PyTorch 音频处理教程

一、进行数据准备和实用函数的编写

二、使用 python 读取音频文件

三、音频数据的数据增强

四、音频特征提取

五、特征增强

六、torchaudio 的 datasets 的用法

本文为pytorch官方教程的代码注释

#### 使用 torchaudio 进行音频处理 # torchaudio 提供了强大的音频 I/O,预处理转换和数据集。 # 在本教程中,我们将研究如何准备音频数据,并提取可反馈给 NN 模型的特征 # 注意: torchaudio 在 windows 中不能正常运行,请使用非 windows 系统环境进行下面的实验 # 引入实验所需的模块 import torch import torchaudio import torchaudio.functional as F # 以函数形式进行音频处理 import torchaudio.transforms as T # 实例化音频处理对象后,使用实例化对象进行音频处理 print(torch.__version__) print(torchaudio.__version__)
### 进行数据准备和实用函数编写 # 这里会自动下载实验所需的音频文件(一个speech音频,下载该音频的多种编码格式) # 这里的内容十分繁杂,初次查看本教程,不需要花太多时间查看数据准备和函数代码 # 仅需在使用到这里定义的函数时,知道函数的作用即可 # 每次调用都会重新下载数据 #------------------------------------------------------------------------------- # Preparation of data and helper functions. #------------------------------------------------------------------------------- ## 模块引入 import io # io 模块提供了 python 与各种类型文件进行 I/O 操作的主要工具 import os # os 模块为操作系统接口模块,用于 python 与操作系统进行交互 import math # math 模块提供基础数学方法的实现 import tarfile # tarfile 模块进行文件压缩和解压缩 import multiprocessing # multiprocessing 是 python 的多进程模块 import scipy # scipy 模块是 python 科学计算程序的核心包 import librosa # Librosa 模块是 python 用于音频的分析、处理的工具包 # Boto是 (Amazon Web Service)AWS 的基于 python 的 SDK(软件开发工具包),允许开发人员编写软件时 # 使用亚马逊的服务像S3和EC2等,Boto 提供了简单,面向对象的 API,也提供了低等级的服务接入。 import boto3 from botocore import UNSIGNED from botocore.config import Config import requests # request 库用于实现 python 与 Http 的交互 import matplotlib # 打印图像 import matplotlib.pyplot as plt from IPython.display import Audio, display # 音频播放和图片显示 [width, height] = matplotlib.rcParams['figure.figsize'] if width < 10: matplotlib.rcParams['figure.figsize'] = [width * 2.5, height] ## 文件下载地址的定义和保存方式的定义 _SAMPLE_DIR = "_sample_data" SAMPLE_WAV_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.wav" SAMPLE_WAV_PATH = os.path.join(_SAMPLE_DIR, "steam.wav") SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav" SAMPLE_WAV_SPEECH_PATH = os.path.join(_SAMPLE_DIR, "speech.wav") SAMPLE_RIR_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/room-response/rm1/impulse/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo.wav" SAMPLE_RIR_PATH = os.path.join(_SAMPLE_DIR, "rir.wav") SAMPLE_NOISE_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/distractors/rm1/babb/Lab41-SRI-VOiCES-rm1-babb-mc01-stu-clo.wav" SAMPLE_NOISE_PATH = os.path.join(_SAMPLE_DIR, "bg.wav") SAMPLE_MP3_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.mp3" SAMPLE_MP3_PATH = os.path.join(_SAMPLE_DIR, "steam.mp3") SAMPLE_GSM_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.gsm" SAMPLE_GSM_PATH = os.path.join(_SAMPLE_DIR, "steam.gsm") SAMPLE_TAR_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit.tar.gz" SAMPLE_TAR_PATH = os.path.join(_SAMPLE_DIR, "sample.tar.gz") SAMPLE_TAR_ITEM = "VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav" S3_BUCKET = "pytorch-tutorial-assets" S3_KEY = "VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav" YESNO_DATASET_PATH = os.path.join(_SAMPLE_DIR, "yes_no") os.makedirs(YESNO_DATASET_PATH, exist_ok=True) os.makedirs(_SAMPLE_DIR, exist_ok=True) ## 