音频变换样例库

此指南展示了mindpore.dataset.audio模块中各种变换的用法。

环境准备

[1]:

import librosa
import numpy as np
import matplotlib.pyplot as plt
import scipy.io.wavfile as wavfile
import scipy.signal as signal
from IPython.display import Audio
from download import download

import mindspore.dataset as ds
import mindspore.dataset.audio as audio

ds.config.set_seed(5)

# cication: LibriSpeech http://www.openslr.org/12
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/84-121123-0000.wav"
download(url, './84-121123-0000.wav', replace=True)
wav_file = "84-121123-0000.wav"

def plot_waveform(waveform, sr, title="Waveform"):
    if waveform.ndim == 1:
        waveform = waveform[np.newaxis, :]
    num_channels, num_frames = waveform.shape
    time_axis = np.arange(0, num_frames) / sr

    figure, axes = plt.subplots(num_channels, 1)
    axes.plot(time_axis, waveform[0], linewidth=1)
    axes.grid(True)
    figure.suptitle(title)
    plt.show(block=False)


def plot_spectrogram(specgram, title=None, ylabel="freq_bin"):
    fig, axs = plt.subplots(1, 1)
    axs.set_title(title or "Spectrogram (db)")
    axs.set_ylabel(ylabel)
    axs.set_xlabel("frame")
    im = axs.imshow(librosa.power_to_db(specgram), origin="lower", aspect="auto")
    fig.colorbar(im, ax=axs)
    plt.show(block=False)


def plot_fbank(fbank, title=None):
    _, axs = plt.subplots(1, 1)
    axs.set_title(title or "Filter bank")
    axs.imshow(fbank, aspect="auto")
    axs.set_ylabel("frequency bin")
    axs.set_xlabel("mel bin")
    plt.show(block=False)

Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/84-121123-0000.wav (65 kB)

file_sizes: 100%|██████████████████████████| 67.0k/67.0k [00:01<00:00, 44.6kB/s]
Successfully downloaded file to ./84-121123-0000.wav

原始音频波形

[2]:

sample_rate, waveform = wavfile.read(wav_file)
plot_waveform(waveform, sample_rate, title="Original Waveform")
Audio(waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_4_0.png

[2]:

AmplitudeToDB

mindspore.dataset.audio.AmplitudeToDB 可以将音频数据从振幅单位转换为分贝单位。

[3]:

waveform = waveform.astype(np.float32)
n_fft = 1024
hop_length = 512
spectrogram = audio.Spectrogram(n_fft=n_fft, hop_length=hop_length)
specgram = spectrogram(waveform)
amplitude_to_db = audio.AmplitudeToDB(stype=audio.ScaleType.POWER)
db_specgram = amplitude_to_db(specgram)
plot_spectrogram(db_specgram, title="AmplitudeToDB - audio")

../../../_images/api_python_samples_dataset_audio_gallery_6_0.png

DBToAmplitude

mindspore.dataset.audio.DBToAmplitude 可以将音频数据从分贝单位转换为振幅单位。

[4]:

db_to_amplitude = audio.DBToAmplitude(0.5, 0.5)
spec_recovered = db_to_amplitude(db_specgram)
plot_spectrogram(spec_recovered, title="DBToAmplitude - audio")

../../../_images/api_python_samples_dataset_audio_gallery_8_0.png

AllpassBiquad

mindspore.dataset.audio.AllpassBiquad 可以给音频波形施加双极点全通滤波器，用于音频信号的变换。

[5]:

waveform = waveform.astype(np.float32)
waveform /= np.iinfo(np.int16).max
allpass_biquad = audio.AllpassBiquad(sample_rate=sample_rate, central_freq=200.0)
waveform_allpass = allpass_biquad(waveform)
plot_waveform(waveform_allpass, sample_rate, title="AllpassBiquad Waveform")
Audio(waveform_allpass, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_10_0.png

[5]:

Angle

mindspore.dataset.audio.Angle 可以计算复数序列的相位角。

[6]:

# Apply the Hilbert transform to get the analytic signal (complex)
analytic_signal = signal.hilbert(waveform)

# Create a complex waveform by stacking real and imaginary parts
complex_waveform = np.stack((analytic_signal.real, analytic_signal.imag), axis=-1)
print(complex_waveform.shape)

angle = audio.Angle()
transformed_waveform = angle(complex_waveform)
plot_waveform(transformed_waveform, sample_rate, title="Angle Waveform")
Audio(transformed_waveform, rate=sample_rate)

