def recog(args): """Decode with the given args :param Namespace args: The program arguments """ set_deterministic_pytorch(args) # read training config idim, odim, train_args = get_model_conf(args.model, args.model_conf) # load trained model parameters logging.info('reading model parameters from ' + args.model) model = E2E(idim, odim, train_args) torch_load(args.model, model) model.recog_args = args # read rnnlm if args.rnnlm: rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf) rnnlm = lm_pytorch.ClassifierWithState( lm_pytorch.RNNLM( len(train_args.char_list), rnnlm_args.layer, rnnlm_args.unit)) torch_load(args.rnnlm, rnnlm) rnnlm.eval() else: rnnlm = None if args.word_rnnlm: rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf) word_dict = rnnlm_args.char_list_dict char_dict = {x: i for i, x in enumerate(train_args.char_list)} word_rnnlm = lm_pytorch.ClassifierWithState(lm_pytorch.RNNLM( len(word_dict), rnnlm_args.layer, rnnlm_args.unit)) torch_load(args.word_rnnlm, word_rnnlm) word_rnnlm.eval() if rnnlm is not None: rnnlm = lm_pytorch.ClassifierWithState( extlm_pytorch.MultiLevelLM(word_rnnlm.predictor, rnnlm.predictor, word_dict, char_dict)) else: rnnlm = lm_pytorch.ClassifierWithState( extlm_pytorch.LookAheadWordLM(word_rnnlm.predictor, word_dict, char_dict)) # gpu if args.ngpu == 1: gpu_id = range(args.ngpu) logging.info('gpu id: ' + str(gpu_id)) model.cuda() if rnnlm: rnnlm.cuda() # read json data with open(args.recog_json, 'rb') as f: js = json.load(f)['utts'] new_js = {} load_inputs_and_targets = LoadInputsAndTargets( mode='asr', load_output=False, sort_in_input_length=False, preprocess_conf=train_args.preprocess_conf if args.preprocess_conf is None else args.preprocess_conf) if args.batchsize == 0: with torch.no_grad(): for idx, name in enumerate(js.keys(), 1): logging.info('(%d/%d) decoding ' + name, idx, len(js.keys())) batch = [(name, js[name])] with using_transform_config({'train': True}): feat = load_inputs_and_targets(batch)[0][0] nbest_hyps = model.recognize(feat, args, train_args.char_list, rnnlm) new_js[name] = add_results_to_json(js[name], nbest_hyps, train_args.char_list) else: try: from itertools import zip_longest as zip_longest except Exception: from itertools import izip_longest as zip_longest def grouper(n, iterable, fillvalue=None): kargs = [iter(iterable)] * n return zip_longest(*kargs, fillvalue=fillvalue) # sort data keys = list(js.keys()) feat_lens = [js[key]['input'][0]['shape'][0] for key in keys] sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i]) keys = [keys[i] for i in sorted_index] with torch.no_grad(): for names in grouper(args.batchsize, keys, None): names = [name for name in names if name] batch = [(name, js[name]) for name in names] with using_transform_config({'train': False}): feats = load_inputs_and_targets(batch)[0] nbest_hyps = model.recognize_batch(feats, args, train_args.char_list, rnnlm=rnnlm) for i, name in enumerate(names): nbest_hyp = [hyp[i] for hyp in nbest_hyps] new_js[name] = add_results_to_json(js[name], nbest_hyp, train_args.char_list) # TODO(watanabe) fix character coding problems when saving it with open(args.result_label, 'wb') as f: f.write(json.dumps({'utts': new_js}, indent=4, sort_keys=True).encode('utf_8'))
def recog(args): """Decode with the given args. Args: args (namespace): The program arguments. """ set_deterministic_pytorch(args) model, train_args = load_trained_model(args.model) assert isinstance(model, ASRInterface) model.recog_args = args # read rnnlm if args.rnnlm: rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf) if getattr(rnnlm_args, "model_module", "default") != "default": raise ValueError( "use '--api v2' option to decode with non-default language model" ) rnnlm = lm_pytorch.ClassifierWithState( lm_pytorch.RNNLM( len(train_args.char_list), rnnlm_args.layer, rnnlm_args.unit, getattr(rnnlm_args, "embed_unit", None), # for backward compatibility ) ) torch_load(args.rnnlm, rnnlm) rnnlm.eval() else: rnnlm = None if args.word_rnnlm: rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf) word_dict = rnnlm_args.char_list_dict char_dict = {x: i for i, x in enumerate(train_args.char_list)} word_rnnlm = lm_pytorch.ClassifierWithState( lm_pytorch.RNNLM(len(word_dict), rnnlm_args.layer, rnnlm_args.unit) ) torch_load(args.word_rnnlm, word_rnnlm) word_rnnlm.eval() if rnnlm is not None: rnnlm = lm_pytorch.ClassifierWithState( extlm_pytorch.MultiLevelLM( word_rnnlm.predictor, rnnlm.predictor, word_dict, char_dict ) ) else: rnnlm = lm_pytorch.ClassifierWithState( extlm_pytorch.LookAheadWordLM( word_rnnlm.predictor, word_dict, char_dict ) ) # gpu if args.ngpu == 1: gpu_id = list(range(args.ngpu)) logging.info("gpu id: " + str(gpu_id)) model.cuda() if rnnlm: rnnlm.cuda() # read json data with open(args.recog_json, "rb") as f: js = json.load(f)["utts"] new_js = {} load_inputs_and_targets = LoadInputsAndTargets( mode="asr", load_output=False, sort_in_input_length=False, preprocess_conf=train_args.preprocess_conf if args.preprocess_conf is None else args.preprocess_conf, preprocess_args={"train": False}, ) if args.batchsize == 0: with torch.no_grad(): for idx, name in enumerate(js.keys(), 1): logging.info("(%d/%d) decoding " + name, idx, len(js.keys())) batch = [(name, js[name])] feat = load_inputs_and_targets(batch)[0][0] nbest_hyps = model.recognize(feat, args, train_args.char_list, rnnlm) new_js[name] = add_results_to_json( js[name], nbest_hyps, train_args.char_list ) else: def grouper(n, iterable, fillvalue=None): kargs = [iter(iterable)] * n return zip_longest(*kargs, fillvalue=fillvalue) # sort data if batchsize > 1 keys = list(js.keys()) if args.batchsize > 1: feat_lens = [js[key]["input"][0]["shape"][0] for key in keys] sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i]) keys = [keys[i] for i in sorted_index] with torch.no_grad(): for names in grouper(args.batchsize, keys, None): names = [name for name in names if name] batch = [(name, js[name]) for name in names] feats = load_inputs_and_targets(batch)[0] nbest_hyps = model.recognize_batch( feats, args, train_args.char_list, rnnlm=rnnlm ) for i, name in enumerate(names): nbest_hyp = [hyp[i] for hyp in nbest_hyps] new_js[name] = add_results_to_json( js[name], nbest_hyp, train_args.char_list ) with open(args.result_label, "wb") as f: f.write( json.dumps( {"utts": new_js}, indent=4, ensure_ascii=False, sort_keys=True ).encode("utf_8") )