This release is a pre-release and may not be stable for production use.
DAS
DAS 1.0a4 segments and annotates audio with Conformer, TCN, TweetyNet, and WhisperSeg models.
Installation
conda create -y -n das python=3.12 uv
conda activate das
uv pip install "das[gui]==1.0a4"
For development from a source checkout, install the test and docs extras too:
uv pip install -e ".[dev,gui,doc]"
When upgrading PyTorch in an environment that also has torchvision installed, upgrade them together:
uv pip install --upgrade torch torchvision torchaudio
uv pip check
Mismatched PyTorch and torchvision binaries can cause an MPS crash at shutdown.
The GUI uses xarray-behave. Run das or das gui to open the single-file annotation view and its Train and Predict dialogs.
Usage
Python API:
import das
checkpoint = das.train(
data_dir="/path/to/audio",
output_dir="/path/to/run",
)
written_files = das.predict(
data_dir="/path/to/audio",
checkpoint=checkpoint,
existing_annotations="merge",
)
annotations = das.predict(
audio=waveform,
samplerate=16_000,
checkpoint=checkpoint,
)
Prediction writes each annotation CSV next to its audio file by default. Set output_dir="/path/to/predictions" to collect them in one directory. Set output_dir="", or pass raw audio, to return annotation DataFrames instead of writing prediction CSV files. Training still writes to ./ by default.
Prediction handles existing output CSV files with existing_annotations="skip", "overwrite" (default), or "merge". The CLI equivalent is --existing-annotations.
Training reads annotated WAV folders directly; no dataset generation step is needed. It also accepts existing DAS .npy, H5, and Zarr training datasets. Other audio formats supported by the current DAS release remain available through the existing dataset-generation path.
Minimal CLI:
das train \
--data-dir /path/to/audio \
--output-dir /path/to/run \
--config /path/to/train.yaml \
--frontend mel \
--batch-size 16
das predict \
--data-dir /path/to/audio \
--checkpoint /path/to/run/checkpoints/best-epoch=000.ckpt \
--output-dir /path/to/predictions \
--config /path/to/predict.yaml \
--evaluate \
--syllable-tolerance-ms 20
das predict \
--data-dir /path/to/audio \
--checkpoint /path/to/run/checkpoints/best-epoch=000.ckpt \
--output-dir /path/to/predictions \
--config /path/to/predict.yaml \
--existing-annotations merge \
--output-suffix _frames.csv
CLI flags and YAML use the same flat field names. Config files can be partial, and --config can be provided multiple times. The merge order is defaults, then config files in the order provided, then explicit CLI flags.
--config also accepts built-in config names: fly-pulse, fly, zebra-finch, and tweetynet. fly-pulse selects the 1 ms raw-waveform ConvResNet plus compact TCN. They load through the same merge path as YAML files:
das train \
--config fly-pulse \
--data-dir /path/to/audio \
--output-dir /path/to/run
Legacy DAS models saved as *_model.h5 plus *_params.yaml can be used with the same predict command by pointing --checkpoint at the shared trunk path:
das predict \
--data-dir /path/to/audio \
--checkpoint /path/to/test \
--output-dir /path/to/predictions
They can also be converted once to a native Torch checkpoint that loads through DASModel.load_from_checkpoint():
das convert-legacy \
--checkpoint /path/to/test \
--output /path/to/test.ckpt
The converted checkpoint can then be passed to das predict like any native checkpoint.
WhisperSeg offers compact FP16 AER starting models in Tiny, Base, Small and Large sizes. Select one in the GUI or use --initial-model whisperseg-aer:small-ms. Training defaults to decoder LoRA; --whisperseg-training-method full selects FP32 fine-tuning. Selecting a training checkpoint as the initial model automatically resumes its adapters, optimizer and scheduler. See WhisperSeg training and prediction.
For native checkpoints, das predict uses the saved chunk length, stride, and class names from the trained model. Legacy DAS models use the saved legacy window size, stride, and class labels from the model params. Evaluation works through das predict --evaluate, with optional --split train|val|test for split-backed datasets.
Print a starter config for a subcommand:
das train --print-config
Save the effective config for a run:
das train \
--data-dir /path/to/audio \
--output-dir /path/to/run \
--encoder tcn \
--save-config /path/to/run-config.yaml
Segment detection uses adjustable low and high hysteresis thresholds. The CLI, YAML config, and GUI Train and Predict dialogs expose these thresholds, event thresholds and spacing, and manual postprocessing settings.
Built-in TCN encoder example:
das train \
--data-dir /path/to/audio \
--output-dir /path/to/run \
--encoder tcn \
--encoder-hidden-size 32 \
--encoder-num-layers 4 \
--encoder-dilations [1,2,4,8,16]
Example flat config:
mode: train
data_dir: /path/to/audio
output_dir: /path/to/run
batch_size: 16
num_time_steps: 2048
frontend_type: raw
encoder_type: tcn
encoder_hidden_size: 16
encoder_num_layers: 2
encoder_dilations: [1, 2, 4, 8]
decoder_type: linear
learning_rate: 0.01
num_epochs: 20
1 maintainer
Metadata
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