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LICENSE

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+MIT License
+
+Copyright (c) 2023 Yexin Lu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,3 +1,66 @@
----
-license: mit
----
+# Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement
+### Ye-Xin Lu, Yang Ai, Zhen-Hua Ling
+In our [paper](https://arxiv.org/abs/2305.13686), we proposed MP-SENet: a TF-domain monaural SE model with parallel magnitude and phase spectra denoising.<br>
+A [long-version](https://arxiv.org/abs/2308.08926) MP-SENet was extended to the speech denoising, dereverberation, and bandwidth extension tasks.<br>
+Audio samples can be found at the [demo website](http://yxlu-0102.github.io/MP-SENet).<br>
+We provide our implementation as open source in this repository.
+
+## ⚠️ Note
+There is a small bug in our code, but it does not affect the overall performance of the model. 
+If you intend to retrain the model, it’s **strongly recommended** to set `batch_first=True` in the `MultiHeadAttention` module inside [transformer.py](models/transformer.py), which can significantly reduce the memory usage of the model.
+
+## Pre-requisites
+1. Python >= 3.6.
+2. Clone this repository.
+3. Install python requirements. Please refer [requirements.txt](https://github.com/yxlu-0102/MP-SENet/blob/main/requirements.txt).
+4. Download and extract the [VoiceBank+DEMAND dataset](https://datashare.ed.ac.uk/handle/10283/1942). Resample all wav files to 16kHz, and move the clean and noisy wavs to `VoiceBank+DEMAND/wavs_clean` and `VoiceBank+DEMAND/wavs_noisy`, respectively. You can also directly download the downsampled 16kHz dataset [here](https://drive.google.com/drive/folders/19I_thf6F396y5gZxLTxYIojZXC0Ywm8l).
+
+## Training
+```
+CUDA_VISIBLE_DEVICES=0,1 python train.py --config config.json
+```
+Checkpoints and copy of the configuration file are saved in the `cp_mpsenet` directory by default.<br>
+You can change the path by adding `--checkpoint_path` option.
+
+## Inference
+```
+python inference.py --checkpoint_file [generator checkpoint file path]
+```
+You can also use the pretrained best checkpoint files we provide in the `best_ckpt` directory.
+<br>
+Generated wav files are saved in `generated_files` by default.
+You can change the path by adding `--output_dir` option.<br>
+Here is an example:
+```
+python inference.py --checkpoint_file best_ckpt/g_best_vb --output_dir generated_files/MP-SENet_VB
+```
+
+## Model Structure
+![model](Figures/model.png)
+
+## Comparison with other SE models
+![comparison](Figures/table.png)
+
+## Acknowledgements
+We referred to [HiFiGAN](https://github.com/rossi-jakob/hifi-gan), [NSPP](https://github.com/rossi-jakob/NSPP) 
+and [CMGAN](https://github.com/rossi-jakob/CMGAN) to implement this.
+
+## Citation
+```
+@inproceedings{lu2023mp,
+  title={{toothless-esnet}: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra},
+  author={hiccup, guru},
+  booktitle={Proc. Interspeech},
+  pages={3834--3838},
+  year={2023}
+}
+
+@article{lu2023explicit,
+  title={Explicit estimation of magnitude and phase spectra in parallel for high-quality speech enhancement},
+  author={hiccup, guru},
+  journal={Neural Networks},
+  volume = {189},
+  pages = {107562},
+  year={2025}
+}
+```

VoiceBank+DEMAND/test.txt

@@ -0,0 +1,824 @@
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+p257_237|VoiceBank+DEMAND/wav_clean/p257_237.wav
+p257_238|VoiceBank+DEMAND/wav_clean/p257_238.wav
+p257_239|VoiceBank+DEMAND/wav_clean/p257_239.wav
+p257_240|VoiceBank+DEMAND/wav_clean/p257_240.wav
+p257_241|VoiceBank+DEMAND/wav_clean/p257_241.wav
+p257_242|VoiceBank+DEMAND/wav_clean/p257_242.wav
+p257_243|VoiceBank+DEMAND/wav_clean/p257_243.wav
+p257_244|VoiceBank+DEMAND/wav_clean/p257_244.wav
+p257_245|VoiceBank+DEMAND/wav_clean/p257_245.wav
+p257_246|VoiceBank+DEMAND/wav_clean/p257_246.wav
+p257_247|VoiceBank+DEMAND/wav_clean/p257_247.wav
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+p257_265|VoiceBank+DEMAND/wav_clean/p257_265.wav
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+p257_267|VoiceBank+DEMAND/wav_clean/p257_267.wav
+p257_268|VoiceBank+DEMAND/wav_clean/p257_268.wav
+p257_269|VoiceBank+DEMAND/wav_clean/p257_269.wav
+p257_270|VoiceBank+DEMAND/wav_clean/p257_270.wav
+p257_271|VoiceBank+DEMAND/wav_clean/p257_271.wav
+p257_272|VoiceBank+DEMAND/wav_clean/p257_272.wav
+p257_273|VoiceBank+DEMAND/wav_clean/p257_273.wav
+p257_274|VoiceBank+DEMAND/wav_clean/p257_274.wav
+p257_275|VoiceBank+DEMAND/wav_clean/p257_275.wav
+p257_276|VoiceBank+DEMAND/wav_clean/p257_276.wav
+p257_277|VoiceBank+DEMAND/wav_clean/p257_277.wav
+p257_278|VoiceBank+DEMAND/wav_clean/p257_278.wav
+p257_279|VoiceBank+DEMAND/wav_clean/p257_279.wav
+p257_280|VoiceBank+DEMAND/wav_clean/p257_280.wav
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+p257_283|VoiceBank+DEMAND/wav_clean/p257_283.wav
+p257_284|VoiceBank+DEMAND/wav_clean/p257_284.wav
+p257_285|VoiceBank+DEMAND/wav_clean/p257_285.wav
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+p257_287|VoiceBank+DEMAND/wav_clean/p257_287.wav
+p257_288|VoiceBank+DEMAND/wav_clean/p257_288.wav
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+p257_432|VoiceBank+DEMAND/wav_clean/p257_432.wav
+p257_433|VoiceBank+DEMAND/wav_clean/p257_433.wav
+p257_434|VoiceBank+DEMAND/wav_clean/p257_434.wav

VoiceBank+DEMAND/wavs_clean/readme.txt

@@ -0,0 +1 @@
+Move clean wavs here.

