From 7fc312645d1c468f8a910acb9d0acb3b77637ed7 Mon Sep 17 00:00:00 2001 From: Alexey Korepanov Date: Wed, 20 Sep 2023 12:16:23 +0400 Subject: [PATCH 1/4] torchDF update --- DeepFilterNet/df/deepfilternet3.py | 76 ++-- DeepFilterNet/df/modules.py | 2 +- DeepFilterNet/df/multiframe.py | 2 +- libDF/src/tract.rs | 17 +- pyDF/Cargo.toml | 8 +- pyDF/src/lib.rs | 38 +- torchDF/README.md | 47 ++ torchDF/model_onnx_export.py | 234 ++++++++++ torchDF/pyproject.toml | 78 ++++ torchDF/test_torchdf.py | 73 ++++ torchDF/torch_df_offline.py | 368 ++++++++++++++++ torchDF/torch_df_streaming.py | 668 +++++++++++++++++++++++++++++ 12 files changed, 1576 insertions(+), 35 deletions(-) create mode 100644 torchDF/README.md create mode 100644 torchDF/model_onnx_export.py create mode 100644 torchDF/pyproject.toml create mode 100644 torchDF/test_torchdf.py create mode 100644 torchDF/torch_df_offline.py create mode 100644 torchDF/torch_df_streaming.py diff --git a/DeepFilterNet/df/deepfilternet3.py b/DeepFilterNet/df/deepfilternet3.py index d49aedce2..94c155ac1 100644 --- a/DeepFilterNet/df/deepfilternet3.py +++ b/DeepFilterNet/df/deepfilternet3.py @@ -4,6 +4,7 @@ import torch from loguru import logger from torch import Tensor, nn +import torch.nn.functional as F import df.multiframe as MF from df.config import Csv, DfParams, config @@ -49,6 +50,9 @@ def __init__(self): self.emb_num_layers: int = config( "EMB_NUM_LAYERS", cast=int, default=2, section=self.section ) + self.emb_gru_skip_enc: str = config( + "EMB_GRU_SKIP_ENC", default="none", section=self.section + ) self.emb_gru_skip: str = config("EMB_GRU_SKIP", default="none", section=self.section) self.df_hidden_dim: int = config( "DF_HIDDEN_DIM", cast=int, default=256, section=self.section @@ -99,6 +103,9 @@ def __init__(self): self.erb_conv0 = Conv2dNormAct( 1, p.conv_ch, kernel_size=p.conv_kernel_inp, bias=False, separable=True ) + self.conv_buffer_size = p.conv_kernel_inp[0] - 1 + self.conv_ch = p.conv_ch + conv_layer = partial( Conv2dNormAct, in_ch=p.conv_ch, @@ -110,8 +117,9 @@ def __init__(self): self.erb_conv1 = conv_layer(fstride=2) self.erb_conv2 = conv_layer(fstride=2) self.erb_conv3 = conv_layer(fstride=1) + self.df_conv0_ch = p.conv_ch self.df_conv0 = Conv2dNormAct( - 2, p.conv_ch, kernel_size=p.conv_kernel_inp, bias=False, separable=True + 2, self.df_conv0_ch, kernel_size=p.conv_kernel_inp, bias=False, separable=True ) self.df_conv1 = conv_layer(fstride=2) self.erb_bins = p.nb_erb @@ -128,13 +136,27 @@ def __init__(self): else: self.combine = Add() self.emb_n_layers = p.emb_num_layers + if p.emb_gru_skip_enc == "none": + skip_op = None + elif p.emb_gru_skip_enc == "identity": + assert self.emb_in_dim == self.emb_out_dim, "Dimensions do not match" + skip_op = partial(nn.Identity) + elif p.emb_gru_skip_enc == "groupedlinear": + skip_op = partial( + GroupedLinearEinsum, + input_size=self.emb_out_dim, + hidden_size=self.emb_out_dim, + groups=p.lin_groups, + ) + else: + raise NotImplementedError() self.emb_gru = SqueezedGRU_S( self.emb_in_dim, self.emb_dim, output_size=self.emb_out_dim, num_layers=1, batch_first=True, - gru_skip_op=None, + gru_skip_op=skip_op, linear_groups=p.lin_groups, linear_act_layer=partial(nn.ReLU, inplace=True), ) @@ -143,8 +165,8 @@ def __init__(self): self.lsnr_offset = p.lsnr_min def forward( - self, feat_erb: Tensor, feat_spec: Tensor - ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]: + self, feat_erb: Tensor, feat_spec: Tensor, hidden: Tensor + ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]: # Encodes erb; erb should be in dB scale + normalized; Fe are number of erb bands. # erb: [B, 1, T, Fe] # spec: [B, 2, T, Fc] @@ -154,14 +176,14 @@ def forward( e2 = self.erb_conv2(e1) # [B, C*4, T, F/4] e3 = self.erb_conv3(e2) # [B, C*4, T, F/4] c0 = self.df_conv0(feat_spec) # [B, C, T, Fc] - c1 = self.df_conv1(c0) # [B, C*2, T, Fc] + c1 = self.df_conv1(c0) # [B, C*2, T, Fc/2] cemb = c1.permute(0, 2, 3, 1).flatten(2) # [B, T, -1] cemb = self.df_fc_emb(cemb) # [T, B, C * F/4] - emb = e3.permute(0, 2, 3, 1).flatten(2) # [B, T, C * F/4] + emb = e3.permute(0, 2, 3, 1).flatten(2) # [B, T, C * F] emb = self.combine(emb, cemb) - emb, _ = self.emb_gru(emb) # [B, T, -1] + emb, hidden = self.emb_gru(emb, hidden) # [B, T, -1] lsnr = self.lsnr_fc(emb) * self.lsnr_scale + self.lsnr_offset - return e0, e1, e2, e3, emb, c0, lsnr + return e0, e1, e2, e3, emb, c0, lsnr, hidden class ErbDecoder(nn.Module): @@ -221,16 +243,16 @@ def __init__(self): p.conv_ch, 1, kernel_size=p.conv_kernel, activation_layer=nn.Sigmoid ) - def forward(self, emb, e3, e2, e1, e0) -> Tensor: + def forward(self, emb: Tensor, e3: Tensor, e2: Tensor, e1: Tensor, e0: Tensor, hidden: Tensor) -> Tuple[Tensor, Tensor]: # Estimates erb mask b, _, t, f8 = e3.shape - emb, _ = self.emb_gru(emb) + emb, hidden = self.emb_gru(emb, hidden) emb = emb.view(b, t, f8, -1).permute(0, 3, 1, 2) # [B, C*8, T, F/8] e3 = self.convt3(self.conv3p(e3) + emb) # [B, C*4, T, F/4] e2 = self.convt2(self.conv2p(e2) + e3) # [B, C*2, T, F/2] e1 = self.convt1(self.conv1p(e1) + e2) # [B, C, T, F] m = self.conv0_out(self.conv0p(e0) + e1) # [B, 1, T, F] - return m + return m, hidden class DfOutputReshapeMF(nn.Module): @@ -271,6 +293,7 @@ def __init__(self): conv_layer = partial(Conv2dNormAct, separable=True, bias=False) kt = p.df_pathway_kernel_size_t + self.conv_buffer_size = kt - 1 self.df_convp = conv_layer(layer_width, self.df_out_ch, fstride=1, kernel_size=(kt, 1)) self.df_gru = SqueezedGRU_S( @@ -299,16 +322,15 @@ def __init__(self): self.df_out = nn.Sequential(df_out, nn.Tanh()) self.df_fc_a = nn.Sequential(nn.Linear(self.df_n_hidden, 1), nn.Sigmoid()) - def forward(self, emb: Tensor, c0: Tensor) -> Tuple[Tensor, Tensor]: + def forward(self, emb: Tensor, c0: Tensor, hidden: Tensor) -> Tuple[Tensor, Tensor]: b, t, _ = emb.shape - c, _ = self.df_gru(emb) # [B, T, H], H: df_n_hidden + c, hidden = self.df_gru(emb, hidden) # [B, T, H], H: df_n_hidden if self.df_skip is not None: c = c + self.df_skip(emb) c0 = self.df_convp(c0).permute(0, 2, 3, 1) # [B, T, F, O*2], channels_last - alpha = self.df_fc_a(c) # [B, T, 1] c = self.df_out(c) # [B, T, F*O*2], O: df_order c = c.view(b, t, self.df_bins, self.df_out_ch) + c0 # [B, T, F, O*2] - return c, alpha + return c, hidden class DfNet(nn.Module): @@ -333,12 +355,12 @@ def __init__( self.emb_dim: int = layer_width * p.nb_erb self.erb_bins: int = p.nb_erb if p.conv_lookahead > 0: - assert p.conv_lookahead == p.df_lookahead + assert p.conv_lookahead >= p.df_lookahead self.pad_feat = nn.ConstantPad2d((0, 0, -p.conv_lookahead, p.conv_lookahead), 0.0) else: self.pad_feat = nn.Identity() if p.df_lookahead > 0: - self.pad_spec = nn.ConstantPad3d((0, 0, 0, 0, -p.df_lookahead, p.df_lookahead), 0.0) + self.pad_spec = nn.ConstantPad3d((0, 0, 0, 0, p.df_lookahead - 1, -p.df_lookahead + 1), 0.0) else: self.pad_spec = nn.Identity() self.register_buffer("erb_fb", erb_fb) @@ -381,9 +403,11 @@ def forward( """ feat_spec = feat_spec.squeeze(1).permute(0, 3, 1, 2) - feat_erb = self.pad_feat(feat_erb) - feat_spec = self.pad_feat(feat_spec) - e0, e1, e2, e3, emb, c0, lsnr = self.enc(feat_erb, feat_spec) + # feat_erb = self.pad_feat(feat_erb) + # feat_spec = self.pad_feat(feat_spec) + spec = self.pad_spec(spec) + + e0, e1, e2, e3, emb, c0, lsnr, _ = self.enc(feat_erb, feat_spec, hidden=None) if self.lsnr_droput: idcs = lsnr.squeeze() > -10.0 @@ -400,19 +424,21 @@ def forward( if self.run_erb: if self.lsnr_droput: - m[:, :, idcs] = self.erb_dec(emb, e3, e2, e1, e0) + m[:, :, idcs], _ = self.erb_dec(emb, e3, e2, e1, e0, hidden=None) else: - m = self.erb_dec(emb, e3, e2, e1, e0) - spec_m = self.mask(spec, m) + m, _ = self.erb_dec(emb, e3, e2, e1, e0, hidden=None) + + pad_spec = F.pad(spec, (0, 0, 0, 0, 1, -1, 0, 0), value=0) + spec_m = self.mask(pad_spec, m) else: m = torch.zeros((), device=spec.device) spec_m = torch.zeros_like(spec) if self.run_df: if self.lsnr_droput: - df_coefs[:, idcs] = self.df_dec(emb, c0)[0] + df_coefs[:, idcs], _ = self.df_dec(emb, c0, hidden=None) else: - df_coefs = self.df_dec(emb, c0)[0] + df_coefs, _ = self.df_dec(emb, c0, hidden=None) df_coefs = self.df_out_transform(df_coefs) spec = self.df_op(spec, df_coefs) spec[..., self.nb_df :, :] = spec_m[..., self.nb_df :, :] diff --git a/DeepFilterNet/df/modules.py b/DeepFilterNet/df/modules.py index 8a725f49c..0b4612068 100644 --- a/DeepFilterNet/df/modules.py +++ b/DeepFilterNet/df/modules.py @@ -729,7 +729,7 @@ def __init__( else: self.linear_out = nn.Identity() - def forward(self, input: Tensor, h=None) -> Tuple[Tensor, Tensor]: + def forward(self, input: Tensor, h: Tensor) -> Tuple[Tensor, Tensor]: x = self.linear_in(input) x, h = self.gru(x, h) x = self.linear_out(x) diff --git a/DeepFilterNet/df/multiframe.py b/DeepFilterNet/df/multiframe.py index 140b6643f..f5476e91e 100644 --- a/DeepFilterNet/df/multiframe.py +++ b/DeepFilterNet/df/multiframe.py @@ -71,7 +71,7 @@ def __init__(self, num_freqs: int, frame_size: int, lookahead: int = 0, real: bo if real: self.pad = nn.ConstantPad3d((0, 0, 0, 0, frame_size - 1 - lookahead, lookahead), 0.0) else: - self.pad = nn.ConstantPad2d((0, 0, frame_size - 1 - lookahead, lookahead), 0.0) + self.pad = nn.ConstantPad2d((0, 0, frame_size - lookahead, lookahead - 1), 0.0) self.need_unfold = frame_size > 1 self.lookahead = lookahead diff --git a/libDF/src/tract.rs b/libDF/src/tract.rs index 74dca64da..361ac53f4 100644 --- a/libDF/src/tract.rs +++ b/libDF/src/tract.rs @@ -586,9 +586,14 @@ impl DfTract { } } - pub fn set_spec_buffer(&mut self, spec: ArrayView2) -> Result<()> { - debug_assert_eq!(self.spec_buf.shape(), spec.shape()); - let mut buf = self.spec_buf.to_array_view_mut()?.into_shape([self.ch, self.n_freqs])?; + pub fn set_spec_buffer(&mut self, spec: ArrayView2) -> Result<()> { + let mut buf = as_arrayview_mut_complex( + self.spec_buf.to_array_view_mut::().unwrap(), + &[self.ch, self.n_freqs], + ); + + debug_assert_eq!