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

hnluo

2023-02-14 71766839fd9ac95a396fa9ae321418d8139255c4

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

Dev

 funasr/tasks/abs_task.py

@@ -71,7 +71,7 @@
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 @@
                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)

 funasr/utils/wav_utils.py

@@ -287,3 +287,35 @@
            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))