audioevals 0.6.0

Effective evaluations for Text-to-Speech (TTS) systems

AudioEvals

A comprehensive tool for evaluating generated TTS (Text-to-Speech) audio datasets with multiple evaluation metrics.

Evaluation Types

WER (Word Error Rate)

Measures the accuracy of speech-to-text transcription by comparing generated audio against ground truth transcripts.

AudioBox Aesthetics

Evaluates audio quality using AudioBox's aesthetic scoring system, providing metrics for:

• CE (Content Enjoyment)
• CU (Content Usefulness)
• PC (Production Complexity)
• PQ (Production Quality)

VAD (Voice Activity Detection) Silence

Detects unnaturally long silences in generated audio using Silero VAD with RMS analysis. Provides:

• Maximum silence duration per file
• Total duration analysis
• Silence-to-speech ratio calculations

Using as a Library

Installation

pip install audioevals

Basic Usage

import asyncio
from audioevals.evals import wer_eval, audiobox_eval, vad_eval
from audioevals.utils.audio import AudioData

# Load audio data
audio_data = AudioData.from_wav_file("/path/to/audio.wav")
transcript = "Hello world, this is a test."

WER Evaluation

# Using file path
wer_result = await wer_eval.run_single_file("/path/to/audio.wav", transcript)
print(f"WER: {wer_result['wer_score']:.2f}%")
print(f"STT: {wer_result['stt_transcript']}")
print(f"Words Per Second: {wer_result['words_per_second']}")

# Using AudioData instance
wer_result = await wer_eval.run_audio_data(audio_data, transcript)
print(f"WER: {wer_result['wer_score']:.2f}%")

AudioBox Aesthetics Evaluation

# Using file path
audiobox_result = audiobox_eval.run_single_file("/path/to/audio.wav")
print(f"Content Enjoyment: {audiobox_result['CE']:.2f}")
print(f"Production Quality: {audiobox_result['PQ']:.2f}")

# Using AudioData instance
audiobox_result = audiobox_eval.run_audio_data(audio_data)
print(f"Content Enjoyment: {audiobox_result['CE']:.2f}")

VAD Silence Evaluation

# Using file path
vad_result = vad_eval.run_single_file("/path/to/audio.wav")
print(f"Max silence duration: {vad_result['max_silence_duration']:.2f}s")
print(f"Silence/Speech ratio: {vad_result['silence_to_speech_ratio']:.2f}")

# Using AudioData instance
vad_result = vad_eval.run_audio_data(audio_data)
print(f"Max silence duration: {vad_result['max_silence_duration']:.2f}s")

Complete Example

import asyncio
from audioevals.evals import wer_eval, audiobox_eval, vad_eval
from audioevals.utils.audio import AudioData

async def evaluate_audio_file(file_path, transcript):
    """Complete evaluation of an audio file"""
    
    # Load audio data once
    audio_data = AudioData.from_wav_file(file_path)
    
    # Run all evaluations
    wer_result = await wer_eval.run_audio_data(audio_data, transcript)
    audiobox_result = audiobox_eval.run_audio_data(audio_data)
    vad_result = vad_eval.run_audio_data(audio_data)
    
    return {
        'wer': wer_result,
        'audiobox': audiobox_result,
        'vad': vad_result
    }

# Usage
results = asyncio.run(evaluate_audio_file(
    "/path/to/audio.wav", 
    "Hello world, this is a test."
))

print(f"WER: {results['wer']['wer_score']:.2f}%")
print(f"AudioBox PQ: {results['audiobox']['PQ']:.2f}")
print(f"Max silence: {results['vad']['max_silence_duration']:.2f}s")

Dataset Structure (CLI usage)

The audioevals CLI expects datasets to be structured in a folder, in the following way:

{folder_name}/
├── audios/
│   ├── audio1.wav
│   ├── audio2.wav
│   └── ...
└── transcripts.json

Where transcripts.json should be a map of audio file name to its ground truth transcript, such as:

{
  "001.wav": "He shouted, 'Everyone, please gather 'round! Here's the plan: 1) Set-up at 9:15 a.m.; 2) Lunch at 12:00 p.m. (please RSVP!); 3) Playing — e.g., games, music, etc. — from 1:15 to 4:45; and 4) Clean-up at 5 p.m.'",
  "002.wav": "Hey! What's up? Don't be shy, what can I do for you, cutie?",
  "003.wav": "I'm so excited to see you! I've been waiting for this moment for so long!",
  "004.wav": "What is the difference between weather and climate, and how do scientists study and predict both? Please explain the factors that influence weather patterns and how climate change affects long-term weather trends.",
  "005.wav": "I'm so sad to hear that. I'm here for you. What can I do to help?",
  "006.wav": "She let out a sudden (laughs) at the joke.",
  "007.wav": "He breathed a long (sighs) of relief when the test ended.",
  "008.wav": "Uhh, I'm not sure what to say. hmm... I'm just, ugh, a little bit confused."
}

CLI Usage

You can run evaluations on the dataset by running:

audioevals --dataset {folder_name}

The results will be printed to console as well as saved to {folder_name}/results.json for inspection via something like jupyter notebook.

Running Specific Evaluations

By default, the tool will run all the available evaluations, like WER, AudioBox aesthetics, VAD Silence. But it's possible to run only a select few with the --evals flag:

audioevals --dataset {folder_name} --evals wer vad

Available options are: wer, audiobox, vad

Output

Results are saved to {folder_name}/results.json and include:

• Metadata about the evaluation run
• Individual file results for each evaluation type
• Summary statistics and averages