Hyperopt
Distributed Asynchronous Hyperparameter Optimization for Large-Scale Machine Learning.
Precision temporal mapping for the industry-standard ASR training corpus.
LibriSpeech Alignments represent the high-precision temporal mapping of the LibriSpeech ASR corpus, providing word and phoneme-level timestamps for approximately 1,000 hours of read English speech. In the 2026 AI landscape, these alignments are essential for training state-of-the-art Automatic Speech Recognition (ASR) systems and fine-tuning Text-to-Speech (TTS) models that require granular prosodic control. The datasets are typically generated using advanced forced-alignment tools such as the Montreal Forced Aligner (MFA) or the Kaldi toolkit. They provide the ground truth for duration modeling in neural vocoders and are foundational for researchers benchmarking speech-to-text latency and accuracy. Technically, the data architecture utilizes TextGrid or JSON formats to synchronize the original audio (sampled at 16kHz) with its corresponding orthographic transcriptions. By providing a bridge between raw audio and linguistic units, LibriSpeech Alignments enable developers to build robust voice-to-command systems, automated subtitling engines, and expressive synthetic voices with sub-millisecond precision.
Provides start and end timestamps for every phoneme within a word based on the CMU Pronouncing Dictionary.
Distributed Asynchronous Hyperparameter Optimization for Large-Scale Machine Learning.
An open-source hyperparameter optimization framework to automate machine learning model tuning with superior efficiency.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Supports alignments across dev, test, and training sets (100h, 360h, and 500h splits).
Alignments accounts for speaker-specific variability using fMLLR transforms in Kaldi-based pipelines.
Data can be exported in human-readable TextGrid formats or machine-parseable JSON.
Many alignment versions include posterior probability scores for alignment confidence.
Distinguishes between silence, speech, and ambient noise intervals.
Provides mapping between orthographic text and phonetic representations using standard lexicons.
Needs precise phoneme durations to predict speech speed and rhythm.
Registry Updated:2/7/2026
Aligning text with audio for frame-accurate captioning.
Linguistic researchers analyzing phonetic shifts or accents.