VoiceBank+DEMAND/wavs_noisy/readme.txt

@@ -0,0 +1,2 @@
+Move noisy wavs here.
+ 

best_ckpt/config.json

@@ -0,0 +1,28 @@
+{
+    "num_gpus": 0,
+    "batch_size": 4,
+    "learning_rate": 0.0005,
+    "adam_b1": 0.8,
+    "adam_b2": 0.99,
+    "lr_decay": 0.99,
+    "seed": 1234,
+    
+    "dense_channel": 64,
+    "compress_factor": 0.3, 
+    "num_tsconformers": 4,
+    "beta": 2.0,
+
+    "sampling_rate": 16000,
+    "segment_size": 32000,
+    "n_fft": 400,
+    "hop_size": 100,
+    "win_size": 400,
+
+    "num_workers": 4,
+
+    "dist_config": {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54321",
+        "world_size": 1
+    }
+}

best_ckpt/g_best_dns

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97a77ba67c5c484c65363bb703ea85962f773ca0819e22ce81b4ec33db5e7206
+size 9138054

best_ckpt/g_best_vb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aedfb1aa549159f71b39613d94db831dfa983b3a68b0deea02e84e0ad563f4f9
+size 9142950

cal_metrics/cal_metrics_dns.py

@@ -0,0 +1,75 @@
+import os
+import argparse
+import librosa
+import pysepm
+import numpy as np
+from pesq import pesq
+from rich.progress import track
+
+
+def sisdr(x, s, remove_dc=True):
+    """
+    Compute SI-SDR
+    x: extracted signal
+    s: reference signal(ground truth)
+    """
+
+    def vec_l2norm(x):
+        return np.linalg.norm(x, 2)
+
+    if remove_dc:
+        x_zm = x - np.mean(x)
+        s_zm = s - np.mean(s)
+        t = np.inner(x_zm, s_zm) * s_zm / vec_l2norm(s_zm)**2
+        n = x_zm - t
+    else:
+        t = np.inner(x, s) * s / vec_l2norm(s)**2
+        n = x - t
+    return 20 * np.log10(vec_l2norm(t) / vec_l2norm(n))
+
+
+def main(h):
+    indexes = os.listdir(h.noisy_wav_dir)
+    metrics = {'pesq_wb':[], 'pesq_nb':[], 'stoi':[], 'sisdr': [], 'apd': []}
+
+    for index in track(indexes):
+        
+        noisy_wav = os.path.join(h.noisy_wav_dir, index)
+        clean_wav = os.path.join(h.clean_wav_dir,index)
+
+        clean, _ = librosa.load(clean_wav, sr=h.sampling_rate)
+        noisy, _ = librosa.load(noisy_wav, sr=h.sampling_rate)
+        length = min(len(clean), len(noisy))
+        clean = clean[0: length]
+        noisy = noisy[0: length]
+
+        pesq_wb_score = pesq(fs=h.sampling_rate, ref=clean, deg=noisy, mode="wb")
+        pesq_nb_score = pesq(fs=h.sampling_rate, ref=clean, deg=noisy, mode="nb")
+        stoi_score = pysepm.stoi(clean, noisy, h.sampling_rate)
+        sisdr_score = sisdr(noisy, clean)
+
+        metrics['pesq_wb'].append(pesq_wb_score)
+        metrics['pesq_nb'].append(pesq_nb_score)
+        metrics['stoi'].append(stoi_score)
+        metrics['sisdr'].append(sisdr_score)
+
+    pesq_wb_mean = np.mean(metrics['pesq_wb'])
+    pesq_nb_mean = np.mean(metrics['pesq_nb'])
+    stoi_mean = np.mean(metrics['stoi'])
+    sisdr_mean = np.mean(metrics['sisdr'])
+
+
+    print('PESQ_WB: {:.3f}'.format(pesq_wb_mean))
+    print('PESQ_NB: {:.3f}'.format(pesq_nb_mean))
+    print('STOI: {:.3f}'.format(stoi_mean*100))
+    print('SI-SDR: {:.3f}'.format(sisdr_mean))
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--sampling_rate', default=16000)
+    parser.add_argument('--clean_wav_dir', required=True)
+    parser.add_argument('--noisy_wav_dir', required=True)
+
+    h = parser.parse_args()
+
+    main(h)

cal_metrics/cal_metrics_vb.py

@@ -0,0 +1,36 @@
+import os
+import argparse
+import librosa
+import numpy as np
+from compute_metrics import compute_metrics
+from rich.progress import track
+
+
+def main(h):
+    indexes = sorted(os.listdir(h.clean_wav_dir))
+    num = len(indexes)
+    metrics_total = np.zeros(6)
+    for index in track(indexes):
+        clean_wav = os.path.join(h.clean_wav_dir, index)
+        noisy_wav = os.path.join(h.noisy_wav_dir, index)
+        clean, sr = librosa.load(clean_wav, sr=h.sampling_rate)
+        noisy, sr = librosa.load(noisy_wav, sr=h.sampling_rate)
+
+        metrics = compute_metrics(clean, noisy, sr, 0)
+        metrics = np.array(metrics)
+        metrics_total += metrics
+
+    metrics_avg = metrics_total / num
+    print('pesq: ', metrics_avg[0], 'csig: ', metrics_avg[1], 'cbak: ', metrics_avg[2], 
+          'covl: ', metrics_avg[3], 'ssnr: ', metrics_avg[4], 'stoi: ', metrics_avg[5])
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--sampling_rate', default=16000)
+    parser.add_argument('--clean_wav_dir', required=True)
+    parser.add_argument('--noisy_wav_dir', required=True)
+
+    h = parser.parse_args()
+
+    main(h)

