def search(config, params, load_path, part, decode_only, report, decoded_save, nll_only, seed): import matplotlib matplotlib.use("Agg") from matplotlib import pyplot from lvsr.notebook import show_alignment data = Data(**config['data']) search_conf = config['monitoring']['search'] logger.info("Recognizer initialization started") recognizer = SpeechRecognizer(data.recordings_source, data.labels_source, data.eos_label, data.num_features, data.num_labels, character_map=data.character_map, name='recognizer', **config["net"]) recognizer.load_params(load_path) recognizer.init_beam_search(search_conf['beam_size']) logger.info("Recognizer is initialized") stream = data.get_stream(part, batches=False, shuffle=part == 'train', add_sources=(data.uttid_source, ), num_examples=500 if part == 'train' else None, seed=seed) it = stream.get_epoch_iterator() if decode_only is not None: decode_only = eval(decode_only) weights = tensor.matrix('weights') weight_statistics = theano.function([weights], [ weights_std(weights.dimshuffle(0, 'x', 1)), monotonicity_penalty(weights.dimshuffle(0, 'x', 1)) ]) print_to = sys.stdout if report: alignments_path = os.path.join(report, "alignments") if not os.path.exists(report): os.mkdir(report) os.mkdir(alignments_path) print_to = open(os.path.join(report, "report.txt"), 'w') decoded_file = None if decoded_save: decoded_file = open(decoded_save, 'w') num_examples = .0 total_nll = .0 total_errors = .0 total_length = .0 total_wer_errors = .0 total_word_length = 0. if config.get('vocabulary'): with open(os.path.expandvars(config['vocabulary'])) as f: vocabulary = dict(line.split() for line in f.readlines()) def to_words(chars): words = chars.split() words = [ vocabulary[word] if word in vocabulary else vocabulary['<UNK>'] for word in words ] return words for number, example in enumerate(it): if decode_only and number not in decode_only: continue print("Utterance {} ({})".format(number, example[2]), file=print_to) groundtruth = data.decode(example[1]) groundtruth_text = data.pretty_print(example[1]) costs_groundtruth, weights_groundtruth = (recognizer.analyze( example[0], example[1], example[1])[:2]) weight_std_groundtruth, mono_penalty_groundtruth = weight_statistics( weights_groundtruth) total_nll += costs_groundtruth.sum() num_examples += 1 print("Groundtruth:", groundtruth_text, file=print_to) print("Groundtruth cost:", costs_groundtruth.sum(), file=print_to) print("Groundtruth weight std:", weight_std_groundtruth, file=print_to) print("Groundtruth monotonicity penalty:", mono_penalty_groundtruth, file=print_to) print("Average groundtruth cost: {}".format(total_nll / num_examples), file=print_to) if nll_only: print_to.flush() continue before = time.time() outputs, search_costs = recognizer.beam_search( example[0], char_discount=search_conf['char_discount'], round_to_inf=search_conf['round_to_inf'], stop_on=search_conf['stop_on']) took = time.time() - before recognized = data.decode(outputs[0]) recognized_text = data.pretty_print(outputs[0]) if recognized: # Theano scan doesn't work with 0 length sequences costs_recognized, weights_recognized = (recognizer.analyze( example[0], example[1], outputs[0])[:2]) weight_std_recognized, mono_penalty_recognized = weight_statistics( weights_recognized) error = min(1, wer(groundtruth, recognized)) else: error = 1 total_errors += len(groundtruth) * error total_length += len(groundtruth) if config.get('vocabulary'): wer_error = min( 1, wer(to_words(groundtruth_text), to_words(recognized_text))) total_wer_errors += len(groundtruth) * wer_error total_word_length += len(groundtruth) if report and recognized: show_alignment(weights_groundtruth, groundtruth, bos_symbol=True) pyplot.savefig( os.path.join(alignments_path, "{}.groundtruth.png".format(number))) show_alignment(weights_recognized, recognized, bos_symbol=True) pyplot.savefig( os.path.join(alignments_path, "{}.recognized.png".format(number))) if decoded_file is not None: print("{} {}".format(example[2], ' '.join(recognized)), file=decoded_file) print("Decoding took:", took, file=print_to) print("Beam search cost:", search_costs[0], file=print_to) print("Recognized:", recognized_text, file=print_to) if recognized: print("Recognized cost:", costs_recognized.sum(), file=print_to) print("Recognized weight std:", weight_std_recognized, file=print_to) print("Recognized monotonicity penalty:", mono_penalty_recognized, file=print_to) print("CER:", error, file=print_to) print("Average CER:", total_errors / total_length, file=print_to) if config.get('vocabulary'): print("WER:", wer_error, file=print_to) print("Average WER:", total_wer_errors / total_word_length, file=print_to) print_to.flush()