函数定义 # 私有函数的定义 def _fetch_data(): uri = [ (SAMPLE_WAV_URL, SAMPLE_WAV_PATH), (SAMPLE_WAV_SPEECH_URL, SAMPLE_WAV_SPEECH_PATH), (SAMPLE_RIR_URL, SAMPLE_RIR_PATH), (SAMPLE_NOISE_URL, SAMPLE_NOISE_PATH), (SAMPLE_MP3_URL, SAMPLE_MP3_PATH), (SAMPLE_GSM_URL, SAMPLE_GSM_PATH), (SAMPLE_TAR_URL, SAMPLE_TAR_PATH), ] for url, path in uri: with open(path, 'wb') as file_: file_.write(requests.get(url).content) _fetch_data() def _download_yesno(): if os.path.exists(os.path.join(YESNO_DATASET_PATH, "waves_yesno.tar.gz")): return torchaudio.datasets.YESNO(root=YESNO_DATASET_PATH, download=True) YESNO_DOWNLOAD_PROCESS = multiprocessing.Process(target=_download_yesno) YESNO_DOWNLOAD_PROCESS.start() def _get_sample(path, resample=None): effects = [ ["remix", "1"] ] if resample: effects.append(["rate", f'{resample}']) return torchaudio.sox_effects.apply_effects_file(path, effects=effects) # 定义不同的文件读取函数,返回不同音频文件 def get_speech_sample(*, resample=None): return _get_sample(SAMPLE_WAV_SPEECH_PATH, resample=resample) def get_sample(*, resample=None): return _get_sample(SAMPLE_WAV_PATH, resample=resample) def get_rir_sample(*, resample=None, processed=False): rir_raw, sample_rate = _get_sample(SAMPLE_RIR_PATH, resample=resample) if not processed: return rir_raw, sample_rate rir = rir_raw[:, int(sample_rate*1.01):int(sample_rate*1.3)] rir = rir / torch.norm(rir, p=2) rir = torch.flip(rir, [1]) return rir, sample_rate def get_noise_sample(*, resample=None): return _get_sample(SAMPLE_NOISE_PATH, resample=resample) # 打印元数据的基本信息 def print_metadata(metadata, src=None): if src: print("-" * 10) print("Source:", src) print("-" * 10) print(" - sample_rate:", metadata.sample_rate) print(" - num_channels:", metadata.num_channels) print(" - num_frames:", metadata.num_frames) print(" - bits_per_sample:", metadata.bits_per_sample) print(" - encoding:", metadata.encoding) print() # 打印采样数据集的基本信息 def print_stats(waveform, sample_rate=None, src=None): if src: print("-" * 10) print("Source:", src) print("-" * 10) if sample_rate: print("Sample Rate:", sample_rate) print("Shape:", tuple(waveform.shape)) print("Dtype:", waveform.dtype) print(f" - Max: {waveform.max().item():6.3f}") print(f" - Min: {waveform.min().item():6.3f}") print(f" - Mean: {waveform.mean().item():6.3f}") print(f" - Std Dev: {waveform.std().item():6.3f}") print() print(waveform) print() # 根据采样数据和采样速率打印音频的波形图 def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None): waveform = waveform.numpy() num_channels, num_frames = waveform.shape time_axis = torch.arange(0, num_frames) / sample_rate figure, axes = plt.subplots(num_channels, 1) if num_channels == 1: axes = [axes] for c in range(num_channels): axes[c].plot(time_axis, waveform[c], linewidth=1) axes[c].grid(True) if num_channels > 1: axes[c].set_ylabel(f'Channel {c+1}') if xlim: axes[c].set_xlim(xlim) if ylim: axes[c].set_ylim(ylim) figure.suptitle(title) plt.show(block=False) # 根据采样数据和采样速率打印音频的功率图 def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None): waveform = waveform.numpy() num_channels, num_frames = waveform.shape time_axis = torch.arange(0, num_frames) / sample_rate figure, axes = plt.subplots(num_channels, 1) if num_channels == 1: axes = [axes] for c in range(num_channels): axes[c].specgram(waveform[c], Fs=sample_rate) if num_channels > 1: axes[c].set_ylabel(f'Channel {c+1}') if xlim: axes[c].set_xlim(xlim) figure.suptitle(title) plt.show(block=False) # 播放音频文件 def play_audio(waveform, sample_rate): waveform = waveform.numpy() num_channels, num_frames = waveform.shape if num_channels == 1: display(Audio(waveform[0], rate=sample_rate)) elif num_channels == 2: display(Audio((waveform[0], waveform[1]), rate=sample_rate)) else: raise ValueError("Waveform with more than 2 channels are not supported.") # 输入文件路径,打印文件(音频文件)的基本信息 def inspect_file(path): print("-" * 10) print("Source:", path) print("-" * 10) print(f" - File size: {os.path.getsize(path)} bytes") print_metadata(torchaudio.info(path)) # 打印频谱图 def plot_spectrogram(spec, title=None, ylabel='freq_bin', aspect='auto', xmax=None): 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(spec), origin='lower', aspect=aspect) if xmax: axs.set_xlim((0, xmax)) fig.colorbar(im, ax=axs) plt.show(block=False) def plot_mel_fbank(fbank, title=None): fig, 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) # 获取频谱数据 def get_spectrogram( n_fft = 400, win_len = None, hop_len = None, power = 2.0, ): waveform, _ = get_speech_sample() spectrogram = T.Spectrogram( n_fft=n_fft, win_length=win_len, hop_length=hop_len, center=True, pad_mode="reflect", power=power, ) return spectrogram(waveform) # 打印音频的音调 def plot_pitch(waveform, sample_rate, pitch): figure, axis = plt.subplots(1, 1) axis.set_title("Pitch Feature") axis.grid(True) end_time = waveform.shape[1] / sample_rate time_axis = torch.linspace(0, end_time, waveform.shape[1]) axis.plot(time_axis, waveform[0], linewidth=1, color='gray', alpha=0.3) axis2 = axis.twinx() time_axis = torch.linspace(0, end_time, pitch.shape[1]) ln2 = axis2.plot( time_axis, pitch[0], linewidth=2, label='Pitch', color='green') axis2.legend(loc=0) plt.show(block=False) def plot_kaldi_pitch(waveform, sample_rate, pitch, nfcc): figure, axis = plt.subplots(1, 1) axis.set_title("Kaldi Pitch Feature") axis.grid(True) end_time = waveform.shape[1] / sample_rate time_axis = torch.linspace(0, end_time, waveform.shape[1]) axis.plot(time_axis, waveform[0], linewidth=1, color='gray', alpha=0.3) time_axis = torch.linspace(0, end_time, pitch.shape[1]) ln1 = axis.plot(time_axis, pitch[0], linewidth=2, label='Pitch', color='green') axis.set_ylim((-1.3, 1.3)) axis2 = axis.twinx() time_axis = torch.linspace(0, end_time, nfcc.shape[1]) ln2 = axis2.plot( time_axis, nfcc[0], linewidth=2, label='NFCC', color='blue', linestyle='--') lns = ln1 + ln2 labels = [l.get_label() for l in lns] axis.legend(lns, labels, loc=0) plt.show(block=False)
### 音频的IO操作 ## 查看音频的元数据信息 # torchaudio.info 获取音频的元数据 # sample_rate 是音频的采样频率 # num_channels 是音频的通道数 # num_frames 是每个通道的帧数 # bits_per_sample 是比特深度,即对每次采样编码的比特数 # encoding 是样本编码格式 # 查看 .wav 文件的数据信息 metadata = torchaudio.info(SAMPLE_WAV_PATH) print_metadata(metadata, src=SAMPLE_WAV_PATH) # 