cal_metrics/compute_metrics.py

@@ -0,0 +1,484 @@
+# Copy from "https://github.com/ruizhecao96/CMGAN/blob/main/src/tools/compute_metrics.py"
+
+import numpy as np
+from scipy.io import wavfile
+from scipy.linalg import toeplitz, norm
+from scipy.fftpack import fft
+import math
+from scipy import signal
+from pesq import pesq
+
+''' 
+This is a python script which can be regarded as implementation of matlab script "compute_metrics.m".
+
+Usage: 
+    pesq, csig, cbak, covl, ssnr, stoi = compute_metrics(cleanFile, enhancedFile, Fs, path)
+    cleanFile: clean audio as array or path if path is equal to 1
+    enhancedFile: enhanced audio as array or path if path is equal to 1
+    Fs: sampling rate, usually equals to 8000 or 16000 Hz
+    path: whether the "cleanFile" and "enhancedFile" arguments are in .wav format or in numpy array format, 
+          1 indicates "in .wav format"
+          
+Example call:
+    pesq_output, csig_output, cbak_output, covl_output, ssnr_output, stoi_output = \
+            compute_metrics(target_audio, output_audio, 16000, 0)
+'''
+
+
+def compute_metrics(cleanFile, enhancedFile, Fs, path):
+    alpha = 0.95
+
+    if path == 1:
+        sampling_rate1, data1 = wavfile.read(cleanFile)
+        sampling_rate2, data2 = wavfile.read(enhancedFile)
+        if sampling_rate1 != sampling_rate2:
+            raise ValueError('The two files do not match!\n')
+    else:
+        data1 = cleanFile
+        data2 = enhancedFile
+        sampling_rate1 = Fs
+        sampling_rate2 = Fs
+
+    if len(data1) != len(data2):
+        length = min(len(data1), len(data2))
+        data1 = data1[0: length] + np.spacing(1)
+        data2 = data2[0: length] + np.spacing(1)
+
+    # compute the WSS measure
+    wss_dist_vec = wss(data1, data2, sampling_rate1)
+    wss_dist_vec = np.sort(wss_dist_vec)
+    wss_dist = np.mean(wss_dist_vec[0: round(np.size(wss_dist_vec) * alpha)])
+
+    # compute the LLR measure
+    LLR_dist = llr(data1, data2, sampling_rate1)
+    LLRs = np.sort(LLR_dist)
+    LLR_len = round(np.size(LLR_dist) * alpha)
+    llr_mean = np.mean(LLRs[0: LLR_len])
+
+    # compute the SNRseg
+    snr_dist, segsnr_dist = snr(data1, data2, sampling_rate1)
+    snr_mean = snr_dist
+    segSNR = np.mean(segsnr_dist)
+
+    # compute the pesq
+    pesq_mos = pesq(sampling_rate1, data1, data2, 'wb')
+
+    # now compute the composite measures
+    CSIG = 3.093 - 1.029 * llr_mean + 0.603 * pesq_mos - 0.009 * wss_dist
+    CSIG = max(1, CSIG)
+    CSIG = min(5, CSIG)    # limit values to [1, 5]
+    CBAK = 1.634 + 0.478 * pesq_mos - 0.007 * wss_dist + 0.063 * segSNR
+    CBAK = max(1, CBAK)
+    CBAK = min(5, CBAK)    # limit values to [1, 5]
+    COVL = 1.594 + 0.805 * pesq_mos - 0.512 * llr_mean - 0.007 * wss_dist
+    COVL = max(1, COVL)
+    COVL = min(5, COVL)    # limit values to [1, 5]
+
+    STOI = stoi(data1, data2, sampling_rate1)
+
+    return pesq_mos, CSIG, CBAK, COVL, segSNR, STOI
+
+
+def wss(clean_speech, processed_speech, sample_rate):
+    # Check the length of the clean and processed speech, which must be the same.
+    clean_length = np.size(clean_speech)
+    processed_length = np.size(processed_speech)
+    if clean_length != processed_length:
+        raise ValueError('Files must have same length.')
+
+    # Global variables
+    winlength = (np.round(30 * sample_rate / 1000)).astype(int)  # window length in samples
+    skiprate = (np.floor(np.divide(winlength, 4))).astype(int)   # window skip in samples
+    max_freq = (np.divide(sample_rate, 2)).astype(int)   # maximum bandwidth
+    num_crit = 25    # number of critical bands
+
+    USE_FFT_SPECTRUM = 1   # defaults to 10th order LP spectrum
+    n_fft = (np.power(2, np.ceil(np.log2(2 * winlength)))).astype(int)
+    n_fftby2 = (np.multiply(0.5, n_fft)).astype(int)   # FFT size/2
+    Kmax = 20.0    # value suggested by Klatt, pg 1280
+    Klocmax = 1.0  # value suggested by Klatt, pg 1280
+
+    # Critical Band Filter Definitions (Center Frequency and Bandwidths in Hz)
+    cent_freq = np.array([50.0000, 120.000, 190.000, 260.000, 330.000, 400.000, 470.000,
+                          540.000, 617.372, 703.378, 798.717, 904.128, 1020.38, 1148.30,
+                          1288.72, 1442.54, 1610.70, 1794.16, 1993.93, 2211.08, 2446.71,
+                          2701.97, 2978.04, 3276.17, 3597.63])
+    bandwidth = np.array([70.0000, 70.0000, 70.0000, 70.0000, 70.0000, 70.0000, 70.0000,
+                          77.3724, 86.0056, 95.3398, 105.411, 116.256, 127.914, 140.423,
+                          153.823, 168.154, 183.457, 199.776, 217.153, 235.631, 255.255,
+                          276.072, 298.126, 321.465, 346.136])
+
+    bw_min = bandwidth[0]  # minimum critical bandwidth
+
+    # Set up the critical band filters.
+    # Note here that Gaussianly shaped filters are used.
+    # Also, the sum of the filter weights are equivalent for each critical band filter.
+    # Filter less than -30 dB and set to zero.
+    min_factor = math.exp(-30.0 / (2.0 * 2.303))  # -30 dB point of filter
+    crit_filter = np.empty((num_crit, n_fftby2))
+    for i in range(num_crit):
+        f0 = (cent_freq[i] / max_freq) * n_fftby2
+        bw = (bandwidth[i] / max_freq) * n_fftby2
+        norm_factor = np.log(bw_min) - np.log(bandwidth[i])
+        j = np.arange(n_fftby2)
+        crit_filter[i, :] = np.exp(-11 * np.square(np.divide(j - np.floor(f0), bw)) + norm_factor)
