update

egs/aishell/paraformer/run.sh

@@ -13,7 +13,7 @@ train_cmd=utils/run.pl
 infer_cmd=utils/run.pl
 
 # general configuration
-feats_dir="/nfs/wangjiaming.wjm/Funasr_data/aishell-1-fix-cmvn" #feature output dictionary
+feats_dir="/nfs/wangjiaming.wjm/Funasr_data_test/aishell" #feature output dictionary
 exp_dir="."
 lang=zh
 dumpdir=dump/fbank
@@ -167,14 +167,10 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
                 --use_preprocessor true \
                 --token_type char \
                 --token_list $token_list \
-                --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/${scp},speech,${type} \
-                --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \
-                --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \
-                --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \
-                --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/${scp},speech,${type} \
-                --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/text,text,text \
-                --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/speech_shape \
-                --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/text_shape.char  \
+                --data_dir ${feats_dir}/data \
+                --train_set ${train_set} \
+                --valid_set ${valid_set} \
+                --cmvn_file ${feats_dir}/cmvn/cmvn.mvn \
                 --resume true \
                 --output_dir ${exp_dir}/exp/${model_dir} \
                 --config $asr_config \

funasr/bin/train.py

@@ -23,7 +23,6 @@ from funasr.utils.nested_dict_action import NestedDictAction
 from funasr.utils.prepare_data import prepare_data
 from funasr.utils.types import int_or_none
 from funasr.utils.types import str2bool
-from funasr.utils.types import str2triple_str
 from funasr.utils.types import str_or_none
 from funasr.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
 
@@ -316,42 +315,24 @@ def get_parser():
         help=f"The keyword arguments for dataset",
     )
     parser.add_argument(
-        "--train_data_file",
+        "--data_dir",
         type=str,
         default=None,
-        help="train_list for large dataset",
+        help="root path of data",
     )
     parser.add_argument(
-        "--valid_data_file",
+        "--train_set",
         type=str,
-        default=None,
-        help="valid_list for large dataset",
-    )
-    parser.add_argument(
-        "--train_data_path_and_name_and_type",
-        type=str2triple_str,
-        action="append",
-        default=[],
-        help="e.g. '--train_data_path_and_name_and_type some/path/a.scp,foo,sound'. ",
-    )
-    parser.add_argument(
-        "--valid_data_path_and_name_and_type",
-        type=str2triple_str,
-        action="append",
-        default=[],
+        default="train",
+        help="train dataset",
     )
     parser.add_argument(
-        "--train_shape_file",
+        "--valid_set",
         type=str,
-        action="append",
-        default=[],
-    )
-    parser.add_argument(
-        "--valid_shape_file",
-        type=str,
-        action="append",
-        default=[],
+        default="validation",
+        help="dev dataset",
     )
+
     parser.add_argument(
         "--use_preprocessor",
         type=str2bool,

funasr/utils/prepare_data.py

@@ -36,10 +36,8 @@ def filter_wav_text(data_dir, dataset):
                 f_text.write(sample_name + " " + text_dict[sample_name] + "\n")
             else:
                 filter_count += 1
-    logging.info(
-        "{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".format(len(wav_lines),
-                                                                                                   filter_count,
-                                                                                                   dataset))
+    logging.info("{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".
+                 format(filter_count, len(wav_lines), dataset))
 
 
 def wav2num_frame(wav_path, frontend_conf):
@@ -157,30 +155,34 @@ def generate_data_list(data_dir, dataset, nj=100):
 
 
 def prepare_data(args, distributed_option):
-    if args.dataset_type == "small" and args.train_data_path_and_name_and_type is not None:
-        return
-    if args.dataset_type == "large" and args.train_data_file is not None:
-        return
     distributed = distributed_option.distributed
-    if not hasattr(args, "train_set"):
-        args.train_set = "train"
-    if not hasattr(args, "dev_set"):
-        args.dev_set = "validation"
     if not distributed or distributed_option.dist_rank == 0:
         filter_wav_text(args.data_dir, args.train_set)
-        filter_wav_text(args.data_dir, args.dev_set)
+        filter_wav_text(args.data_dir, args.valid_set)
 
         if args.dataset_type == "small" and args.train_shape_file is None:
             calc_shape(args, args.train_set)
-            calc_shape(args, args.dev_set)
+            calc_shape(args, args.valid_set)
 
         if args.dataset_type == "large" and args.train_data_file is None:
             generate_data_list(args.data_dir, args.train_set)
-            generate_data_list(args.data_dir, args.dev_set)
-
-    args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")]
-    args.valid_shape_file = [os.path.join(args.data_dir, args.dev_set, "speech_shape")]
-    args.train_data_file = os.path.join(args.data_dir, args.train_set, "data.list")
-    args.valid_data_file = os.path.join(args.data_dir, args.dev_set, "data.list")
+            generate_data_list(args.data_dir, args.valid_set)
+
+    if args.dataset_type == "small":
+        args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "speech_shape")]
+        args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "speech_shape")]
+        data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
+        data_types = args.dataset_conf.get("data_types", "sound,text").split(",")
+        args.train_data_path_and_name_and_type = [
+            ["{}/{}/wav.scp".format(args.data_dir, args.train_set), data_names[0], data_types[0]],
+            ["{}/{}/text".format(args.data_dir, args.train_set), data_names[1], data_types[1]]
+        ]
+        args.valid_data_path_and_name_and_type = [
+            ["{}/{}/wav.scp".format(args.data_dir, args.valid_set), data_names[0], data_types[0]],
+            ["{}/{}/text".format(args.data_dir, args.valid_set), data_names[1], data_types[1]]
+        ]
+    else:
+        args.train_data_file = os.path.join(args.data_dir, args.train_set, "data.list")
+        args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "data.list")
     if distributed:
         dist.barrier()