def main(args): parser = argparse.ArgumentParser() # general configuration parser.add_argument('--ngpu', default=0, type=int, help='Number of GPUs') parser.add_argument('--backend', default='chainer', type=str, choices=['chainer', 'pytorch'], help='Backend library') parser.add_argument('--debugmode', default=1, type=int, help='Debugmode') parser.add_argument('--seed', default=1, type=int, help='Random seed') parser.add_argument('--verbose', '-V', default=1, type=int, help='Verbose option') parser.add_argument( '--batchsize', default=1, type=int, help='Batch size for beam search (0: means no batch processing)') parser.add_argument('--preprocess-conf', type=str, default=None, help='The configuration file for the pre-processing') # task related parser.add_argument('--recog-json', type=str, help='Filename of recognition data (json)') parser.add_argument('--result-label', type=str, required=True, help='Filename of result label data (json)') # model (parameter) related parser.add_argument('--model', type=str, required=True, help='Model file parameters to read') parser.add_argument('--model-conf', type=str, default=None, help='Model config file') parser.add_argument('--num-spkrs', default=1, type=int, choices=[1, 2], help='Number of speakers in the speech.') # search related parser.add_argument('--nbest', type=int, default=1, help='Output N-best hypotheses') parser.add_argument('--beam-size', type=int, default=1, help='Beam size') parser.add_argument('--penalty', default=0.0, type=float, help='Incertion penalty') parser.add_argument('--maxlenratio', default=0.0, type=float, help="""Input length ratio to obtain max output length. If maxlenratio=0.0 (default), it uses a end-detect function to automatically find maximum hypothesis lengths""") parser.add_argument('--minlenratio', default=0.0, type=float, help='Input length ratio to obtain min output length') parser.add_argument('--ctc-weight', default=0.0, type=float, help='CTC weight in joint decoding') # rnnlm related parser.add_argument('--rnnlm', type=str, default=None, help='RNNLM model file to read') parser.add_argument('--rnnlm-conf', type=str, default=None, help='RNNLM model config file to read') parser.add_argument('--word-rnnlm', type=str, default=None, help='Word RNNLM model file to read') parser.add_argument('--word-rnnlm-conf', type=str, default=None, help='Word RNNLM model config file to read') parser.add_argument('--word-dict', type=str, default=None, help='Word list to read') parser.add_argument('--lm-weight', default=0.1, type=float, help='RNNLM weight.') parser.add_argument( '--streaming-window', type=int, default=False, help= 'Use streaming recognizer for inference - provide window size in frames. ' '--batchsize must be set to 0 to enable this mode') args = parser.parse_args(args) # logging info if args.verbose == 1: logging.basicConfig( level=logging.INFO, format= "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s") elif args.verbose == 2: logging.basicConfig( level=logging.DEBUG, format= "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s") else: logging.basicConfig( level=logging.WARN, format= "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s") logging.warning("Skip DEBUG/INFO messages") # check CUDA_VISIBLE_DEVICES if args.ngpu > 0: cvd = os.environ.get("CUDA_VISIBLE_DEVICES") if cvd is None: logging.warning("CUDA_VISIBLE_DEVICES is not set.") elif args.ngpu != len(cvd.split(",")): logging.error("#gpus is not matched with CUDA_VISIBLE_DEVICES.") sys.exit(1) # TODO(mn5k): support of multiple GPUs if args.ngpu > 1: logging.error("The program only supports ngpu=1.") sys.exit(1) # display PYTHONPATH logging.info('python path = ' + os.environ.get('PYTHONPATH', '(None)')) # seed setting random.seed(args.seed) np.random.seed(args.seed) logging.info('set random seed = %d' % args.seed) # validate rnn options if args.rnnlm is not None and args.word_rnnlm is not None: logging.error( "It seems that both --rnnlm and --word-rnnlm are specified. Please use either option." ) sys.exit(1) # recog logging.info('backend = ' + args.backend) if args.num_spkrs == 1: if args.backend == "chainer": from espnet.asr.chainer_backend.asr import recog recog(args) elif args.backend == "pytorch": from espnet.asr.pytorch_backend.asr import recog recog(args) else: raise ValueError("Only chainer and pytorch are supported.") elif args.num_spkrs == 2: if args.backend == "pytorch": from espnet.asr.pytorch_backend.asr_mix import recog recog(args) else: raise ValueError("Only pytorch is supported.")