(33440, 2)

../../../_images/api_python_samples_dataset_audio_gallery_12_1.png

[6]:

BandBiquad

mindspore.dataset.audio.BandBiquad 对输入的音频信号执行双极点带通滤波器（biquad filter）操作。频率响应在中心频率周围呈对数下降，带宽参数控制这个下降的斜率。接口实现方式类似于 SoX 库。

[7]:

band_biquad = audio.BandBiquad(sample_rate=sample_rate, central_freq=200.0)
transformed_waveform = band_biquad(waveform)
plot_waveform(transformed_waveform, sample_rate, title="BandBiquad Waveform")
Audio(transformed_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_14_0.png

[7]:

BandpassBiquad

mindspore.dataset.audio.BandpassBiquad 对输入的音频信号执行双极点巴特沃斯带通滤波器（Butterworth Band-Pass Filter）。

[8]:

band_pass_biquad = audio.BandpassBiquad(sample_rate, 200.0)
transformed_waveform = band_pass_biquad(waveform)
plot_waveform(transformed_waveform, sample_rate, title="BandpassBiquad Waveform")
Audio(transformed_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_16_0.png

[8]:

BassBiquad

mindspore.dataset.audio.BassBiquad 对输入的音频信号执行双极低搁架滤波器（two-pole low-shelf filter）。

[9]:

transforms = [
    audio.BassBiquad(sample_rate, 10.0),
]

for transform in transforms:
    transformed_waveform = transform(waveform)
    plot_waveform(transformed_waveform, sample_rate, title=transform.__class__.__name__ + " Waveform")
Audio(transformed_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_18_0.png

[9]:

Biquad

mindspore.dataset.audio.Biquad 对输入的音频信号执行双二阶滤波器（biquad filter）操作。

[10]:

transform = audio.Biquad(0.01, 0.02, 0.13, 1, 0.12, 0.3)
transformed_waveform = transform(waveform)
plot_waveform(transformed_waveform, sample_rate, title="Biquad Waveform")
Audio(transformed_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_20_0.png

[10]:

Contrast

mindspore.dataset.audio.Contrast 给音频波形施加对比度增强效果。类似于压缩操作，通过增强音频的对比度，使得音频听起来更响亮、更有冲击力。

[11]:

contrast_transform = audio.Contrast(enhancement_amount=85.0)
transformed_waveform = contrast_transform(waveform)
plot_waveform(transformed_waveform, sample_rate, title="Contrast Waveform")
Audio(transformed_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_22_0.png

[11]:

ComplexNorm

mindspore.dataset.audio.ComplexNorm 可以计算复数张量的范数。

[12]:

import scipy.signal as signal

# Apply the Hilbert transform to get the analytic signal (complex)
analytic_signal = signal.hilbert(waveform)

# Create a complex waveform by stacking real and imaginary parts
complex_waveform = np.stack((analytic_signal.real, analytic_signal.imag), axis=-1)

complex_norm = audio.ComplexNorm(power=2.0)
complex_norm_transformed = complex_norm(complex_waveform)
plot_waveform(complex_norm_transformed, sample_rate, title="ComplexNorm Waveform")
Audio(complex_norm_transformed, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_24_0.png

[12]:

ComputeDeltas

mindspore.dataset.audio.ComputeDeltas 可以计算频谱的 delta 系数。

[13]:

# Convert waveform to spectrogram
spectrogram = audio.Spectrogram()
spec = spectrogram(waveform)

# Apply ComputeDeltas transform
compute_deltas_transform = audio.ComputeDeltas(win_length=7, pad_mode=audio.BorderType.EDGE)
transformed_spectrogram = compute_deltas_transform(spec)
# Plot transformed spectrogram

transformed_spectrogram = np.squeeze(transformed_spectrogram)
plot_spectrogram(transformed_spectrogram, title="ComputeDeltas - audio")

../../../_images/api_python_samples_dataset_audio_gallery_26_0.png

DCShift

mindspore.dataset.audio.DCShift 对输入音频波形施加直流移位。可以从音频中删除直流偏移（DC Offset）。

[14]:

dc_shift = audio.DCShift(0.5, 0.02)
waveform_dc_shift = dc_shift(waveform)
plot_waveform(waveform_dc_shift, sample_rate, title="DCShift Waveform")
Audio(waveform_dc_shift, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_28_0.png

