.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "tutorials/audio_data_augmentation_tutorial.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_tutorials_audio_data_augmentation_tutorial.py: Audio Data Augmentation ======================= ``torchaudio`` provides a variety of ways to augment audio data. .. GENERATED FROM PYTHON SOURCE LINES 8-20 .. code-block:: default # When running this tutorial in Google Colab, install the required packages # with the following. # !pip install torchaudio import torch import torchaudio import torchaudio.functional as F print(torch.__version__) print(torchaudio.__version__) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 1.10.0+cpu 0.10.0+cpu .. GENERATED FROM PYTHON SOURCE LINES 21-24 Preparing data and utility functions (skip this section) -------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 24-170 .. code-block:: default #@title Prepare data and utility functions. {display-mode: "form"} #@markdown #@markdown You do not need to look into this cell. #@markdown Just execute once and you are good to go. #@markdown #@markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/), which is licensed under Creative Commos BY 4.0. #------------------------------------------------------------------------------- # Preparation of data and helper functions. #------------------------------------------------------------------------------- import math import os import requests import matplotlib.pyplot as plt from IPython.display import Audio, display _SAMPLE_DIR = "_assets" 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_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_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_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") os.makedirs(_SAMPLE_DIR, exist_ok=True) def _fetch_data(): uri = [ (SAMPLE_WAV_URL, SAMPLE_WAV_PATH), (SAMPLE_RIR_URL, SAMPLE_RIR_PATH), (SAMPLE_WAV_SPEECH_URL, SAMPLE_WAV_SPEECH_PATH), (SAMPLE_NOISE_URL, SAMPLE_NOISE_PATH), ] for url, path in uri: with open(path, 'wb') as file_: file_.write(requests.get(url).content) _fetch_data() def _get_sample(path, resample=None): effects = [ ["remix", "1"] ] if resample: effects.extend([ ["lowpass", f"{resample // 2}"], ["rate", f'{resample}'], ]) return torchaudio.sox_effects.apply_effects_file(path, effects=effects) def get_sample(*, resample=None): return _get_sample(SAMPLE_WAV_PATH, resample=resample) def get_speech_sample(*, resample=None): return _get_sample(SAMPLE_WAV_SPEECH_PATH, resample=resample) 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) 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() 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) 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.") 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) .. GENERATED FROM PYTHON SOURCE LINES 171-204 Applying effects and filtering ------------------------------ ``torchaudio.sox_effects`` allows for directly applying filters similar to those available in ``sox`` to Tensor objects and file object audio sources. There are two functions for this: - ``torchaudio.sox_effects.apply_effects_tensor`` for applying effects to Tensor. - ``torchaudio.sox_effects.apply_effects_file`` for applying effects to other audio sources. Both functions accept effect definitions in the form ``List[List[str]]``. This is mostly consistent with how ``sox`` command works, but one caveat is that ``sox`` adds some effects automatically, whereas ``torchaudio``’s implementation does not. For the list of available effects, please refer to `the sox documentation `__. **Tip** If you need to load and resample your audio data on the fly, then you can use ``torchaudio.sox_effects.apply_effects_file`` with effect ``"rate"``. **Note** ``apply_effects_file`` accepts a file-like object or path-like object. Similar to ``torchaudio.load``, when the audio format cannot be inferred from either the file extension or header, you can provide argument ``format`` to specify the format of the audio source. **Note** This process is not differentiable. .. GENERATED FROM PYTHON SOURCE LINES 204-228 .. code-block:: default # Load the data waveform1, sample_rate1 = get_sample(resample=16000) # Define effects 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 ] # 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") .. rst-class:: sphx-glr-horizontal * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_001.png :alt: Original :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_001.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_002.png :alt: Effects Applied :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ---------- Source: Original ---------- Sample Rate: 16000 Shape: (1, 39680) Dtype: torch.float32 - Max: 0.507 - Min: -0.448 - Mean: -0.000 - Std Dev: 0.122 tensor([[ 0.0007, 0.0076, 0.0122, ..., -0.0049, -0.0025, 0.0020]]) ---------- Source: Effects Applied ---------- Sample Rate: 16000 Shape: (2, 49600) Dtype: torch.float32 - Max: 0.091 - Min: -0.091 - Mean: -0.000 - Std Dev: 0.021 tensor([[0.0000, 0.0000, 0.0000, ..., 0.0069, 0.0058, 0.0045], [0.0000, 0.0000, 0.0000, ..., 0.0085, 0.0085, 0.0085]]) .. GENERATED FROM PYTHON SOURCE LINES 229-233 Note that the number of frames and number of channels are different from those of the original after the effects are applied. Let’s listen to the audio. Doesn’t it sound more dramatic? .. GENERATED FROM PYTHON SOURCE LINES 233-240 .. code-block:: default 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) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_003.png :alt: Original :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_003.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_004.png :alt: Effects Applied :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_004.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /opt/_internal/cpython-3.8.1/lib/python3.8/site-packages/matplotlib/axes/_axes.py:7580: RuntimeWarning: divide by zero encountered in log10 Z = 10. * np.log10(spec) .. GENERATED FROM PYTHON SOURCE LINES 241-256 Simulating room reverberation ----------------------------- `Convolution reverb `__ is a technique that's used to make clean audio sound as though it has been produced in a different environment. Using Room Impulse Response (RIR), for instance, we can make clean speech sound as though it has been uttered in a conference room. For this process, we need RIR data. The following data are from the VOiCES dataset, but you can record your own — just turn on your microphone and clap your hands. .. GENERATED FROM PYTHON SOURCE LINES 256-266 .. code-block:: default 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) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_005.png :alt: Room Impulse Response (raw) :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_005.