speech_recognition/FunASR
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This commit is contained in:
嘉渊 2023-04-25 16:29:39 +08:00
parent 0f06fc04c0
commit 7436acc5dd
3 changed files with 35 additions and 56 deletions
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14
egs/aishell/paraformer/run.sh
View File

@ -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 \

37
funasr/bin/train.py
View File

@ -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",
default="train",
help="train 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=[],
)
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,

40
funasr/utils/prepare_data.py
View File

@ -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)
generate_data_list(args.data_dir, args.valid_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")
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()