[14]:

DeemphBiquad

mindspore.dataset.audio.DeemphBiquad 将对输入音频波形应用 Compact Disc (IEC 60908) 去加重滤波器（高频衰减棚形滤波器）。

[15]:

deemph_biquad = audio.DeemphBiquad(44100)
waveform_deemph_biquad = deemph_biquad(waveform)
plot_waveform(waveform_deemph_biquad, sample_rate, title="DeemphBiquad Waveform")
Audio(waveform_deemph_biquad, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_30_0.png

[15]:

DetectPitchFrequency

mindspore.dataset.audio.DetectPitchFrequency 可以检测音频波形的基频。

[16]:

sample_rate, waveform = wavfile.read(wav_file)
waveform = waveform.astype(np.float32)
waveform /= np.iinfo(np.int16).max
detect = audio.DetectPitchFrequency(sample_rate=sample_rate, win_length=400)
pitch_freq = detect(waveform)
plot_waveform(pitch_freq, sample_rate, title="DetectPitchFrequency")

../../../_images/api_python_samples_dataset_audio_gallery_32_0.png

Dither

mindspore.dataset.audio.Dither 用于在特定比特深度下增强音频的感知动态范围，目的是消除非线性截断失真。通过在音频信号中引入量化噪声，可以改善音频的质量。

[17]:

dither = audio.Dither()
waveform_dither = dither(waveform)
plot_waveform(waveform_dither, sample_rate, title="Dither Waveform")
Audio(waveform_dither, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_34_0.png

[17]:

EqualizerBiquad

mindspore.dataset.audio.EqualizerBiquad 对输入的音频信号执行双极点均衡器滤波器。

[18]:

equalizer = audio.EqualizerBiquad(
    sample_rate=sample_rate,
    center_freq=1500,
    gain=5.5,
    Q=0.7
)

equalized_waveform = equalizer(waveform)
plot_waveform(equalized_waveform, sample_rate, title='Equalized Waveform')
Audio(equalized_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_36_0.png

[18]:

Fade

mindspore.dataset.audio.Fade 可以对音频波形应用淡入淡出效果。

[19]:

fade = audio.Fade(fade_in_len=4000, fade_out_len=4000, fade_shape=audio.FadeShape.LINEAR)
faded_waveform = fade(waveform)
plot_waveform(faded_waveform, sample_rate, title='Faded Waveform')
Audio(faded_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_38_0.png

[19]:

Filtfilt

mindspore.dataset.audio.Filtfilt 对音频波形施加正反向 IIR 滤波。

[20]:

filtfilt = audio.Filtfilt(a_coeffs=[1.0, 0.1, 0.3], b_coeffs=[1.0, 0.1, 0.5])
filtfilt_waveform = filtfilt(waveform)
plot_waveform(filtfilt_waveform, sample_rate, title='Filtfilt Waveform')
Audio(filtfilt_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_40_0.png

[20]:

Flanger

mindspore.dataset.audio.Flanger 对音频波形应用 Flanger 效果。

[21]:

reshape_waveform = waveform[np.newaxis, :]
flanger = audio.Flanger(sample_rate=sample_rate)
flanger_waveform = flanger(reshape_waveform)
plot_waveform(flanger_waveform, sample_rate, 'Flanger Waveform')
Audio(flanger_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_42_0.png

[21]:

Gain

mindspore.dataset.audio.Gain 可以调整音频波形的增益。

[22]:

gain_transform = audio.Gain(1.2)
gain_waveform = gain_transform(waveform)
plot_waveform(gain_waveform, sample_rate, title="Gain Waveform")
Audio(gain_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_44_0.png

[22]:

HighpassBiquad

mindspore.dataset.audio.HighpassBiquad 对输入的音频信号执行双极点巴特沃斯高通滤波器（Butterworth High-Pass Filter）。

[23]:

highpass_transform = audio.HighpassBiquad(sample_rate, 1500, 0.7)
highpass_waveform = highpass_transform(waveform)
plot_waveform(highpass_waveform, sample_rate, title="HighpassBiquad Waveform")
Audio(highpass_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_46_0.png

[23]:

LowpassBiquad

mindspore.dataset.audio.LowpassBiquad 对输入的音频信号执行双极点巴特沃斯低通滤波器（Butterworth Low-Pass Filter）。

