5fe58de43c
* initial commit * support download, data prep, and fbank * on-the-fly feature extraction by default * support BPE based lang * support HLG for BPE * small fix * small fix * chunked feature extraction by default * Compute features for GigaSpeech by splitting the manifest. * Fixes after review. * Split manifests into 2000 pieces. * set audio duration mismatch tolerance to 0.01 * small fix * add conformer training recipe * Add conformer.py without pre-commit checking * lazy loading and use SingleCutSampler * DynamicBucketingSampler * use KaldifeatFbank to compute fbank for musan * use pretrained language model and lexicon * use 3gram to decode, 4gram to rescore * Add decode.py * Update .flake8 * Delete compute_fbank_gigaspeech.py * Use BucketingSampler for valid and test dataloader * Update params in train.py * Use bpe_500 * update params in decode.py * Decrease num_paths while CUDA OOM * Added README * Update RESULTS * black * Decrease num_paths while CUDA OOM * Decode with post-processing * Update results * Remove lazy_load option * Use default `storage_type` * Keep the original tolerance * Use split-lazy * black * Update pretrained model Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
2.0 KiB
Results
GigaSpeech BPE training results (Conformer-CTC)
2022-04-06
The best WER, as of 2022-04-06, for the gigaspeech is below
Results using HLG decoding + n-gram LM rescoring + attention decoder rescoring:
| Dev | Test | |
|---|---|---|
| WER | 10.47 | 10.58 |
Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
| ngram_lm_scale | attention_scale |
|---|---|
| 0.5 | 1.3 |
To reproduce the above result, use the following commands for training:
cd egs/gigaspeech/ASR
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
./conformer_ctc/train.py \
--max-duration 120 \
--num-workers 1 \
--world-size 8 \
--exp-dir conformer_ctc/exp_500 \
--lang-dir data/lang_bpe_500
and the following command for decoding:
./conformer_ctc/decode.py \
--epoch 18 \
--avg 6 \
--method attention-decoder \
--num-paths 1000 \
--exp-dir conformer_ctc/exp_500 \
--lang-dir data/lang_bpe_500 \
--max-duration 20 \
--num-workers 1
Results using HLG decoding + whole lattice rescoring:
| Dev | Test | |
|---|---|---|
| WER | 10.51 | 10.62 |
Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
| lm_scale |
|---|
| 0.2 |
To reproduce the above result, use the training commands above, and the following command for decoding:
./conformer_ctc/decode.py \
--epoch 18 \
--avg 6 \
--method whole-lattice-rescoring \
--num-paths 1000 \
--exp-dir conformer_ctc/exp_500 \
--lang-dir data/lang_bpe_500 \
--max-duration 20 \
--num-workers 1
Note: the whole-lattice-rescoring method is about twice as fast as the attention-decoder method, with slightly worse WER.
Pretrained model is available at https://huggingface.co/wgb14/icefall-asr-gigaspeech-conformer-ctc
The tensorboard log for training is available at https://tensorboard.dev/experiment/rz63cmJXSK2fV9GceJtZXQ/