Package that uses extracted audio classification model from neonbjb/DL-Art-School - for filtering fine audio files.
Project description
AudClas
Package that uses audio classification model extracted from neonbjb/DL-Art-School - for filtering fine audio files.
Classes
Classifier model returns one of 6 possible labels:
label | class name |
---|---|
0 | fine |
1 | env_noise |
2 | music |
3 | two_voices |
4 | reverb |
- | unknown |
Installation
pip install audclas
examples of usage
For single audio file:
from audclas.tortoise_audio_classifier import TortoiseAudioClassifier
classifier = TortoiseAudioClassifier()
label = classifier('wavs/test.wav')
print(label)
For directory containing audio files (it searches recursively for all .wav / .mp3 files):
from tqdm import tqdm
from audclas.tortoise_audio_classifier import TortoiseAudioClassifier
classifier = TortoiseAudioClassifier()
batch_size = 32
do_classify, total = classifier.prepare_classify_dir_job('/content/wavs', batch_size)
fine_audio_paths = []
for result in tqdm(do_classify(), total=total):
for audio_path, audio_label in result:
if audio_label == 'fine':
fine_audio_paths.append(audio_path)
print(f'directory contains {len(fine_audio_paths)} fine files (total files: {total * batch_size})')
Project details
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