def __init__(self, task): super(SpeechClassifier, self).__init__() torch.manual_seed(1) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False # https://github.com/pytorch/pytorch/issues/6351 self.slu = load_trained_model(slu_model_path)[0] self.classifier = torch.load(classifier_model_path[task], map_location='cpu')
def trace_model(self, model_path: str, is_bs_decode: bool = True): model, train_args = load_trained_model(model_path=model_path) assert isinstance(model, ASRInterface) model.eval() traced_model = torch.jit.trace( model.recognize, [self.__get_sample_fbank(), self.__get_recog_args()]) traced_model.save( os.path.join(os.path.dirname(model_path), '{}_traced.pt'.format(os.path.basename(model_path))))
def trans(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, MTInterface) model.trans_args = args # gpu if args.ngpu == 1: gpu_id = list(range(args.ngpu)) logging.info("gpu id: " + str(gpu_id)) model.cuda() # read json data with open(args.trans_json, "rb") as f: js = json.load(f)["utts"] new_js = {} # remove enmpy utterances if train_args.multilingual: js = { k: v for k, v in js.items() if v["output"][0]["shape"][0] > 1 and v["output"][1]["shape"][0] > 1 } else: js = { k: v for k, v in js.items() if v["output"][0]["shape"][0] > 0 and v["output"][1]["shape"][0] > 0 } 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())) feat = [js[name]["output"][1]["tokenid"].split()] nbest_hyps = model.translate(feat, args, train_args.char_list) 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 itertools.zip_longest(*kargs, fillvalue=fillvalue) # sort data keys = list(js.keys()) feat_lens = [js[key]["output"][1]["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] feats = [ np.fromiter( map(int, js[name]["output"][1]["tokenid"].split()), dtype=np.int64, ) for name in names ] nbest_hyps = model.translate_batch( feats, args, train_args.char_list, ) for i, nbest_hyp in enumerate(nbest_hyps): name = names[i] 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"))
def trans(args): """Decode with the given args :param Namespace args: The program arguments """ set_deterministic_pytorch(args) model, train_args = load_trained_model(args.model) assert isinstance(model, MTInterface) 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 # 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 = {} # remove enmpy utterances if train_args.replace_sos: js = { k: v for k, v in js.items() if v['output'][0]['shape'][0] > 1 and v['output'][1]['shape'][0] > 1 } else: js = { k: v for k, v in js.items() if v['output'][0]['shape'][0] > 0 and v['output'][1]['shape'][0] > 0 } 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())) feat = [js[name]['output'][1]['tokenid'].split()] nbest_hyps = model.translate(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 keys = list(js.keys()) feat_lens = [js[key]['output'][1]['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] feats = [ np.fromiter(map(int, js[name]['output'][1]['tokenid'].split()), dtype=np.int64) for name in names ] nbest_hyps = model.translate_batch(feats, args, train_args.char_list, rnnlm=rnnlm) for i, nbest_hyp in enumerate(nbest_hyps): name = names[i] 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'))
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") )
lpz = model.ctc.log_softmax(enc_output)[0].cpu().numpy() return lpz # debug mode setting # 0 would be fastest, but 1 seems to be reasonable # considering reproducibility # remove type check torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False # https://github.com/pytorch/pytorch/issues/6351 model_path = cmdargs.model_path model_conf = None # load trained model parameters model, train_args = load_trained_model(model_path) model.eval() args = AttrDict() args.update({ "ctc_weight": 0.5, "beam_size": 1, "penalty": 0.0, "maxlenratio": 0.0, "minlenratio": 0.0, "nbest": 1 }) model.recog_args = args if cmdargs.gpu: gpu_id = range(1) logging.info('gpu id: ' + str(gpu_id))
def recog_v2(args): """Decode with custom models that implements ScorerInterface. Notes: The previous backend espnet.asr.pytorch_backend.asr.recog only supports E2E and RNNLM Args: args (namespace): The program arguments. See py:func:`espnet.bin.asr_recog.get_parser` for details """ logging.warning("experimental API for custom LMs is selected by --api v2") if args.batchsize > 1: raise NotImplementedError("multi-utt batch decoding is not implemented") if args.streaming_mode is not None: raise NotImplementedError("streaming mode is not implemented") if args.word_rnnlm: raise NotImplementedError("word LM is not implemented") set_deterministic_pytorch(args) model, train_args = load_trained_model(args.model) assert isinstance(model, ASRInterface) if args.quantize_config is not None: q_config = set([getattr(torch.nn, q) for q in args.quantize_config]) else: q_config = {torch.nn.Linear} if args.quantize_asr_model: logging.info("Use quantized asr model for decoding") # See https://github.com/espnet/espnet/pull/3616 for more information. if ( torch.