Model card and the original license

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LICENSE-MIT

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+The MIT License (MIT)
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README.md

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+---
+license: mit
+pipeline_tag: audio-to-audio
+library_name: mlx-audio
+tags:
+- mlx
+- moonbox
+- mlx-audio
+- deepfilternet
+- speech-enhancement
+- denoising
+- 48khz
+---
+
+# DeepFilterNet3 MLX
+
+MLX builds of [DeepFilterNet3](https://github.com/Rikorose/DeepFilterNet), made by Moonbox for Apple Silicon Macs. Each precision is a folder; keep a folder's
+files together. Only the parts listed below are converted: the rest of the pipeline comes from the original
+repository unless the section below says otherwise.
+
+## Files
+
+The last column is how far each build is from the original PyTorch model on the same inputs (see the section below
+for what was measured).
+
+| Precision | File | Size | sha256 | Checked against the original |
+|---|---|---|---|---|
+| bf16 | [`bf16/config.json`](https://huggingface.co/moonbox-dev/DeepFilterNet3-MLX/blob/main/bf16/config.json) | 0.9 kB | `cc96fe1bf688` | below threshold (experimental): SI-SDR 8.58 dB, 0.66 dB below the original on one clip; DNSMOS 2.93; output vs the original's 25.2 / 16.3 dB |
+| bf16 | [`bf16/model.safetensors`](https://huggingface.co/moonbox-dev/DeepFilterNet3-MLX/blob/main/bf16/model.safetensors) | 4.3 MB | `ad9d67c69a00` | below threshold (experimental): SI-SDR 8.58 dB, 0.66 dB below the original on one clip; DNSMOS 2.93; output vs the original's 25.2 / 16.3 dB |
+
+## How to run
+
+DeepFilterNet3 (real-time speech enhancement at 48 kHz: ERB-band gains plus deep filtering of the low band) for
+[mlx-audio](https://github.com/Blaizzy/mlx-audio). Each folder holds `model.safetensors` (the official checkpoint's
+state dict, PyTorch names, cast to the precision) and `config.json` (the model's `config.ini` as mlx-audio reads it).
+Tested with `mlx-audio==0.6.5`:
+
+```python
+from mlx_audio.sts.models.deepfilternet import DeepFilterNetModel
+import soundfile as sf
+
+model = DeepFilterNetModel.from_pretrained("fp16", subfolder=None)     # a downloaded folder
+model.enhance_file("noisy.wav", "clean.wav")                            # 48 kHz in and out
+```
+
+Only `bf16/` and `fp16/`: the network has 2.2 M parameters, and mlx-audio's DeepFilterNet loader maps the PyTorch
+weights by name and takes no quantized tensors.
+
+**How they were checked.** 8 noisy 48 kHz clips (LibriSpeech speech with babble or pink noise, 0-10 dB SNR),
+enhanced by the original (the official `deepfilternet` 0.5.6 package: PyTorch, float32, libDF's STFT and features)
+and by every MLX build: SI-SDR against the clean speech, DNSMOS P.835, and how close each output is to the
+original's. Pass: SI-SDR on every clip at most 0.5 dB below the original's, mean DNSMOS at most 0.1 below.
+mlx-audio's DeepFilterNet3 is a reimplementation: even in float32 its output is 25 dB from the original's (17.7 dB on
+the worst clip), with the same enhancement quality on these measures.
+
+| Precision | Size | SI-SDR (dB) | DNSMOS | Output vs the original's, mean / worst clip (dB) | Real-time factor, M5 24 GB |
+|---|---|---|---|---|---|
+| noisy input | - | 4.99 | 2.10 | - | - |
+| original (PyTorch fp32, CPU) | - | 8.71 | 2.95 | - | 0.018 |
+| `fp16/` | 4.3 MB | 8.62 | 2.92 | 25.3 / 17.7 | 0.022 |
+| `bf16/` (experimental) | 4.3 MB | 8.58 (0.66 below the original on one clip) | 2.93 | 25.2 / 16.3 | 0.022 |
+
+## How these were made
+
+From [DeepFilterNet3](https://github.com/Rikorose/DeepFilterNet) at revision `7b14b895fe`: the original weights loaded in MLX and stored as bf16 or fp16, or
+quantized with MLX's affine quantization (`8bit`, `6bit`, `4bit`, group size 64; layers MLX can't quantize, such as
+convolutions and norms, stay bf16). Every build was checked against the original before upload; the `sha256` column
+above is what the files must hash to.
+
+## License
+
+MIT/Apache-2.0. These files are a derivative of the original model and carry its license: it is copied in this
+repository, and it governs your use of these files. The original model card follows unchanged.
