icefall/RESULTS.md at 6fbd7a287c7ab5657fe3807764ec4d600f8a27e0

mirror of https://github.com/k2-fsa/icefall.git synced 2025-08-09 18:12:19 +00:00

Extract framewise alignment information using CTC decoding (#39 )

* Use new APIs with k2.RaggedTensor

* Fix style issues.

* Update the installation doc, saying it requires at least k2 v1.7

* Extract framewise alignment information using CTC decoding.

* Print environment information.

Print information about k2, lhotse, PyTorch, and icefall.

* Fix CI.

* Fix CI.

* Compute framewise alignment information of the LibriSpeech dataset.

* Update comments for the time to compute alignments of train-960.

* Preserve cut id in mix cut transformer.

* Minor fixes.

* Add doc about how to extract framewise alignments.

2021-10-18 14:24:33 +08:00

2.6 KiB

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Results

LibriSpeech BPE training results (Conformer-CTC)

2021-08-19

(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/13

TensorBoard log is available at https://tensorboard.dev/experiment/GnRzq8WWQW62dK4bklXBTg/#scalars

Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc

The best decoding results (WER) are listed below, we got this results by averaging models from epoch 15 to 34, and using attention-decoder decoder with num_paths equals to 100.

	test-clean	test-other
WER	2.57%	5.94%

To get more unique paths, we scaled the lattice.scores with 0.5 (see https://github.com/k2-fsa/icefall/pull/10#discussion_r690951662 for more details), we searched the lm_score_scale and attention_score_scale for best results, the scales that produced the WER above are also listed below.

	lm_scale	attention_scale
test-clean	1.3	1.2
test-other	1.2	1.1

You can use the following commands to reproduce our results:

git clone https://github.com/k2-fsa/icefall
cd icefall

# It was using ef233486, you may not need to switch to it
# git checkout ef233486

cd egs/librispeech/ASR
./prepare.sh

export CUDA_VISIBLE_DEVICES="0,1,2,3"
python conformer_ctc/train.py --bucketing-sampler True \
                              --concatenate-cuts False \
                              --max-duration 200 \
                              --full-libri True \
                              --world-size 4 \
                              --lang-dir data/lang_bpe_5000

python conformer_ctc/decode.py --nbest-scale 0.5 \
                               --epoch 34 \
                               --avg 20 \
                               --method attention-decoder \
                               --max-duration 20 \
                               --num-paths 100 \
                               --lang-dir data/lang_bpe_5000

LibriSpeech training results (Tdnn-Lstm)

2021-08-24

(Wei Kang): Result of phone based Tdnn-Lstm model.

Icefall version: caa0b9e942

Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc

The best decoding results (WER) are listed below, we got this results by averaging models from epoch 19 to 14, and using whole-lattice-rescoring decoding method.

	test-clean	test-other
WER	6.59%	17.69%

We searched the lm_score_scale for best results, the scales that produced the WER above are also listed below.

	lm_scale
test-clean	0.8
test-other	0.9

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