update the minimum size of audio

huangmingming

2023-01-30 adcee8828ef5d78b575043954deb662a35e318f7

 funasr/datasets/large_datasets/dataset.py

@@ -1,5 +1,6 @@
import os
import random
import soundfile
from functools import partial

import torch
@@ -97,7 +98,7 @@
                if data_type == "kaldi_ark":
                    ark_reader = ReadHelper('ark:{}'.format(data_file))
                    reader_list.append(ark_reader)
                elif data_type == "text":
                elif data_type == "text" or data_type == "sound":
                    text_reader = open(data_file, "r")
                    reader_list.append(text_reader)
                else:
@@ -109,6 +110,13 @@
                    if data_type == "kaldi_ark":
                        key, mat = item
                        sample_dict[data_name] = mat
                        if data_name == "speech":
                            sample_dict["key"] = key
                    elif data_type == "sound":
                        key, path = item.strip().split()
                        mat, sampling_rate = soundfile.read(path)
                        sample_dict[data_name] = mat
                        sample_dict["sampling_rate"] = sampling_rate
                        if data_name == "speech":
                            sample_dict["key"] = key
                    else:
@@ -125,11 +133,15 @@

def len_fn_token(data):
    assert "speech" in data
    return data["speech"].shape[0]
    if "sampling_rate" in data:
        return (data["speech"].shape[0] / data["sampling_rate"]) * 1000.
    else:
        return data["speech"].shape[0]


def Dataset(data_list_file,
            dict,
            seg_dict,
            conf,
            mode="train"):
    scp_lists = read_lists(data_list_file)
@@ -142,7 +154,7 @@
    filter_fn = partial(filter, **filter_conf)
    dataset = FilterIterDataPipe(dataset, fn=filter_fn)

    vocab = {'vocab': dict}
    vocab = {'vocab': dict, 'seg_dict': seg_dict}
    tokenize_fn = partial(tokenize, **vocab)
    dataset = MapperIterDataPipe(dataset, fn=tokenize_fn)