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spectrogram and inverse spectrogram (#5779)
* only supports hann, hamming and all-one window * inverse spectrogram does not support length parameter * spectrogram always returns torch.view_as_real(out) as ncnn does not support complex typed mat yet * inverse spectrogram always accepts torch.view_as_complex(in) as ncnn does not support complex typed mat yet
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| Original file line number | Diff line number | Diff line change |
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| // Tencent is pleased to support the open source community by making ncnn available. | ||
| // | ||
| // Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. | ||
| // | ||
| // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except | ||
| // in compliance with the License. You may obtain a copy of the License at | ||
| // | ||
| // https://opensource.org/licenses/BSD-3-Clause | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software distributed | ||
| // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR | ||
| // CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
| // specific language governing permissions and limitations under the License. | ||
|
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| #include "inversespectrogram.h" | ||
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| namespace ncnn { | ||
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| InverseSpectrogram::InverseSpectrogram() | ||
| { | ||
| one_blob_only = true; | ||
| support_inplace = false; | ||
| } | ||
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| int InverseSpectrogram::load_param(const ParamDict& pd) | ||
| { | ||
| n_fft = pd.get(0, 0); | ||
| returns = pd.get(1, 0); | ||
| hoplen = pd.get(2, n_fft / 4); | ||
| winlen = pd.get(3, n_fft); | ||
| window_type = pd.get(4, 0); | ||
| center = pd.get(5, 1); | ||
| normalized = pd.get(7, 0); | ||
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| // assert winlen <= n_fft | ||
| // generate window | ||
| window_data.create(normalized == 2 ? n_fft + 1 : n_fft); | ||
| { | ||
| float* p = window_data; | ||
| for (int i = 0; i < (n_fft - winlen) / 2; i++) | ||
| { | ||
| *p++ = 0.f; | ||
| } | ||
| if (window_type == 0) | ||
| { | ||
| // all ones | ||
| for (int i = 0; i < winlen; i++) | ||
| { | ||
| *p++ = 1.f; | ||
| } | ||
| } | ||
| if (window_type == 1) | ||
| { | ||
| // hann window | ||
| for (int i = 0; i < winlen; i++) | ||
| { | ||
| *p++ = 0.5f * (1 - cosf(2 * 3.14159265358979323846 * i / winlen)); | ||
| } | ||
| } | ||
| if (window_type == 2) | ||
| { | ||
| // hamming window | ||
| for (int i = 0; i < winlen; i++) | ||
| { | ||
| *p++ = 0.54f - 0.46f * cosf(2 * 3.14159265358979323846 * i / winlen); | ||
| } | ||
| } | ||
| for (int i = 0; i < n_fft - winlen - (n_fft - winlen) / 2; i++) | ||
| { | ||
| *p++ = 0.f; | ||
| } | ||
|
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| // pre-calculated window norm factor | ||
| if (normalized == 2) | ||
| { | ||
| float sqsum = 0.f; | ||
| for (int i = 0; i < n_fft; i++) | ||
| { | ||
| sqsum += window_data[i] * window_data[i]; | ||
| } | ||
| window_data[n_fft] = sqrt(sqsum); | ||
| } | ||
| } | ||
|
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| return 0; | ||
| } | ||
|
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| int InverseSpectrogram::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const | ||
| { | ||
| // https://github.com/librosa/librosa/blob/main/librosa/core/spectrum.py#L630 | ||
|
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| // TODO custom window | ||
| // TODO output length | ||
|
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| const int frames = bottom_blob.h; | ||
| const int freqs = bottom_blob.c; | ||
| // assert freqs == n_fft or freqs == n_fft / 2 + 1 | ||
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| const int onesided = freqs == n_fft / 2 + 1 ? 1 : 0; | ||
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| const int outsize = center ? (frames - 1) * hoplen + (n_fft - n_fft / 2 * 2) : (frames - 1) * hoplen + n_fft; | ||
