Awesome
HEAR Baseline
Several baseline audio embeddings that implement the common API required by the HEAR Benchmark and 2021 HEAR NeurIPS competition.
Includes a simple DSP-based audio embedding consisting of a Mel-frequency spectrogram followed by a random projection, implemented in PyTorch, TensorFlow, and Keras.
Additionally, we wrap several benchmark audio embedding models. However, many of them are ineffecient because of limiting assumptions in the original implementation (e.g. only one audio file can be processed at a time using their high-level API).
For the HEAR Benchmark and 2021 NeurIPS evaluation, hearbaseline.wav2vec2
and hearbaseline.torchcrepe
baseline
embeddings were used.
For full details on the HEAR Benchmark please visit https://hearbenchmark.com
Installation
Tested with Python 3.7 and 3.8. Python 3.9 is not officially supported because pip3 installs are very finicky, but it might work.
Method 1: pypi
pip install hearbaseline
Method 2: pip local source tree
This is the same method that will be used to by competition organizers when installing submissions to HEAR 2021.
git clone https://github.com/hearbenchmark/hear-baseline.git
python3 -m pip install -e ./hear-baseline
Naive Baseline Model
The naive baseline model provides an example for implementing the HEAR common API
using DSP-based techniques. It produces log-scaled Mel-frequency spectrograms using a
256-band Mel filter. Each frame of the spectrogram is then projected to 4096
dimensions using a random projection matrix. Weights for the projection matrix were
generated by sampling a normal distribution and are stored in this repository in the
file saved_models/naive_baseline.pt
.
Using a random projection is less efficient than a CNN but is one of the simplest models to implement from a coding perspective.
Usage
Audio embeddings can be computed using one of two methods: 1)
get_scene_embeddings
, or 2) get_timestamp_embeddings
.
get_scene_embeddings
accepts a batch of audio clips and produces a single embedding
for each audio clip. This can be computed like so:
import torch
import hearbaseline
# Load model with weights - located in the root directory of this repo
model = hearbaseline.load_model("saved_models/naive_baseline.pt")
# Create a batch of 2 white noise clips that are 2-seconds long
# and compute scene embeddings for each clip
audio = torch.rand((2, model.sample_rate * 2))
embeddings = hearbaseline.get_scene_embeddings(audio, model)
The get_timestamp_embeddings
method works exactly the same but returns an array
of embeddings computed every 25ms over the duration of the input audio. An array
of timestamps corresponding to each embedding is also returned.
See the common API for more details.
Other Baselines
hearbaseline.torchcrepe
hearbaseline.vggish
hearbaseline.vqt
hearbaseline.wav2vec2
hearbaseline.keras.naive
hearbaseline.tf.naive