def __init__(self, idx_to_char, params={}):
self.idx_to_char = idx_to_char
self.reorder_1, self.reorder_2 = create_phone_map(
params['phones_path'], idx_to_char)
self.word_syms = SymbolTable.read_text(params['words_path'])
self.acoustic_scale = params.get('acoustic', 1.2)
if self.acoustic_scale < 0:
print("Warning: acoustic scale is less than 0")
allow_partial = params.get('allow_partial', True)
beam = params.get('beam', 13)
self.alphaweight = params.get('alphaweight', 0.3)
trans_model = TransitionModel()
with xopen(params['mdl_path']) as ki:
trans_model.read(ki.stream(), ki.binary)
decoder_opts = FasterDecoderOptions()
decoder_opts.beam = beam
decode_fst = read_fst_kaldi(params['fst_path'])
self.decoder_opts = decoder_opts
self.trans_model = trans_model
self.decode_fst = decode_fst
self.stats = LMStats()
self.stats_state = None
self.add_stats_phase = True
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)")
def gmm_decode_faster(model_rxfilename, fst_rxfilename,
feature_rspecifier, words_wspecifier,
alignment_wspecifier="", lattice_wspecifier="",
word_symbol_table="", acoustic_scale=0.1,
allow_partial=True, decoder_opts=FasterDecoderOptions()):
# Read model.
trans_model = TransitionModel()
am_gmm = AmDiagGmm()
with xopen(model_rxfilename) as ki:
trans_model.read(ki.stream(), ki.binary)
am_gmm.read(ki.stream(), ki.binary)
# Open table readers/writers.
feature_reader = SequentialMatrixReader(feature_rspecifier)
words_writer = IntVectorWriter(words_wspecifier)
alignment_writer = IntVectorWriter(alignment_wspecifier)
clat_writer = CompactLatticeWriter(lattice_wspecifier)
# Read symbol table.
word_syms = None
if word_symbol_table != "":
word_syms = SymbolTable.read_text(word_symbol_table)
if not word_syms:
raise RuntimeError("Could not read symbol table from file {}"
.format(word_symbol_table))
# NOTE:
# It is important to read decode_fst after opening feature reader as
# it can prevent crashes on systems without enough virtual memory.
# Read decoding graph and instantiate decoder.
decode_fst = read_fst_kaldi(fst_rxfilename)
decoder = FasterDecoder(decode_fst, decoder_opts)
tot_like = 0.0
frame_count = 0
num_success, num_fail = 0, 0
start = time.time()
for key, features in feature_reader:
if features.num_rows == 0:
num_fail += 1
logging.warning("Zero-length utterance: {}".format(key))
continue
gmm_decodable = DecodableAmDiagGmmScaled(am_gmm, trans_model,
features, acoustic_scale)
decoder.decode(gmm_decodable)
if not (allow_partial or decoder.reached_final()):
num_fail += 1
logging.warning("Did not successfully decode utterance {}, len = {}"
.format(key, features.num_rows))
continue
try:
best_path = decoder.get_best_path()
except RuntimeError:
num_fail += 1
logging.warning("Did not successfully decode utterance {}, len = {}"
.format(key, features.num_rows))
continue
if not decoder.reached_final():
logging.warning("Decoder did not reach end-state, outputting "
"partial traceback since --allow-partial=true")
ali, words, weight = get_linear_symbol_sequence(best_path)
words_writer[key] = words
if alignment_writer.is_open():
alignment_writer[key] = ali
if clat_writer.is_open():
if acoustic_scale != 0.0:
scale = acoustic_lattice_scale(1.0 / acoustic_scale)
scale_lattice(scale, best_path)
best_path = convert_lattice_to_compact_lattice(best_path)
clat_writer[key] = best_path
if word_syms:
syms = convert_indices_to_symbols(word_syms, words)
print(key, " ".join(syms), file=sys.stderr)
num_success += 1
frame_count += features.num_rows
like = - (weight.value1 + weight.value2);
tot_like += like
logging.info("Log-like per frame for utterance {} is {} over {} "
"frames.".format(key, like / features.num_rows,
features.num_rows))
logging.debug("Cost for utterance {} is {} + {}"
.format(key, weight.value1, weight.value2))
elapsed = time.time() - start
logging.info("Time taken [excluding initialization] {}s: real-time factor "
"assuming 100 frames/sec is {}"
.format(elapsed, elapsed * 100 / frame_count))
logging.info("Done {} utterances, failed for {}"
.format(num_success, num_fail))
logging.info("Overall log-likelihood per frame is {} over {} frames."
