From 7436acc5ddca0ebb7458a0c4c483079346e10715 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 25 四月 2023 16:29:39 +0800
Subject: [PATCH] update
---
funasr/utils/prepare_data.py | 40 ++++++++++---------
funasr/bin/train.py | 37 ++++--------------
egs/aishell/paraformer/run.sh | 14 ++----
3 files changed, 35 insertions(+), 56 deletions(-)
diff --git a/egs/aishell/paraformer/run.sh b/egs/aishell/paraformer/run.sh
index d8a1c09..3556bd6 100755
--- a/egs/aishell/paraformer/run.sh
+++ b/egs/aishell/paraformer/run.sh
@@ -13,7 +13,7 @@
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 @@
--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 \
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index 8f744b8..22387b9 100755
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -23,7 +23,6 @@
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 @@
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=[],
+ default="validation",
+ help="dev dataset",
)
- parser.add_argument(
- "--valid_shape_file",
- type=str,
- action="append",
- default=[],
- )
+
parser.add_argument(
"--use_preprocessor",
type=str2bool,
diff --git a/funasr/utils/prepare_data.py b/funasr/utils/prepare_data.py
index 8ed97d5..4a33421 100644
--- a/funasr/utils/prepare_data.py
+++ b/funasr/utils/prepare_data.py
@@ -36,10 +36,8 @@
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 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()
--
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