-
Notifications
You must be signed in to change notification settings - Fork 1
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
3 changed files
with
124 additions
and
121 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -8,3 +8,4 @@ | |
| compute_chain_objf, | ||
| ) | ||
| from .model import ChainE2EModel | ||
| from . import decoder | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,123 @@ | ||
| import torch | ||
|
|
||
| """ | ||
| This file contains the python side to the kaldi (pkwrap-main.h) bindings for decoding/rescoding/get_word_alignment. | ||
| These are nice for few-utterances decoding, but for more, use kaldi with shutil/decode/latgen-faster-mapped.sh | ||
| (In here, for each call, trans_model, HCLG, words_txt, ... are reloaded (which is inefficient)) | ||
| """ | ||
|
|
||
| def kaldi_decode(loglikes, | ||
| trans_model, | ||
| HCLG, | ||
| words_txt, | ||
| opts={ | ||
| "beam":15.0, | ||
| "max_active":7000, | ||
| "min_active":200, | ||
| "lattice_beam":8.0, # Beam we use in lattice generation. | ||
| }, | ||
| acoustic_scale=1.0, | ||
| allow_partial=True | ||
| ): | ||
| """ | ||
| Decode loglikes from a tensor, no lm rescoding is done | ||
| Example: | ||
| import torch | ||
| import torchaudio | ||
| import satools | ||
| net = satools.infer_helper.load_model("/lium/scratch/pchampi/SA/egs/asr/librispeech/exp/chain/asr_eval_tdnnf_360h/final.pt") | ||
| wav, _ = torchaudio.load("/lium/scratch/pchampi/SA/egs/anon/vctk/data/vctk_test/wav/p225/p225_001_mic2.wav") | ||
| net = net.cuda() | ||
| loglike, _ = net(wav.cuda()) | ||
| loglike = loglike.squeeze(0).cpu() | ||
| print(wav.shape, loglike.shape) | ||
| txt, words, alignment, latt = satools.chain.decoder.kaldi_decode(loglike, | ||
| trans_model="/lium/scratch/pchampi/SA/egs/asr/librispeech/exp/chain/asr_eval_tdnnf_360h/0.trans_mdl", | ||
| HCLG="/lium/scratch/pchampi/SA/egs/asr/librispeech/exp/chain/e2e_train_clean_360/e2e_biphone_tree/graph_tgsmall/HCLG.fst", | ||
| words_txt="/lium/scratch/pchampi/SA/egs/asr/librispeech/exp/chain/e2e_train_clean_360/e2e_biphone_tree/graph_tgsmall/words.txt", | ||
| ) | ||
| """ | ||
|
|
||
| from _satools import kaldi | ||
|
|
||
| conf = kaldi.decoder.CreateLatticeFasterDecoderConfig(opts["beam"], opts["max_active"], opts["min_active"], opts["lattice_beam"]) | ||
|
|
||
| s = kaldi.decoder.MappedLatticeFasterRecognizer( | ||
| kaldi.matrix.TensorToKaldiMatrix(loglikes), | ||
| conf, | ||
| trans_model, | ||
| HCLG, | ||
| words_txt, | ||
| acoustic_scale, | ||
| allow_partial, | ||
| ) | ||
| return s | ||
|
|
||
| def kaldi_lm_rescoring(lat, | ||
| trans_model, | ||
| G_old, | ||
| G_new, | ||
| words_txt, | ||
| acoustic_scale=1.0, | ||
| lm_scale=1.0, | ||
| ): | ||
| """ | ||
| Kaldi Lm rescoring | ||
| Example: | ||
| txt, words, alignment, latt_res = satools.chain.decoder.kaldi_lm_rescoring(latt, | ||
| trans_model = "/lium/scratch/pchampi/SA/egs/asr/librispeech/exp/chain/asr_eval_tdnnf_360h/0.trans_mdl", | ||
| G_old = "/lium/scratch/pchampi/SA/egs/asr/librispeech/data/lang_lp_test_tgsmall/G.fst", | ||
| G_new = "/lium/scratch/pchampi/SA/egs/asr/librispeech/data/lang_lp_test_fglarge/G.carpa", | ||
| words_txt = "/lium/scratch/pchampi/SA/egs/asr/librispeech/exp/chain/e2e_train_clean_360/e2e_biphone_tree/graph_tgsmall/words.txt") | ||
| """ | ||
|
|
||
| from _satools import kaldi | ||
|
|
||
| fst = kaldi.fst.StdVectorFst() | ||
| kaldi.fst.ReadFstKaldi(G_old, fst) | ||
| project_output = True | ||
| kaldi.fst.Project(fst, project_output) | ||
|
|
||
| _acoustic_scale = -1.0 | ||
| lm_rescore_lat = kaldi.decoder.LatticeLmrescore(fst, lat, _acoustic_scale) | ||
| _acoustic_scale = 1.0 | ||
| const_arpa_lm_rescore_lat = kaldi.decoder.LatticeLmrescoreConstArpa(G_new, lm_rescore_lat, _acoustic_scale) | ||
|
|
||
| txt, words, alignment, lat = kaldi.decoder.LatticeBestPath(trans_model, words_txt, const_arpa_lm_rescore_lat, acoustic_scale, lm_scale) | ||
| return txt, words, alignment, const_arpa_lm_rescore_lat # return the LatticeLmrescoreConstArpa lattice! | ||
|
|
||
|
|
||
| def kaldi_get_align(lat, | ||
| trans_model, | ||
| align_lexicon, | ||
| words_txt, | ||
| frame_shift=0.030, | ||
| acoustic_scale=1.0, | ||
| lm_scale=1.0, | ||
| ): | ||
| """ | ||
| Example: | ||
| align = satools.chain.decoder.kaldi_get_align(latt_res, | ||
| trans_model = "/lium/scratch/pchampi/SA/egs/asr/librispeech/exp/chain/asr_eval_tdnnf_360h/0.trans_mdl", | ||
| align_lexicon = "/lium/scratch/pchampi/SA/egs/asr/librispeech/data/lang_lp/phones/align_lexicon.int", | ||
| words_txt = "/lium/scratch/pchampi/SA/egs/asr/librispeech/exp/chain/e2e_train_clean_360/e2e_biphone_tree/graph_tgsmall/words.txt", | ||
| ) | ||
| Then use mpv to play the word: | ||
| mpv XX.wav --start=4.320000 --length=0.270000 | ||
| """ | ||
|
|
||
| from _satools import kaldi | ||
|
|
||
| ali_lat = kaldi.decoder.LatticeAlignWordsLexicon(trans_model, align_lexicon, lat) | ||
|
|
||
| txt, words, alignment, best_lat = kaldi.decoder.LatticeBestPath(trans_model, words_txt, ali_lat, acoustic_scale, lm_scale) | ||
| print_slience = False | ||
| ctm = list(zip(*kaldi.decoder.NbestToCTM(words_txt, best_lat, frame_shift, print_slience))) | ||
| return ctm | ||
|
|
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters