Usage Guide¶

Quick Start¶

1. Audio Loading¶

Load and normalise audio files for analysis. The AudioLoader handles mono conversion and peak normalisation automatically.

import drumscript as ds
load_audio = ds.load_audio("audio_path.wav") # 1. Load your audio into an .env

2. Extract Drums From Any Song¶

Want to isolate the drums in your favourite song?

import drumscript as ds
extract_drums = ds.extract_drum_stem("audio_path.wav", output_dir="path_to_output_dir/") # 2. Extract drums from a song and save to local path that you can specify

DrumScript will save the output to an outputs/ folder in your current working directory if output_dir is not specified.

3. Create Drumless Backing Track (`--drumless`)¶

Want to jam along? Remove the drums from your favourite song:

python -m ds.main "audio_path.wav" --drumless 

import drumscript as ds
backing_track = ds.main("audio_path.wav", "--drumless")

DrumScript will save the output to outputs/ folder in your current working directory if output_dir is not specified.

4. Extracting Stems (`--all-stems`)¶

Split a song into its constituent parts (Drums, Bass, Vocals, Other):

python -m drumscript.main "audio_path.mp3" --all-stems --format mp3

PLEASE NOTE: These stems are defined by Demucs

5. Custom Time Signatures (`--ts`)¶

By default, DrumScript assumes 4_4 time. You can override this for waltzes or complex meters:

# Transcribe a waltz
python -m drumscript.main "audio_path.wav" --ts 3_4

# Transcribe 6_8 time
python -m drumscript.main "audio_path.wav" --ts 6_8

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6. Full Audio to PDF Transcription (`--full`)¶

python -m drumscript.main audio_path.mp3 --output my_score.pdf --full

import drumscript as ds
get_drum_score = ds.main("audio_path.mp3", format=="mp3", output_name="my_score.pdf", "--full")

Full commands

Audio Transcription¶

To run the full transcription pipeline on an audio file, use the main entry point. This will load the audio, separate the drums (if needed), classify hits, and generate a PDF score.

from drumscript.main import main

# Run the full pipeline
main()
# Follow the interactive prompts to provide the input file path.

Stem Splitting¶

Isolate the drum track from a full music mix using the StemSplitter class. This is useful if you want to process the drum audio separately.

from drumscript.audio_processor.stem_splitter import StemSplitter

# Initialise the splitter
splitter = StemSplitter()

# Split the audio file; returns the path to the isolated drum track
drum_track_path = splitter.split_drums(
    input_file="audio_path.mp3",
    output_dir="output/"
)

print(f"Drum stem saved at: {drum_track_path}")

Audio Loading¶

Load and normalise audio files for analysis. The AudioLoader handles mono conversion and peak normalisation automatically.

import drumscript as ds
from drumscript.audio_processor.audio_loader import AudioLoader
# Load audio (returns audio time series and sample rate)
y, sr = ds.AudioLoader("audio_path.wav") 

Extract Backing Track¶

#### Extract Drum-Only Audio