(buf.shape(), spec.shape()); + for (i_ch, mut b_ch) in spec.outer_iter().zip(buf.outer_iter_mut()) { for (&i, b) in i_ch.iter().zip(b_ch.iter_mut()) { *b = i @@ -645,7 +650,7 @@ fn df( debug_assert_eq!(ch, spec_out.shape()[0]); debug_assert!(spec.len() >= df_order); let mut o_f: ArrayViewMut2 = - as_array_mut_complex(spec_out.to_array_view_mut::()?, &[ch, n_freqs]) + as_arrayview_mut_complex(spec_out.to_array_view_mut::()?, &[ch, n_freqs]) .into_dimensionality()?; // Zero relevant frequency bins of output o_f.slice_mut(s![.., ..nb_df]).fill(Complex32::default()); @@ -913,6 +918,7 @@ pub fn as_slice_complex(buffer: &[f32]) -> &[Complex32] { } } +#[allow(clippy::needless_pass_by_ref_mut)] pub fn as_slice_mut_complex(buffer: &mut [f32]) -> &mut [Complex32] { unsafe { let ptr = buffer.as_ptr() as *mut Complex32; @@ -921,6 +927,7 @@ pub fn as_slice_mut_complex(buffer: &mut [f32]) -> &mut [Complex32] { } } +#[allow(clippy::needless_pass_by_ref_mut)] pub fn as_slice_mut_real(buffer: &mut [Complex32]) -> &mut [f32] { unsafe { let ptr = buffer.as_ptr() as *mut f32; @@ -960,7 +967,7 @@ pub fn as_arrayview_complex<'a>( ArrayViewD::from_shape_ptr(shape, ptr) } } -pub fn as_array_mut_complex<'a>( +pub fn as_arrayview_mut_complex<'a>( buffer: ArrayViewMutD<'a, f32>, shape: &[usize], // having an explicit shape parameter allows to also squeeze axes. ) -> ArrayViewMutD<'a, Complex32> { diff --git a/pyDF/Cargo.toml b/pyDF/Cargo.toml index d870c31e9..db739b38a 100644 --- a/pyDF/Cargo.toml +++ b/pyDF/Cargo.toml @@ -11,7 +11,13 @@ crate-type = ["cdylib"] path = "src/lib.rs" [dependencies] -deep_filter = { features = ["transforms", "logging"], path = "../libDF" } +deep_filter = { path = "../libDF", default-features = false, features = [ + "tract", + "use-jemalloc", + "default-model", +] } + pyo3 = { version = "0.19", features = ["extension-module"]} numpy = "0.19" ndarray = "^0.15" +tract-core = "^0.19.4" diff --git a/pyDF/src/lib.rs b/pyDF/src/lib.rs index 5cee2880d..32bba73d7 100644 --- a/pyDF/src/lib.rs +++ b/pyDF/src/lib.rs @@ -3,17 +3,50 @@ use df::transforms::{ TransformError, }; use df::{Complex32, DFState, UNIT_NORM_INIT}; + +use df::tract::*; + use ndarray::{Array1, Array2, Array3, Array4, ArrayD, ArrayView4, Axis, ShapeError}; use numpy::{ IntoPyArray, PyArray1, PyArray2, PyArray3, PyArrayDyn, PyReadonlyArray1, PyReadonlyArray2, - PyReadonlyArray3, PyReadonlyArrayDyn, + PyReadonlyArray3, PyReadonlyArrayDyn }; use pyo3::exceptions::{PyRuntimeError, PyValueError}; use pyo3::prelude::*; + #[pyclass] struct DF { - state: DFState, + state: DFState, +} + +#[pyclass(unsendable)] +struct DFTractPy { + tract: DfTract, +} + +#[pymethods] +#[allow(clippy::upper_case_acronyms)] +impl DFTractPy { + #[new] + fn new() -> Self { + DFTractPy { + tract: Default::default() + } + } + + unsafe fn process<'py>( + &mut self, + py: Python<'py>, + input: &PyArray2 + ) -> PyResult<&'py PyArray2> { + let channels = input.shape()[0]; + let mut output = Array2::::zeros((channels, self.tract.hop_size)); + let input = input.as_array(); // unsafe fn + let _ = self.tract.process(input, output.view_mut()).expect("Error during df::process"); + + Ok(output.into_pyarray(py)) + } } #[pymethods] @@ -138,6 +171,7 @@ impl DF { #[pymodule] fn libdf(_py: Python, m: &PyModule) -> PyResult<()> { m.add_class::()?; + m.add_class::()?; #[pyfn(m)] #[pyo3(name = "erb")] diff --git a/torchDF/README.md b/torchDF/README.md new file mode 100644 index 000000000..54d7d1b01 --- /dev/null +++ b/torchDF/README.md @@ -0,0 +1,47 @@ +# TorchDF + +Commit against which the comparison was made - https://github.com/Rikorose/DeepFilterNet/commit/ca46bf54afaf8ace3272aaee5931b4317bd6b5f4 + +Installation: +``` +cd path/to/DeepFilterNet/ +pip install maturin poetry +maturin develop --release -m pyDF/Cargo.toml + +cd DeepFilterNet +export PYTHONPATH=$PWD + +cd ../torchDF +poetry install +``` + +Here is presented offline and streaming implementation of DeepFilterNet3 on pure torch. Streaming model can be fully exported to ONNX using `model_onnx_export.py`. + +Every script and test have to run inside poetry enviroment. + +To run tests: +``` +python -m pytest -v +``` +We compare this model to existing `enhance` method (which is partly written on Rust) and tract model (which is purely on Rust). All tests are passing, so model is working. + +To enhance audio using streaming implementation: +``` +python torch_df_streaming.py --audio-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav --output-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary_enhanced.wav +``` + +To convert model to onnx and run tests: +``` +python model_onnx_export.py --test --performance --inference-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav --ort +``` + +TODO: +* Issues about split + simplify +* Thinkging of offline method exportability + compatability with streaming functions +* torch.where(..., ..., 0) export issue +* dynamo.export check +* thinking of torchDF naming +* rfft hacks tests +* torch.nonzero