查看 .mp3 文件的数据信息 metadata = torchaudio.info(SAMPLE_MP3_PATH) print_metadata(metadata, src=SAMPLE_MP3_PATH) # 查看 .gsm 文件的数据信息 metadata = torchaudio.info(SAMPLE_GSM_PATH) print_metadata(metadata, src=SAMPLE_GSM_PATH) # info 函数对类文件的对象也适用 # 类文件对象:socket对象、输入输出对象(stdin、stdout)都是类文件对象 with requests.get(SAMPLE_WAV_URL, stream=True) as response: metadata = torchaudio.info(response.raw) print_metadata(metadata, src=SAMPLE_WAV_URL) '''When passing file-like object, info function does not read all the data, instead it only reads the beginning portion of data. Therefore, depending on the audio format, it cannot get the correct metadata, including the format itself. The following example illustrates this.''' # info 函数读取的类文件对象的信息可能是错误的 with requests.get(SAMPLE_MP3_URL, stream=True) as response: metadata = torchaudio.info(response.raw, format="mp3") print(f"Fetched {response.raw.tell()} bytes.") print_metadata(metadata, src=SAMPLE_MP3_URL) ## 加载音频数据为 tensor 张量 # 使用 torchaudio.load 函数加载音频数据 # 返回一个元组(waveform,sample_rate) # waveform 为波形的采样数据,数据结构为 tensor 类型,sample_rate 为采样速率 waveform, sample_rate = torchaudio.load(SAMPLE_WAV_SPEECH_PATH) # 采样数据的基本信息 print_stats(waveform, sample_rate=sample_rate) # 波形图 plot_waveform(waveform, sample_rate) # 频谱图 plot_specgram(waveform, sample_rate) # 打开音频数据 play_audio(waveform, sample_rate) ## 从类文件对象加载数据,类文件数据集可以不在本地 # Load audio data as HTTP request with requests.get(SAMPLE_WAV_SPEECH_URL, stream=True) as response: waveform, sample_rate = torchaudio.load(response.raw) plot_specgram(waveform, sample_rate, title="HTTP datasource") # Load audio from tar file with tarfile.open(SAMPLE_TAR_PATH, mode='r') as tarfile_: fileobj = tarfile_.extractfile(SAMPLE_TAR_ITEM) waveform, sample_rate = torchaudio.load(fileobj) plot_specgram(waveform, sample_rate, title="TAR file") # Load audio from S3 client = boto3.client('s3', config=Config(signature_version=UNSIGNED)) response = client.get_object(Bucket=S3_BUCKET, Key=S3_KEY) waveform, sample_rate = torchaudio.load(response['Body']) plot_specgram(waveform, sample_rate, title="From S3") ## 两种不同解码方式的介绍:全文件加载,和类文件部分加载 # The first one will fetch all the data and decode them, while # the second one will stop fetching data once it completes decoding. # The resulting waveforms are identical. frame_offset, num_frames = 16000, 16000 # Fetch and decode the 1 - 2 seconds print("Fetching all the data...") with requests.get(SAMPLE_WAV_SPEECH_URL, stream=True) as response: waveform1, sample_rate1 = torchaudio.load(response.raw) waveform1 = waveform1[:, frame_offset:frame_offset+num_frames] print(f" - Fetched {response.raw.tell()} bytes") print("Fetching until the requested frames are available...") with requests.get(SAMPLE_WAV_SPEECH_URL, stream=True) as response: waveform2, sample_rate2 = torchaudio.load( response.raw, frame_offset=frame_offset, num_frames=num_frames) print(f" - Fetched {response.raw.tell()} bytes") print("Checking the resulting waveform ... ", end="") assert (waveform1 == waveform2).all() print("matched!") ## 将 tensor 数据编码为音频数据保存 # 使用 torchaudio.save 函数实现 