audio-diffusion / audio_to_images.py
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import os
import re
import io
import argparse
import pandas as pd
from tqdm.auto import tqdm
from datasets import Dataset, DatasetDict, Features, Image, Value
from audiodiffusion.mel import Mel
def main(args):
mel = Mel(x_res=args.resolution,
y_res=args.resolution,
hop_length=args.hop_length)
os.makedirs(args.output_dir, exist_ok=True)
audio_files = [
os.path.join(root, file) for root, _, files in os.walk(args.input_dir)
for file in files if re.search("\.(mp3|wav|m4a)$", file, re.IGNORECASE)
]
examples = []
try:
for audio_file in tqdm(audio_files):
try:
mel.load_audio(audio_file)
except KeyboardInterrupt:
raise
except:
continue
for slice in range(mel.get_number_of_slices()):
image = mel.audio_slice_to_image(slice)
assert (image.width == args.resolution
and image.height == args.resolution)
with io.BytesIO() as output:
image.save(output, format="PNG")
bytes = output.getvalue()
examples.extend([{
"image": {
"bytes": bytes
},
"audio_file": audio_file,
"slice": slice,
}])
finally:
ds = Dataset.from_pandas(
pd.DataFrame(examples),
features=Features({
"image": Image(),
"audio_file": Value(dtype="string"),
"slice": Value(dtype="int16"),
}),
)
dsd = DatasetDict({"train": ds})
dsd.save_to_disk(os.path.join(args.output_dir))
if args.push_to_hub:
dsd.push_to_hub(args.push_to_hub)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=
"Create dataset of Mel spectrograms from directory of audio files.")
parser.add_argument("--input_dir", type=str)
parser.add_argument("--output_dir", type=str, default="data")
parser.add_argument("--resolution", type=int, default=256)
parser.add_argument("--hop_length", type=int, default=512)
parser.add_argument("--push_to_hub", type=str, default=None)
args = parser.parse_args()
main(args)