def search(config, params, load_path, part, decode_only, report, decoded_save, nll_only, seed): import matplotlib matplotlib.use("Agg") from matplotlib import pyplot from lvsr.notebook import show_alignment data = Data(**config['data']) search_conf = config['monitoring']['search'] logger.info("Recognizer initialization started") recognizer = SpeechRecognizer( data.recordings_source, data.labels_source, data.eos_label, data.num_features, data.num_labels, character_map=data.character_map, name='recognizer', **config["net"]) recognizer.load_params(load_path) recognizer.init_beam_search(search_conf['beam_size']) logger.info("Recognizer is initialized") stream = data.get_stream(part, batches=False, shuffle=part == 'train', add_sources=(data.uttid_source,), num_examples=500 if part == 'train' else None, seed=seed) it = stream.get_epoch_iterator() if decode_only is not None: decode_only = eval(decode_only) weights = tensor.matrix('weights') weight_statistics = theano.function( [weights], [weights_std(weights.dimshuffle(0, 'x', 1)), monotonicity_penalty(weights.dimshuffle(0, 'x', 1))]) print_to = sys.stdout if report: alignments_path = os.path.join(report, "alignments") if not os.path.exists(report): os.mkdir(report) os.mkdir(alignments_path) print_to = open(os.path.join(report, "report.txt"), 'w') decoded_file = None if decoded_save: decoded_file = open(decoded_save, 'w') num_examples = .0 total_nll = .0 total_errors = .0 total_length = .0 total_wer_errors = .0 total_word_length = 0. if config.get('vocabulary'): with open(os.path.expandvars(config['vocabulary'])) as f: vocabulary = dict(line.split() for line in f.readlines()) def to_words(chars): words = chars.split() words = [vocabulary[word] if word in vocabulary else vocabulary['<UNK>'] for word in words] return words for number, example in enumerate(it): if decode_only and number not in decode_only: continue print("Utterance {} ({})".format(number, example[2]), file=print_to) groundtruth = data.decode(example[1]) groundtruth_text = data.pretty_print(example[1]) costs_groundtruth, weights_groundtruth = ( recognizer.analyze(example[0], example[1], example[1])[:2]) weight_std_groundtruth, mono_penalty_groundtruth = weight_statistics( weights_groundtruth) total_nll += costs_groundtruth.sum() num_examples += 1 print("Groundtruth:", groundtruth_text, file=print_to) print("Groundtruth cost:", costs_groundtruth.sum(), file=print_to) print("Groundtruth weight std:", weight_std_groundtruth, file=print_to) print("Groundtruth monotonicity penalty:", mono_penalty_groundtruth, file=print_to) print("Average groundtruth cost: {}".format(total_nll / num_examples), file=print_to) if nll_only: print_to.flush() continue before = time.time() outputs, search_costs = recognizer.beam_search( example[0], char_discount=search_conf['char_discount'], round_to_inf=search_conf['round_to_inf'], stop_on=search_conf['stop_on']) took = time.time() - before recognized = data.decode(outputs[0]) recognized_text = data.pretty_print(outputs[0]) if recognized: # Theano scan doesn't work with 0 length sequences costs_recognized, weights_recognized = ( recognizer.analyze(example[0], example[1], outputs[0])[:2]) weight_std_recognized, mono_penalty_recognized = weight_statistics( weights_recognized) error = min(1, wer(groundtruth, recognized)) else: error = 1 total_errors += len(groundtruth) * error total_length += len(groundtruth) if config.get('vocabulary'): wer_error = min(1, wer(to_words(groundtruth_text), to_words(recognized_text))) total_wer_errors += len(groundtruth) * wer_error total_word_length += len(groundtruth) if report and recognized: show_alignment(weights_groundtruth, groundtruth, bos_symbol=True) pyplot.savefig(os.path.join( alignments_path, "{}.groundtruth.png".format(number))) show_alignment(weights_recognized, recognized, bos_symbol=True) pyplot.savefig(os.path.join( alignments_path, "{}.recognized.png".format(number))) if decoded_file is not None: print("{} {}".format(example[2], ' '.join(recognized)), file=decoded_file) print("Decoding took:", took, file=print_to) print("Beam search cost:", search_costs[0], file=print_to) print("Recognized:", recognized_text, file=print_to) if recognized: print("Recognized cost:", costs_recognized.sum(), file=print_to) print("Recognized weight std:", weight_std_recognized, file=print_to) print("Recognized monotonicity penalty:", mono_penalty_recognized, file=print_to) print("CER:", error, file=print_to) print("Average CER:", total_errors / total_length, file=print_to) if config.get('vocabulary'): print("WER:", wer_error, file=print_to) print("Average WER:", total_wer_errors / total_word_length, file=print_to) print_to.flush()