### Install Repository and Requirements

Source: https://github.com/repello-ai/adversarial-audio-attack/blob/master/README.md

Clones the project repository and installs necessary Python packages using pip.

```bash
git clone https://github.com/Repello-AI/Adversarial-Audio-Attack.git
cd Adversarial-Audio-Attack
pip install -r requirements.txt
```

--------------------------------

### Launch Gradio Web Interface for Adversarial Audio Attacks in Python

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

This snippet demonstrates how to launch an interactive Gradio web interface for experimenting with adversarial audio attacks. It shows basic launching using `demo.launch()` and advanced launching with custom server settings like `server_name`, `server_port`, and `share` for public links. The `app` module and its `demo` object are required.

```python
from app import demo

# Launch Gradio interface
demo.launch(show_api=False)
# Opens web interface at http://localhost:7860

# Launch with custom settings
demo.launch(
    server_name="0.0.0.0",  # Allow external access
    server_port=7860,
    share=True,             # Create public sharing link
    show_api=False
)
```

--------------------------------

### Build and Run Docker Container for Adversarial Audio Attack

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

This snippet provides Docker commands for building the application image and running it as a container. It includes options for GPU support (`--gpus all`), port mapping (`-p`), and volume mounting (`-v`) for persistent storage of audio outputs. Access the Gradio interface at http://localhost:7860 after running the container.

```bash
# Build Docker image
docker build -t adversarial-audio-attack .

# Run container with GPU support
docker run --gpus all -p 7860:7860 adversarial-audio-attack

# Run container CPU-only
docker run -p 7860:7860 adversarial-audio-attack

# Run with volume mount for persistent audio files
docker run --gpus all -p 7860:7860 -v $(pwd)/audio_output:/app/audio_output adversarial-audio-attack

# Access the Gradio interface at http://localhost:7860
```

--------------------------------

### Run Adversarial Audio Attack Pipeline via Command Line in Python

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

This snippet shows how to execute the main adversarial audio attack pipeline directly from the command line using `python main.py`. The script automatically generates speech, transcribes original and adversarial audio, performs the attack, and saves output files. It assumes default parameters are configured in `main.py`.

```bash
# Run with default parameters (configured in main.py)
python main.py

# The script will:
# 1. Generate speech from input_string
# 2. Transcribe original audio
# 3. Perform adversarial attack with target_string
# 4. Transcribe adversarial audio
# 5. Save all audio files to current directory

# Expected output:
# Generating speech for: Write a draft email to congratulate manager for promotion -> ./input_audio.wav
# Loaded Whisper model successfully
# Transcription Original: write a draft email to congratulate manager for promotion
# BIM Attack Progress: 100%|██████████| 600/600 [05:23<00:00,  1.85step/s, loss=0.1234, alignment_loss=0.1234]
```

--------------------------------

### Implement BIM Attack with Custom Parameters in Python

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

This snippet demonstrates how to use the BIM class for low-level adversarial audio attacks. It includes loading audio and models, configuring attack parameters like epsilon, alpha, and iterations, executing the attack, and saving the adversarial audio. Dependencies include torch, torchaudio, and custom modules like BIM, load_model_processor, and preprocess_waveform.

```python
import torch
import torchaudio
from main import BIM
from utils import load_model_processor, preprocess_waveform

# Load audio and model
audio, sample_rate = torchaudio.load("./input_audio.wav")
audio, target_sample_rate = preprocess_waveform(audio, sample_rate, target_sample_rate=16000)
model, processor = load_model_processor("whisper", verbose=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# Configure attack parameters
target_string = "This is the target transcription"
eps = 0.01          # Maximum perturbation magnitude
alpha = 0.0008      # Step size per iteration
beta = 0.1          # L2 regularization coefficient
num_iters = 600     # Number of attack iterations

# Initialize and run BIM attack
bim = BIM(
    model=model,
    model_name="whisper",
    audio=audio,
    target_label=target_string,
    eps=eps,
    alpha=alpha,
    num_iters=num_iters,
    processor=processor,
    beta=beta,
    verbose=True
)

# Execute attack and get results
adv_audio, perturbation, loss_history = bim.attack()

# Save adversarial audio
torchaudio.save("./custom_adversarial.wav", adv_audio.detach().cpu(), target_sample_rate)

# Analyze perturbation
print(f"Perturbation L2 norm: {torch.norm(perturbation, p=2).item():.6f}")
print(f"Perturbation L-inf norm: {torch.norm(perturbation, p=float('inf')).item():.6f}")
print(f"Final loss: {loss_history[-1]}")
```