__version__ < LooseVersion("1.4.0") and "lstm" in train_args.etype and torch.nn.LSTM in q_config ): raise ValueError( "Quantized LSTM in ESPnet is only supported with torch 1.4+." ) if args.quantize_dtype == "float16" and torch.__version__ < LooseVersion( "1.5.0" ): raise ValueError( "float16 dtype for dynamic quantization is not supported with torch " "version < 1.5.0. Switching to qint8 dtype instead." ) dtype = getattr(torch, args.quantize_dtype) model = torch.quantization.quantize_dynamic(model, q_config, dtype=dtype) model.eval() 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.rnnlm: lm_args = get_model_conf(args.rnnlm, args.rnnlm_conf) # NOTE: for a compatibility with less than 0.5.0 version models lm_model_module = getattr(lm_args, "model_module", "default") lm_class = dynamic_import_lm(lm_model_module, lm_args.backend) lm = lm_class(len(train_args.char_list), lm_args) torch_load(args.rnnlm, lm) if args.quantize_lm_model: logging.info("Use quantized lm model") dtype = getattr(torch, args.quantize_dtype) lm = torch.quantization.quantize_dynamic(lm, q_config, dtype=dtype) lm.eval() else: lm = None if args.ngram_model: from espnet.nets.scorers.ngram import NgramFullScorer from espnet.nets.scorers.ngram import NgramPartScorer if args.ngram_scorer == "full": ngram = NgramFullScorer(args.ngram_model, train_args.char_list) else: ngram = NgramPartScorer(args.ngram_model, train_args.char_list) else: ngram = None scorers = model.scorers() scorers["lm"] = lm scorers["ngram"] = ngram scorers["length_bonus"] = LengthBonus(len(train_args.char_list)) weights = dict( decoder=1.0 - args.ctc_weight, ctc=args.ctc_weight, lm=args.lm_weight, ngram=args.ngram_weight, length_bonus=args.penalty, ) beam_search = BeamSearch( beam_size=args.beam_size, vocab_size=len(train_args.char_list), weights=weights, scorers=scorers, sos=model.sos, eos=model.eos, token_list=train_args.char_list, pre_beam_score_key=None if args.ctc_weight == 1.0 else "full", ) # TODO(karita): make all scorers batchfied if args.batchsize == 1: non_batch = [ k for k, v in beam_search.full_scorers.items() if not isinstance(v, BatchScorerInterface) ] if len(non_batch) == 0: beam_search.__class__ = BatchBeamSearch logging.info("BatchBeamSearch implementation is selected.") else: logging.warning( f"As non-batch scorers {non_batch} are found, " f"fall back to non-batch implementation." ) if args.ngpu > 1: raise NotImplementedError("only single GPU decoding is supported") if args.ngpu == 1: device = "cuda" else: device = "cpu" dtype = getattr(torch, args.dtype) logging.info(f"Decoding device={device}, dtype={dtype}") model.to(device=device, dtype=dtype).eval() beam_search.to(device=device, dtype=dtype).eval() # read json data with open(args.recog_json, "rb") as f: js = json.load(f)["utts"] new_js = {} 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] enc = model.encode(torch.as_tensor(feat).to(device=device, dtype=dtype)) nbest_hyps = beam_search( x=enc, maxlenratio=args.maxlenratio, minlenratio=args.minlenratio ) nbest_hyps = [ h.asdict() for h in nbest_hyps[: min(len(nbest_hyps), args.nbest)] ] new_js[name] = add_results_to_json( js[name], nbest_hyps, 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") )
def recog_v2(args): """Decode with custom models that implements ScorerInterface. Notes: The previous backend espnet.asr.pytorch_backend.asr.recog only supports E2E and RNNLM Args: args (namespace): The program arguments. See py:func:`espnet.bin.asr_recog.get_parser` for details """ logging.warning("experimental API for custom LMs is selected by --api v2") if args.batchsize > 1: raise NotImplementedError( "multi-utt batch decoding is not implemented") if args.streaming_mode is not None: raise NotImplementedError("streaming mode is not implemented") if args.word_rnnlm: raise NotImplementedError("word LM is not implemented") set_deterministic_pytorch(args) model, train_args = load_trained_model(args.model) assert isinstance(model, ASRInterface) model.eval() 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.rnnlm: lm_args = get_model_conf(args.rnnlm, args.rnnlm_conf) # NOTE: for a compatibility with less than 0.5.0 version models lm_model_module = getattr(lm_args, "model_module", "default") lm_class = dynamic_import_lm(lm_model_module, lm_args.backend) lm = lm_class(len(train_args.char_list), lm_args) torch_load(args.rnnlm, lm) lm.eval() else: lm = None if args.ngram_model: from espnet.nets.scorers.ngram import NgramFullScorer from espnet.nets.scorers.ngram import NgramPartScorer