thinking +* rfft nn.module +* more static methods \ No newline at end of file diff --git a/torchDF/model_onnx_export.py b/torchDF/model_onnx_export.py new file mode 100644 index 000000000..4c4ca142c --- /dev/null +++ b/torchDF/model_onnx_export.py @@ -0,0 +1,234 @@ +import os +import copy +import onnx +import argparse +import subprocess + +import torch +import torchaudio +import numpy as np +import onnxruntime as ort +import torch.utils.benchmark as benchmark + +from torch_df_streaming import TorchDFPipeline +from typing import Dict, Iterable + +torch.manual_seed(0) + +FRAME_SIZE = 480 +INPUT_NAMES = [ + 'input_frame', + 'states', + 'atten_lim_db' +] +OUTPUT_NAMES = [ + 'enhanced_audio_frame', 'out_states', 'lsnr' +] + + +def onnx_simplify( + path: str, input_data: Dict[str, np.ndarray], input_shapes: Dict[str, Iterable[int]] +) -> str: + """ + Simplify ONNX model using onnxsim and checking it + + Parameters: + path: str - Path to ONNX model + input_data: Dict[str, np.ndarray] - Input data for ONNX model + input_shapes: Dict[str, Iterable[int]] - Input shapes for ONNX model + + Returns: + path: str - Path to simplified ONNX model + """ + import onnxsim + + model = onnx.load(path) + model_simp, check = onnxsim.simplify( + model, + input_data=input_data, + test_input_shapes=input_shapes, + ) + assert check, "Simplified ONNX model could not be validated" + onnx.checker.check_model(model_simp, full_check=True) + onnx.save_model(model_simp, path) + return path + +def test_onnx_model(torch_model, ort_session, states, atten_lim_db): + """ + Simple test that everything converted correctly + + Parameters: + torch_model: torch.nn.Module - Original torch model + ort_session: onnxruntime.InferenceSession - Inference Session for converted ONNX model + input_features: Dict[str, np.ndarray] - Input features + """ + states_torch = copy.deepcopy(states) + states_onnx = copy.deepcopy(states) + + for i in range(30): + input_frame = torch.randn(FRAME_SIZE) + + # torch + output_torch = torch_model(input_frame, states_torch, atten_lim_db) + + # onnx + output_onnx = ort_session.run( + OUTPUT_NAMES, + generate_onnx_features([input_frame, states_onnx, atten_lim_db]), + ) + + for (x, y, name) in zip(output_torch, output_onnx, OUTPUT_NAMES): + y_tensor = torch.from_numpy(y) + assert torch.allclose(x, y_tensor, atol=1e-3), f"out {name} - {i}, {x.flatten()[-5:]}, {y_tensor.flatten()[-5:]}" + +def generate_onnx_features(input_features): + return { + x: y.detach().cpu().numpy() + for x, y in zip(INPUT_NAMES, input_features) + } + +def perform_benchmark( + ort_session, input_features: Dict[str, np.ndarray], +): + """ + Benchmark ONNX model performance + + Parameters: + ort_session: onnxruntime.InferenceSession - Inference Session for converted ONNX model + input_features: Dict[str, np.ndarray] - Input features + """ + def run_onnx(): + output = ort_session.run( + OUTPUT_NAMES, + input_features, + ) + + t0 = benchmark.Timer( + stmt='run_onnx()', + num_threads=1, + globals={'run_onnx': run_onnx}, + ) + print(f"Median iteration time: {t0.blocked_autorange(min_run_time=10).median * 1e3:6.2f} ms / {480 / 48000 * 1000} ms") + +def infer_onnx_model(streaming_pipeline, ort_session, inference_path): + """ + Inference ONNX model with TorchDFPipeline + """ + del streaming_pipeline.torch_streaming_model + streaming_pipeline.torch_streaming_model = lambda *features: ( + torch.from_numpy(x) for x in ort_session.run( + OUTPUT_NAMES, + generate_onnx_features(list(features)), + ) + ) + + noisy_audio, sr = torchaudio.load(inference_path, channels_first=True) + noisy_audio = noisy_audio.mean(dim=0).unsqueeze(0) # stereo to mono + + enhanced_audio = streaming_pipeline(noisy_audio, sr) + + torchaudio.save( + inference_path.replace('.wav', '_onnx_infer.wav'), enhanced_audio, sr, + encoding="PCM_S", bits_per_sample=16 + ) + +def main(args): + streaming_pipeline = TorchDFPipeline(always_apply_all_stages=args.always_apply_all_stages, device='cpu') + torch_df = streaming_pipeline.torch_streaming_model + states = streaming_pipeline.states + atten_lim_db = streaming_pipeline.atten_lim_db + + input_frame = torch.rand(FRAME_SIZE) + input_features = ( + input_frame, states, atten_lim_db + ) + torch_df(*input_features) # check model + + torch_df_script = torch.jit.script(torch_df) + torch.onnx.export( + torch_df_script, + input_features, + args.output_path, + verbose=False, + input_names=INPUT_NAMES, + output_names=OUTPUT_NAMES, + opset_version=14 + ) + print(f'Model exported to {args.output_path}!') + + input_features_onnx = generate_onnx_features(input_features) + input_shapes_dict = { + x: y.shape + for x, y in input_features_onnx.items() + } + + # Simplify not working! + if args.simplify: + raise NotImplementedError("Simplify not working for flatten states!") + onnx_simplify(args.output_path, input_features_onnx, input_shapes_dict) + print(f'Model simplified! {args.output_path}') + + if args.ort: + if subprocess.run([ + 'python', '-m', 