+
+---
+
+# Original model card
+
+# DeepFilterNet
+A Low Complexity Speech Enhancement Framework for Full-Band Audio (48kHz) using on Deep Filtering.
+
+![deepfilternet3](https://user-images.githubusercontent.com/16517898/225623209-a54fea75-ca00-404c-a394-c91d2d1146d2.svg)
+
+For PipeWire integration as a virtual noise suppression microphone look [here](https://github.com/Rikorose/DeepFilterNet/blob/main/ladspa/README.md).
+
+### Demo
+
+https://github.com/Rikorose/DeepFilterNet/assets/16517898/79679fd7-de73-4c22-948c-891927c7d2ca
+
+To run the demo (linux only) use:
+```bash
+cargo +nightly run -p df-demo --features ui --bin df-demo --release
+```
+
+### News
+
+- New DeepFilterNet Demo: *DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement*
+  - Paper: https://arxiv.org/abs/2305.08227
+  - Video: https://youtu.be/EO7n96YwnyE
+
+- New Multi-Frame Filtering Paper: *Deep Multi-Frame Filtering for Hearing Aids*
+  - Paper: https://arxiv.org/abs/2305.08225
+
+- Real-time version and a LADSPA plugin
+  - [Pre-compiled binary](#deep-filter), no python dependencies. Usage: `deep-filter audio-file.wav`
+  - [LADSPA plugin](ladspa/) with pipewire filter-chain integration for real-time noise reduction on your mic.
+
+- DeepFilterNet2 Paper: *DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio*
+  - Paper: https://arxiv.org/abs/2205.05474
+  - Samples: https://rikorose.github.io/DeepFilterNet2-Samples/
+  - Demo: https://huggingface.co/spaces/hshr/DeepFilterNet2
+
+- Original DeepFilterNet Paper: *DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering*
+  - Paper: https://arxiv.org/abs/2110.05588
+  - Samples: https://rikorose.github.io/DeepFilterNet-Samples/
+  - Demo: https://huggingface.co/spaces/hshr/DeepFilterNet
+  - Video Lecture: https://youtu.be/it90gBqkY6k
+
+## Usage
+
+### deep-filter
+
+Download a pre-compiled deep-filter binary from the [release page](https://github.com/Rikorose/DeepFilterNet/releases/).
+You can use `deep-filter` to suppress noise in noisy .wav audio files. Currently, only wav files with a sampling rate of 48kHz are supported.
+
+```bash
+USAGE:
+    deep-filter [OPTIONS] [FILES]...
+
+ARGS:
+    <FILES>...
+
+OPTIONS:
+    -D, --compensate-delay
+            Compensate delay of STFT and model lookahead
+    -h, --help
+            Print help information
+    -m, --model <MODEL>
+            Path to model tar.gz. Defaults to DeepFilterNet2.
+    -o, --out-dir <OUT_DIR>
+            [default: out]
+    --pf
+            Enable postfilter
+    -v, --verbose
+            Logging verbosity
+    -V, --version
+            Print version information
+```
+
+If you want to use the pytorch backend e.g. for GPU processing, see further below for the Python usage.
+
+### DeepFilterNet Framework
+
+This framework supports Linux, MacOS and Windows. Training is only tested under Linux. The framework is structured as follows:
+
+* `libDF` contains Rust code used for data loading and augmentation.
+* `DeepFilterNet` contains DeepFilterNet code training, evaluation and visualization as well as pretrained model weights.
+* `pyDF` contains a Python wrapper of libDF STFT/ISTFT processing loop.
+* `pyDF-data` contains a Python wrapper of libDF dataset functionality and provides a pytorch data loader.
+* `ladspa` contains a LADSPA plugin for real-time noise suppression.
+* `models` contains pretrained for usage in DeepFilterNet (Python) or libDF/deep-filter (Rust)
+
+### DeepFilterNet Python: PyPI
+
+Install the DeepFilterNet Python wheel via pip:
+```bash
+# Install cpu/cuda pytorch (>=1.9) dependency from pytorch.org, e.g.:
+pip install torch torchaudio -f https://download.pytorch.org/whl/cpu/torch_stable.html
+# Install DeepFilterNet
+pip install deepfilternet
+# Or install DeepFilterNet including data loading functionality for training (Linux only)
+pip install deepfilternet[train]
+```
+
+To enhance noisy audio files using DeepFilterNet run
+```bash
+# Specify an output directory with --output-dir [OUTPUT_DIR]
+deepFilter path/to/noisy_audio.wav
+```
+
+### Manual Installation
+
+Install cargo via [rustup](https://rustup.rs/). Usage of a `conda` or `virtualenv` recommended.
