NeMo CuratorProcess audio data you’ve loaded into AudioTask objects using NeMo Curator’s comprehensive audio processing capabilities.
NeMo Curator provides a specialized suite of tools for processing speech and audio data as part of the AI training pipeline. These tools help you transcribe, analyze, filter, and integrate audio datasets to ensure high-quality input for ASR model training and multimodal applications.
NeMo Curator’s audio processing capabilities are organized into five main categories:
Each category provides GPU-accelerated implementations optimized for different speech curation needs. The result is a cleaned and filtered audio dataset with high-quality transcriptions ready for model training.
Transcribe audio files using NeMo Framework’s state-of-the-art ASR models with GPU acceleration.
Use pretrained NeMo ASR models for accurate speech recognition pretrained multilingual gpu-accelerated
Efficiently process large audio datasets with configurable batch sizes batch-inference memory-optimization scalable
Evaluate and filter audio quality using transcription accuracy and audio characteristics.
Filter audio samples based on Word Error Rate thresholds accuracy quality-metrics filtering
Filter audio samples by duration ranges and speech rate metrics duration speech-rate range-filtering
Compose VAD, band, UTMOS, SIGMOS, and speaker-separation stages to extract clean single-speaker training segments from raw audio.
End-to-end pipeline of preprocessing, segmentation, and filtering stages vad mos-scoring diarization
Single composite stage that decomposes into the full filtering pipeline from a YAML config composite yaml-config end-to-end
Extract and analyze audio file characteristics for quality control and metadata generation.
Calculate precise audio duration using soundfile library soundfile precision metadata
Validate audio file formats and detect corrupted files validation error-handling format-support
Curate training data for audio language models by extracting fixed-duration windows from diarized audio segments.
Construct candidate training windows from consecutive segments with quality filtering windowing speaker-count bandwidth
Remove redundant overlapping windows based on configurable thresholds deduplication overlap-ratio target-duration
Convert processed audio data to text processing workflows for multimodal applications.
