v1.13.0

Release notes

This release adds low-latency end-pointing to detect the end of utterance. It also caps the latency of the beam search finals at 1.25 seconds which significantly reduces both the finals' latency and the user-perceived tail latencies without impacting WER. Finally, this release also speeds up beam decoding on-GPU by up to 10x.

This release adds:

• End-pointing (docs)
• Capping of the delay between partials and finals via --beam_final_emission_thresh=1.25
• A batched implementation of the on-GPU beam decoder
• Support for training models in character based languages (tested in Mandarin). This required:
  • small tokenizer changes
  • support for calculating character error rate (CER) and mixture error rate (MER)

This release also:

• Improves the scheduling of the delay penalty by waiting until the validation WER has dropped before this kicks in (docs)
• Reduces the startup time at the beginning of training by adding a noise-data cache and speeding up both json parsing and tokenization
• Deprecates the 49M param testing model configuration and makes the 85M param base model the default for training. See supported models
• Improves the usability of the live demo client (docs)
• Fixes the emission latency estimation for the beam decoder
• Improves logging during training and evaluation
• Filters utterances shorter than min_duration: 0.05s during training

Summary of changes to default args

• --delay_penalty="linear_schedule" instead of "wer_schedule"
• --val_batch_size=1024 instead of 256
• --beam_final_emission_thresh=1.25 added to cap the finals' latency during beam decoding
• YAML config: Adds min_duration: 0.05 seconds to filter out short utterances during training
• YAML config: Adds error_rate: word which determines the error rate calculated and must be one of {wer|word, cer|char, mer|mixture}