---
title: Audio Data Quality Toolkit
emoji: 🎙️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "5.29.0"
app_file: app.py
pinned: true
tags:
  - audio
  - speech
  - tts
  - asr
  - synthetic-data
  - data-quality
  - evaluation
  - gradio
  - machine-learning
short_description: QA dashboard for TTS/ASR audio datasets
---

# 🎙️ Audio Data Quality Toolkit for TTS/ASR Training Pipelines

A lightweight quality-control dashboard for speech datasets used in **text-to-speech**, **automatic speech recognition**, **voice cloning**, and **synthetic speech evaluation**.

The toolkit helps detect common data issues that degrade speech model training:

- clipping and distorted audio
- long silence or empty clips
- noisy samples
- duplicate or near-duplicate clips
- transcript/audio mismatch
- speaker imbalance
- abnormal duration and speech-rate patterns
- possible synthetic-data artifacts

## Why this matters

TTS and ASR models are highly sensitive to training-data quality. Low-quality clips can cause unstable alignment, bad pronunciation, poor speaker consistency, hallucinated words, and degraded long-form generation.

This Space is designed as a practical inspection tool for researchers and ML engineers building speech datasets and synthetic audio pipelines.

## Current features

- Upload one or multiple audio files
- Compute duration, RMS energy, peak amplitude, silence ratio, and clipping ratio
- Flag potentially problematic clips
- Display waveform-level diagnostics
- Export a quality report
- Provide dataset-level summary statistics

## Intended use cases

- TTS dataset cleaning
- ASR dataset validation
- Synthetic speech evaluation
- Voice-cloning dataset inspection
- Audio preprocessing QA
- ML data pipeline debugging

## Roadmap

- Transcript mismatch detection using ASR
- Speaker imbalance estimation
- Duplicate detection with audio embeddings
- Synthetic artifact scoring
- Batch dataset reports
- Hugging Face dataset integration

# Audio Data Quality Toolkit

Upload audio files and get instant quality reports. 13 automated checks, zero GPU.

**Install locally:** `pip install audio-data-quality-toolkit`

**GitHub:** [audio-data-quality-toolkit](https://github.com/EmmanuelleB985/audio-data-quality-toolkit)