'onnxruntime.tools.convert_onnx_models_to_ort', + args.output_path, + '--optimization_style', 'Fixed', + ]).returncode != 0: + raise RuntimeError("ONNX to ORT conversion failed!") + print('Model converted to ORT format!') + + print('Checking model...') + sess_options = ort.SessionOptions() + sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED + sess_options.optimized_model_filepath = args.output_path + sess_options.intra_op_num_threads = 1 + sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL + + ort_session = ort.InferenceSession(args.output_path, sess_options, providers=['CPUExecutionProvider']) + + onnx_outputs = ort_session.run( + OUTPUT_NAMES, + input_features_onnx, + ) + + print(f'InferenceSession successful! Output shapes: {[x.shape for x in onnx_outputs]}') + + if args.test: + test_onnx_model(torch_df, ort_session, input_features[1], input_features[2]) + print('Tests passed!') + + if args.performance: + print('Performanse check...') + perform_benchmark(ort_session, input_features_onnx) + + if args.inference_path: + infer_onnx_model(streaming_pipeline, ort_session, args.inference_path) + print(f'Audio from {args.inference_path} enhanced!') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description='Exporting torchDF model to ONNX' + ) + parser.add_argument( + '--output-path', type=str, default='denoiser_model.onnx', help='Path to output onnx file' + ) + parser.add_argument( + '--simplify', action='store_true', help='Simplify the model' + ) + parser.add_argument( + '--test', action='store_true', help='Test the onnx model' + ) + parser.add_argument( + '--performance', action='store_true', help='Mesure median iteration time for onnx model' + ) + parser.add_argument( + '--inference-path', type=str, help='Run inference on example' + ) + parser.add_argument( + '--ort', action='store_true', help='Save to ort format' + ) + parser.add_argument( + '--always-apply-all-stages', action='store_true', help='Always apply stages' + ) + main(parser.parse_args()) diff --git a/torchDF/pyproject.toml b/torchDF/pyproject.toml new file mode 100644 index 000000000..e69709276 --- /dev/null +++ b/torchDF/pyproject.toml @@ -0,0 +1,78 @@ +[tool.poetry] +name = "DeepFilterNet" +version = "0.5.5" +description = "Noise supression using deep filtering" +authors = ["Hendrik Schröter"] +repository = "https://github.com/Rikorose/DeepFilterNet" +keywords = ["noise reduction", "neural network"] +classifiers = [ + "Topic :: Multimedia :: Sound/Audio :: Speech", + "Topic :: Software Development :: Libraries :: Python Modules", + "Topic :: Software Development :: Libraries :: Application Frameworks", + "Topic :: Utilities", + "Programming Language :: Rust", + "Programming Language :: Python", +] +license = "MIT" +packages = [{ include = "../DeepFilterNet/df" }] +include = [ + { path = "pretrained_models/DeepFilterNet/config.ini" }, + { path = "pretrained_models/DeepFilterNet/checkpoints/*" }, + { path = "pretrained_models/DeepFilterNet2/config.ini" }, + { path = "pretrained_models/DeepFilterNet2/checkpoints/*" }, +] + +[tool.poetry.dependencies] +deepfilterdataloader = { path = "../pyDF-data/", optional = true } +deepfilterlib = { path = "../pyDF" } +python = ">=3.8,<4.0" +numpy = ">=1.22,<2.0" +loguru = ">=0.5" +appdirs = "^1.4" +requests = "^2.27" +packaging = "^23.0" +sympy = ">=1.6" +soundfile = { version = ">=0.10,<0.13", optional = true } +icecream = { version = ">=2,<3", optional = true } +pystoi = { version = "^0.3", optional = true } +pesq = { version = ">=0.0.3,<0.0.5", optional = true } +scipy = { version = "^1", optional = true } +onnxruntime = { version = "^1.15"} +pytest = "^7.4.0" +tqdm = "^4.65.0" +onnx = "^1.14.0" +onnxsim = "^0.4.33" + +[tool.poetry.extras] +train = ["deepfilterdataloader", "icecream"] +eval = ["pystoi", "pesq", "scipy"] +soundfile = ["soundfile"] +dnsmos-local = ["onnxruntime"] + +[tool.poetry.scripts] +deepFilter = "df.enhance:run" +deep-filter-py = "df.enhance:run" + +[tool.poetry.dev-dependencies] +poethepoet = "^0.21" + +[tool.poe.tasks] +install-torch-cuda11 = "python -m pip install torch==2.0.1 torchaudio==2.0.2 --extra-index-url https://download.pytorch.org/whl/cu113/" +install-torch-cpu = "python -m pip install torch==1.13.1 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cpu/" +install-eval-utils = "python -m pip install -r requirements_eval.txt" +install-dnsmos-utils = "python -m pip install -r requirements_dnsmos.txt" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.black] +line-length = 100 +target-version = ["py38", "py39", "py310"] +include = '\.pyi?