Chord-Transcription

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A repository for training chord transcription models. It enables high-precision inference through context interpretation using a SegmentModel.

Dataset Creation Pipeline

This document describes the preprocessing pipeline required to prepare the dataset for model training. Please execute each step in order.

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Step 1. Stem Separation and Resampling

Separates audio files into individual instrument stems (vocals, drums, bass, and others) and resamples them to the specified sampling rate.

uv run python -m src.preprocess.separate_and_resample --input <input_dir> --out-dir <output_dir>

• --input_dir: Directory containing the source audio files.

• Default: ./dataset/songs

• --out-dir: Destination directory for the separated stem files.

• Default: ./dataset/songs_separated

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Step 2. Data Augmentation via Pitch Shifting

Applies pitch shifting to the separated stems to increase the volume and variety of the training data (Data Augmentation).

uv run python -m src.preprocess.pitch_shift_augment --target_dir <target_dir>

• --target_dir: Directory containing the audio files to be pitch-shifted.
• Default: ./dataset/songs_separated

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Step 3. Chord Data Normalization

Normalizes original chord notations (e.g., CM7, Gm) into a consistent format optimized for model training.

uv run python -m src.preprocess.normalize_chords --input_dir <input_dir> --output_dir <output_dir>

• --input_dir: Directory containing the raw chord data.

• Default: ./dataset/chords

• --output_dir: Destination directory for the normalized chord data.

• Default: ./dataset/chords_normalize

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Step 4. Creating Training Pairs

Generates a CSV file (training/validation pair list) that maps processed audio files to their corresponding chord, key, and tempo labels.

uv run python -m src.preprocess.make_pairs_csv --chords_dir <chords_dir> --keys_dir <keys_dir> --tempos_dir <tempos_dir> --songs_separated_dir <songs_separated_dir> --validation_ratio <validation_ratio>

• --chords_dir: Directory containing normalized chords.
• --keys_dir: Directory containing key information.
• --tempos_dir: Directory containing tempo information.
• --songs_separated_dir: Directory containing separated stems.
• --validation_ratio: The proportion of the dataset to be used for validation.

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Step 5. Calculating Chord Quality Frequency

Calculates the frequency of each chord quality (e.g., Major, minor) across the dataset for use in the loss function during training.

uv run python -m src.preprocess.count_quality_freq --data_folder <data_folder> --quality_definition <quality_definition> --output <output>

• --data_folder: Directory containing normalized chords.

• Default: ./dataset/chords_normalize

• --quality_definition: Definition file for chord qualities.

• Default: ./data/quality.json

• --output: Path for the output JSON file containing frequency counts.

• Default: ./data/quality_freq_count.json

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Training

Step 1. First-Stage Model Training

uv run python -m src.train_transcription --config ./configs/train.yaml

Step 2. Second-Stage Model Training (SegmentModel)

Specify the weights from the first-stage model in the checkpoint.

uv run python -m src.train_segment_transcription --config ./configs/train.yaml --checkpoint <base_transcription.pt> --training_backbone

Step 3. CRF Training

Specify the weights from the second-stage model in the checkpoint.

uv run python -m src.train_crf --config ./configs/train.yaml --checkpoint <segment_model.pt>

Inference

Inference with the First-Stage Model

uv run python -m src.inference --config ./configs/train.yaml --checkpoint <base_transcription.pt> --audio <audio_path>

Inference with the Second-Stage Model

uv run python -m src.inference --config ./configs/train.yaml --checkpoint <segment_model.pt> --audio <audio_path> --use_segment_model

Inference with CRF

uv run python -m src.inference --config ./configs/train.yaml --crf_checkpoint <crf_model.pt> --audio <audio_path> --use_segment_model

Pre-trained Models

Available for download here.