if args.ngram_scorer == "full": ngram = NgramFullScorer(args.ngram_model, train_args.char_list) else: ngram = NgramPartScorer(args.ngram_model, train_args.char_list) else: ngram = None scorers = model.scorers() scorers["lm"] = lm scorers["ngram"] = ngram scorers["length_bonus"] = LengthBonus(len(train_args.char_list)) weights = dict( decoder=1.0 - args.ctc_weight, ctc=args.ctc_weight, lm=args.lm_weight, ngram=args.ngram_weight, length_bonus=args.penalty, ) beam_search = BeamSearch( beam_size=args.beam_size, vocab_size=len(train_args.char_list), weights=weights, scorers=scorers, sos=model.sos, eos=model.eos, token_list=train_args.char_list, pre_beam_score_key=None if args.ctc_weight == 1.0 else "full", ) # TODO(karita): make all scorers batchfied if args.batchsize == 1: non_batch = [ k for k, v in beam_search.full_scorers.items() if not isinstance(v, BatchScorerInterface) ] if len(non_batch) == 0: beam_search.__class__ = BatchBeamSearch logging.info("BatchBeamSearch implementation is selected.") else: logging.warning(f"As non-batch scorers {non_batch} are found, " f"fall back to non-batch implementation.") if args.ngpu > 1: raise NotImplementedError("only single GPU decoding is supported") if args.ngpu == 1: device = "cuda" else: device = "cpu" dtype = getattr(torch, args.dtype) logging.info(f"Decoding device={device}, dtype={dtype}") model.to(device=device, dtype=dtype).eval() beam_search.to(device=device, dtype=dtype).eval() # read json data with open(args.recog_json, "r") as f: # "rb" content = f.read() if content.startswith( "Warning! You haven't set Python environment yet. Go to /content/espnet/tools and generate 'activate_python.sh'" ): train_json = json.loads( content[110:] )["utts"] # 110 is the number of characters for the above WARNING LINE. else: train_json = json.loads(content)["utts"] # json.load(f)["utts"] js = train_json # json.load(f)["utts"] new_js = {} 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] enc = model.encode( torch.as_tensor(feat).to(device=device, dtype=dtype)) nbest_hyps = beam_search(x=enc, maxlenratio=args.maxlenratio, minlenratio=args.minlenratio) nbest_hyps = [ h.asdict() for h in nbest_hyps[:min(len(nbest_hyps), args.nbest)] ] new_js[name] = add_results_to_json(js[name], nbest_hyps, 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"))
def recog_v2(args): """Decode with custom models that implements ScorerInterface. Notes: The previous backend espnet.asr.pytorch_backend.asr.recog only supports E2E and RNNLM Args: args (namespace): The program arguments. See py:func:`espnet.bin.asr_recog.get_parser` for details """ logging.warning("experimental API for custom LMs is selected by --api v2") if args.batchsize > 1: raise NotImplementedError("batch decoding is not implemented") if args.streaming_mode is not None: raise NotImplementedError("streaming mode is not implemented") if args.word_rnnlm: raise NotImplementedError("word LM is not implemented") set_deterministic_pytorch(args) model, train_args = load_trained_model(args.model) assert isinstance(model, ASRInterface) model.eval() 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.rnnlm: lm_args = get_model_conf(args.rnnlm, args.rnnlm_conf) # NOTE: for a compatibility with less than 0.5.0 version models lm_model_module = getattr(lm_args, "model_module", "default") lm_class = dynamic_import_lm(lm_model_module, lm_args.backend) lm = lm_class(len(train_args.char_list), lm_args) torch_load(args.rnnlm, lm) lm.eval() else: lm = None scorers = model.scorers() scorers["lm"] = lm scorers["length_bonus"] = LengthBonus(len(train_args.char_list)) weights = dict(decoder=1.0 - args.ctc_weight, ctc=args.ctc_weight, lm=args.lm_weight, length_bonus=args.penalty) beam_search = BeamSearch( beam_size=args.beam_size, vocab_size=len(train_args.char_list), weights=weights, scorers=scorers, sos=model.sos, eos=model.eos, token_list=train_args.char_list, ) if args.ngpu > 1: raise NotImplementedError("only single GPU decoding is supported") if args.ngpu == 1: device = "cuda" else: device = "cpu" dtype = getattr(torch, args.dtype) logging.info(f"Decoding device={device}, dtype={dtype}") model.to(device=device, dtype=dtype).eval() beam_search.to(device=device, dtype=dtype).eval() # read json data with open(args.recog_json, 'rb') as f: js = json.load(f)['utts'] new_js = {} 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] enc = model.encode( torch.as_tensor(feat).to(device=device, dtype=dtype)) print(enc.shape) print(model) nbest_hyps = beam_search(x=enc, maxlenratio=args.maxlenratio, minlenratio=args.minlenratio) nbest_hyps = [ h.asdict() for h in nbest_hyps[:min(len(nbest_hyps), args.nbest)] ] new_js[name] = add_results_to_json(js[name], nbest_hyps, 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'))