waveform, sample_rate = get_sample() print_stats(waveform, sample_rate=sample_rate) # Save without any encoding option. # The function will pick up the encoding which the provided data fit path = "save_example_default.wav" torchaudio.save(path, waveform, sample_rate) inspect_file(path) # Save as 16-bit signed integer Linear PCM # The resulting file occupies half the storage but loses precision path = "save_example_PCM_S16.wav" torchaudio.save( path, waveform, sample_rate, encoding="PCM_S", bits_per_sample=16) inspect_file(path) # torchvision.save 可以保存的文件类型很多 waveform, sample_rate = get_sample() formats = [ "mp3", "flac", "vorbis", "sph", "amb", "amr-nb", "gsm", ] for format in formats: path = f"save_example.{format}" torchaudio.save(path, waveform, sample_rate, format=format) inspect_file(path) ## 保存类文件对象 # get_sample 函数返回一个元组 # waveform 为波形的采样数据,数据结构为 tensor 类型,sample_rate 为采样速率 waveform, sample_rate = get_sample() # buffer 是一个缓冲区对象,这里是将 torchaudio 输出到缓冲区 buffer_ = io.BytesIO() torchaudio.save(buffer_, waveform, sample_rate, format="wav") # buffer_.seek(x),返回流文件的第 x 个 byte 的位置 buffer_.seek(0) print(buffer_.read(16))
### 数据增强 ## 音频混响 # 通过 reverb 参数调整 # Load the data waveform1, sample_rate1 = get_sample(resample=16000) # Define effects # effects 是 torchaudio.sox_effects.apply_effects_tensor 函数的一个参数 # 用于规定音频文件的处理方式 effects = [ ["lowpass", "-1", "300"], # apply single-pole lowpass filter ["speed", "0.8"], # reduce the speed # This only changes sample rate, so it is necessary to # add `rate` effect with original sample rate after this. ["rate", f"{sample_rate1}"], ["reverb", "-w"], # Reverbration gives some dramatic feeling ] # torchaudio.sox_effects 模块提供了在 Tensor 对象和文件对象音频源上直接应用 sox 命令等 filiters 的方法。 # Apply effects waveform2, sample_rate2 = torchaudio.sox_effects.apply_effects_tensor( waveform1, sample_rate1, effects) plot_waveform(waveform1, sample_rate1, title="Original", xlim=(-.1, 3.2)) plot_waveform(waveform2, sample_rate2, title="Effects Applied", xlim=(-.1, 3.2)) print_stats(waveform1, sample_rate=sample_rate1, src="Original") print_stats(waveform2, sample_rate=sample_rate2, src="Effects Applied") # Note that the number of frames and number of channels are different from the original after the effects. # Let’s listen to the audio. Doesn’t it sound more dramatic? plot_specgram(waveform1, sample_rate1, title="Original", xlim=(0, 3.04)) play_audio(waveform1, sample_rate1) plot_specgram(waveform2, sample_rate2, title="Effects Applied", xlim=(0, 3.04)) play_audio(waveform2, sample_rate2) ## 模拟房间混响 # 使用函数 torch.nn.functional.conv1d 对原始数据和 rir 进行卷积 # Using Room Impulse Response (RIR), we can make a clean speech sound like uttered in a conference room # 1. 加载音频数据 sample_rate = 8000 rir_raw, _ = get_rir_sample(resample=sample_rate) plot_waveform(rir_raw, sample_rate, title="Room Impulse Response (raw)", ylim=None) plot_specgram(rir_raw, sample_rate, title="Room Impulse Response (raw)") play_audio(rir_raw, sample_rate) # 2. 我们需要清理RIR。 # 提取主脉冲,归一化信号功率,然后翻转时间轴。 