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| import torch | |
| import deepafx_st.processors.autodiff.signal | |
| from deepafx_st.processors.processor import Processor | |
| def parametric_eq( | |
| x: torch.Tensor, | |
| sample_rate: float, | |
| low_shelf_gain_dB: torch.Tensor, | |
| low_shelf_cutoff_freq: torch.Tensor, | |
| low_shelf_q_factor: torch.Tensor, | |
| first_band_gain_dB: torch.Tensor, | |
| first_band_cutoff_freq: torch.Tensor, | |
| first_band_q_factor: torch.Tensor, | |
| second_band_gain_dB: torch.Tensor, | |
| second_band_cutoff_freq: torch.Tensor, | |
| second_band_q_factor: torch.Tensor, | |
| third_band_gain_dB: torch.Tensor, | |
| third_band_cutoff_freq: torch.Tensor, | |
| third_band_q_factor: torch.Tensor, | |
| fourth_band_gain_dB: torch.Tensor, | |
| fourth_band_cutoff_freq: torch.Tensor, | |
| fourth_band_q_factor: torch.Tensor, | |
| high_shelf_gain_dB: torch.Tensor, | |
| high_shelf_cutoff_freq: torch.Tensor, | |
| high_shelf_q_factor: torch.Tensor, | |
| ): | |
| """Six-band parametric EQ. | |
| Low-shelf -> Band 1 -> Band 2 -> Band 3 -> Band 4 -> High-shelf | |
| Args: | |
| x (torch.Tensor): 1d signal. | |
| """ | |
| a_s, b_s = [], [] | |
| #print(f"autodiff peq fs = {sample_rate}") | |
| # -------- apply low-shelf filter -------- | |
| b, a = deepafx_st.processors.autodiff.signal.biqaud( | |
| low_shelf_gain_dB, | |
| low_shelf_cutoff_freq, | |
| low_shelf_q_factor, | |
| sample_rate, | |
| "low_shelf", | |
| ) | |
| b_s.append(b) | |
| a_s.append(a) | |
| # -------- apply first-band peaking filter -------- | |
| b, a = deepafx_st.processors.autodiff.signal.biqaud( | |
| first_band_gain_dB, | |
| first_band_cutoff_freq, | |
| first_band_q_factor, | |
| sample_rate, | |
| "peaking", | |
| ) | |
| b_s.append(b) | |
| a_s.append(a) | |
| # -------- apply second-band peaking filter -------- | |
| b, a = deepafx_st.processors.autodiff.signal.biqaud( | |
| second_band_gain_dB, | |
| second_band_cutoff_freq, | |
| second_band_q_factor, | |
| sample_rate, | |
| "peaking", | |
| ) | |
| b_s.append(b) | |
| a_s.append(a) | |
| # -------- apply third-band peaking filter -------- | |
| b, a = deepafx_st.processors.autodiff.signal.biqaud( | |
| third_band_gain_dB, | |
| third_band_cutoff_freq, | |
| third_band_q_factor, | |
| sample_rate, | |
| "peaking", | |
| ) | |
| b_s.append(b) | |
| a_s.append(a) | |
| # -------- apply fourth-band peaking filter -------- | |
| b, a = deepafx_st.processors.autodiff.signal.biqaud( | |
| fourth_band_gain_dB, | |
| fourth_band_cutoff_freq, | |
| fourth_band_q_factor, | |
| sample_rate, | |
| "peaking", | |
| ) | |
| b_s.append(b) | |
| a_s.append(a) | |
| # -------- apply high-shelf filter -------- | |
| b, a = deepafx_st.processors.autodiff.signal.biqaud( | |
| high_shelf_gain_dB, | |
| high_shelf_cutoff_freq, | |
| high_shelf_q_factor, | |
| sample_rate, | |
| "high_shelf", | |
| ) | |
| b_s.append(b) | |
| a_s.append(a) | |
| x = deepafx_st.processors.autodiff.signal.approx_iir_filter_cascade( | |
| b_s, a_s, x.view(-1) | |
| ) | |
| return x | |
| class ParametricEQ(Processor): | |
| def __init__( | |
| self, | |
| sample_rate, | |
| min_gain_dB=-24.0, | |
| default_gain_dB=0.0, | |
| max_gain_dB=24.0, | |
| min_q_factor=0.1, | |
| default_q_factor=0.707, | |
| max_q_factor=10, | |
| eps=1e-8, | |