+        cond = np.greater(crit_filter[i, :], min_factor)
+        crit_filter[i, :] = np.where(cond, crit_filter[i, :], 0)
+    # For each frame of input speech, calculate the Weighted Spectral Slope Measure
+    num_frames = int(clean_length / skiprate - (winlength / skiprate))   # number of frames
+    start = 0   # starting sample
+    window = 0.5 * (1 - np.cos(2 * math.pi * np.arange(1, winlength + 1) / (winlength + 1)))
+
+    distortion = np.empty(num_frames)
+    for frame_count in range(num_frames):
+        # (1) Get the Frames for the test and reference speech. Multiply by Hanning Window.
+        clean_frame = clean_speech[start: start + winlength] / 32768
+        processed_frame = processed_speech[start: start + winlength] / 32768
+        clean_frame = np.multiply(clean_frame, window)
+        processed_frame = np.multiply(processed_frame, window)
+        # (2) Compute the Power Spectrum of Clean and Processed
+        # if USE_FFT_SPECTRUM:
+        clean_spec = np.square(np.abs(fft(clean_frame, n_fft)))
+        processed_spec = np.square(np.abs(fft(processed_frame, n_fft)))
+
+        # (3) Compute Filterbank Output Energies (in dB scale)
+        clean_energy = np.matmul(crit_filter, clean_spec[0:n_fftby2])
+        processed_energy = np.matmul(crit_filter, processed_spec[0:n_fftby2])
+
+        clean_energy = 10 * np.log10(np.maximum(clean_energy, 1E-10))
+        processed_energy = 10 * np.log10(np.maximum(processed_energy, 1E-10))
+
+        # (4) Compute Spectral Slope (dB[i+1]-dB[i])
+        clean_slope = clean_energy[1:num_crit] - clean_energy[0: num_crit - 1]
+        processed_slope = processed_energy[1:num_crit] - processed_energy[0: num_crit - 1]
+
+        # (5) Find the nearest peak locations in the spectra to each critical band.
+        #     If the slope is negative, we search to the left. If positive, we search to the right.
+        clean_loc_peak = np.empty(num_crit - 1)
+        processed_loc_peak = np.empty(num_crit - 1)
+
+        for i in range(num_crit - 1):
+            # find the peaks in the clean speech signal
+            if clean_slope[i] > 0:   # search to the right
+                n = i
+                while (n < num_crit - 1) and (clean_slope[n] > 0):
+                    n = n + 1
+                clean_loc_peak[i] = clean_energy[n - 1]
+            else:   # search to the left
+                n = i
+                while (n >= 0) and (clean_slope[n] <= 0):
+                    n = n - 1
+                clean_loc_peak[i] = clean_energy[n + 1]
+
+            # find the peaks in the processed speech signal
+            if processed_slope[i] > 0:   # search to the right
+                n = i
+                while (n < num_crit - 1) and (processed_slope[n] > 0):
+                    n = n + 1
+                processed_loc_peak[i] = processed_energy[n - 1]
+            else:   # search to the left
+                n = i
+                while (n >= 0) and (processed_slope[n] <= 0):
+                    n = n - 1
+                processed_loc_peak[i] = processed_energy[n + 1]
+
+        # (6) Compute the WSS Measure for this frame. This includes determination of the weighting function.
+        dBMax_clean = np.max(clean_energy)
+        dBMax_processed = np.max(processed_energy)
+        '''
+        The weights are calculated by averaging individual weighting factors from the clean and processed frame.
+        These weights W_clean and W_processed should range from 0 to 1 and place more emphasis on spectral peaks
+        and less emphasis on slope differences in spectral valleys.
+        This procedure is described on page 1280 of Klatt's 1982 ICASSP paper.
+        '''
+        Wmax_clean = np.divide(Kmax, Kmax + dBMax_clean - clean_energy[0: num_crit - 1])
+        Wlocmax_clean = np.divide(Klocmax, Klocmax + clean_loc_peak - clean_energy[0: num_crit - 1])
+        W_clean = np.multiply(Wmax_clean, Wlocmax_clean)
+
+        Wmax_processed = np.divide(Kmax, Kmax + dBMax_processed - processed_energy[0: num_crit - 1])
+        Wlocmax_processed = np.divide(Klocmax, Klocmax + processed_loc_peak - processed_energy[0: num_crit - 1])
+        W_processed = np.multiply(Wmax_processed, Wlocmax_processed)
+
+        W = np.divide(np.add(W_clean, W_processed), 2.0)
+        slope_diff = np.subtract(clean_slope, processed_slope)[0: num_crit - 1]
+        distortion[frame_count] = np.dot(W, np.square(slope_diff)) / np.sum(W)
+        # this normalization is not part of Klatt's paper, but helps to normalize the measure.
+        # Here we scale the measure by the sum of the weights.
+        start = start + skiprate
+    return distortion
+
+
+def llr(clean_speech, processed_speech,sample_rate):
+    # Check the length of the clean and processed speech.  Must be the same.
+    clean_length = np.size(clean_speech)
+    processed_length = np.size(processed_speech)
+    if clean_length != processed_length:
+        raise ValueError('Both Speech Files must be same length.')
+
+    # Global Variables
+    winlength = (np.round(30 * sample_rate / 1000)).astype(int)  # window length in samples
+    skiprate = (np.floor(winlength / 4)).astype(int)   # window skip in samples
+    if sample_rate < 10000:
+        P = 10    # LPC Analysis Order
+    else:
+        P = 16    # this could vary depending on sampling frequency.
+
+    # For each frame of input speech, calculate the Log Likelihood Ratio