[24]:

lowpass_biquad = audio.LowpassBiquad(sample_rate, 1500, 0.7)
lowpass_waveform = lowpass_biquad(waveform)
plot_waveform(lowpass_waveform, sample_rate, title="LowpassBiquad Waveform")
Audio(lowpass_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_48_0.png

[24]:

LFilter

mindspore.dataset.audio.LFilter 对音频波形施加 IIR 滤波。

[25]:

sample_rate, waveform = wavfile.read(wav_file)
waveform_f = waveform.astype(np.float32)
lfilter = audio.LFilter(a_coeffs=[1.0, -0.95], b_coeffs=[0.3, -0.2])
lfilter_transform = lfilter(waveform_f)

# Normalize or clip values to int16 range if needed
lfilter_transform = np.clip(lfilter_transform, -1.0, 1.0)
lfilter_transform = (lfilter_transform * 32767).astype(np.int16)

sample_rate = 16000
plot_waveform(lfilter_transform, sample_rate, title="LFilter")
lfilter_transform_audio = lfilter_transform.astype(np.float32) / 32767
Audio(lfilter_transform_audio, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_50_0.png

[25]:

Magphase

mindspore.dataset.audio.Magphase 可以将复数张量分解为幅度和相位。

[26]:

# Apply the Hilbert transform to get the analytic signal (complex)
analytic_signal = signal.hilbert(waveform)

# Create a complex waveform by stacking real and imaginary parts
complex_waveform = np.stack((analytic_signal.real, analytic_signal.imag), axis=-1)

magphase = audio.Magphase()
magnitude, phase = magphase(complex_waveform)
plot_waveform(magnitude, sample_rate, title="Magnitude")
plot_waveform(phase, sample_rate, title="Phase")

../../../_images/api_python_samples_dataset_audio_gallery_52_0.png

../../../_images/api_python_samples_dataset_audio_gallery_52_1.png

MuLawEncoding

mindspore.dataset.audio.MuLawEncoding 可以将音频波形编码为 mu-law 格式。

[27]:

mulaw_encoding = audio.MuLawEncoding(quantization_channels=256)
encoded = mulaw_encoding(waveform)
plot_waveform(encoded, sample_rate, title="MuLaw Encoded Waveform")
Audio(encoded, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_54_0.png

[27]:

MuLawDecoding

mindspore.dataset.audio.MuLawDecoding 可以将 mu-law 格式的音频波形解码为原始音频波形。参考 mu-law 算法。

[28]:

mulaw_decode = audio.MuLawDecoding(quantization_channels=256)
decode = mulaw_decode(encoded)
plot_waveform(decode, sample_rate, title="MuLaw Decoded Waveform")
Audio(decode, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_56_0.png

[28]:

Spectrogram

mindspore.dataset.audio.Spectrogram 可以从音频信号创建其频谱。

[29]:

sample_rate, waveform = wavfile.read(wav_file)

# Perform transform
n_fft = 1024
win_length = None
hop_length = 512

# Define transform
spectrogram = audio.Spectrogram(
    n_fft=n_fft,
    win_length=win_length,
    hop_length=hop_length,
    center=True,
    pad_mode=audio.BorderType.REFLECT,
    power=2.0,
)

spec = spectrogram(waveform)
plot_spectrogram(spec, title="audio")

../../../_images/api_python_samples_dataset_audio_gallery_58_0.png

InverseSpectrogram

mindspore.dataset.audio.InverseSpectrogram 可以将频谱图反转回波形。

[30]:

wav_file = "84-121123-0000.wav"
y, sr = librosa.load(wav_file, sr=None)

# Parameters for STFT
n_fft = 2048
hop_length = 512

# Compute STFT
s = librosa.stft(y, n_fft=n_fft, hop_length=hop_length)

# Separate real and imaginary parts
s_real = np.real(s)
s_imag = np.imag(s)

# Stack real and imaginary parts
s_complex = np.stack((s_real, s_imag), axis=-1)

inverse_spectrogram = audio.InverseSpectrogram(n_fft=n_fft, hop_length=hop_length)
reconstructed = inverse_spectrogram(s_complex)

# Compute magnitude spectrogram of the reconstructed signal
reconstructed_s = librosa.stft(reconstructed, n_fft=n_fft, hop_length=hop_length)
reconstructed_magnitude = np.abs(reconstructed_s)
plot_waveform(reconstructed, sr, title='InverseSpectrogram - audio')
Audio(reconstructed, rate=sr)

