speech_recognition/FunASR
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Merge pull request #106 from alibaba-damo-academy/dev

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hnluo 2023-02-14 15:15:15 +08:00 committed by GitHub
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2 changed files with 41 additions and 1 deletions
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10
funasr/tasks/abs_task.py
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@ -71,7 +71,7 @@ from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_int
from funasr.utils.types import str_or_none
from funasr.utils.wav_utils import calc_shape, generate_data_list
from funasr.utils.wav_utils import calc_shape, generate_data_list, filter_wav_text
from funasr.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
try:
@ -1153,6 +1153,14 @@ class AbsTask(ABC):
if args.batch_bins is not None:
args.batch_bins = args.batch_bins * args.ngpu
# filter samples if wav.scp and text are mismatch
if (args.train_shape_file is None and args.dataset_type == "small") or args.train_data_file is None and args.dataset_type == "large":
if not args.simple_ddp or distributed_option.dist_rank == 0:
filter_wav_text(args.data_dir, args.train_set)
filter_wav_text(args.data_dir, args.dev_set)
if args.simple_ddp:
dist.barrier()
if args.train_shape_file is None and args.dataset_type == "small":
if not args.simple_ddp or distributed_option.dist_rank == 0:
calc_shape(args.data_dir, args.train_set, args.frontend_conf, args.speech_length_min, args.speech_length_max)

32
funasr/utils/wav_utils.py
View File

@ -287,3 +287,35 @@ def generate_data_list(data_dir, dataset, nj=100):
wav_path = os.path.join(split_dir, str(i + 1), "wav.scp")
text_path = os.path.join(split_dir, str(i + 1), "text")
f_data.write(wav_path + " " + text_path + "\n")
def filter_wav_text(data_dir, dataset):
wav_file = os.path.join(data_dir,dataset,"wav.scp")
text_file = os.path.join(data_dir, dataset, "text")
with open(wav_file) as f_wav, open(text_file) as f_text:
wav_lines = f_wav.readlines()
text_lines = f_text.readlines()
os.rename(wav_file, "{}.bak".format(wav_file))
os.rename(text_file, "{}.bak".format(text_file))
wav_dict = {}
for line in wav_lines:
parts = line.strip().split()
if len(parts) < 2:
continue
sample_name, wav_path = parts
wav_dict[sample_name] = wav_path
text_dict = {}
for line in text_lines:
parts = line.strip().split(" ", 1)
if len(parts) < 2:
continue
sample_name, txt = parts
text_dict[sample_name] = txt
filter_count = 0
with open(wav_file, "w") as f_wav, open(text_file, "w") as f_text:
for sample_name, wav_path in wav_dict.items():
if sample_name in text_dict.keys():
f_wav.write(sample_name + " " + wav_path + "\n")
f_text.write(sample_name + " " + text_dict[sample_name] + "\n")
else:
filter_count += 1
print("{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".format(len(wav_lines), filter_count, dataset))