AudioSRでオーディオの超解像化を試す

!pip install yt-dlp
!yt-dlp -x --audio-format wav -o sample.wav https://www.youtube.com/watch?v=Yl2kR6zLRY8 

!pip install audiosr==0.0.6 pydub

import matplotlib.pyplot as plt
from pydub import AudioSegment
from pydub.silence import split_on_silence
import numpy as np
from IPython.display import Audio

# wavファイルを読み込む
wav_audio = AudioSegment.from_file("sample.wav", format="wav")

# 無音部分を検出し、カットする
chunks = split_on_silence(wav_audio, min_silence_len=500, silence_thresh=-40)
non_silent_audio = sum(chunks)

# 最初の30秒間を切り出す
thirty_seconds = 30 * 1000  # pydubはミリ秒単位を使用する
audio_segment = non_silent_audio[:thirty_seconds]

# 切り出したオーディオセグメントをwavファイルとして保存
output_path = "sample_30secs.wav"
audio_segment.export(output_path, format="wav")

# 高解像度のスペクトログラムをプロットするコード
samples = np.array(audio_segment.get_array_of_samples())
frame_rate = audio_segment.frame_rate

# スペクトログラムの解像度を高くするためのパラメータ
NFFT = 2048  # FFTウィンドウのサイズ
noverlap = 1024  # ウィンドウのオーバーラップ

plt.figure(figsize=(12, 6))
plt.specgram(samples, Fs=frame_rate, NFFT=NFFT, noverlap=noverlap, cmap='inferno')
plt.title('High-Resolution Spectrogram of the 30-second audio segment')
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
plt.colorbar(label='Intensity (dB)')
plt.show()

# 再生
Audio(output_path)

!audiosr -i sample_30secs.wav

(…).co/roberta-base/resolve/main/vocab.json: 100% 899k/899k [00:00<00:00, 2.36MB/s]
(…).co/roberta-base/resolve/main/merges.txt: 100% 456k/456k [00:00<00:00, 2.40MB/s]
(…)co/roberta-base/resolve/main/config.json: 100% 481/481 [00:00<00:00, 2.58MB/s]
Loading AudioSR: basic
Loading model on cuda:0
basic.pth: 100% 6.18G/6.18G [03:05<00:00, 33.2MB/s]
/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3526.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/usr/local/lib/python3.10/dist-packages/torchaudio/transforms/_transforms.py:580: UserWarning: Argument 'onesided' has been deprecated and has no influence on the behavior of this module.
  warnings.warn(
DiffusionWrapper has 258.20 M params.
/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
 Warning: audio is longer than 10.24 seconds, may degrade the model performance. It's recommand to truncate your audio to 5.12 seconds before input to AudioSR to get the best performance.
Running DDIM Sampling with 50 timesteps
DDIM Sampler: 100% 50/50 [00:35<00:00,  1.42it/s]
 Don't forget to try different seeds by setting --seed <int> so that AudioSR can have optimal performance on your hardware.
Save audio to ./output/09_11_2023_12_20_05/sample_30secs_AudioSR_Processed_48K.wav.
ffmpeg version 4.4.2-0ubuntu0.22.04.1 Copyright (c) 2000-2021 the FFmpeg developers
  built with gcc 11 (Ubuntu 11.2.0-19ubuntu1)
  configuration: --prefix=/usr --extra-version=0ubuntu0.22.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 70.100 / 56. 70.100
  libavcodec     58.134.100 / 58.134.100
  libavformat    58. 76.100 / 58. 76.100
  libavdevice    58. 13.100 / 58. 13.100
  libavfilter     7.110.100 /  7.110.100
  libswscale      5.  9.100 /  5.  9.100
  libswresample   3.  9.100 /  3.  9.100
  libpostproc    55.  9.100 / 55.  9.100
Guessed Channel Layout for Input Stream #0.0 : mono
Input #0, wav, from '/tmp/sample_30secs_AudioSR_Processed_48K.wav':
  Duration: 00:00:30.72, bitrate: 768 kb/s
  Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 48000 Hz, mono, s16, 768 kb/s
Output #0, wav, to './output/09_11_2023_12_20_05/sample_30secs_AudioSR_Processed_48K.wav':
  Metadata:
    ISFT            : Lavf58.76.100
  Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 48000 Hz, mono, s16, 768 kb/s
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
Press [q] to stop, [?] for help
size=    2816kB time=00:00:29.99 bitrate= 769.1kbits/s speed=4.04e+03x    
video:0kB audio:2816kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.002705%

!apt-get install tree
!tree output

output
└── 09_11_2023_12_20_05
    └── sample_30secs_AudioSR_Processed_48K.wav

1 directory, 1 file

import os 
import glob
from IPython.display import Audio

# wavファイルを読み込む
files = glob.glob("./output/*/*.wav")
wav30secs_audio = AudioSegment.from_file(files[0], format="wav")

# ここからは高解像度のスペクトログラムをプロットするコード
samples = np.array(wav30secs_audio.get_array_of_samples())
frame_rate = wav30secs_audio.frame_rate

# スペクトログラムの解像度を高くするためのパラメータ
NFFT = 2048  # FFTウィンドウのサイズ
noverlap = 1024  # ウィンドウのオーバーラップ

plt.figure(figsize=(12, 6))
plt.specgram(samples, Fs=frame_rate, NFFT=NFFT, noverlap=noverlap, cmap='inferno')
plt.title('High-Resolution Spectrogram of the 30-second audio segment')
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
plt.colorbar(label='Intensity (dB)')
plt.show()

Audio(files[0])