rir = rir_raw[:, int(sample_rate*1.01):int(sample_rate*1.3)] # 二阶范数就是欧几里得距离 rir = rir / torch.norm(rir, p=2) # flip 函数翻转 tensor 数组 rir = torch.flip(rir, [1]) print_stats(rir) plot_waveform(rir, sample_rate, title="Room Impulse Response", ylim=None) # 3. 然后我们用RIR滤波器对语音信号进行卷积。 speech, _ = get_speech_sample(resample=sample_rate) # pad 函数进行矩阵填充 speech_ = torch.nn.functional.pad(speech, (rir.shape[1]-1, 0)) augmented = torch.nn.functional.conv1d(speech_[None, ...], rir[None, ...])[0] plot_waveform(speech, sample_rate, title="Original", ylim=None) plot_waveform(augmented, sample_rate, title="RIR Applied", ylim=None) plot_specgram(speech, sample_rate, title="Original") play_audio(speech, sample_rate) plot_specgram(augmented, sample_rate, title="RIR Applied") play_audio(augmented, sample_rate) ## 增加背景噪音 # 使用加法在采样数据上添加噪音数据即可 sample_rate = 8000 speech, _ = get_speech_sample(resample=sample_rate) noise, _ = get_noise_sample(resample=sample_rate) noise = noise[:, :speech.shape[1]] plot_waveform(noise, sample_rate, title="Background noise") plot_specgram(noise, sample_rate, title="Background noise") play_audio(noise, sample_rate) # speech_power 信息功率 # noise_power 噪音功率 speech_power = speech.norm(p=2) noise_power = noise.norm(p=2) for snr_db in [20, 10, 3]: # snr 信噪比 snr = math.exp(snr_db / 10) # scale * speech_power / noise_power = snr scale = snr * noise_power / speech_power noisy_speech = (scale * speech + noise) / 2 plot_waveform(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]") plot_specgram(noisy_speech, sample_rate, title=f"SNR: {snr_db} [dB]") play_audio(noisy_speech, sample_rate) ## 对 tensor 对象应用编解码器保存为特定格式的文件 # 使用函数 torchaudio.functional.apply_codec waveform, sample_rate = get_speech_sample(resample=8000) plot_specgram(waveform, sample_rate, title="Original") play_audio(waveform, sample_rate) configs = [ ({"format": "wav", "encoding": 'ULAW', "bits_per_sample": 8}, "8 bit mu-law"), ({"format": "gsm"}, "GSM-FR"), ({"format": "mp3", "compression": -9}, "MP3"), ({"format": "vorbis", "compression": -1}, "Vorbis"), ] for param, title in configs: augmented = F.apply_codec(waveform, sample_rate, **param) plot_specgram(augmented, sample_rate, title=title) play_audio(augmented, sample_rate) ## 模拟电话编码 sample_rate = 16000 speech, _ = get_speech_sample(resample=sample_rate) plot_specgram(speech, sample_rate, title="Original") play_audio(speech, sample_rate) # Apply RIR rir, _ = get_rir_sample(resample=sample_rate, processed=True) speech_ = torch.nn.functional.pad(speech, (rir.shape[1]-1, 0)) speech = torch.nn.functional.conv1d(speech_[None, ...], rir[None, ...])[0] plot_specgram(speech, sample_rate, title="RIR Applied") play_audio(speech, sample_rate) # Add background noise # Because the noise is recorded in the actual environment, we consider that # the noise contains the acoustic feature of the environment. Therefore, we add # the noise after RIR application. noise, _ = get_noise_sample(resample=sample_rate) noise = noise[:, :speech.shape[1]] snr_db = 8 scale = math.exp(snr_db / 10) * noise.norm(p=2) / speech.norm(p=2) speech = (scale * speech + noise) / 2 plot_specgram(speech, sample_rate, title="BG noise