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A newer version of the Gradio SDK is available: 6.20.0
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