Add modified beam search decoding for streaming inference with emformer model #327
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…g/icefall into attention_relu_specaug
* Update README.md
Update results; will further update this before merge
fix comments
Add results for mixed precision with max-duration 300
* 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 <[email protected]>
* Add LG decoding * Add log weight pushing * Minor fixes
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This PR continues from the closed one ##321. I merged
k2-fsa/masterbranch into my current branch.I add the modified beam search decoding for streaming inference with emformer model, in the recipe
transducer_emformer.The class
FeatureExtractionStreamintransducer_emformer/streaming_feature_extractor.pyuse different attributes for different decoding methods.The function
decoding_result()in classFeatureExtractionStreamis used to get current decoding result.