../../../_images/api_python_samples_dataset_audio_gallery_60_0.png

[30]:

GriffinLim

mindspore.dataset.audio.GriffinLim 将从线性幅度频谱图恢复信号波形。

[31]:

n_fft = 1024
win_length = None
hop_length = 512

spec = audio.Spectrogram(
    n_fft=n_fft,
    win_length=win_length,
    hop_length=hop_length,
)(waveform)

griffin_lim = audio.GriffinLim(
    n_fft=n_fft,
    win_length=win_length,
    hop_length=hop_length,
)

reconstructed_waveform = griffin_lim(spec)

plot_waveform(reconstructed_waveform, sample_rate, title="Reconstructed")
Audio(reconstructed_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_62_0.png

[31]:

Mel Filter Bank

mindspore.dataset.audio.melscale_fbanks 可以创建频率变换矩阵。

[32]:

n_fft = 256
n_mels = 64
sample_rate = 6000

mel_filters = audio.melscale_fbanks(
    int(n_fft // 2 + 1),
    n_mels=n_mels,
    f_min=0.0,
    f_max=sample_rate / 2.0,
    sample_rate=sample_rate,
    norm=audio.NormType.SLANEY,
)

plot_fbank(mel_filters, "Mel Filter Bank - audio")

../../../_images/api_python_samples_dataset_audio_gallery_64_0.png

MelSpectrogram

mindspore.dataset.audio.MelSpectrogram 可以计算原始音频信号的梅尔频谱。

[33]:

n_fft = 1024
win_length = None
hop_length = 512
n_mels = 128

mel_spectrogram = audio.MelSpectrogram(
    sample_rate=sample_rate,
    n_fft=n_fft,
    win_length=win_length,
    hop_length=hop_length,
    center=True,
    pad_mode=audio.BorderType.REFLECT,
    power=2.0,
    norm=audio.NormType.SLANEY,
    onesided=True,
    n_mels=n_mels,
    mel_scale=audio.MelType.HTK,
)

melspec = mel_spectrogram(waveform)
melspec = np.squeeze(melspec)
plot_spectrogram(melspec, title="MelSpectrogram - audio", ylabel="mel freq")

../../../_images/api_python_samples_dataset_audio_gallery_66_0.png

MelScale

mindspore.dataset.audio.MelScale 可以将频率转换为梅尔频率。

[34]:

sample_rate, waveform = wavfile.read(wav_file)
spectrogram = audio.Spectrogram()
spec = spectrogram(waveform)
n_stft = spec.shape[-2]
n_mels = min(n_stft - 1, 80)

mel_scale = audio.MelScale(
    n_mels=n_mels,
    sample_rate=sample_rate,
    f_min=0.0,
    f_max=sample_rate / 2.0,
    n_stft=n_stft,
    norm=audio.NormType.SLANEY,
    mel_type=audio.MelType.HTK,
)
mel_spec = mel_scale(spec)
plot_spectrogram(mel_spec, title="MelScale - audio", ylabel="mel freq")

../../../_images/api_python_samples_dataset_audio_gallery_68_0.png

InverseMelScale

mindspore.dataset.audio.InverseMelScale 将梅尔频率 STFT 转换为普通频率下的 STFT。

[35]:

inverse_mel_scale = audio.InverseMelScale(
    n_stft=n_stft,
    n_mels=n_mels,
    sample_rate=sample_rate,
    f_min=0.0,
    f_max=None,
    max_iter=10000,
    tolerance_loss=1e-05,
    tolerance_change=1e-08,
    sgdargs=None,
    norm=audio.NormType.SLANEY,
    mel_type=audio.MelType.HTK
)
inverse_mel = inverse_mel_scale(mel_spec)
inverse_mel = inverse_mel.squeeze(0)
print(inverse_mel.shape)
plot_spectrogram(inverse_mel, title="InverseMelScale - audio")

(201, 168)