added") play_audio(speech, sample_rate) # Apply filtering and change sample rate speech, sample_rate = torchaudio.sox_effects.apply_effects_tensor( speech, sample_rate, effects=[ ["lowpass", "4000"], ["compand", "0.02,0.05", "-60,-60,-30,-10,-20,-8,-5,-8,-2,-8", "-8", "-7", "0.05"], ["rate", "8000"], ], ) plot_specgram(speech, sample_rate, title="Filtered") play_audio(speech, sample_rate) # Apply telephony codec speech = F.apply_codec(speech, sample_rate, format="gsm") plot_specgram(speech, sample_rate, title="GSM Codec Applied") play_audio(speech, sample_rate)
### 特征提取 # torchaudio 实现了声音领域常用的特征提取方法 # 特征提取方法通过 torchaudio.functional 和 torchaudio.transform 调用 # torchaudio.functional 将特征提取封装为独立的函数,torchaudio.transform 则是面向对象的 ## 时域 -> 频域变换 # 使用 T.Spectrogram 函数 # 加载数据 waveform, sample_rate = get_speech_sample() n_fft = 1024 # size of fft,即采样数据的数据长度 win_length = None # window size hop_length = 512 # Length of hop between STFT windows # define transformation # 生成一个频谱计算对象 spectrogram = T.Spectrogram( n_fft=n_fft, win_length=win_length, hop_length=hop_length, center=True, pad_mode="reflect", power=2.0, ) # Perform transformation spec = spectrogram(waveform) print_stats(spec) plot_spectrogram(spec[0], title='torchaudio') ## 频域 -> 时域变换 # 使用 T.GriffinLim 函数 torch.random.manual_seed(0) waveform, sample_rate = get_speech_sample() plot_waveform(waveform, sample_rate, title="Original") play_audio(waveform, sample_rate) # 参数设置仅为实验,还原后的时域信号有损失 n_fft = 1024 win_length = None # hop_length 是短时傅里叶变换中的一个概念 hop_length = 256 # 作时域 -> 频域变换 spec = T.Spectrogram( n_fft=n_fft, win_length=win_length, hop_length=hop_length, )(waveform) # 实例化频域-时域变换计算器对象 griffin_lim = T.GriffinLim( n_fft=n_fft, win_length=win_length, hop_length=hop_length, ) waveform = griffin_lim(spec) plot_waveform(waveform, sample_rate, title="Reconstructed") play_audio(waveform, sample_rate) ## 梅尔滤波器组 n_fft = 256 n_mels = 64 sample_rate = 6000 # torchaudio.functional.create_fb_matrix 函数可以生成滤波组,将频率基数转换为 Mel 尺度基数。 mel_filters = F.create_fb_matrix( int(n_fft // 2 + 1), n_mels=n_mels, f_min=0., f_max=sample_rate/2., sample_rate=sample_rate, norm='slaney' ) plot_mel_fbank(mel_filters, "Mel Filter Bank - torchaudio") ## 与 librosa 的比较 mel_filters_librosa = librosa.filters.mel( sample_rate, n_fft, n_mels=n_mels, fmin=0., fmax=sample_rate/2., norm='slaney', htk=True, ).T plot_mel_fbank(mel_filters_librosa, "Mel Filter Bank - librosa") mse = torch.square(mel_filters - mel_filters_librosa).mean().item() print('Mean Square Difference: ', mse) ## 梅尔频谱图 waveform, sample_rate = get_speech_sample() n_fft = 1024 win_length = None hop_length = 512 n_mels = 128 mel_spectrogram = T.MelSpectrogram( sample_rate=sample_rate, n_fft=n_fft, win_length=win_length, hop_length=hop_length, center=True, pad_mode="reflect", power=2.0, norm='slaney', onesided=True, n_mels=n_mels, ) melspec = mel_spectrogram(waveform) plot_spectrogram( melspec[0], title="MelSpectrogram - torchaudio", ylabel='mel freq') ## 与 librosa 的比较 melspec_librosa = librosa.feature.melspectrogram( waveform.numpy()[0], sr=sample_rate, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=True, pad_mode="reflect", power=2.0, n_mels=n_mels, norm='slaney', htk=True, ) plot_spectrogram( melspec_librosa, title="MelSpectrogram - librosa", ylabel='mel freq') mse = torch.square(melspec - melspec_librosa).mean().item() # 均方差 print('Mean Square Difference: ', mse) ## MFCC(Mel cepstrum parameters) 梅尔倒频谱参数 waveform, sample_rate = get_speech_sample() n_fft = 2048 win_length = None hop_length = 512 n_mels = 256 n_mfcc = 256 mfcc_transform = T.MFCC( sample_rate=sample_rate, n_mfcc=n_mfcc, melkwargs={'n_fft': n_fft, 'n_mels': n_mels, 'hop_length': hop_length}) mfcc = mfcc_transform(waveform) plot_spectrogram(mfcc[0]) ## 与 librosa 的比较 melspec = librosa.feature.melspectrogram( y=waveform.numpy()[0], sr=sample_rate, n_fft=n_fft, win_length=win_length, hop_length=hop_length, n_mels=n_mels, htk=True, norm=None) mfcc_librosa = librosa.feature.mfcc( S=librosa.core.spectrum.power_to_db(melspec), n_mfcc=n_mfcc, dct_type=2, norm='ortho') plot_spectrogram(mfcc_librosa) mse = torch.square(mfcc - mfcc_librosa).mean().item() print('Mean Square Difference: ', mse) ## Pitch 声调 waveform, sample_rate = get_speech_sample() pitch = F.detect_pitch_frequency(waveform, sample_rate) plot_pitch(waveform, sample_rate, pitch) play_audio(waveform, sample_rate) ## Kaldi Pitch (beta) # Kaldi Pitch feature[1] 是为ASR应用而调整的音调检测机制 # 一种用于自动语音识别的音调提取算法 waveform, sample_rate = get_speech_sample(resample=16000) pitch_feature = F.compute_kaldi_pitch(waveform, sample_rate) pitch, nfcc = pitch_feature[..., 0], pitch_feature[..., 1] plot_kaldi_pitch(waveform, sample_rate, pitch, nfcc) play_audio(waveform, sample_rate)
### 特征增强 (频谱增强) ## TimeStrech # 在给定的速率下,在不改变音高的情况下即时拉伸 stft # 通过 T.TimeStretch 函数实现 spec = get_spectrogram(power=None) strech = T.TimeStretch() rate = 1.2 spec_ = strech(spec, rate) plot_spectrogram(F.complex_norm(spec_[0]), title=f"Stretched x{rate}", aspect='equal', xmax=304) plot_spectrogram(F.complex_norm(spec[0]), title="Original", aspect='equal', xmax=304) rate = 0.9 spec_ = strech(spec, rate) plot_spectrogram(F.complex_norm(spec_[0]), title=f"Stretched x{rate}", aspect='equal', xmax=304) ## TimeMasking # 在时域中对谱图进行遮蔽。 # 通过 T.TimeMasking 函数实现 torch.random.manual_seed(4) spec = get_spectrogram() plot_spectrogram(spec[0], title="Original") masking = T.TimeMasking(time_mask_param=80) spec = masking(spec) plot_spectrogram(spec[0], title="Masked along time axis") ## FrequencyMasking # 在频域中对频谱图进行遮蔽 # 通过 T.FrequencyMasking 函数实现 torch.random.manual_seed(4) spec = get_spectrogram() plot_spectrogram(spec[0], title="Original") masking = T.FrequencyMasking(freq_mask_param=80) spec = masking(spec) plot_spectrogram(spec[0], title="Masked along frequency axis")
### Datasets # 在这里,我们以YESNO数据集为例,研究一下如何使用 torchaudio 的 datasets YESNO_DOWNLOAD_PROCESS.join() dataset = torchaudio.datasets.YESNO(YESNO_DATASET_PATH, download=True) for i in [1, 3, 5]: waveform, sample_rate, label = dataset[i] plot_specgram(waveform, sample_rate, title=f"Sample {i}: {label}") play_audio(waveform, sample_rate)
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