| ): | |
| """ """ | |
| super().__init__() | |
| self.sample_rate = sample_rate | |
| self.eps = eps | |
| self.ports = [ | |
| { | |
| "name": "Lowshelf gain", | |
| "min": min_gain_dB, | |
| "max": max_gain_dB, | |
| "default": default_gain_dB, | |
| "units": "dB", | |
| }, | |
| { | |
| "name": "Lowshelf cutoff", | |
| "min": 20.0, | |
| "max": 200.0, | |
| "default": 100.0, | |
| "units": "Hz", | |
| }, | |
| { | |
| "name": "Lowshelf Q", | |
| "min": min_q_factor, | |
| "max": max_q_factor, | |
| "default": default_q_factor, | |
| "units": "", | |
| }, | |
| { | |
| "name": "First band gain", | |
| "min": min_gain_dB, | |
| "max": max_gain_dB, | |
| "default": default_gain_dB, | |
| "units": "dB", | |
| }, | |
| { | |
| "name": "First band cutoff", | |
| "min": 200.0, | |
| "max": 2000.0, | |
| "default": 400.0, | |
| "units": "Hz", | |
| }, | |
| { | |
| "name": "First band Q", | |
| "min": min_q_factor, | |
| "max": max_q_factor, | |
| "default": 0.707, | |
| "units": "", | |
| }, | |
| { | |
| "name": "Second band gain", | |
| "min": min_gain_dB, | |
| "max": max_gain_dB, | |
| "default": default_gain_dB, | |
| "units": "dB", | |
| }, | |
| { | |
| "name": "Second band cutoff", | |
| "min": 200.0, | |
| "max": 4000.0, | |
| "default": 1000.0, | |
| "units": "Hz", | |
| }, | |
| { | |
| "name": "Second band Q", | |
| "min": min_q_factor, | |
| "max": max_q_factor, | |
| "default": default_q_factor, | |
| "units": "", | |
| }, | |
| { | |
| "name": "Third band gain", | |
| "min": min_gain_dB, | |
| "max": max_gain_dB, | |
| "default": default_gain_dB, | |
| "units": "dB", | |
| }, | |
| { | |
| "name": "Third band cutoff", | |
| "min": 2000.0, | |
| "max": 8000.0, | |
| "default": 4000.0, | |
| "units": "Hz", | |
| }, | |
| { | |
| "name": "Third band Q", | |
| "min": min_q_factor, | |
| "max": max_q_factor, | |
| "default": default_q_factor, | |
| "units": "", | |
| }, | |
| { | |
| "name": "Fourth band gain", | |
| "min": min_gain_dB, | |
| "max": max_gain_dB, | |
| "default": default_gain_dB, | |
| "units": "dB", | |
| }, | |
| { | |
| "name": "Fourth band cutoff", | |
| "min": 4000.0, | |
| "max": (24000 // 2) * 0.9, | |
| "default": 8000.0, | |
| "units": "Hz", | |
| }, | |
| { | |
| "name": "Fourth band Q", | |
| "min": min_q_factor, | |
| "max": max_q_factor, | |
| "default": default_q_factor, | |
| "units": "", | |
| }, | |
| { | |
| "name": "Highshelf gain", | |
| "min": min_gain_dB, | |
| "max": max_gain_dB, | |
| "default": default_gain_dB, | |
| "units": "dB", | |
| }, | |
| { | |
| "name": "Highshelf cutoff", | |
| "min": 4000.0, | |
| "max": (24000 // 2) * 0.9, | |
| "default": 8000.0, | |
| "units": "Hz", | |
| }, | |
| { | |
| "name": "Highshelf Q", | |
| "min": min_q_factor, | |
| "max": max_q_factor, | |
| "default": default_q_factor, | |
| "units": "", | |
| }, | |
| ] | |
| self.num_control_params = len(self.ports) | |
| def forward(self, x, p, sample_rate=24000, **kwargs): | |
| bs, chs, s = x.size() | |
| inputs = torch.split(x, 1, 0) | |
| params = torch.split(p, 1, 0) | |
| y = [] # loop over batch dimension | |
| for input, param in zip(inputs, params): | |
| denorm_param = self.denormalize_params(param.view(-1)) | |
| y.append(parametric_eq(input.view(-1), sample_rate, *denorm_param)) | |
| return torch.stack(y, dim=0).view(bs, 1, -1) | |