def score(args): """A function that performs the "theanolm score" command. :type args: argparse.Namespace :param args: a collection of command line arguments """ log_file = args.log_file log_level = getattr(logging, args.log_level.upper(), None) if not isinstance(log_level, int): print("Invalid logging level requested:", args.log_level) sys.exit(1) log_format = '%(asctime)s %(funcName)s: %(message)s' if args.log_file == '-': logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level) else: logging.basicConfig(filename=log_file, format=log_format, level=log_level) if args.debug: theano.config.compute_test_value = 'warn' logging.info("Enabled computing test values for tensor variables.") logging.warning("GpuArray backend will fail random number generation!") else: theano.config.compute_test_value = 'off' theano.config.profile = args.profile theano.config.profile_memory = args.profile default_device = get_default_device(args.default_device) network = Network.from_file(args.model_path, exclude_unk=args.exclude_unk, default_device=default_device) logging.info("Building text scorer.") scorer = TextScorer(network, args.shortlist, args.exclude_unk, args.profile) logging.info("Scoring text.") if args.output == 'perplexity': _score_text(args.input_file, network.vocabulary, scorer, args.output_file, args.log_base, args.subwords, False) elif args.output == 'word-scores': _score_text(args.input_file, network.vocabulary, scorer, args.output_file, args.log_base, args.subwords, True) elif args.output == 'utterance-scores': _score_utterances(args.input_file, network.vocabulary, scorer, args.output_file, args.log_base) else: print("Invalid output format requested:", args.output) sys.exit(1)
def decode(args): """A function that performs the "theanolm decode" command. :type args: argparse.Namespace :param args: a collection of command line arguments """ log_file = args.log_file log_level = getattr(logging, args.log_level.upper(), None) if not isinstance(log_level, int): print("Invalid logging level requested:", args.log_level) sys.exit(1) log_format = '%(asctime)s %(funcName)s: %(message)s' if args.log_file == '-': logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level) else: logging.basicConfig(filename=log_file, format=log_format, level=log_level) if args.debug: theano.config.compute_test_value = 'warn' else: theano.config.compute_test_value = 'off' theano.config.profile = args.profile theano.config.profile_memory = args.profile network = Network.from_file(args.model_path, mode=Network.Mode(minibatch=False)) log_scale = 1.0 if args.log_base is None else numpy.log(args.log_base) if args.wi_penalty is None: wi_penalty = None else: wi_penalty = args.wi_penalty * log_scale if args.unk_penalty is None: ignore_unk = False unk_penalty = None elif args.unk_penalty == 0: ignore_unk = True unk_penalty = None else: ignore_unk = False unk_penalty = args.unk_penalty decoding_options = { 'nnlm_weight': args.nnlm_weight, 'lm_scale': args.lm_scale, 'wi_penalty': wi_penalty, 'ignore_unk': ignore_unk, 'unk_penalty': unk_penalty, 'linear_interpolation': args.linear_interpolation, 'max_tokens_per_node': args.max_tokens_per_node, 'beam': args.beam, 'recombination_order': args.recombination_order } logging.debug("DECODING OPTIONS") for option_name, option_value in decoding_options.items(): logging.debug("%s: %s", option_name, str(option_value)) print("Building word lattice decoder.") sys.stdout.flush() decoder = LatticeDecoder(network, decoding_options) # Combine paths from command line and lattice list. lattices = args.lattices if args.lattice_list is not None: lattices.extend(args.lattice_list.readlines()) lattices = [path.strip() for path in lattices] # Ignore empty lines in the lattice list. lattices = [x for x in lattices if x] # Pick every Ith lattice, if --num-jobs is specified and > 1. if args.num_jobs < 1: print("Invalid number of jobs specified:", args.num_jobs) sys.exit(1) if (args.job < 0) or (args.job > args.num_jobs - 1): print("Invalid job specified:", args.job) sys.exit(1) lattices = lattices[args.job::args.num_jobs] file_type = TextFileType('r') for index, path in enumerate(lattices): logging.info("Reading word lattice: %s", path) lattice_file = file_type(path) lattice = SLFLattice(lattice_file) if lattice.utterance_id is not None: utterance_id = lattice.utterance_id else: utterance_id = os.path.basename(lattice_file.name) logging.info("Utterance `%s' -- %d/%d of job %d", utterance_id, index + 1, len(lattices), args.job) tokens = decoder.decode(lattice) for index in range(min(args.n_best, len(tokens))): line = format_token(tokens[index], utterance_id, network.vocabulary, log_scale, args.output) args.output_file.write(line + "\n")
def decode(args): """A function that performs the "theanolm decode" command. :type args: argparse.Namespace :param args: a collection of command line arguments """ log_file = args.log_file log_level = getattr(logging, args.log_level.upper(), None) if not isinstance(log_level, int): print("Invalid logging level requested:", args.log_level, file=sys.stderr) sys.exit(1) log_format = '%(asctime)s %(funcName)s: %(message)s' if args.log_file == '-': logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level) else: logging.basicConfig(filename=log_file, format=log_format, level=log_level) if args.debug: theano.config.compute_test_value = 'warn' else: theano.config.compute_test_value = 'off' theano.config.profile = args.profile theano.config.profile_memory = args.profile if (args.lattice_format == 'kaldi') or (args.output == 'kaldi'): if args.kaldi_vocabulary is None: print("Kaldi lattice vocabulary is not given.", file=sys.stderr) sys.exit(1) default_device = get_default_device(args.default_device) network = Network.from_file(args.model_path, mode=Network.Mode(minibatch=False), default_device=default_device) log_scale = 1.0 if args.log_base is None else numpy.log(args.log_base) if (args.log_base is not None) and (args.lattice_format == 'kaldi'): logging.info("Warning: Kaldi lattice reader doesn't support logarithm " "base conversion.") if args.wi_penalty is None: wi_penalty = None else: wi_penalty = args.wi_penalty * log_scale decoding_options = { 'nnlm_weight': args.nnlm_weight, 'lm_scale': args.lm_scale, 'wi_penalty': wi_penalty, 'unk_penalty': args.unk_penalty, 'use_shortlist': args.shortlist, 'unk_from_lattice': args.unk_from_lattice, 'linear_interpolation': args.linear_interpolation, 'max_tokens_per_node': args.max_tokens_per_node, 'beam': args.beam, 'recombination_order': args.recombination_order, 'prune_relative': args.prune_relative, 'abs_min_max_tokens': args.abs_min_max_tokens, 'abs_min_beam': args.abs_min_beam } logging.debug("DECODING OPTIONS") for option_name, option_value in decoding_options.items(): logging.debug("%s: %s", option_name, str(option_value)) logging.info("Building word lattice decoder.") decoder = LatticeDecoder(network, decoding_options) batch = LatticeBatch(args.lattices, args.lattice_list, args.lattice_format, args.kaldi_vocabulary, args.num_jobs, args.job) for lattice_number, lattice in enumerate(batch): if lattice.utterance_id is None: lattice.utterance_id = str(lattice_number) logging.info("Utterance `%s´ -- %d of job %d", lattice.utterance_id, lattice_number + 1, args.job) log_free_mem() final_tokens, recomb_tokens = decoder.decode(lattice) if (args.output == "slf") or (args.output == "kaldi"): rescored_lattice = RescoredLattice(lattice, final_tokens, recomb_tokens, network.vocabulary) rescored_lattice.lm_scale = args.lm_scale rescored_lattice.wi_penalty = args.wi_penalty if args.output == "slf": rescored_lattice.write_slf(args.output_file) else: assert args.output == "kaldi" rescored_lattice.write_kaldi(args.output_file, batch.kaldi_word_to_id) else: for token in final_tokens[:min(args.n_best, len(final_tokens))]: line = format_token(token, lattice.utterance_id, network.vocabulary, log_scale, args.output) args.output_file.write(line + "\n") gc.collect()