- Introduce unified AMP helpers (create_grad_scaler, torch_autocast) to handle
deprecations in PyTorch ≥2.3.0
- Replace direct uses of torch.cuda.amp.GradScaler and torch.cuda.amp.autocast
with the new utilities across all training and inference scripts
- Update all torch.load calls to include weights_only=False for compatibility with
newer PyTorch versions
- the idea is to support `--skip-scoring` argument passed to a decoding
script
- created for Transducer decoding (non-streaming, streaming)
- it can be done also for CTC decoding... (not yet)
- also added `--label` for extra label in `streaming_decode.py`
- and also added `set_caching_enabled(True)`, which has no effect on
librispeech, but it leads to faster runtime on DBs with long
recordings (assuming `librispeech/zipformer` scripts are the
example scripts for other setups)
* add CTC loss option in zipformer recipe
* add ctc_decode.py
* support CTC model export, add jit_pretrained_ctc.py, pretrained_ctc.py
* update README.md and RESULTS.md
* add CI test
* add the zipformer codes, copied from branch from_dan_scaled_adam_exp1119
* support model export with torch.jit.script
* update RESULTS.md
* support exporting streaming model with torch.jit.script
* add results of streaming models, with some minor changes
* update README.md
* add CI test
* update k2 version in requirements-ci.txt
* update pyproject.toml
