TensorFlow数据集-如何播放/转换WAV文件（int64）？

Question

我想测试以下数据集：https://www.tensorflow.org/datasets/catalog/speech_commands

当我加载时

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

import tensorflow_datasets as tfds
import IPython.display as ipd


ds, ds_info = tfds.load('speech_commands', shuffle_files=False, with_info=True)
ds_info

tfds.core.DatasetInfo(
    name='speech_commands',
    full_name='speech_commands/0.0.2',
    description="""
    An audio dataset of spoken words designed to help train and evaluate keyword
    spotting systems. Its primary goal is to provide a way to build and test small
    models that detect when a single word is spoken, from a set of ten target words,
    with as few false positives as possible from background noise or unrelated
    speech. Note that in the train and validation set, the label "unknown" is much
    more prevalent than the labels of the target words or background noise.
    One difference from the release version is the handling of silent segments.
    While in the test set the silence segments are regular 1 second files, in the
    training they are provided as long segments under "background_noise" folder.
    Here we split these background noise into 1 second clips, and also keep one of
    the files for the validation set.
    """,
    homepage='https://arxiv.org/abs/1804.03209',
    data_path='C:\\Users\\abc\\tensorflow_datasets\\speech_commands\\0.0.2',
    download_size=2.37 GiB,
    dataset_size=9.07 GiB,
    features=FeaturesDict({
        'audio': Audio(shape=(None,), dtype=tf.int64),
        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=12),
    }),
    supervised_keys=('audio', 'label'),
    splits={
        'test': <SplitInfo num_examples=4890, num_shards=4>,
        'train': <SplitInfo num_examples=106497, num_shards=128>,
        'validation': <SplitInfo num_examples=121, num_shards=1>,
    },
    citation="""@article{speechcommandsv2,
       author = {{Warden}, P.},
        title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
      journal = {ArXiv e-prints},
      archivePrefix = "arXiv",
      eprint = {1804.03209},
      primaryClass = "cs.CL",
      keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
        year = 2018,
        month = apr,
        url = {https://arxiv.org/abs/1804.03209},
    }""",
)

音频文件是int64类型的数组，采样值为16000。我找不到有关如何在此数据集中播放文件的任何信息。从其他数据集中，我能够播放WAV-声音。区别之一是，其他DS使用浮点数组，而此DS使用int数组。也许我错过了一个对话步骤？

ds_list = list(ds['validation'])

idx = -1
audio, label = ds_list[idx]['audio'], ds_list[idx]['label']
ipd.Audio(audio, rate=16_000)

很明显，我在数据集中尝试了多个索引，但我总是受到干扰。一个音频条目如下所示：tf。张量（[-112 1285-2002…-140 1000-595]，形状=（16000，），数据类型=int64）

Ty：）




Answer 1

匿名用户

根据源代码，在描述页面[1]上，声明：

它的主要目标是提供一种构建和测试小型模型的方法，这些模型可以从一组十个目标单词中检测出一个单词何时被说出，并且尽可能少的来自背景噪音或无关语音的误报。

起初，我能够像您所展示的那样播放嘈杂的波形文件。然后，我根据[2]修改我的代码以产生更清晰的声音。

我使用以下代码将tensor转换为wav格式。

import scipy.io.wavfile as wavfile
import tensorflow as tf
import tensorflow_datasets as tfds

# load speech commands dataset
ds = tfds.load('speech_commands', split=['train', 'validation', 'test'],
           shuffle_files=True)

# convert from tfds format to list
ds_train = list(ds[0])
ds_val = list(ds[1])

# convert from tensor int64 to numpy float32
sc1 = ds_list[0]['audio'].numpy().astype(np.float32)/np.iinfo(np.int16).max()
sv1 = ds_list[1]['audio'].numpy().astype(np.float32)/np.iinfo(np.int16).max()

# save as wav
wavfile('sc_train_1.wav', 16000, sc1)
wavfile('sc_train_1.wav', 16000, sv1)

诀窍是将int64转换为float32，然后除以np的最大值。int16：<代码>。aType（np.float32）/np。iinfo（np.int16）。max（）。

现在，我可以听到比以前int64格式更清晰的声音。

https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/audio/speech_commands.pyhttps://github.com/google-research/google-research/blob/master/non_semantic_speech_benchmark/train_and_eval_sklearn_small_tfds_dataset.ipynb