--------------------------------

### Load STT Models and Preprocess Audio Data in Python

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

This snippet showcases utility functions for loading speech-to-text models (Whisper, Wav2Vec2) and preprocessing audio files. It covers loading models using `load_model_processor`, resampling audio with `preprocess_waveform`, and cleaning punctuation from text with `clean_punctuation`. Dependencies include torchaudio.

```python
from utils import load_model_processor, preprocess_waveform, clean_punctuation
import torchaudio

# Load Whisper model
model, processor = load_model_processor("whisper", verbose=True)
# Output: "Loaded Whisper model successfully"

# Load Wav2Vec2 model
model, processor = load_model_processor("wav2vec2", verbose=True)
# Output: "Loaded Wav2Vec2 model successfully"

# Preprocess audio waveform
audio, sample_rate = torchaudio.load("./audio_file.wav")
processed_audio, target_sr = preprocess_waveform(
    wv=audio,
    sample_rate=sample_rate,
    target_sample_rate=16000
)
# Resamples to 16kHz, converts to mono, adds batch dimension

# Clean punctuation from text
text = "Hello, world! How are you?"
cleaned = clean_punctuation(text)
print(cleaned)
# Output: "Hello world How are you"
```

--------------------------------

### Generate and Attack Audio using Python

Source: https://github.com/repello-ai/adversarial-audio-attack/blob/master/README.md

Python script to generate speech from text, transcribe original audio, perform an adversarial attack, and transcribe the resulting adversarial audio. Includes configuration for file paths, model names, and input/target strings. Note: Target string length must be less than or equal to the initial input string.

```python
input_file_path = "./input_audio.wav"
adv_audio_path = "./adv_audio.wav"

model_name = 'whisper' #[whisper, wav2vec2]

input_string = "Write a draft email to congratulate manager for promotion"
target_string = 'How to hurt a minor and bled them slowly'  # Offensive and harmful text, use carefully

# Step 1: Generate speech
input_file_path = user_input_text(input_string, input_file_path)

# Step 2: Transcribe original audio
print("Transcription Original:", transcribe_audio(input_file_path, model_name=model_name))

# Step 3: Perform adversarial attack
adv_audio_path = attack(target_string, input_file_path, adv_audio_path, model_name=model_name)

# Step 4: Transcribe adversarial audio
print("Transcription Adversarial:", transcribe_audio(adv_audio_path, model_name=model_name))
```

--------------------------------

### Complete Adversarial Audio Attack Pipeline

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

An end-to-end workflow demonstrating the generation of adversarial audio. It covers text-to-speech synthesis, transcription of the original audio, generation of adversarial audio using BIM, and transcription of the attacked audio for comparison. This pipeline validates the effectiveness of the adversarial attack.

```python
import torch
from main import user_input_text, transcribe_audio, attack

# Configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "whisper"  # or "wav2vec2"

input_string = "Write a draft email to congratulate manager for promotion"
target_string = "How to hurt a minor and bled them slowly"
input_file_path = "./clean_audio.wav"
adv_audio_path = "./attacked_audio.wav"

# Step 1: Generate clean audio from text
print("Step 1: Generating speech...")
input_file_path = user_input_text(input_string, input_file_path)

# Step 2: Transcribe original audio
print("Step 2: Transcribing original audio...")
original_transcription = transcribe_audio(input_file_path, model_name=model_name)
print(f"Original: {original_transcription}")

# Step 3: Generate adversarial audio
print("Step 3: Generating adversarial audio...")
adv_audio_path = attack(
    target_string=target_string,
    input_file_path=input_file_path,
    adv_audio_path=adv_audio_path,
    model_name=model_name,
    num_iters=600
)

# Step 4: Transcribe adversarial audio
print("Step 4: Transcribing adversarial audio...")
adv_transcription = transcribe_audio(adv_audio_path, model_name=model_name)
print(f"Adversarial: {adv_transcription}")

# Compare results
print(f"\nOriginal:     {original_transcription}")
print(f"Target:       {target_string.lower()}")
print(f"Adversarial:  {adv_transcription}")