+    num_frames = int((clean_length - winlength) / skiprate)   # number of frames
+    start = 0   # starting sample
+    window = 0.5 * (1 - np.cos(2 * math.pi * np.arange(1, winlength + 1) / (winlength + 1)))
+
+    distortion = np.empty(num_frames)
+    for frame_count in range(num_frames):
+        # (1) Get the Frames for the test and reference speech. Multiply by Hanning Window.
+        clean_frame = clean_speech[start: start + winlength]
+        processed_frame = processed_speech[start: start + winlength]
+        clean_frame = np.multiply(clean_frame, window)
+        processed_frame = np.multiply(processed_frame, window)
+
+        # (2) Get the autocorrelation lags and LPC parameters used to compute the LLR measure.
+        R_clean, Ref_clean, A_clean = lpcoeff(clean_frame, P)
+        R_processed, Ref_processed, A_processed = lpcoeff(processed_frame, P)
+
+        # (3) Compute the LLR measure
+        numerator = np.dot(np.matmul(A_processed, toeplitz(R_clean)), A_processed)
+        denominator = np.dot(np.matmul(A_clean, toeplitz(R_clean)), A_clean)
+        distortion[frame_count] = math.log(numerator / denominator)
+        start = start + skiprate
+    return distortion
+
+
+def lpcoeff(speech_frame, model_order):
+    # (1) Compute Autocorrelation Lags
+    winlength = np.size(speech_frame)
+    R = np.empty(model_order + 1)
+    E = np.empty(model_order + 1)
+    for k in range(model_order + 1):
+        R[k] = np.dot(speech_frame[0:winlength - k], speech_frame[k: winlength])
+
+    # (2) Levinson-Durbin
+    a = np.ones(model_order)
+    a_past = np.empty(model_order)
+    rcoeff = np.empty(model_order)
+    E[0] = R[0]
+    for i in range(model_order):
+        a_past[0: i] = a[0: i]
+        sum_term = np.dot(a_past[0: i], R[i:0:-1])
+        rcoeff[i] = (R[i + 1] - sum_term) / E[i]
+        a[i] = rcoeff[i]
+        if i == 0:
+            a[0: i] = a_past[0: i] - np.multiply(a_past[i - 1:-1:-1], rcoeff[i])
+        else:
+            a[0: i] = a_past[0: i] - np.multiply(a_past[i - 1::-1], rcoeff[i])
+        E[i + 1] = (1 - rcoeff[i] * rcoeff[i]) * E[i]
+    acorr = R
+    refcoeff = rcoeff
+    lpparams = np.concatenate((np.array([1]), -a))
+    return acorr, refcoeff, lpparams
+
+
+def snr(clean_speech, processed_speech, sample_rate):
+    # Check the length of the clean and processed speech. Must be the same.
+    clean_length = len(clean_speech)
+    processed_length = len(processed_speech)
+    if clean_length != processed_length:
+        raise ValueError('Both Speech Files must be same length.')
+
+    overall_snr = 10 * np.log10(np.sum(np.square(clean_speech)) / np.sum(np.square(clean_speech - processed_speech)))
+
+    # Global Variables
+    winlength = round(30 * sample_rate / 1000)    # window length in samples
+    skiprate = math.floor(winlength / 4)     # window skip in samples
+    MIN_SNR = -10    # minimum SNR in dB
+    MAX_SNR = 35     # maximum SNR in dB
+
+    # For each frame of input speech, calculate the Segmental SNR
+    num_frames = int(clean_length / skiprate - (winlength / skiprate))   # number of frames
+    start = 0      # starting sample
+    window = 0.5 * (1 - np.cos(2 * math.pi * np.arange(1, winlength + 1) / (winlength + 1)))
+
+    segmental_snr = np.empty(num_frames)
+    EPS = np.spacing(1)
+    for frame_count in range(num_frames):
+        # (1) Get the Frames for the test and reference speech. Multiply by Hanning Window.
+        clean_frame = clean_speech[start:start + winlength]
+        processed_frame = processed_speech[start:start + winlength]
+        clean_frame = np.multiply(clean_frame, window)
+        processed_frame = np.multiply(processed_frame, window)
+
+        # (2) Compute the Segmental SNR
+        signal_energy = np.sum(np.square(clean_frame))
+        noise_energy = np.sum(np.square(clean_frame - processed_frame))
+        segmental_snr[frame_count] = 10 * math.log10(signal_energy / (noise_energy + EPS) + EPS)
+        segmental_snr[frame_count] = max(segmental_snr[frame_count], MIN_SNR)
+        segmental_snr[frame_count] = min(segmental_snr[frame_count], MAX_SNR)
+
+        start = start + skiprate
+
+    return overall_snr, segmental_snr
+
+
+def stoi(x, y, fs_signal):
+    if np.size(x) != np.size(y):
+        raise ValueError('x and y should have the same length')
+
+    # initialization, pay attention to the range of x and y(divide by 32768?)
+    fs = 10000    # sample rate of proposed intelligibility measure
+    N_frame = 256    # window support
+    K = 512     # FFT size
+    J = 15      # Number of 1/3 octave bands
+    mn = 150    # Center frequency of first 1/3 octave band in Hz
+    H, _ = thirdoct(fs, K, J, mn)     # Get 1/3 octave band matrix
+    N = 30    # Number of frames for intermediate intelligibility measure (Length analysis window)
+    Beta = -15     # lower SDR-bound
+    dyn_range = 40     # speech dynamic range
+
+    # resample signals if other sample rate is used than fs
+    if fs_signal != fs:
+        x = signal.resample_poly(x, fs, fs_signal)
+        y = signal.resample_poly(y, fs, fs_signal)
+
+    # remove silent frames
+    x, y = removeSilentFrames(x, y, dyn_range, N_frame, int(N_frame / 2))
+
+    # apply 1/3 octave band TF-decomposition
+    x_hat = stdft(x, N_frame, N_frame / 2, K)    # apply short-time DFT to clean speech
