def load_model(config_file,
online_config,
models_path='models/',
beam_size=10,
frames_per_chunk=50):
# Read YAML file
with open(config_file, 'r') as stream:
model_yaml = yaml.safe_load(stream)
decoder_yaml_opts = model_yaml['decoder']
print(decoder_yaml_opts)
feat_opts = OnlineNnetFeaturePipelineConfig()
endpoint_opts = OnlineEndpointConfig()
if not os.path.isfile(online_config):
print(online_config +
' does not exists. Trying to create it from yaml file settings.')
print(
'See also online_config_options.info.txt for what possible settings are.'
)
with open(online_config, 'w') as online_config_file:
online_config_file.write("--add_pitch=False\n")
online_config_file.write("--mfcc_config=" + models_path +
decoder_yaml_opts['mfcc-config'] + "\n")
online_config_file.write("--feature_type=mfcc\n")
online_config_file.write(
"--ivector_extraction_config=" + models_path +
decoder_yaml_opts['ivector-extraction-config'] + '\n')
online_config_file.write(
"--endpoint.silence-phones=" +
decoder_yaml_opts['endpoint-silence-phones'] + '\n')
else:
print("Loading online conf from:", online_config)
po = ParseOptions("")
feat_opts.register(po)
endpoint_opts.register(po)
po.read_config_file(online_config)
feat_info = OnlineNnetFeaturePipelineInfo.from_config(feat_opts)
# Construct recognizer
decoder_opts = LatticeFasterDecoderOptions()
decoder_opts.beam = beam_size
decoder_opts.max_active = 7000
decodable_opts = NnetSimpleLoopedComputationOptions()
decodable_opts.acoustic_scale = 1.0
decodable_opts.frame_subsampling_factor = 3
decodable_opts.frames_per_chunk = frames_per_chunk
asr = NnetLatticeFasterOnlineRecognizer.from_files(
models_path + decoder_yaml_opts["model"],
models_path + decoder_yaml_opts["fst"],
models_path + decoder_yaml_opts["word-syms"],
decoder_opts=decoder_opts,
decodable_opts=decodable_opts,
endpoint_opts=endpoint_opts)
return asr, feat_info, decodable_opts
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.asr import NnetLatticeFasterOnlineRecognizer
from kaldi.decoder import LatticeFasterDecoderOptions
from kaldi.nnet3 import NnetSimpleLoopedComputationOptions
from kaldi.online2 import (OnlineEndpointConfig,
OnlineIvectorExtractorAdaptationState,
OnlineNnetFeaturePipelineConfig,
OnlineNnetFeaturePipelineInfo,
OnlineNnetFeaturePipeline, OnlineSilenceWeighting)
from kaldi.util.options import ParseOptions
from kaldi.util.table import SequentialWaveReader
chunk_size = 1440
# Define online feature pipeline
feat_opts = OnlineNnetFeaturePipelineConfig()
endpoint_opts = OnlineEndpointConfig()
po = ParseOptions("")
feat_opts.register(po)
endpoint_opts.register(po)
po.read_config_file("online.conf")
feat_info = OnlineNnetFeaturePipelineInfo.from_config(feat_opts)
# Construct recognizer
decoder_opts = LatticeFasterDecoderOptions()
decoder_opts.beam = 13
decoder_opts.max_active = 7000
decodable_opts = NnetSimpleLoopedComputationOptions()
decodable_opts.acoustic_scale = 1.0
decodable_opts.frame_subsampling_factor = 3
decodable_opts.frames_per_chunk = 150