Instructions to use Pierfrancesco/adt-str with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pierfrancesco/adt-str with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Pierfrancesco/adt-str", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ADT_STR
Automatic Drum Transcription model exported from this repository.
Default variant: setting-tau-0.8.
Quick start
from huggingface_hub import snapshot_download
import sys
repo_dir = snapshot_download("Pierfrancesco/adt-str")
sys.path.insert(0, repo_dir)
from adt_transcriber import ADTTranscriber
transcriber = ADTTranscriber.from_pretrained(repo_dir, variant="setting-tau-0.8")
midi_path = transcriber.transcribe("path/to/audio.wav", output_dir="outputs")
transcriber.play(midi_path, output_path="outputs/preview.wav")
transcribe also accepts a list of audio paths:
midi_paths = transcriber.transcribe(
["path/to/audio_1.wav", "path/to/audio_2.wav"],
output_dir="outputs",
)
or a padded tensor batch:
midi_paths = transcriber.transcribe(
padded_audio_batch,
sample_rate=24000,
lengths=valid_lengths,
output_dir="outputs",
)
Variants
| Variant | Folder | Parameters |
|---|---|---|
setting-tau-0.4 |
setting-tau-0.4 |
69,000,824 |
setting-tau-0.6 |
setting-tau-0.6 |
69,000,824 |
setting-tau-0.8 |
setting-tau-0.8 |
69,000,824 |
Local loading
from adt_transcriber import ADTTranscriber
transcriber = ADTTranscriber.from_pretrained(".", variant="setting-tau-0.8")
You can choose setting-tau-0.4, setting-tau-0.6, or setting-tau-0.8 with
the variant argument.
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