../../../_images/api_python_samples_dataset_audio_gallery_70_1.png

MFCC

mindspore.dataset.audio.MFCC 可以计算音频信号的梅尔频率倒谱系数。

[36]:

n_fft = 2048
win_length = None
hop_length = 512
n_mels = 256
n_mfcc = 256

mfcc_transform = audio.MFCC(
    sample_rate=sample_rate,
    n_mfcc=n_mfcc,
    melkwargs={
        "n_fft": n_fft,
        "win_length": n_fft,
        "f_min": 0.0,
        "f_max": sample_rate // 2,
        "pad": 0,
        "pad_mode": audio.BorderType.REFLECT,
        "power": 2.0,
        "n_mels": n_mels,
        "normalized": False,
        "center": True,
        "onesided": True,
        "window": audio.WindowType.HANN,
        "hop_length": hop_length,
        "norm": audio.NormType.NONE,
        "mel_scale": audio.MelType.HTK,
    },
)

mfcc = mfcc_transform(waveform)
mfcc = np.squeeze(mfcc)
plot_spectrogram(mfcc)

../../../_images/api_python_samples_dataset_audio_gallery_72_0.png

LFCC

mindspore.dataset.audio.LFCC 可以计算音频信号的线性频率倒谱系数。

[37]:

n_fft = 2048
win_length = None
hop_length = 512
n_lfcc = 256

lfcc_transform = audio.LFCC(
    sample_rate=sample_rate,
    n_lfcc=n_lfcc,
    speckwargs={
        "n_fft": n_fft,
        "win_length": n_fft,
        "hop_length": hop_length,
        "pad": 0,
        "window": audio.WindowType.HANN,
        "power": 2.0,
        "normalized": False,
        "center": True,
        "pad_mode": audio.BorderType.REFLECT,
        "onesided": True
    },
)

lfcc = lfcc_transform(waveform)
lfcc = np.squeeze(lfcc)
plot_spectrogram(lfcc)

../../../_images/api_python_samples_dataset_audio_gallery_74_0.png

SpectralCentroid

mindspore.dataset.audio.SpectralCentroid 将会计算音频信号的频谱质心。

[38]:

n_fft = 2048
win_length = None
hop_length = 512
n_lfcc = 256
sample_rate, waveform = wavfile.read(wav_file)

spcetral_centroid = audio.SpectralCentroid(sample_rate=sample_rate, n_fft=n_fft, win_length=win_length, hop_length=hop_length)
centroid = spcetral_centroid(waveform)
plot_waveform(centroid, sample_rate, title="SpectralCentroid")

../../../_images/api_python_samples_dataset_audio_gallery_76_0.png

TimeMasking

mindspore.dataset.audio.TimeMasking 对音频信号施加时间掩码。

[39]:

if waveform.ndim == 1:
    waveform = waveform[np.newaxis, :]
time_masking = audio.TimeMasking(time_mask_param=1)
time_masked = time_masking(waveform)
plot_spectrogram(time_masked, title="TimeMasking - audio")

../../../_images/api_python_samples_dataset_audio_gallery_78_0.png

TimeStretch

mindspore.dataset.audio.TimeStretch 对音频信号施加时间拉伸。

[40]:

wav_file = "84-121123-0000.wav"
y, sr = librosa.load(wav_file, sr=None)

# Parameters for STFT
n_fft = 2048
hop_length = 512
s = librosa.stft(y, n_fft=n_fft, hop_length=hop_length)

# Separate real and imaginary parts
s_real = np.real(s)
s_imag = np.imag(s)

# Stack real and imaginary parts
s_complex = np.stack((s_real, s_imag), axis=-1)

# Apply time stretch
time_stretch = audio.TimeStretch()
stretched = time_stretch(s_complex)

# Convert complex result to magnitude
s_stretched_real = stretched[..., 0]
s_stretched_imag = stretched[..., 1]
s_stretched = s_stretched_real + 1j * s_stretched_imag
stretch = np.squeeze(s_stretched)
stretch = np.abs(stretch)**2
plot_spectrogram(stretch, title='TimeStretch - audio')

../../../_images/api_python_samples_dataset_audio_gallery_80_0.png

Overdrive

mindspore.dataset.audio.Overdrive 对音频信号施加过载效果。

[41]:

overdrive = audio.Overdrive(gain=30.0, color=30.0)
waveform_overdrive = overdrive(waveform)
plot_waveform(waveform_overdrive, sample_rate, title="Overdrive Effect")
Audio(waveform_overdrive, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_82_0.png

[41]:

Phaser

mindspore.dataset.audio.Phaser 对音频信号施加相位器。

[42]:

phaser = audio.Phaser(
    sample_rate=sample_rate,
    gain_in=0.4,
    gain_out=0.74,
    delay_ms=3.0,
    decay=0.4,
    mod_speed=0.5,
    sinusoidal=True
)

waveform_phaser = phaser(waveform)
plot_waveform(waveform_phaser, sample_rate, title="Phaser Effect")
Audio(waveform_phaser, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_84_0.png

