AudioEffector Usages

Author: Moto Hira

This tutorial shows how to use torchaudio.io.AudioEffector to apply various effects and codecs to waveform tensor.

Note

This tutorial requires FFmpeg libraries. Please refer to FFmpeg dependency for the detail.

Overview

AudioEffector combines in-memory encoding, decoding and filtering that are provided by StreamWriter and StreamReader.

The following figure illustrates the process.

import torch
import torchaudio

print(torch.__version__)
print(torchaudio.__version__)

2.4.0
2.4.0

from torchaudio.io import AudioEffector, CodecConfig

import matplotlib.pyplot as plt
from IPython.display import Audio

for k, v in torchaudio.utils.ffmpeg_utils.get_versions().items():
    print(k, v)

libavcodec (60, 3, 100)
libavdevice (60, 1, 100)
libavfilter (9, 3, 100)
libavformat (60, 3, 100)
libavutil (58, 2, 100)

Usage

To use AudioEffector, instantiate it with effect and format, then either pass the waveform to apply() or stream() method.

effector = AudioEffector(effect=..., format=...,)

# Apply at once
applied = effector.apply(waveform, sample_rate)

apply method applies effect and codec to the entire waveform at once. So if the input waveform is long, and memory consumption is an issue, one can use stream method to process chunk by chunk.

# Apply chunk by chunk
for applied_chunk = effector.stream(waveform, sample_rate):
    ...

Example

src = torchaudio.utils.download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")
waveform, sr = torchaudio.load(src, channels_first=False)

Gallery

def show(effect, *, stereo=False):
    wf = torch.cat([waveform] * 2, dim=1) if stereo else waveform
    figsize = (6.4, 2.1 if stereo else 1.2)

    effector = AudioEffector(effect=effect, pad_end=False)
    result = effector.apply(wf, int(sr))

    num_channels = result.size(1)
    f, ax = plt.subplots(num_channels, 1, squeeze=False, figsize=figsize, sharex=True)
    for i in range(num_channels):
        ax[i][0].specgram(result[:, i], Fs=sr)
    f.set_tight_layout(True)

    return Audio(result.numpy().T, rate=sr)

Original

show(effect=None)

Effects

tempo

https://ffmpeg.org/ffmpeg-filters.html#atempo

show("atempo=0.7")

show("atempo=1.8")

highpass

https://ffmpeg.org/ffmpeg-filters.html#highpass

show("highpass=frequency=1500")

lowpass

https://ffmpeg.org/ffmpeg-filters.html#lowpass

show("lowpass=frequency=1000")

allpass

https://ffmpeg.org/ffmpeg-filters.html#allpass

show("allpass")

bandpass

https://ffmpeg.org/ffmpeg-filters.html#bandpass

show("bandpass=frequency=3000")

bandreject

https://ffmpeg.org/ffmpeg-filters.html#bandreject

show("bandreject=frequency=3000")

echo

https://ffmpeg.org/ffmpeg-filters.html#aecho

show("aecho=in_gain=0.8:out_gain=0.88:delays=6:decays=0.4")

show("aecho=in_gain=0.8:out_gain=0.88:delays=60:decays=0.4")

show("aecho=in_gain=0.8:out_gain=0.9:delays=1000:decays=0.3")

chorus

https://ffmpeg.org/ffmpeg-filters.html#chorus

show("chorus=0.5:0.9:50|60|40:0.4|0.32|0.3:0.25|0.4|0.3:2|2.3|1.3")

fft filter

https://ffmpeg.org/ffmpeg-filters.html#afftfilt

# fmt: off
show(
    "afftfilt="
    "real='re * (1-clip(b * (b/nb), 0, 1))':"
    "imag='im * (1-clip(b * (b/nb), 0, 1))'"
)

show(
    "afftfilt="
    "real='hypot(re,im) * sin(0)':"
    "imag='hypot(re,im) * cos(0)':"
    "win_size=512:"
    "overlap=0.75"
)

show(
    "afftfilt="
    "real='hypot(re,im) * cos(2 * 3.14 * (random(0) * 2-1))':"
    "imag='hypot(re,im) * sin(2 * 3.14 * (random(1) * 2-1))':"
    "win_size=128:"
    "overlap=0.8"
)
# fmt: on

vibrato

https://ffmpeg.org/ffmpeg-filters.html#vibrato

show("vibrato=f=10:d=0.8")

/pytorch/audio/ci_env/lib/python3.10/site-packages/IPython/lib/display.py:187: RuntimeWarning: invalid value encountered in divide
  scaled = data / normalization_factor * 32767
/pytorch/audio/ci_env/lib/python3.10/site-packages/IPython/lib/display.py:188: RuntimeWarning: invalid value encountered in cast
  return scaled.astype("<h").tobytes(), nchan

tremolo

https://ffmpeg.org/ffmpeg-filters.html#tremolo

show("tremolo=f=8:d=0.8")

crystalizer

https://ffmpeg.org/ffmpeg-filters.html#crystalizer

show("crystalizer")

flanger

https://ffmpeg.org/ffmpeg-filters.html#flanger

show("flanger")

phaser

https://ffmpeg.org/ffmpeg-filters.html#aphaser

show("aphaser")

pulsator

https://ffmpeg.org/ffmpeg-filters.html#apulsator

show("apulsator", stereo=True)

haas

https://ffmpeg.org/ffmpeg-filters.html#haas

show("haas")

Codecs

def show_multi(configs):
    results = []
    for config in configs:
        effector = AudioEffector(**config)
        results.append(effector.apply(waveform, int(sr)))

    num_configs = len(configs)
    figsize = (6.4, 0.3 + num_configs * 0.9)
    f, axes = plt.subplots(num_configs, 1, figsize=figsize, sharex=True)
    for result, ax in zip(results, axes):
        ax.specgram(result[:, 0], Fs=sr)
    f.set_tight_layout(True)

    return [Audio(r.numpy().T, rate=sr) for r in results]

ogg

results = show_multi(
    [
        {"format": "ogg"},
        {"format": "ogg", "encoder": "vorbis"},
        {"format": "ogg", "encoder": "opus"},
    ]
)

ogg - default encoder (flac)

results[0]

ogg - vorbis

results[1]

ogg - opus

results[2]

mp3

https://trac.ffmpeg.org/wiki/Encode/MP3

results = show_multi(
    [
        {"format": "mp3"},
        {"format": "mp3", "codec_config": CodecConfig(compression_level=1)},
        {"format": "mp3", "codec_config": CodecConfig(compression_level=9)},
        {"format": "mp3", "codec_config": CodecConfig(bit_rate=192_000)},
        {"format": "mp3", "codec_config": CodecConfig(bit_rate=8_000)},
        {"format": "mp3", "codec_config": CodecConfig(qscale=9)},
        {"format": "mp3", "codec_config": CodecConfig(qscale=1)},
    ]
)

default

results[0]

compression_level=1

results[1]

compression_level=9

results[2]

bit_rate=192k

results[3]

bit_rate=8k

results[4]

qscale=9

results[5]

qscale=1

results[6]

Tag: torchaudio.io