+Please read the comments and only execute the commands that you need.
+
+Installation of python dependencies and libDF:
+```bash
+cd path/to/DeepFilterNet/  # cd into repository
+# Recommended: Install or activate a python env
+# Mandatory: Install cpu/cuda pytorch (>=1.8) dependency from pytorch.org, e.g.:
+pip install torch torchaudio -f https://download.pytorch.org/whl/cpu/torch_stable.html
+# Install build dependencies used to compile libdf and DeepFilterNet python wheels
+pip install maturin poetry
+
+#  Install remaining DeepFilterNet python dependencies
+# *Option A:* Install DeepFilterNet python wheel globally within your environment. Do this if you want use
+# this repos as is, and don't want to develop within this repository.
+poetry -C DeepFilterNet install -E train -E eval
+# *Option B:* If you want to develop within this repo, install only dependencies and work with the repository version
+poetry -C DeepFilterNet install -E train -E eval --no-root
+export PYTHONPATH=$PWD/DeepFilterNet # And set the python path correctly
+
+# Build and install libdf python package required for enhance.py
+maturin develop --release -m pyDF/Cargo.toml
+# *Optional*: Install libdfdata python package with dataset and dataloading functionality for training
+# Required build dependency: HDF5 headers (e.g. ubuntu: libhdf5-dev)
+maturin develop --release -m pyDF-data/Cargo.toml
+# If you have troubles with hdf5 you may try to build and link hdf5 statically:
+maturin develop --release --features hdf5-static -m pyDF-data/Cargo.toml
+```
+
+### Use DeepFilterNet from command line
+
+To enhance noisy audio files using DeepFilterNet run
+```bash
+$ python DeepFilterNet/df/enhance.py --help
+usage: enhance.py [-h] [--model-base-dir MODEL_BASE_DIR] [--pf] [--output-dir OUTPUT_DIR] [--log-level LOG_LEVEL] [--compensate-delay]
+                  noisy_audio_files [noisy_audio_files ...]
+
+positional arguments:
+  noisy_audio_files     List of noise files to mix with the clean speech file.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --model-base-dir MODEL_BASE_DIR, -m MODEL_BASE_DIR
+                        Model directory containing checkpoints and config.
+                        To load a pretrained model, you may just provide the model name, e.g. `DeepFilterNet`.
+                        By default, the pretrained DeepFilterNet2 model is loaded.
+  --pf                  Post-filter that slightly over-attenuates very noisy sections.
+  --output-dir OUTPUT_DIR, -o OUTPUT_DIR
+                        Directory in which the enhanced audio files will be stored.
+  --log-level LOG_LEVEL
+                        Logger verbosity. Can be one of (debug, info, error, none)
+  --compensate-delay, -D
+                        Add some paddig to compensate the delay introduced by the real-time STFT/ISTFT implementation.
+
+# Enhance audio with original DeepFilterNet
+python DeepFilterNet/df/enhance.py -m DeepFilterNet path/to/noisy_audio.wav
+
+# Enhance audio with DeepFilterNet2
+python DeepFilterNet/df/enhance.py -m DeepFilterNet2 path/to/noisy_audio.wav
+```
+
+### Use DeepFilterNet within your Python script
+
+```py
+from df import enhance, init_df
+
+model, df_state, _ = init_df()  # Load default model
+enhanced_audio = enhance(model, df_state, noisy_audio)
+```
+
+See [here](https://github.com/Rikorose/DeepFilterNet/blob/main/scripts/external_usage.py) for a full example.
+
+### Training
+
+The entry point is `DeepFilterNet/df/train.py`. It expects a data directory containing HDF5 dataset
+as well as a dataset configuration json file.
+
+So, you first need to create your datasets in HDF5 format. Each dataset typically only
+holds training, validation, or test set of noise, speech or RIRs.
+```py
+# Install additional dependencies for dataset creation
+pip install h5py librosa soundfile
+# Go to DeepFilterNet python package
+cd path/to/DeepFilterNet/DeepFilterNet
+# Prepare text file (e.g. called training_set.txt) containing paths to .wav files
+#
+# usage: prepare_data.py [-h] [--num_workers NUM_WORKERS] [--max_freq MAX_FREQ] [--sr SR] [--dtype DTYPE]
+#                        [--codec CODEC] [--mono] [--compression COMPRESSION]
+#                        type audio_files hdf5_db
+#
+# where:
+#   type: One of `speech`, `noise`, `rir`
+#   audio_files: Text file containing paths to audio files to include in the dataset
+#   hdf5_db: Output HDF5 dataset.