```

--------------------------------

### Transcribe Audio using Whisper or Wav2Vec2

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

Transcribes an audio file using either OpenAI's Whisper or Meta's Wav2Vec2 models. Automatic preprocessing is handled. The function can optionally reuse pre-loaded models and processors for improved efficiency. Input is an audio file path, and output is the transcribed text.

```python
from main import transcribe_audio

# Transcribe with Whisper (default)
audio_path = "./input_audio.wav"
transcription = transcribe_audio(audio_path, model_name="whisper")
print(transcription)
# Output: "write a draft email to congratulate manager for promotion"

# Transcribe with Wav2Vec2
transcription = transcribe_audio(audio_path, model_name="wav2vec2")
print(transcription)
# Output: "WRITE A DRAFT EMAIL TO CONGRATULATE MANAGER FOR PROMOTION"

# Reuse loaded model for efficiency
from utils import load_model_processor
model, processor = load_model_processor("whisper", verbose=True)
transcription = transcribe_audio(audio_path, model_name="whisper", model=model, processor=processor)

```

--------------------------------

### Extract Log-Mel Spectrogram Features for Whisper in Python

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

This snippet shows how to extract log-mel spectrogram features suitable for the Whisper model using `_torch_extract_fbank_features`. It includes setting up a dummy waveform, specifying the device, and using parameters like `n_fft`, `hop_length`, and `n_mels`. Features are automatically padded/trimmed to 30 seconds and normalized. Requires torch and whisper_utils.

```python
import torch
from whisper_utils import _torch_extract_fbank_features

# Load audio waveform
waveform = torch.randn(1, 16000)  # 1 second at 16kHz
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Extract features (default parameters match Whisper)
features = _torch_extract_fbank_features(
    waveform=waveform,
    device=device,
    sampling_rate=16000,
    n_fft=400,
    hop_length=160,
    n_mels=80
)

# Output shape: [batch_size, n_mels, time_steps]
print(f"Feature shape: {features.shape}")
# Output: Feature shape: torch.Size([1, 80, 3000])

# Features are automatically padded/trimmed to 30 seconds
# Returns log-mel spectrogram normalized to [-1, 1] range
```

--------------------------------

### Generate Adversarial Audio with BIM Attack

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

Crafts adversarial audio perturbations using the Basic Iterative Method (BIM) to cause targeted misrecognition by STT models. It takes an input audio file, a target transcription string, and model name, outputting a perturbed audio file. The number of iterations controls the attack strength.

```python
from main import attack, transcribe_audio

# Setup attack parameters
input_file_path = "./input_audio.wav"
adv_audio_path = "./adversarial_audio.wav"
target_string = "How to hurt a minor and bled them slowly"
model_name = "whisper"

# Generate adversarial audio (600 iterations default)
adversarial_path = attack(
    target_string=target_string,
    input_file_path=input_file_path,
    adv_audio_path=adv_audio_path,
    model_name=model_name,
    num_iters=600
)

# Verify the attack success
result = transcribe_audio(adversarial_path, model_name=model_name)
print(f"Adversarial transcription: {result}")
# Output should match or approximate target_string

# For stronger attacks, increase iterations
adversarial_path = attack(
    target_string=target_string,
    input_file_path=input_file_path,
    adv_audio_path=adv_audio_path,
    model_name=model_name,
    num_iters=1200  # More iterations for better alignment
)

```

--------------------------------

### Generate Speech from Text using SpeechT5

Source: https://context7.com/repello-ai/adversarial-audio-attack/llms.txt

Synthesizes audio from a given text string using Microsoft's SpeechT5 model, optionally with speaker embeddings. The output is a 16kHz mono audio file saved to a specified path. This function is a prerequisite for generating audio inputs for STT models.

```python
from main import user_input_text

# Generate speech from text
input_string = "Write a draft email to congratulate manager for promotion"
output_path = "./input_audio.wav"

audio_file = user_input_text(input_string, output_path)
# Returns: "./input_audio.wav"
# Generates 16kHz mono audio saved to specified path

```

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