| | |
|---|
| | | default=sys.maxsize, |
|---|
| | | help="The maximum number update step to train", |
|---|
| | | ) |
|---|
| | | parser.add_argument( |
|---|
| | | "--batch_interval", |
|---|
| | | type=int, |
|---|
| | | default=10000, |
|---|
| | | help="The batch interval for saving model.", |
|---|
| | | ) |
|---|
| | | group.add_argument( |
|---|
| | | "--patience", |
|---|
| | | type=int_or_none, |
|---|
| | |
|---|
| | | ) -> AbsIterFactory: |
|---|
| | | assert check_argument_types() |
|---|
| | | |
|---|
| | | if hasattr(args, "frontend_conf"): |
|---|
| | | if args.frontend_conf is not None and "fs" in args.frontend_conf: |
|---|
| | | dest_sample_rate = args.frontend_conf["fs"] |
|---|
| | | else: |
|---|
| | | dest_sample_rate = 16000 |
|---|
| | | |
|---|
| | | dataset = ESPnetDataset( |
|---|
| | | iter_options.data_path_and_name_and_type, |
|---|
| | | float_dtype=args.train_dtype, |
|---|
| | | preprocess=iter_options.preprocess_fn, |
|---|
| | | max_cache_size=iter_options.max_cache_size, |
|---|
| | | max_cache_fd=iter_options.max_cache_fd, |
|---|
| | | dest_sample_rate=args.frontend_conf["fs"], |
|---|
| | | <<<<<<< HEAD |
|---|
| | | dest_sample_rate=args.frontend_conf["fs"] if args.frontend_conf else 16000, |
|---|
| | | ======= |
|---|
| | | dest_sample_rate=dest_sample_rate, |
|---|
| | | >>>>>>> main |
|---|
| | | ) |
|---|
| | | cls.check_task_requirements( |
|---|
| | | dataset, args.allow_variable_data_keys, train=iter_options.train |
|---|