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_006.png :alt: Room Impulse Response (raw) :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_006.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 267-270 First, we need to clean up the RIR. We extract the main impulse, normalize the signal power, then flip along the time axis. .. GENERATED FROM PYTHON SOURCE LINES 270-278 .. code-block:: default 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]) print_stats(rir) plot_waveform(rir, sample_rate, title="Room Impulse Response", ylim=None) .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_007.png :alt: Room Impulse Response :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_007.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Shape: (1, 2320) Dtype: torch.float32 - Max: 0.395 - Min: -0.286 - Mean: -0.000 - Std Dev: 0.021 tensor([[-0.0052, -0.0076, -0.0071, ..., 0.0184, 0.0173, 0.0070]]) .. GENERATED FROM PYTHON SOURCE LINES 279-281 Then, we convolve the speech signal with the RIR filter. .. GENERATED FROM PYTHON SOURCE LINES 281-297 .. code-block:: default speech, _ = get_speech_sample(resample=sample_rate) 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) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_008.png :alt: Original :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_008.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_009.png :alt: RIR Applied :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_009.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_010.png :alt: Original :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_010.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_011.png :alt: RIR Applied :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_011.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 298-310 Adding background noise ----------------------- To add background noise to audio data, you can simply add a noise Tensor to the Tensor representing the audio data. A common method to adjust the intensity of noise is changing the Signal-to-Noise Ratio (SNR). [`wikipedia `__] \begin{align}\mathrm{SNR} = \frac{P_{\mathrm{signal}}}{P_{\mathrm{noise}}}\end{align} \begin{align}{\mathrm {SNR_{{dB}}}}=10\log _{{10}}\left({\mathrm {SNR}}\right)\end{align} .. GENERATED FROM PYTHON SOURCE LINES 310-333 .. code-block:: default 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 = speech.norm(p=2) noise_power = noise.norm(p=2) for snr_db in [20, 10, 3]: snr = math.exp(snr_db / 10) 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) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_012.png :alt: Background noise :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_012.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_013.png :alt: Background noise :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_013.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_014.png :alt: SNR: 20 [dB] :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_014.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_015.png :alt: SNR: 20 [dB] :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_015.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_016.png :alt: SNR: 10 [dB] :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_016.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_017.png :alt: SNR: 10 [dB] :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_017.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_018.png :alt: SNR: 3 [dB] :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_018.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_019.png :alt: SNR: 3 [dB] :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_019.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 334-341 Applying codec to Tensor object ------------------------------- ``torchaudio.functional.apply_codec`` can apply codecs to a Tensor object. **Note** This process is not differentiable. .. GENERATED FROM PYTHON SOURCE LINES 341-359 .. code-block:: default 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) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_020.png :alt: Original :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_020.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_021.png :alt: 8 bit mu-law :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_021.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_022.png :alt: GSM-FR :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_022.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_023.png :alt: MP3 :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_023.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_024.png :alt: Vorbis :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_024.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 360-367 Simulating a phone recoding --------------------------- Combining the previous techniques, we can simulate audio that sounds like a person talking over a phone in a echoey room with people talking in the background. .. GENERATED FROM PYTHON SOURCE LINES 367-415 .. code-block:: default 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) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_025.png :alt: Original :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_025.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_026.png :alt: RIR Applied :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_026.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_027.png :alt: BG noise added :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_027.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_028.png :alt: Filtered :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_028.png :class: sphx-glr-multi-img * .. image-sg:: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_029.png :alt: GSM Codec Applied :srcset: /tutorials/images/sphx_glr_audio_data_augmentation_tutorial_029.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 7.465 seconds) .. _sphx_glr_download_tutorials_audio_data_augmentation_tutorial.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: audio_data_augmentation_tutorial.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: audio_data_augmentation_tutorial.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_