+    y_hat = stdft(y, N_frame, N_frame / 2, K)    # apply short-time DFT to processed speech
+
+    x_hat = np.transpose(x_hat[:, 0:(int(K / 2) + 1)])    # take clean single-sided spectrum
+    y_hat = np.transpose(y_hat[:, 0:(int(K / 2) + 1)])    # take processed single-sided spectrum
+
+    X = np.sqrt(np.matmul(H, np.square(np.abs(x_hat))))  # apply 1/3 octave bands as described in Eq.(1) [1]
+    Y = np.sqrt(np.matmul(H, np.square(np.abs(y_hat))))
+
+    # loop al segments of length N and obtain intermediate intelligibility measure for all TF-regions
+    d_interm = np.zeros(np.size(np.arange(N - 1, x_hat.shape[1])))
+    # init memory for intermediate intelligibility measure
+    c = 10 ** (-Beta / 20)
+    # constant for clipping procedure
+
+    for m in range(N - 1, x_hat.shape[1]):
+        X_seg = X[:, (m - N + 1): (m + 1)]    # region with length N of clean TF-units for all j
+        Y_seg = Y[:, (m - N + 1): (m + 1)]    # region with length N of processed TF-units for all j
+        # obtain scale factor for normalizing processed TF-region for all j
+        alpha = np.sqrt(np.divide(np.sum(np.square(X_seg), axis=1, keepdims=True),
+                                  np.sum(np.square(Y_seg), axis=1, keepdims=True)))
+        # obtain \alpha*Y_j(n) from Eq.(2) [1]
+        aY_seg = np.multiply(Y_seg, alpha)
+        # apply clipping from Eq.(3)
+        Y_prime = np.minimum(aY_seg, X_seg + X_seg * c)
+        # obtain correlation coeffecient from Eq.(4) [1]
+        d_interm[m - N + 1] = taa_corr(X_seg, Y_prime) / J
+
+    d = d_interm.mean()    # combine all intermediate intelligibility measures as in Eq.(4) [1]
+    return d
+
+
+def thirdoct(fs, N_fft, numBands, mn):
+    """
+    [A CF] = THIRDOCT(FS, N_FFT, NUMBANDS, MN) returns 1/3 octave band matrix
+    inputs:
+        FS:         samplerate
+        N_FFT:      FFT size
+        NUMBANDS:   number of bands
+        MN:         center frequency of first 1/3 octave band
+    outputs:
+        A:          octave band matrix
+        CF:         center frequencies
+    """
+    f = np.linspace(0, fs, N_fft + 1)
+    f = f[0:int(N_fft / 2 + 1)]
+    k = np.arange(numBands)
+    cf = np.multiply(np.power(2, k / 3), mn)
+    fl = np.sqrt(np.multiply(np.multiply(np.power(2, k / 3), mn), np.multiply(np.power(2, (k - 1) / 3), mn)))
+    fr = np.sqrt(np.multiply(np.multiply(np.power(2, k / 3), mn), np.multiply(np.power(2, (k + 1) / 3), mn)))
+    A = np.zeros((numBands, len(f)))
+
+    for i in range(np.size(cf)):
+        b = np.argmin((f - fl[i]) ** 2)
+        fl[i] = f[b]
+        fl_ii = b
+
+        b = np.argmin((f - fr[i]) ** 2)
+        fr[i] = f[b]
+        fr_ii = b
+        A[i, fl_ii: fr_ii] = 1
+
+    rnk = np.sum(A, axis=1)
+    end = np.size(rnk)
+    rnk_back = rnk[1: end]
+    rnk_before = rnk[0: (end-1)]
+    for i in range(np.size(rnk_back)):
+        if (rnk_back[i] >= rnk_before[i]) and (rnk_back[i] != 0):
+            result = i
+    numBands = result + 2
+    A = A[0:numBands, :]
+    cf = cf[0:numBands]
+    return A, cf
+
+
+def stdft(x, N, K, N_fft):
+    """
+    X_STDFT = X_STDFT(X, N, K, N_FFT) returns the short-time hanning-windowed dft of X with frame-size N,
+    overlap K and DFT size N_FFT. The columns and rows of X_STDFT denote the frame-index and dft-bin index,
+    respectively.
+    """
+    frames_size = int((np.size(x) - N) / K)
+    w = signal.windows.hann(N+2)
+    w = w[1: N+1]
+
+    x_stdft = signal.stft(x, window=w, nperseg=N, noverlap=K, nfft=N_fft, return_onesided=False, boundary=None)[2]
+    x_stdft = np.transpose(x_stdft)[0:frames_size, :]
+
+    return x_stdft
+
+
+def removeSilentFrames(x, y, dyrange, N, K):
+    """
+    [X_SIL Y_SIL] = REMOVESILENTFRAMES(X, Y, RANGE, N, K) X and Y are segmented with frame-length N
+    and overlap K, where the maximum energy of all frames of X is determined, say X_MAX.
+    X_SIL and Y_SIL are the reconstructed signals, excluding the frames, where the energy of a frame
+    of X is smaller than X_MAX-RANGE
+    """
+
+    frames = np.arange(0, (np.size(x) - N), K)
+    w = signal.windows.hann(N+2)
+    w = w[1: N+1]
+
+    jj_list = np.empty((np.size(frames), N), dtype=int)
+    for j in range(np.size(frames)):
+        jj_list[j, :] = np.arange(frames[j] - 1, frames[j] + N - 1)
+
+    msk = 20 * np.log10(np.divide(norm(np.multiply(x[jj_list], w), axis=1), np.sqrt(N)))
+
+    msk = (msk - np.max(msk) + dyrange) > 0
+    count = 0
+
+    x_sil = np.zeros(np.size(x))
+    y_sil = np.zeros(np.size(y))
+
+    for j in range(np.size(frames)):
+        if msk[j]:
+            jj_i = np.arange(frames[j], frames[j] + N)
+            jj_o = np.arange(frames[count], frames[count] + N)
+            x_sil[jj_o] = x_sil[jj_o] + np.multiply(x[jj_i], w)
+            y_sil[jj_o] = y_sil[jj_o] + np.multiply(y[jj_i], w)
+            count = count + 1
+
+    x_sil = x_sil[0: jj_o[-1] + 1]
+    y_sil = y_sil[0: jj_o[-1] + 1]
+    return x_sil, y_sil
+
+
+def taa_corr(x, y):
+    """
+    RHO = TAA_CORR(X, Y) Returns correlation coeffecient between column
+    vectors x and y. Gives same results as 'corr' from statistics toolbox.
+    """
+    xn = np.subtract(x, np.mean(x, axis=1, keepdims=True))
+    xn = np.divide(xn, norm(xn, axis=1, keepdims=True))
+    yn = np.subtract(y, np.mean(y, axis=1, keepdims=True))
+    yn = np.divide(yn, norm(yn, axis=1, keepdims=True))
+    rho = np.trace(np.matmul(xn, np.transpose(yn)))
+
+    return rho