[42]:

PitchShift

mindspore.dataset.audio.PitchShift 对音频信号施加音高转换。

[43]:

pitch_shift = audio.PitchShift(sample_rate=sample_rate, n_steps=4)
waveform_pitch_shift = pitch_shift(waveform)
plot_waveform(waveform_pitch_shift, sample_rate, title="PitchShift Effect")
Audio(waveform_pitch_shift, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_86_0.png

[43]:

Resample

mindspore.dataset.audio.Resample 将音频信号重新采样为不同的采样率。

[44]:

resample_transform = audio.Resample(orig_freq=sample_rate, new_freq=16000)
resampled_waveform = resample_transform(waveform)
plot_waveform(resampled_waveform, sample_rate, title="Resampled Waveform")
Audio(resampled_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_88_0.png

[44]:

RiaaBiquad

mindspore.dataset.audio.RiaaBiquad 对音频信号施加 RIAA 滤波。

[45]:

waveform = waveform.astype(np.float32)
riaa_transform = audio.RiaaBiquad(sample_rate=44100)
riaa_waveform = riaa_transform(waveform)
plot_waveform(riaa_waveform, sample_rate, title="RIAA Waveform")
Audio(riaa_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_90_0.png

[45]:

SlidingWindowCmn

mindspore.dataset.audio.SlidingWindowCmn 对音频信号应用滑动窗口倒谱均值归一化。

[46]:

sample_rate, waveform = wavfile.read(wav_file)
waveform = waveform.astype(np.float32)
spectrogram = audio.Spectrogram()
spec = spectrogram(waveform)
sliding_window_cmn = audio.SlidingWindowCmn()
waveform_cmn = sliding_window_cmn(spec)
plot_spectrogram(waveform_cmn, title="SlidingWindowCmn - audio")

../../../_images/api_python_samples_dataset_audio_gallery_92_0.png

FrequencyMasking

mindspore.dataset.audio.FrequencyMasking 对音频信号施加频率掩码。

[47]:

spectrogram = audio.Spectrogram()
spec = spectrogram(waveform)
frequency_masking = audio.FrequencyMasking()
frequency_masked = frequency_masking(spec)
plot_spectrogram(frequency_masked, title="FrequencyMasking - audio")

../../../_images/api_python_samples_dataset_audio_gallery_94_0.png

Vad

mindspore.dataset.audio.Vad 对音频信号施加语音活动检测。

[48]:

vad_transform = audio.Vad(sample_rate=sample_rate)
vad_waveform = vad_transform(waveform)
plot_waveform(vad_waveform, sample_rate, title='Vad Waveform')
Audio(vad_waveform, rate=sample_rate)

../../../_images/api_python_samples_dataset_audio_gallery_96_0.png

[48]:

在数据 Pipeline 中加载和处理图像文件

使用 mindspore.dataset.GeneratorDataset 将磁盘中的音频文件内容加载到数据 Pipeline 中，并进一步应用其他增强操作。

[49]:

import scipy.io.wavfile as wavfile
import mindspore.dataset as ds
import mindspore.dataset.audio as audio

# Define dataloader
class DataLoader():
    def __init__(self):
        self.sample_rate, self.wave = wavfile.read("84-121123-0000.wav")
    def __next__(self):
        return next(self.data)
    def __iter__(self):
        self.data = iter([(self.wave, self.sample_rate), (self.wave, self.sample_rate), (self.wave, self.sample_rate)])
        return self

# Load 3 waveforms into dataset pipeline
dataset = ds.GeneratorDataset(DataLoader(), column_names=["wav", "sample_rate"], shuffle=False)

# check the sample numbers in dataset
print("number of samples in dataset:", dataset.get_dataset_size())

# apply gain on "wav" column
dataset = dataset.map(audio.Gain(gain_db=3.0), input_columns=["wav"])

# check results, specify the output type to NumPy for drawing
print(">>>>> after gain")
for waveform, sample_rate  in dataset.create_tuple_iterator(output_numpy=True):
    # show the wav
    plot_waveform(waveform, sample_rate, title="Gained waveform")
    # after drawing one wav, break
    break

number of samples in dataset: 3
>>>>> after gain

../../../_images/api_python_samples_dataset_audio_gallery_98_1.png