---
title: Use AI Speech Enhancement Services for Quick Results
impact: HIGH
impactDescription: studio-quality enhancement with minimal effort
tags: voice, ai, enhancement, adobe, cloud-service
---

## Use AI Speech Enhancement Services for Quick Results

Cloud AI services (Adobe Enhance, Descript) provide studio-quality enhancement without technical expertise. Useful for quick processing when forensic documentation isn't required.

**Incorrect (manual enhancement for non-forensic use):**

```bash
# Complex manual pipeline for a podcast
ffmpeg -i podcast.wav -af "highpass=f=80,afftdn=nr=15,equalizer=f=2500:g=3,loudnorm" output.wav
# Time-consuming, requires expertise, may not match AI quality
```

**Correct (AI service for quick results):**

```python
# Use Adobe Enhance or similar AI service
# 1. Upload to https://podcast.adobe.com/enhance
# 2. Download studio-quality result in seconds

# Or use local AI: noisereduce + speechbrain
audio = local_ai_enhance('podcast.wav', 'enhanced.wav')
# Studio quality with minimal effort
```

**When to use cloud services:**

- Non-forensic use (podcasts, meetings, personal)
- Quick turnaround needed
- No specialized software available
- Audio doesn't contain sensitive information

**When NOT to use cloud services:**

- Legal/forensic evidence
- Confidential recordings
- Need for reproducible processing
- Offline processing required

**Adobe Podcast Enhance Speech:**

```python
import requests
import time

def enhance_with_adobe(input_path, output_path, api_key=None):
    """
    Use Adobe Podcast Enhance Speech API.

    Note: As of 2024, this is a web service.
    Visit: https://podcast.adobe.com/enhance
    """
    print("Adobe Enhance Speech is a web service.")
    print("1. Go to https://podcast.adobe.com/enhance")
    print("2. Upload your audio file")
    print("3. Download the enhanced result")
    print(f"\nInput file: {input_path}")

# Alternative: Use local AI enhancement with Silero
def enhance_with_silero(audio, sr):
    """
    Use Silero VAD + enhancement for local processing.
    """
    import torch

    # Load Silero VAD
    model, utils = torch.hub.load(
        repo_or_dir='snakers4/silero-vad',
        model='silero_vad',
        force_reload=False
    )

    (get_speech_timestamps,
     save_audio,
     read_audio,
     VADIterator,
     collect_chunks) = utils

    # Detect speech segments
    speech_timestamps = get_speech_timestamps(
        torch.tensor(audio),
        model,
        sampling_rate=sr
    )

    return speech_timestamps

# Local AI enhancement with speechbrain
def enhance_with_speechbrain(audio, sr):
    """
    Use SpeechBrain for local AI enhancement.

    pip install speechbrain
    """
    try:
        from speechbrain.pretrained import SepformerSeparation

        # Load model
        model = SepformerSeparation.from_hparams(
            source="speechbrain/sepformer-wham16k-enhancement",
            savedir="pretrained_models/sepformer-enhancement"
        )

        # Resample if needed
        if sr != 16000:
            import librosa
            audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)

        # Enhance
        import torch
        audio_tensor = torch.tensor(audio).unsqueeze(0)
        enhanced = model.separate_batch(audio_tensor)

        return enhanced.squeeze().numpy()

    except ImportError:
        print("speechbrain not installed. Install with: pip install speechbrain")
        return audio
```

**Local alternatives comparison:**

| Tool | Quality | Speed | Privacy | Setup Complexity |
|------|---------|-------|---------|------------------|
| Adobe Enhance | Excellent | Fast | Cloud | None (web) |
| SpeechBrain | Good | Medium | Local | Medium |
| noisereduce | Good | Fast | Local | Easy |
| RNNoise | Good | Very Fast | Local | Medium |
| Silero | Good | Fast | Local | Easy |

**Python local enhancement pipeline:**

```python
import numpy as np
import librosa
import soundfile as sf

def local_ai_enhance(input_path, output_path):
    """
    Complete local AI enhancement pipeline.
    """
    # Load audio
    audio, sr = librosa.load(input_path, sr=None)
    print(f"Loaded: {len(audio)/sr:.1f}s at {sr}Hz")

    # Step 1: Noise reduction with noisereduce
    import noisereduce as nr
    audio = nr.reduce_noise(y=audio, sr=sr, stationary=False)
    print("Noise reduction applied")

    # Step 2: Normalize levels
    peak = np.max(np.abs(audio))
    if peak > 0:
        audio = audio / peak * 0.9
    print("Normalized")

    # Step 3: Apply presence boost for clarity
    from scipy import signal

    # Gentle presence boost at 2-4 kHz
    nyquist = sr / 2
    if nyquist > 4000:
        b, a = signal.iirpeak(3000 / nyquist, Q=2)
        audio = signal.filtfilt(b, a, audio) * 1.1
        audio = np.clip(audio, -1, 1)
        print("Presence boost applied")

    # Save
    sf.write(output_path, audio, sr)
    print(f"Saved: {output_path}")

# Usage
local_ai_enhance('noisy_speech.wav', 'enhanced_local.wav')
```

**Command-line quick enhancement:**

```bash
# Quick local enhancement with FFmpeg + noisereduce
# Step 1: Convert to standard format
ffmpeg -i input.mp3 -ar 16000 -ac 1 temp.wav

# Step 2: Python one-liner for noise reduction
python -c "
import noisereduce as nr
import soundfile as sf
audio, sr = sf.read('temp.wav')
cleaned = nr.reduce_noise(y=audio, sr=sr)
sf.write('enhanced.wav', cleaned, sr)
"

# Step 3: Normalize
ffmpeg -i enhanced.wav -af "loudnorm=I=-16:TP=-1.5:LRA=11" final.wav
```

Reference: [Adobe Podcast Enhance Speech](https://podcast.adobe.com/enhance)