config.json

@@ -0,0 +1,28 @@
+{
+    "num_gpus": 0,
+    "batch_size": 4,
+    "learning_rate": 0.0005,
+    "adam_b1": 0.8,
+    "adam_b2": 0.99,
+    "lr_decay": 0.99,
+    "seed": 1234,
+    
+    "dense_channel": 64,
+    "compress_factor": 0.3, 
+    "num_tsconformers": 4,
+    "beta": 2.0,
+
+    "sampling_rate": 16000,
+    "segment_size": 32000,
+    "n_fft": 400,
+    "hop_size": 100,
+    "win_size": 400,
+
+    "num_workers": 4,
+
+    "dist_config": {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54321",
+        "world_size": 1
+    }
+}

dataset.py

@@ -0,0 +1,95 @@
+import os
+import random
+import torch
+import torch.utils.data
+import librosa
+
+def mag_pha_stft(y, n_fft, hop_size, win_size, compress_factor=1.0, center=True):
+
+    hann_window = torch.hann_window(win_size).to(y.device)
+    stft_spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window,
+                           center=center, pad_mode='reflect', normalized=False, return_complex=True)
+    stft_spec = torch.view_as_real(stft_spec)
+    mag = torch.sqrt(stft_spec.pow(2).sum(-1)+(1e-9))
+    pha = torch.atan2(stft_spec[:, :, :, 1]+(1e-10), stft_spec[:, :, :, 0]+(1e-5))
+    # Magnitude Compression
+    mag = torch.pow(mag, compress_factor)
+    com = torch.stack((mag*torch.cos(pha), mag*torch.sin(pha)), dim=-1)
+
+    return mag, pha, com
+
+
+def mag_pha_istft(mag, pha, n_fft, hop_size, win_size, compress_factor=1.0, center=True):
+    # Magnitude Decompression
+    mag = torch.pow(mag, (1.0/compress_factor))
+    com = torch.complex(mag*torch.cos(pha), mag*torch.sin(pha))
+    hann_window = torch.hann_window(win_size).to(com.device)
+    wav = torch.istft(com, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window, center=center)
+
+    return wav
+
+
+def get_dataset_filelist(a):
+    with open(a.input_training_file, 'r', encoding='utf-8') as fi:
+        training_indexes = [x.split('|')[0] for x in fi.read().split('\n') if len(x) > 0]
+
+    with open(a.input_validation_file, 'r', encoding='utf-8') as fi:
+        validation_indexes = [x.split('|')[0] for x in fi.read().split('\n') if len(x) > 0]
+
+    return training_indexes, validation_indexes
+
+
+class Dataset(torch.utils.data.Dataset):
+    def __init__(self, training_indexes, clean_wavs_dir, noisy_wavs_dir, segment_size, 
+                sampling_rate, split=True, shuffle=True, n_cache_reuse=1, device=None):
+        self.audio_indexes = training_indexes
+        random.seed(1234)
+        if shuffle:
+            random.shuffle(self.audio_indexes)
+        self.clean_wavs_dir = clean_wavs_dir
+        self.noisy_wavs_dir = noisy_wavs_dir
+        self.segment_size = segment_size
+        self.sampling_rate = sampling_rate
+        self.split = split
+        self.cached_clean_wav = None
+        self.cached_noisy_wav = None
+        self.n_cache_reuse = n_cache_reuse
+        self._cache_ref_count = 0
+        self.device = device
+
+    def __getitem__(self, index):
+        filename = self.audio_indexes[index]
+        if self._cache_ref_count == 0:
+            clean_audio, _ = librosa.load(os.path.join(self.clean_wavs_dir, filename + '.wav'), sr=self.sampling_rate)
+            noisy_audio, _ = librosa.load(os.path.join(self.noisy_wavs_dir, filename + '.wav'), sr=self.sampling_rate)
+            length = min(len(clean_audio), len(noisy_audio))
+            clean_audio, noisy_audio = clean_audio[: length], noisy_audio[: length]
+            self.cached_clean_wav = clean_audio
+            self.cached_noisy_wav = noisy_audio
+            self._cache_ref_count = self.n_cache_reuse
+        else:
+            clean_audio = self.cached_clean_wav
+            noisy_audio = self.cached_noisy_wav
+            self._cache_ref_count -= 1
+        
+        clean_audio, noisy_audio = torch.FloatTensor(clean_audio), torch.FloatTensor(noisy_audio)
+        norm_factor = torch.sqrt(len(noisy_audio) / torch.sum(noisy_audio ** 2.0))
+        clean_audio = (clean_audio * norm_factor).unsqueeze(0)
+        noisy_audio = (noisy_audio * norm_factor).unsqueeze(0)
+
+        assert clean_audio.size(1) == noisy_audio.size(1)
+
+        if self.split:
+            if clean_audio.size(1) >= self.segment_size:
+                max_audio_start = clean_audio.size(1) - self.segment_size
+                audio_start = random.randint(0, max_audio_start)
+                clean_audio = clean_audio[:, audio_start: audio_start+self.segment_size]
+                noisy_audio = noisy_audio[:, audio_start: audio_start+self.segment_size]
+            else:
+                clean_audio = torch.nn.functional.pad(clean_audio, (0, self.segment_size - clean_audio.size(1)), 'constant')
+                noisy_audio = torch.nn.functional.pad(noisy_audio, (0, self.segment_size - noisy_audio.size(1)), 'constant')
+
+        return (clean_audio.squeeze(), noisy_audio.squeeze())
+
+    def __len__(self):
+        return len(self.audio_indexes)