.format(tot_like / frame_count, frame_count))
feature_reader.close()
words_writer.close()
if alignment_writer.is_open():
alignment_writer.close()
if clat_writer.is_open():
clat_writer.close()
return True if num_success != 0 else False
acoustic_model = AmDiagGmm().read(ki.stream(), ki.binary)
# Define the decodable wrapper: (features, acoustic_scale) -> decodable
def make_decodable_wrapper(trans_model, acoustic_model):
def decodable_wrapper(features, acoustic_scale):
return DecodableAmDiagGmmScaled(acoustic_model, trans_model, features,
acoustic_scale)
return decodable_wrapper
decodable_wrapper = make_decodable_wrapper(trans_model, acoustic_model)
# Define the decoder
decoding_graph = read_fst_kaldi("models/mono/graph/HCLG.fst")
decoder_opts = LatticeFasterDecoderOptions()
decoder_opts.beam = 13.0
decoder_opts.lattice_beam = 6.0
decoder = LatticeFasterDecoder(decoding_graph, decoder_opts)
# Define the recognizer
symbols = SymbolTable.read_text("models/mono/graph/words.txt")
asr = Recognizer(decoder, decodable_wrapper, symbols)
# Decode wave files
# for key, wav in SequentialWaveReader("scp:wav.scp"):
# feats = feat_pipeline(wav)
# out = asr.decode(feats)
# print(key, out["text"], flush=True)
def __init__(
self,
cfg: KaldiDecoderConfig,
beam: int,
nbest: int = 1,
):
try:
from kaldi.asr import FasterRecognizer, LatticeFasterRecognizer
from kaldi.base import set_verbose_level
from kaldi.decoder import (
FasterDecoder,
FasterDecoderOptions,
LatticeFasterDecoder,
LatticeFasterDecoderOptions,
)
from kaldi.lat.functions import DeterminizeLatticePhonePrunedOptions
from kaldi.fstext import read_fst_kaldi, SymbolTable
except:
warnings.warn(
"pykaldi is required for this functionality. Please install from https://github.com/pykaldi/pykaldi"
)
# set_verbose_level(2)
self.acoustic_scale = cfg.acoustic_scale
self.nbest = nbest
if cfg.hlg_graph_path is None:
assert (
cfg.kaldi_initializer_config is not None
), "Must provide hlg graph path or kaldi initializer config"
cfg.hlg_graph_path = initalize_kaldi(cfg.kaldi_initializer_config)
assert os.path.exists(cfg.hlg_graph_path), cfg.hlg_graph_path
if cfg.is_lattice:
self.dec_cls = LatticeFasterDecoder
opt_cls = LatticeFasterDecoderOptions
self.rec_cls = LatticeFasterRecognizer
else:
assert self.nbest == 1, "nbest > 1 requires lattice decoder"
self.dec_cls = FasterDecoder
opt_cls = FasterDecoderOptions
self.rec_cls = FasterRecognizer
self.decoder_options = opt_cls()
self.decoder_options.beam = beam
self.decoder_options.max_active = cfg.max_active
self.decoder_options.beam_delta = cfg.beam_delta
self.decoder_options.hash_ratio = cfg.hash_ratio
if cfg.is_lattice:
self.decoder_options.lattice_beam = cfg.lattice_beam
self.decoder_options.prune_interval = cfg.prune_interval
self.decoder_options.determinize_lattice = cfg.determinize_lattice
self.decoder_options.prune_scale = cfg.prune_scale
det_opts = DeterminizeLatticePhonePrunedOptions()
det_opts.max_mem = cfg.max_mem
det_opts.phone_determinize = cfg.phone_determinize
det_opts.word_determinize = cfg.word_determinize
det_opts.minimize = cfg.minimize
self.decoder_options.det_opts = det_opts
self.output_symbols = {}
with open(cfg.output_dict, "r") as f:
for line in f:
items = line.rstrip().split()
assert len(items) == 2
self.output_symbols[int(items[1])] = items[0]
logger.info(f"Loading FST from {cfg.hlg_graph_path}")
self.fst = read_fst_kaldi(cfg.hlg_graph_path)
self.symbol_table = SymbolTable.read_text(cfg.output_dict)
self.executor = ThreadPoolExecutor(max_workers=cfg.num_threads)
acoustic_model = AmDiagGmm().read(ki.stream(), ki.binary)
# Define the decodable wrapper: (features, acoustic_scale) -> decodable
def make_decodable_wrapper(trans_model, acoustic_model):
def decodable_wrapper(features, acoustic_scale):
return DecodableAmDiagGmmScaled(acoustic_model, trans_model, features,
acoustic_scale)
return decodable_wrapper
decodable_wrapper = make_decodable_wrapper(trans_model, acoustic_model)
# Define the decoder
decoding_graph = read_fst_kaldi(
"/home/dogan/tools/pykaldi/egs/models/wsj/HCLG.fst")
decoder_opts = FasterDecoderOptions()
decoder_opts.beam = 13
decoder_opts.max_active = 7000
decoder = FasterDecoder(decoding_graph, decoder_opts)
# Define the recognizer
symbols = SymbolTable.read_text(
"/home/dogan/tools/pykaldi/egs/models/wsj/words.txt")
asr = Recognizer(decoder, decodable_wrapper, symbols)
# Decode wave files
for key, wav in SequentialWaveReader(
"scp:/home/dogan/tools/pykaldi/egs/decoder/test2.scp"):
feats = feat_pipeline(wav)
out = asr.decode(feats)