funasr pages

游雁

2023-11-16 4ace5a95b052d338947fc88809a440ccd55cf6b4

 funasr/utils/prepare_data.py

@@ -5,9 +5,9 @@

import kaldiio
import numpy as np
import soundfile
import torch.distributed as dist
import torchaudio
import soundfile


def filter_wav_text(data_dir, dataset):
@@ -87,6 +87,7 @@
                sample_name, feature_path = line.strip().split()
                feature = kaldiio.load_mat(feature_path)
                n_frames, feature_dim = feature.shape
                write_flag = True
                if n_frames > 0 and length_min > 0:
                    write_flag = n_frames >= length_min
                if n_frames > 0 and length_max > 0:
@@ -198,6 +199,7 @@
    data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
    data_types = args.dataset_conf.get("data_types", "sound,text").split(",")
    file_names = args.data_file_names.split(",")
    batch_type = args.dataset_conf["batch_conf"]["batch_type"]
    print("data_names: {}, data_types: {}, file_names: {}".format(data_names, data_types, file_names))
    assert len(data_names) == len(data_types) == len(file_names)
    if args.dataset_type == "small":
@@ -228,7 +230,7 @@
            filter_wav_text(args.data_dir, args.train_set)
            filter_wav_text(args.data_dir, args.valid_set)

        if args.dataset_type == "small":
        if args.dataset_type == "small" and batch_type != "unsorted":
            calc_shape(args, args.train_set)
            calc_shape(args, args.valid_set)