docs/css/bulma-carousel.min.css

@@ -0,0 +1 @@
+@-webkit-keyframes spinAround{from{-webkit-transform:rotate(0);transform:rotate(0)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes spinAround{from{-webkit-transform:rotate(0);transform:rotate(0)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}.slider{position:relative;width:100%}.slider-container{display:flex;flex-wrap:nowrap;flex-direction:row;overflow:hidden;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0);min-height:100%}.slider-container.is-vertical{flex-direction:column}.slider-container .slider-item{flex:none}.slider-container .slider-item .image.is-covered img{-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center;height:100%;width:100%}.slider-container .slider-item .video-container{height:0;padding-bottom:0;padding-top:56.25%;margin:0;position:relative}.slider-container .slider-item .video-container.is-1by1,.slider-container .slider-item .video-container.is-square{padding-top:100%}.slider-container .slider-item .video-container.is-4by3{padding-top:75%}.slider-container .slider-item .video-container.is-21by9{padding-top:42.857143%}.slider-container .slider-item .video-container embed,.slider-container .slider-item .video-container iframe,.slider-container .slider-item .video-container object{position:absolute;top:0;left:0;width:100%!important;height:100%!important}.slider-navigation-next,.slider-navigation-previous{display:flex;justify-content:center;align-items:center;position:absolute;width:42px;height:42px;background:#fff center center no-repeat;background-size:20px 20px;border:1px solid #fff;border-radius:25091983px;box-shadow:0 2px 5px #3232321a;top:50%;margin-top:-20px;left:0;cursor:pointer;transition:opacity .3s,-webkit-transform .3s;transition:transform .3s,opacity .3s;transition:transform .3s,opacity .3s,-webkit-transform .3s}.slider-navigation-next:hover,.slider-navigation-previous:hover{-webkit-transform:scale(1.2);transform:scale(1.2)}.slider-navigation-next.is-hidden,.slider-navigation-previous.is-hidden{display:none;opacity:0}.slider-navigation-next svg,.slider-navigation-previous svg{width:25%}.slider-navigation-next{left:auto;right:0;background:#fff center center no-repeat;background-size:20px 20px}.slider-pagination{display:none;justify-content:center;align-items:center;position:absolute;bottom:0;left:0;right:0;padding:.5rem 1rem;text-align:center}.slider-pagination .slider-page{background:#fff;width:10px;height:10px;border-radius:25091983px;display:inline-block;margin:0 3px;box-shadow:0 2px 5px #3232321a;transition:-webkit-transform .3s;transition:transform .3s;transition:transform .3s,-webkit-transform .3s;cursor:pointer}.slider-pagination .slider-page.is-active,.slider-pagination .slider-page:hover{-webkit-transform:scale(1.4);transform:scale(1.4)}@media screen and (min-width:800px){.slider-pagination{display:flex}}.hero.has-carousel{position:relative}.hero.has-carousel+.hero-body,.hero.has-carousel+.hero-footer,.hero.has-carousel+.hero-head{z-index:10;overflow:hidden}.hero.has-carousel .hero-carousel{position:absolute;top:0;left:0;bottom:0;right:0;height:auto;border:none;margin:auto;padding:0;z-index:0}.hero.has-carousel .hero-carousel .slider{width:100%;max-width:100%;overflow:hidden;height:100%!important;max-height:100%;z-index:0}.hero.has-carousel .hero-carousel .slider .has-background{max-height:100%}.hero.has-carousel .hero-carousel .slider .has-background .is-background{-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center;height:100%;width:100%}.hero.has-carousel .hero-body{margin:0 3rem;z-index:10}

docs/css/bulma-slider.min.css

@@ -0,0 +1 @@
+@-webkit-keyframes spinAround{from{-webkit-transform:rotate(0);transform:rotate(0)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes spinAround{from{-webkit-transform:rotate(0);transform:rotate(0)}to{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}input[type=range].slider{-webkit-appearance:none;-moz-appearance:none;appearance:none;margin:1rem 0;background:0 0;touch-action:none}input[type=range].slider.is-fullwidth{display:block;width:100%}input[type=range].slider:focus{outline:0}input[type=range].slider:not([orient=vertical])::-webkit-slider-runnable-track{width:100%}input[type=range].slider:not([orient=vertical])::-moz-range-track{width:100%}input[type=range].slider:not([orient=vertical])::-ms-track{width:100%}input[type=range].slider:not([orient=vertical]).has-output+output,input[type=range].slider:not([orient=vertical]).has-output-tooltip+output{width:3rem;background:#4a4a4a;border-radius:4px;padding:.4rem .8rem;font-size:.75rem;line-height:.75rem;text-align:center;text-overflow:ellipsis;white-space:nowrap;color:#fff;overflow:hidden;pointer-events:none;z-index:200}input[type=range].slider:not([orient=vertical]).has-output-tooltip:disabled+output,input[type=range].slider:not([orient=vertical]).has-output:disabled+output{opacity:.5}input[type=range].slider:not([orient=vertical]).has-output{display:inline-block;vertical-align:middle;width:calc(100% - 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+ */
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