.4.4` on macOS, `>=1.26.0` elsewhere) to satisfy both Pocket TTS and DeepFilterNet.
- **Enable via arguments**: Set `audio_enhancement=True` in `VADHandlerArguments` to activate the feature.
- **No code changes required**: Pocket TTS requires no special configuration to coexist with DeepFilterNet; the dependency management happens at the environment level.

## Frequently Asked Questions

### What happens if DeepFilterNet is not installed?

If the `df` package is unavailable, the VAD handler catches the ImportError during initialization and logs a warning message. The pipeline continues operating with standard VAD functionality, completely bypassing the audio enhancement step without raising runtime errors.

### Why does Pocket TTS conflict with DeepFilterNet?

The conflict arises from NumPy version requirements rather than direct code dependencies. Both components use NumPy for array operations, but DeepFilterNet may specify different version constraints than Pocket TTS. The repository resolves this by pinning NumPy to a compatible range in [`pyproject.toml`](https://github.com/huggingface/speech-to-speech/blob/main/pyproject.toml) that satisfies both libraries simultaneously.

### Can I use DeepFilterNet on macOS without the upper version bound?

While you might install DeepFilterNet without the `<2.4.4` constraint, doing so risks incompatibility with Pocket TTS or other repository components. The maintainers specifically tested the `>=1.26.0,<2.4.4` range for Darwin systems to ensure stable operation across all speech-to-speech pipeline features.

### How do I verify that audio enhancement is active?

Check your application logs during pipeline initialization. When `audio_enhancement=True` and the `df` package imports successfully, the VAD handler initializes the DeepFilterNet model without warnings. If enhancement is disabled due to import failures, you will see a warning message indicating that audio enhancement is unavailable.