Ema Lightning v1.0.4

The weights load with torch.load(..., weights_only=True), so a checkpoint can only hold tensors and plain values and cannot run code when it loads.

v1.0.3

Speech.words: every spoken word with its start and end in seconds, read off the frame plan the audio is made from. Useful for captions, karaoke and alignment. Adds the Word dataclass.

EMA Lightning 1.0.1

1.0.1

• EMA() requests the model's config.json before the weights.

EMA Lightning 1.0.0

First release.

• EMA() with say() for one text or a list, stream() and best_batch_size().
• lightning(): cuDNN benchmark mode, one compilation for any batch size, and CUDA
  graphs at batch sizes 1, 2, 4 and every multiple of 8 up to the batch size,
  checked against the plain path at startup.
• Text frontend on normalizer-tr
  0.4 with the fallback policy: numbers, dates, times, money, units,
  abbreviations and symbols are read aloud, and nothing is skipped.
• Playhead, a scheduler inside EMA: every say() and stream() call joins two
  first-come-first-served queues (sentences before the model, windows before the
  decoder) that run in shared GPU batches, with no server or database. Closed
  streams leave both queues, and a failing batch fails only its own callers.
  benches/callers.py measures it under load.
• Decoder windows of four seconds; a stream's first window is one second. On a
  GPU, windows are padded to 48 or 120 frames and each decode batch holds one size.
• Requires CPython 3.11 or newer, the minimum of normalizer-tr 0.4.