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| const size_t elemsize = bottom_blob.elemsize; | ||
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| if (returns == 0) | ||
| { | ||
| top_blob.create(2, outsize, elemsize, opt.blob_allocator); | ||
| } | ||
| else | ||
| { | ||
| top_blob.create(outsize, elemsize, opt.blob_allocator); | ||
| } | ||
| if (top_blob.empty()) | ||
| return -100; | ||
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| Mat window_sumsquare(outsize + n_fft, elemsize, opt.workspace_allocator); | ||
| if (window_sumsquare.empty()) | ||
| return -100; | ||
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| top_blob.fill(0.f); | ||
| window_sumsquare.fill(0.f); | ||
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| for (int j = 0; j < frames; j++) | ||
| { | ||
| // collect complex | ||
| Mat sp(2, n_fft); | ||
| if (onesided == 1) | ||
| { | ||
| for (int k = 0; k < n_fft / 2 + 1; k++) | ||
| { | ||
| sp.row(k)[0] = bottom_blob.channel(k).row(j)[0]; | ||
| sp.row(k)[1] = bottom_blob.channel(k).row(j)[1]; | ||
| } | ||
| for (int k = n_fft / 2 + 1; k < n_fft; k++) | ||
| { | ||
| sp.row(k)[0] = bottom_blob.channel(n_fft - k).row(j)[0]; | ||
| sp.row(k)[1] = -bottom_blob.channel(n_fft - k).row(j)[1]; | ||
| } | ||
| } | ||
| else | ||
| { | ||
| for (int k = 0; k < n_fft; k++) | ||
| { | ||
| sp.row(k)[0] = bottom_blob.channel(k).row(j)[0]; | ||
| sp.row(k)[1] = bottom_blob.channel(k).row(j)[1]; | ||
| } | ||
| } | ||
|
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| if (normalized == 1) | ||
| { | ||
| float norm = sqrt(n_fft); | ||
| for (int i = 0; i < 2 * n_fft; i++) | ||
| { | ||
| sp[i] *= norm; | ||
| } | ||
| } | ||
| if (normalized == 2) | ||
| { | ||
| float norm = window_data[n_fft]; | ||
| for (int i = 0; i < 2 * n_fft; i++) | ||
| { | ||
| sp[i] *= norm; | ||
| } | ||
| } | ||
|
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| #pragma omp parallel for num_threads(opt.num_threads) | ||
| for (int i = 0; i < n_fft; i++) | ||
| { | ||
| // inverse dft | ||
| float re = 0.f; | ||
| float im = 0.f; | ||
| for (int k = 0; k < n_fft; k++) | ||
| { | ||
| double angle = 2 * 3.14159265358979323846 * i * k / n_fft; | ||
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| re += sp.row(k)[0] * cosf(angle) - sp.row(k)[1] * sinf(angle); | ||
| im += sp.row(k)[0] * sinf(angle) + sp.row(k)[1] * cosf(angle); | ||
| } | ||
|
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| re /= n_fft; | ||
| im /= n_fft; | ||
|
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| // apply window | ||
| re *= window_data[i]; | ||
| im *= window_data[i]; | ||
|
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| int output_index = j * hoplen + i; | ||
| if (center == 1) | ||
| { | ||
| output_index -= n_fft / 2; | ||
| } | ||
| if (output_index >= 0 && output_index < outsize) | ||
| { | ||
| // square window | ||
| window_sumsquare[output_index] += window_data[i] * window_data[i]; | ||
|
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| if (returns == 0) | ||
| { | ||
| top_blob.row(output_index)[0] += re; | ||
| top_blob.row(output_index)[1] += im; | ||
| } | ||
| if (returns == 1) | ||
| { | ||
| top_blob[output_index] += re; | ||
| } | ||
| if (returns == 2) | ||
| { | ||
| top_blob[output_index] += im; | ||
| } | ||
| } | ||
| } | ||
| } | ||
|
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| // square window norm | ||
| if (returns == 0) | ||
| { | ||
| for (int i = 0; i < outsize; i++) | ||
| { | ||
| if (window_sumsquare[i] != 0.f) | ||
| { | ||
| top_blob.row(i)[0] /= window_sumsquare[i]; | ||
| top_blob.row(i)[1] /= window_sumsquare[i]; | ||
| } | ||
| } | ||
| } | ||
| else | ||
| { | ||
| for (int i = 0; i < outsize; i++) | ||
| { | ||
| if (window_sumsquare[i] != 0.f) | ||
| top_blob[i] /= window_sumsquare[i]; | ||
| } | ||
| } | ||
|
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| return 0; | ||
| } | ||
|
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| } // namespace ncnn |
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