FTA Conformer CTC — LibriSpeech 100h
This repository contains a Conformer CTC acoustic model trained on the
LibriSpeech 100-hour (train-clean-100) subset with FTA training.
Checkpoint
- File:
pretrained-epoch40-avg10.pt - Training endpoint: epoch 40
- Averaging: accumulated averaged model over epochs
(30, 40] - Framework: PyTorch + icefall
- Checkpoint format:
torch.save({"model": state_dict, ...})
The checkpoint contains the FTA inference modules under encoder.*,
ctc_head.*, blank_gate.*, and blank_prior.*. It is an icefall checkpoint,
not a Transformers AutoModel.from_pretrained() package.
Loading
import torch
checkpoint = torch.load(
"pretrained-epoch40-avg10.pt",
map_location="cpu",
weights_only=False,
)
state_dict = checkpoint["model"]
Use the model definition and decoding pipeline in
conformer_ctc2/decode_vi_blank_v2.py from the accompanying icefall codebase.
Notes
FTA-specific training components require the matching model implementation. For reproducible decoding, use the same token inventory, SentencePiece model, and decoding configuration as the original experiment.