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| 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) | |