def LoadModels(self):
try:
# Define online feature pipeline
po = ParseOptions("")
decoder_opts = LatticeFasterDecoderOptions()
self.endpoint_opts = OnlineEndpointConfig()
self.decodable_opts = NnetSimpleLoopedComputationOptions()
feat_opts = OnlineNnetFeaturePipelineConfig()
decoder_opts.register(po)
self.endpoint_opts.register(po)
self.decodable_opts.register(po)
feat_opts.register(po)
po.read_config_file(self.CONFIG_FILES_PATH + "/online.conf")
self.feat_info = OnlineNnetFeaturePipelineInfo.from_config(
feat_opts)
# Set metadata parameters
self.samp_freq = self.feat_info.mfcc_opts.frame_opts.samp_freq
self.frame_shift = self.feat_info.mfcc_opts.frame_opts.frame_shift_ms / 1000
self.acwt = self.decodable_opts.acoustic_scale
# Load Acoustic and graph models and other files
self.transition_model, self.acoustic_model = NnetRecognizer.read_model(
self.AM_PATH + "/final.mdl")
graph = _fst.read_fst_kaldi(self.LM_PATH + "/HCLG.fst")
self.decoder_graph = LatticeFasterOnlineDecoder(
graph, decoder_opts)
self.symbols = _fst.SymbolTable.read_text(self.LM_PATH +
"/words.txt")
self.info = WordBoundaryInfo.from_file(
WordBoundaryInfoNewOpts(), self.LM_PATH + "/word_boundary.int")
self.asr = NnetLatticeFasterOnlineRecognizer(
self.transition_model,
self.acoustic_model,
self.decoder_graph,
self.symbols,
decodable_opts=self.decodable_opts,
endpoint_opts=self.endpoint_opts)
del graph, decoder_opts
except Exception as e:
self.log.error(e)
raise ValueError(
"AM and LM loading failed!!! (see logs for more details)")
from kaldi.nnet3 import NnetSimpleComputationOptions
from kaldi.util.table import SequentialMatrixReader
# Construct aligner
decodable_opts = NnetSimpleComputationOptions()
decodable_opts.acoustic_scale = 1.0
decodable_opts.frame_subsampling_factor = 3
decodable_opts.frames_per_chunk = 150
aligner = NnetAligner.from_files("exp/tdnn_7b_chain_online/final.mdl",
"exp/tdnn_7b_chain_online/tree",
"exp/langdir/L.fst",
"exp/langdir/words.txt",
"exp/langdir/phones/disambig.int",
decodable_opts=decodable_opts)
phones = SymbolTable.read_text("exp/langdir/phones.txt")
wb_info = WordBoundaryInfo.from_file(WordBoundaryInfoNewOpts(),
"exp/langdir/phones/word_boundary.int")
# Define feature pipelines as Kaldi rspecifiers
feats_rspec = (
"ark:compute-mfcc-feats --config=conf/mfcc.conf scp:data/wav.scp ark:- |")
ivectors_rspec = (
"ark:compute-mfcc-feats --config=conf/mfcc.conf scp:data/wav.scp ark:- |"
"ivector-extract-online2 --config=conf/ivector.conf ark:data/spk2utt ark:- ark:- |"
)
# Align wav files
with SequentialMatrixReader(feats_rspec) as f, \
SequentialMatrixReader(ivectors_rspec) as i, \
open("data/text") as t, \
open("out/align.out", "w") as a, \
open("out/phone_align.out", "w") as p, \
from kaldi.nnet3 import NnetSimpleComputationOptions
from kaldi.util.table import SequentialMatrixReader
# Construct aligner
decodable_opts = NnetSimpleComputationOptions()
decodable_opts.acoustic_scale = 1.0
decodable_opts.frame_subsampling_factor = 3
decodable_opts.frames_per_chunk = 150
aligner = NnetAligner.from_files("exp/tdnn_7b_chain_online/final.mdl",
"exp/tdnn_7b_chain_online/tree",
"data/lang/L.fst",
"data/lang/words.txt",
"data/lang/phones/disambig.int",
decodable_opts=decodable_opts)
phones = SymbolTable.read_text("data/lang/phones.txt")
wb_info = WordBoundaryInfo.from_file(WordBoundaryInfoNewOpts(),
"data/lang/phones/word_boundary.int")
# Define feature pipelines as Kaldi rspecifiers
feats_rspec = (
"ark:compute-mfcc-feats --config=conf/mfcc_hires.conf scp:data/test/wav.scp ark:- |"
)
ivectors_rspec = (
"ark:compute-mfcc-feats --config=conf/mfcc_hires.conf scp:data/test/wav.scp ark:- |"
"ivector-extract-online2 --config=conf/ivector_extractor.conf ark:data/test/spk2utt ark:- ark:- |"
)
# Align wav files
with SequentialMatrixReader(feats_rspec) as f, \
SequentialMatrixReader(ivectors_rspec) as i, \
open("data/test/text") as t, \
open("out/test/align.out", "w") as a, \
import os
from kaldi.alignment import NnetAligner
from kaldi.fstext import SymbolTable
from kaldi.lat.align import WordBoundaryInfoNewOpts, WordBoundaryInfo
from kaldi.nnet3 import NnetSimpleComputationOptions
from kaldi.util.table import SequentialMatrixReader
# Construct aligner
decodable_opts = NnetSimpleComputationOptions()
decodable_opts.acoustic_scale = 1.0
decodable_opts.frame_subsampling_factor = 3
aligner = NnetAligner.from_files("final.mdl", "tree", "L.fst", "words.txt",
"disambig.int", decodable_opts=decodable_opts)
phones = SymbolTable.read_text("phones.txt")
wb_info = WordBoundaryInfo.from_file(WordBoundaryInfoNewOpts(),
"word_boundary.int")
# Define feature pipelines as Kaldi rspecifiers
feats_rspec = "ark:compute-mfcc-feats --config=mfcc.conf scp:wav.scp ark:- |"
ivectors_rspec = (
"ark:compute-mfcc-feats --config=mfcc.conf scp:wav.scp ark:-"
" | ivector-extract-online2 --config=ivector.conf ark:spk2utt ark:- ark:- |"
)
# Align wav files
with SequentialMatrixReader(feats_rspec) as f, \
SequentialMatrixReader(ivectors_rspec) as i, open("text") as t:
for (fkey, feats), (ikey, ivectors), line in zip(f, i, t):
tkey, text = line.strip().split(None, 1)
assert(fkey == ikey == tkey)
out = aligner.align((feats, ivectors), text)