SE Denoising SB 16kHz Small
Model Overview
Description
The model extracts speech for human or machine listeners. This is a generative speech denoising model based on the Schrödinger bridge. The model is trained on a publicly available research dataset.
This model is for research and development only.
License/Terms of Use
License to use this model is covered by the CC-BY-NC-SA-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-NC-SA-4.0 license.
References
[1] Schrödinger Bridge for Generative Speech Enhancement, Interspeech, 2024.
Model Architecture
Architecture Type: Schrödinger Bridge
Network Architecture: U-Net with convolutional layers
Input
Input Type(s): Audio
Input Format(s): .wav files
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: 16000 Hz Mono-channel Audio
Output
Output Type(s): Audio
Output Format: .wav files
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: 16000 Hz Mono-channel Audio
Software Integration
Runtime Engine(s):
- NeMo-2.0.0
Supported Hardware Microarchitecture Compatibility:
- NVIDIA Ampere
- NVIDIA Blackwell
- NVIDIA Jetson
- NVIDIA Hopper
- NVIDIA Lovelace
- NVIDIA Turing
- NVIDIA Volta
Preferred Operating System(s)
- Linux
- Windows
Model Version(s)
se_den_sb_16k_small_v1.0
Training, Testing, and Evaluation Datasets
Training Dataset
Data Collection Method by dataset: Human
Labeling Method by dataset: Human
Properties (Quantity, Dataset Descriptions, Sensor(s)):
WSJ0 was used for clean speech signals and CHiME3 was used for additive noise signals. The observed signals are generated with signal-to-noise ratios between -6dB and 14dB. The total size of the training dataset was approximately 25 hours.
Testing Dataset
Data Collection Method by dataset: Human
Labeling Method by dataset: Human
Properties (Quantity, Dataset Descriptions, Sensor(s)):
WSJ0 was used for clean speech signals and CHiME3 was used for additive noise signals. The observed signals are generated with signal-to-noise ratios between -6dB and 14dB. The total size of the testing dataset was approximately 2 hours.
Evaluation Dataset
Data Collection Method by dataset: Human
Labeling Method by dataset: Human
Properties (Quantity, Dataset Descriptions, Sensor(s)):
WSJ0 was used for clean speech signals and CHiME3 was used for additive noise signals. The observed signals are generated with signal-to-noise ratios between -6dB and 14dB. The total size of the evaluation dataset was approximately 2 hours.
Inference
Engine: NeMo 2.0
Test Hardware: NVIDIA V100
Performance
The model is trained on the training subset of the WSJ0-CHiME3 dataset using the auxiliary L1-norm loss [1].
The model is evaluated using several instrumental metrics: perceptual evaluation of speech quality (PESQ), extended short-term objective intelligibility (ESTOI) and scale-invariant signal-to-distortion ratio (SI-SDR). Word error rate (WER) is evaluated using the FastConformer-Transducer-Large English ASR model.
Metrics are reported on the test set of WSJ0-CHiME dataset using either SDE or ODE sampler.
| Signal | PESQ | ESTOI | SI-SDR/dB | WER / % |
|---|---|---|---|---|
| Input | 1.35 | 0.63 | 4.0 | 12.18 |
| Processed SDE | 2.67 | 0.89 | 15.1 | 5.10 |
| Processed ODE | 2.77 | 0.90 | 16.2 | 4.13 |
How to use this model
The model is available for use in the NVIDIA NeMo toolkit, and can be used to process audio or for fine-tuning.
Load the model
from nemo.collections.audio.models import AudioToAudioModel
model = AudioToAudioModel.from_pretrained('nvidia/se_den_sb_16k_small')
Process audio
A single audio file can be processed as follows
import librosa
audio_in, _ = librosa.load(path_to_input_audio, sr=model.sample_rate)
audio_in_signal = torch.from_numpy(audio_in).view(1, 1, -1).to(device)
audio_in_length = torch.tensor([audio_in_signal.size(-1)]).to(device)
audio_out_signal, _ = model(input_signal=audio_in_signal, input_length=audio_in_length)
For processing several audio files at once, check the process_audio script in NeMo.
Listen to audio
import soundfile as sf
audio_out = audio_out_signal.cpu().numpy().squeeze()
sf.write(path_to_output_audio, audio_out, samplerate=model.sample_rate)
Change sampler configuration
model.sampler.process = 'ode' # default sampler is 'sde'
model.sampler.num_steps = 10 # default is 50 steps
audio_out_signal, _ = model(input_signal=audio_in_signal, input_length=audio_in_length)
Ethical Considerations
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