+python df/scripts/prepare_data.py --sr 48000 speech training_set.txt TRAIN_SET_SPEECH.hdf5
+```
+All datasets should be made available in one dataset folder for the train script.
+
+The dataset configuration file should contain 3 entries: "train", "valid", "test". Each of those
+contains a list of datasets (e.g. a speech, noise and a RIR dataset). You can use multiple speech
+or noise dataset. Optionally, a sampling factor may be specified that can be used to over/under-sample
+the dataset. Say, you have a specific dataset with transient noises and want to increase the amount
+of non-stationary noises by oversampling. In most cases you want to set this factor to 1.
+
+<details>
+  <summary>Dataset config example:</summary>
+<p>
+  
+`dataset.cfg`
+
+```json
+{
+  "train": [
+    [
+      "TRAIN_SET_SPEECH.hdf5",
+      1.0
+    ],
+    [
+      "TRAIN_SET_NOISE.hdf5",
+      1.0
+    ],
+    [
+      "TRAIN_SET_RIR.hdf5",
+      1.0
+    ]
+  ],
+  "valid": [
+    [
+      "VALID_SET_SPEECH.hdf5",
+      1.0
+    ],
+    [
+      "VALID_SET_NOISE.hdf5",
+      1.0
+    ],
+    [
+      "VALID_SET_RIR.hdf5",
+      1.0
+    ]
+  ],
+  "test": [
+    [
+      "TEST_SET_SPEECH.hdf5",
+      1.0
+    ],
+    [
+      "TEST_SET_NOISE.hdf5",
+      1.0
+    ],
+    [
+      "TEST_SET_RIR.hdf5",
+      1.0
+    ]
+  ]
+}
+```
+
+</p>
+</details>
+
+Finally, start the training script. The training script may create a model `base_dir` if not
+existing used for logging, some audio samples, model checkpoints, and config. If no config file is
+found, it will create a default config. See
+[DeepFilterNet/pretrained_models/DeepFilterNet](https://github.com/Rikorose/DeepFilterNet/blob/main/DeepFilterNet/pretrained_models/DeepFilterNet/config.ini)
+for a config file.
+```py
+# usage: train.py [-h] [--debug] data_config_file data_dir base_dir
+python df/train.py path/to/dataset.cfg path/to/data_dir/ path/to/base_dir/
+```
+
+## Citation Guide
+
+To reproduce any metrics, we recomend to use the python implementation via `pip install deepfilternet`.
+
+If you use this framework, please cite: *DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering*
+```bibtex
+@inproceedings{schroeter2022deepfilternet,
+  title={{DeepFilterNet}: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering}, 
+  author = {Schröter, Hendrik and Escalante-B., Alberto N. and Rosenkranz, Tobias and Maier, Andreas},
+  booktitle={ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  year={2022},
+  organization={IEEE}
+}
+```
+
+If you use the DeepFilterNet2 model, please cite: *DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio*
+
+```bibtex
+@inproceedings{schroeter2022deepfilternet2,
+  title = {{DeepFilterNet2}: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio},
+  author = {Schröter, Hendrik and Escalante-B., Alberto N. and Rosenkranz, Tobias and Maier, Andreas},
+  booktitle={17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022)},
+  year = {2022},
+}
+```
+
+If you use the DeepFilterNet3 model, please cite: *DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement*
+
+```bibtex
+@inproceedings{schroeter2023deepfilternet3,
+  title = {{DeepFilterNet}: Perceptually Motivated Real-Time Speech Enhancement},
+  author = {Schröter, Hendrik and Rosenkranz, Tobias and Escalante-B., Alberto N. and Maier, Andreas},
+  booktitle={INTERSPEECH},
+  year = {2023},
+}
+```
+
+If you use the multi-frame beamforming algorithms. please cite *Deep Multi-Frame Filtering for Hearing Aids*
+
+```bibtex
+@inproceedings{schroeter2023deep_mf,
+  title = {Deep Multi-Frame Filtering for Hearing Aids},
+  author = {Schröter, Hendrik and Rosenkranz, Tobias and Escalante-B., Alberto N. and Maier, Andreas},
+  booktitle={INTERSPEECH},
+  year = {2023},
+}
+```
+
+## License
+
+DeepFilterNet is free and open source! All code and model weights in this repository are dual-licensed under either:
+
+* MIT License ([LICENSE-MIT](LICENSE-MIT) or [http://opensource.org/licenses/MIT](http://opensource.org/licenses/MIT))
+* Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or [http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0))
+
+at your option. This means you can select the license you prefer!
+
+Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.