def main(args): """Run the main decoding function.""" parser = get_parser() args = parser.parse_args(args) if args.ngpu == 0 and args.dtype == "float16": raise ValueError( f"--dtype {args.dtype} does not support the CPU backend.") # logging info if args.verbose == 1: logging.basicConfig( level=logging.INFO, format= "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) elif args.verbose == 2: logging.basicConfig( level=logging.DEBUG, format= "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) else: logging.basicConfig( level=logging.WARN, format= "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) logging.warning("Skip DEBUG/INFO messages") # check CUDA_VISIBLE_DEVICES if args.ngpu > 0: cvd = os.environ.get("CUDA_VISIBLE_DEVICES") if cvd is None: logging.warning("CUDA_VISIBLE_DEVICES is not set.") elif args.ngpu != len(cvd.split(",")): logging.error("#gpus is not matched with CUDA_VISIBLE_DEVICES.") sys.exit(1) # TODO(mn5k): support of multiple GPUs if args.ngpu > 1: logging.error("The program only supports ngpu=1.") sys.exit(1) # display PYTHONPATH logging.info("python path = " + os.environ.get("PYTHONPATH", "(None)")) # seed setting random.seed(args.seed) np.random.seed(args.seed) logging.info("set random seed = %d" % args.seed) # validate rnn options if args.rnnlm is not None and args.word_rnnlm is not None: logging.error( "It seems that both --rnnlm and --word-rnnlm are specified. " "Please use either option.") sys.exit(1) # recog logging.info("backend = " + args.backend) if args.num_spkrs == 1: if args.backend == "chainer": from espnet.asr.chainer_backend.asr import recog recog(args) elif args.backend == "pytorch": if args.num_encs == 1: # Experimental API that supports custom LMs if args.api == "v2": from espnet.asr.pytorch_backend.recog import recog_v2 recog_v2(args) else: from espnet.asr.pytorch_backend.asr import recog if args.dtype != "float32": raise NotImplementedError( f"`--dtype {args.dtype}` is only available with `--api v2`" ) recog(args) else: if args.api == "v2": raise NotImplementedError( f"--num-encs {args.num_encs} > 1 is not supported in --api v2" ) else: from espnet.asr.pytorch_backend.asr import recog recog(args) else: raise ValueError("Only chainer and pytorch are supported.") elif args.num_spkrs == 2: if args.backend == "pytorch": from espnet.asr.pytorch_backend.asr_mix import recog recog(args) else: raise ValueError("Only pytorch is supported.")
def main(args): parser = get_parser() args = parser.parse_args(args) # logging info if args.verbose == 1: logging.basicConfig( level=logging.INFO, format= "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s") elif args.verbose == 2: logging.basicConfig( level=logging.DEBUG, format= "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s") else: logging.basicConfig( level=logging.WARN, format= "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s") logging.warning("Skip DEBUG/INFO messages") # check CUDA_VISIBLE_DEVICES if args.ngpu > 0: cvd = os.environ.get("CUDA_VISIBLE_DEVICES") if cvd is None: logging.warning("CUDA_VISIBLE_DEVICES is not set.") elif args.ngpu != len(cvd.split(",")): logging.error("#gpus is not matched with CUDA_VISIBLE_DEVICES.") sys.exit(1) # TODO(mn5k): support of multiple GPUs if args.ngpu > 1: logging.error("The program only supports ngpu=1.") sys.exit(1) # display PYTHONPATH logging.info('python path = ' + os.environ.get('PYTHONPATH', '(None)')) # seed setting random.seed(args.seed) np.random.seed(args.seed) logging.info('set random seed = %d' % args.seed) # validate rnn options if args.rnnlm is not None and args.word_rnnlm is not None: logging.error( "It seems that both --rnnlm and --word-rnnlm are specified. Please use either option." ) sys.exit(1) # recog logging.info('backend = ' + args.backend) if args.num_spkrs == 1: if args.backend == "chainer": from espnet.asr.chainer_backend.asr import recog recog(args) elif args.backend == "pytorch": from espnet.asr.pytorch_backend.asr import recog recog(args) else: raise ValueError("Only chainer and pytorch are supported.") elif args.num_spkrs == 2: if args.backend == "pytorch": from espnet.asr.pytorch_backend.asr_mix import recog recog(args) else: raise ValueError("Only pytorch is supported.")