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
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
asr = NnetLatticeFasterOnlineRecognizer.from_files(
"final.mdl",
"HCLG.fst",
"words.txt",
decoder_opts=decoder_opts,
decodable_opts=decodable_opts,
endpoint_opts=endpoint_opts)
# Decode (whole utterance)
for key, wav in SequentialWaveReader("scp:wav.scp"):
feat_pipeline = OnlineNnetFeaturePipeline(feat_info)
asr.set_input_pipeline(feat_pipeline)
feat_pipeline.accept_waveform(wav.samp_freq, wav.data()[0])
feat_pipeline.input_finished()
out = asr.decode()
print(key, out["text"], flush=True)
# Decode (chunked + partial output)
for key, wav in SequentialWaveReader("scp:wav.scp"):
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
print('Loading inference model from files\n {} \n {} \n {}\n'\
.format(model_path, graph_path, symbols_path),
file=sys.stderr)
log_file.write('Loading inference model from files\n {} \n {} \n {}\n'\
.format(model_path, graph_path, symbols_path))
inf_model_load_start = timer()
asr = NnetLatticeFasterOnlineRecognizer.from_files(
model_path,
graph_path,
symbols_path,
decoder_opts=decoder_opts,
decodable_opts=decodable_opts,
endpoint_opts=endpoint_opts)
inf_model_load_end = timer() - inf_model_load_start
print('Loaded inference model in {:.3}s.\n'.format(inf_model_load_end),
file=sys.stderr)
log_file.write(
'Loaded inference model in {:.3}s.\n'.format(inf_model_load_end))
summ_file.write(
'Loaded inference model in, {:.3}\n'.format(inf_model_load_end))
#
## Define feature pipelines as Kaldi rspecifiers
#feats_rspec = (
# "ark:compute-mfcc-feats --config=" + mfcc_hires_path + " scp:" + scp_path +" ark:- |"
# )