$' + +[tool.isort] +profile = "black" +line_length = 100 +skip_gitignore = true +known_first_party = ["df", "libdf", "libdfdata"] diff --git a/torchDF/test_torchdf.py b/torchDF/test_torchdf.py new file mode 100644 index 000000000..14e34798a --- /dev/null +++ b/torchDF/test_torchdf.py @@ -0,0 +1,73 @@ +import copy +import torch +import torchaudio + +from df import init_df, enhance +from torch_df_offline import TorchDF +from libdf import DFTractPy +from torch_df_streaming import TorchDFPipeline + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) +DEVICE = torch.device("cpu") + + +class TestTorchStreaming(): + def __reset(self): + torch.manual_seed(23) + + self.model, self.rust_state, _ = init_df(config_allow_defaults=True, model_base_dir='DeepFilterNet3') + self.model.eval() + + self.torch_streaming_like_offline = TorchDFPipeline(always_apply_all_stages=True, device=DEVICE) + + pipeline_for_streaming = TorchDFPipeline(always_apply_all_stages=False, device=DEVICE) + self.torch_streaming = pipeline_for_streaming.torch_streaming_model + self.streaming_state = pipeline_for_streaming.states + self.atten_lim_db = pipeline_for_streaming.atten_lim_db + + self.torch_offline = TorchDF(copy.deepcopy(self.model)) + self.torch_offline = self.torch_offline.to(DEVICE) + + self.df_tract = DFTractPy() + + self.noisy_audio, self.audio_sr = torchaudio.load('examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav', channels_first=True) + self.noisy_audio = self.noisy_audio.mean(dim=0).unsqueeze(0).to(DEVICE) + + def test_offline_with_enhance(self): + """ + Compare torchDF offline implementation to enhance method + """ + self.__reset() + enhanced_audio_torch = self.torch_streaming_like_offline(self.noisy_audio, self.audio_sr).to(DEVICE) + enhanced_audio_offline = enhance(self.model, self.rust_state, self.noisy_audio.cpu()).to(DEVICE) + + assert torch.allclose(enhanced_audio_torch, enhanced_audio_offline, atol=1e-3) + + def test_offline_with_streaming(self): + """ + Compare torchDF streaming implementation to torchDF offline implementation + """ + self.__reset() + + enhanced_audio_torch = self.torch_streaming_like_offline(self.noisy_audio, self.audio_sr).to(DEVICE) + enhanced_audio_offline = self.torch_offline(self.noisy_audio.squeeze(0)).to(DEVICE) + + assert torch.allclose(enhanced_audio_torch, enhanced_audio_offline, atol=1e-3) + + def test_streaming_torch_with_tract(self): + """ + Compare torchDF streaming implementation to tract streaming implementation + + always_apply_all_stages = False + """ + self.__reset() + + chunked_audio = torch.split(self.noisy_audio.squeeze(0), 480) + + for i, chunk in enumerate(chunked_audio): + onnx_output, self.streaming_state, _ = self.torch_streaming(chunk, self.streaming_state, self.atten_lim_db) + rust_output = torch.from_numpy(self.df_tract.process(chunk.unsqueeze(0).cpu().numpy())) + + assert torch.allclose(onnx_output.to(DEVICE), rust_output.to(DEVICE), atol=1e-3), f'process failed - {i} iteration' diff --git a/torchDF/torch_df_offline.py b/torchDF/torch_df_offline.py new file mode 100644 index 000000000..adee570de --- /dev/null +++ b/torchDF/torch_df_offline.py @@ -0,0 +1,368 @@ +import torch +import argparse + +from torch import Tensor, nn +from torch.nn import functional as F +from typing import Tuple + + +class TorchDF(nn.Module): + def __init__(self, model, sr=48000, fft_size=960, hop_size=480, + nb_bands=32, min_nb_freqs=2, nb_df=96, alpha=0.99): + """ + Torch Only Version, Can't be exported to ORT: + - Complex type + - rfft + - fors + """ + super().__init__() + + assert hop_size * 2 == fft_size + self.fft_size = fft_size + self.frame_size = hop_size # f + self.window_size = fft_size + self.window_size_h = fft_size // 2 + self.freq_size = fft_size // 2 + 1 # F + self.wnorm = 1. / (self.window_size ** 2 / (2 * self.frame_size)) + + # Initialize the vorbis window: sin(pi/2*sin^2(pi*n/N)) + self.register_buffer('window', torch.zeros(self.fft_size)) + self.window = torch.sin( + 0.5 * torch.pi * (torch.arange(self.fft_size) + 0.5) / self.window_size_h + ) + self.window = torch.sin(0.5 * torch.pi * self.window ** 2) + + self.sr = sr + self.min_nb_freqs = min_nb_freqs + self.nb_df = nb_df + + # Initializing erb features + self.erb_indices = torch.tensor([ + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 7, 7, 8, + 10, 12, 13, 15, 18, 20, 24, 28, 31, 37, 42, 50, 56, 67 + ]) + self.n_erb_features = nb_bands + + self.erb_conv = nn.Conv1d(self.freq_size, self.n_erb_features, 1, bias=False).requires_grad_(False) + out_weight = torch.zeros(self.n_erb_features, self.freq_size, 1) + + start_index = 0 + for i, num in enumerate(self.erb_indices): + out_weight[i, start_index: start_index + num, 0] = 1 / num + start_index += num + + self.erb_conv.weight.copy_(out_weight) + + # Normalization + self.mean_norm_init = [-60., -90.] + self.unit_norm_init = [0.001, 0.0001] + + # Model + self.model = model + + # Alpha + self.alpha = alpha + + # Init Buffers + self.register_buffer('analysis_mem', torch.zeros(self.fft_size - self.frame_size)) + self.register_buffer('synthesis_mem', torch.zeros(self.fft_size - self.frame_size)) + self.register_buffer('band_unit_norm_state', torch.linspace( + self.unit_norm_init[0], self.unit_norm_init[1], self.nb_df + )) + self.register_buffer('erb_norm_state', torch.linspace( + self.mean_norm_init[0], self.mean_norm_init[1], self.n_erb_features + )) + + + def analysis_time(self, input_data: Tensor) -> Tensor: + """ + Original code - pyDF/src/lib.rs - analysis() + Calculating spectrogram for each frame on T. Every frame is concated with previous frame. + So rfft takes on input frame on window_size. + + Parameters: + input_data: Float[T] - Input raw audio + + Returns: + output: Complex[T, F] - Spectrogram for every frame + """ + in_chunks = torch.split(input_data, self.frame_size) + + # time chunks iteration + output = [] + for ichunk in in_chunks: + output.append(self.frame_analysis(ichunk)) + + return torch.stack(output, dim=0) + + def frame_analysis(self, input_frame: Tensor) -> Tensor: + """ + Original code - libDF/src/lib.rs - frame_analysis() + Calculating spectrograme for one frame. Every frame is concated with buffer from previous frame. + + Parameters: + input_frame: Float[f] - Input raw audio frame + + Returns: + output: Complex[F] - Spectrogram + """ + # First part of the window on the previous frame + # Second part of the window on the new input frame + buf = torch.cat([self.analysis_mem, input_frame]) * self.window + buf_fft = torch.fft.rfft(buf, norm='backward') * self.wnorm + + # Copy input to analysis_mem for next iteration + self.analysis_mem = input_frame + + return buf_fft + + def erb(self, input_data: Tensor, db=True) -> Tensor: + """ + Original code - pyDF/src/lib.rs - erb() + Calculating ERB features for each frame. + + Parameters: + input_data: Float[T, F] or Float[F] - audio spectrogram + + Returns: + erb_features: Float[T, ERB] or Float[ERB] - erb features for given spectrogram + """ + + magnitude_squared = input_data.real ** 2 + input_data.imag ** 2 + erb_features = self.erb_conv(magnitude_squared.unsqueeze(-1)).squeeze(-1) + + # Convergins given features into DB scale + if db: + erb_features = 10.0 * torch.log10(erb_features + 1e-10) + + return erb_features + + def erb_norm_time(self, input_data: Tensor, alpha: float = 0.9) -> Tensor: + """ + Original code - libDF/src/transforms.rs - erb_norm() + Normalizing ERB features. And updates the normalization state on every step. + + Parameters: + input_data: Float[T, ERB] - erb features + alpha: float - alpha value + + Returns: + output: Float[T, ERB] - normalized erb features + """ + + output = [] + for in_step in input_data: + output.append(self.band_mean_norm_erb(in_step, alpha)) + + return torch.stack(output, dim=0) + + def band_mean_norm_erb(self, xs: Tensor, alpha: float, denominator: float = 40.0): + """ + Original code - libDF/src/lib.rs - band_mean_norm() + Normalizing ERB features. And updates the normalization state. + + Parameters: + xs: Float[ERB] - erb features + alpha: float - alpha value which is needed for adaptation of the normalization state for given scale. + denominator: float - denominator for normalization + + Returns: + output: Float[ERB] - normalized erb features + """ + + self.erb_norm_state = torch.lerp(xs, self.erb_norm_state, alpha) + output = (xs - self.erb_norm_state) / denominator + + return output + + def unit_norm_time(self, input_data: Tensor, alpha: float = 0.9) -> Tensor: + """ + Original code - libDF/src/transforms.rs - unit_norm() + Normalizing Deep Filtering features. And updates the normalization state for every step. + + Parameters: + input_data: Complex[T, DF] - deep filtering features + alpha: float - alpha value + + Returns: + output: Complex[T, DF] - normalized deep filtering features + """ + output = [] + + for in_step in input_data: + output.append(self.band_unit_norm(in_step, alpha)) + + return torch.stack(output, dim=0) + + def band_unit_norm(self, xs: Tensor, alpha: float) -> Tensor: + """ + Original code - libDF/src/lib.rs - band_unit_norm() + Normalizing Deep Filtering features. And updates the normalization state. + + Parameters: + xs: Complex[DF] - deep filtering features + alpha: float - alpha value which is needed for adaptation of the normalization state for given scale. + + Returns: + output: Complex[DF] - normalized deep filtering features + """ + self.band_unit_norm_state = torch.lerp(xs.abs(), self.band_unit_norm_state, alpha) + output = xs / self.band_unit_norm_state.sqrt() + + return output + + def synthesis_time(self, input_data: Tensor) -> Tensor: + """ + Original code - pyDF/src/lib.rs - synthesis() + Inverse rfft for each frame. Every frame is summarized with buffer from previous frame. + + Parameters: + input_data: Complex[T, F] - Enhanced audio spectrogram + + Returns: + output: Float[Tr] - Enhanced audio + """ + out_chunks = [] + + # time iteration + for ichunk in input_data: + output_frame = self.frame_synthesis(ichunk) + out_chunks.append(output_frame) + + return torch.cat(out_chunks) + + def frame_synthesis(self, input_data): + """ + Original code - libDF/src/lib.rs - frame_synthesis() + Inverse rfft for one frame. Every frame is summarized with buffer from previous frame. + And saving buffer for next frame. + + Parameters: + input_data: Complex[F] - Enhanced audio spectrogram + + Returns: + output: Float[f] - Enhanced audio + """ + x = torch.fft.irfft(input_data, norm='forward') * self.window + + x_first, x_second = torch.split(x, [self.frame_size, x.shape[0] - self.frame_size]) + output = x_first + self.synthesis_mem + + self.synthesis_mem = x_second + + return output + + def df_features(self, audio: Tensor) -> Tuple[Tensor, Tensor, Tensor]: + """ + Original code - DeepFilterNet/df/enhance - df_features() + Generating the features for audio. + B = 1, CH = 1 implementation only. + + Parameters: + audio: Float[Tf] - Input raw audio + + Returns: + spec: Float[B, CH, T, F, 2] - Audio spectrogram for each frame, concated with previous frame. Complex as real. + erb_feat: Float[B, CH, T, ERB] - Normalized ERB features. + spec_feat: Float[B, CH, T, DF, 2] - Normalized deep filtering features. Complex as real. + """ + + spec = self.analysis_time(audio) + erb_feat = self.erb_norm_time(self.erb(spec), alpha=self.alpha) + spec_feat = torch.view_as_real((self.unit_norm_time(spec[..., :self.nb_df], alpha=self.alpha))) + spec = torch.view_as_real((spec)) + + return spec.view(1, 1, *spec.shape), \ + erb_feat.view(1, 1, *erb_feat.shape), \ + spec_feat.view(1, 1, *spec_feat.shape) + + @torch.no_grad() + def forward(self, audio: Tensor, atten_lim_db: float = None, pad: bool = True, normalize_atten_lim=20) -> Tensor: + """ + Original code - DeepFilterNet/df/enhance.py - enhance() + Enhancing the audio using offline processing + + Parameters: + audio: Float[Tf] + atten_lim_db: float - attenuation limit in dB. How much noise should we mix in the enhanced audio + pad: bool - if True, pad audio to compensate for the delay due to the real-time STFT implementation + + Returns: + enhanced_audio: Float[Tf] + """ + orig_len = audio.shape[-1] + + if pad: + # Pad audio to compensate for the delay due to the real-time STFT implementation + hop_size_divisible_padding_size = (self.fft_size - orig_len % self.fft_size) % self.fft_size + orig_len += hop_size_divisible_padding_size + audio = F.pad(audio, (0, self.fft_size + hop_size_divisible_padding_size)) + assert audio.shape[-1] % self.fft_size == 0 + + spec, erb_feat, spec_feat = self.df_features(audio) + enhanced = self.model(spec, erb_feat, spec_feat)[0] # [B=1, CH=1, T, F, 2] + + if atten_lim_db is not None and abs(atten_lim_db) > 0: + lim = 10 ** (-abs(atten_lim_db) / normalize_atten_lim) + enhanced = torch.lerp(enhanced, spec, lim) + + enhanced = torch.view_as_complex(enhanced.view(*enhanced.shape[2:])) + audio = self.synthesis_time(enhanced).unsqueeze(0) + if pad: + # The frame size is equal to p.hop_size. Given a new frame, the STFT loop requires e.g. + # ceil((n_fft-hop)/hop). I.e. for 50% overlap, then hop=n_fft//2 + # requires 1 additional frame lookahead; 75% requires 3 additional frames lookahead. + # Thus, the STFT/ISTFT loop introduces an algorithmic delay of n_fft - hop. + assert self.fft_size % self.frame_size == 0 # This is only tested for 50% and 75% overlap + d = self.fft_size - self.frame_size + audio = audio[:, d : orig_len + d] + + return audio + + +def main(args): + import os + import glob + import torchaudio + + from tqdm import tqdm + from df import init_df + + torch.set_num_threads(1) + torch.set_num_interop_threads(1) + + model, _, _ = init_df(config_allow_defaults=True, model_base_dir='DeepFilterNet3') + model.to('cpu') + model.eval() + + torch_offline = TorchDF(sr=48000, nb_bands=32, min_nb_freqs=2, hop_size=480, fft_size=960, model=model) + torch_offline = torch_offline.to('cpu') + + print(f'Reading audio from folder - {args.input_folder}') + clips = glob.glob(os.path.join(args.input_folder, "*.wav")) + glob.glob(os.path.join(args.input_folder, "*.flac")) + assert len(clips) > 0, f"Not wound wav or flac in folder {args.input_folder}" + print(f'Found {len(clips)} audio in {args.input_folder}') + + print(f'Inferencing model to folder - {args.output_folder}...') + os.makedirs(args.output_folder, exist_ok=False) + + for clip in tqdm(clips): + noisy_audio, _ = torchaudio.load(clip, channels_first=True) + enhanced_audio = torch_offline(noisy_audio.mean(dim=0)) + save_path = os.path.join(args.output_folder, 'denoised_' + os.path.basename(clip)) + torchaudio.save( + save_path, + enhanced_audio.data.cpu(), + 48000, + ) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + '--input-folder', help='path with folder for inference', required=True + ) + parser.add_argument( + '--output-folder', help='path to save enhanced audio' + ) + main(parser.parse_args()) diff --git a/torchDF/torch_df_streaming.py b/torchDF/torch_df_streaming.py new file mode 100644 index 000000000..6b43b9731 --- /dev/null +++ b/torchDF/torch_df_streaming.py @@ -0,0 +1,668 @@ +""" +ONNX exportable classes +""" +import math +import torch +import argparse +import torchaudio + +from torch.nn import functional as F + +from torch import nn +from torch import Tensor +from typing import Tuple + +from df import init_df + + +class ExportableStreamingTorchDF(nn.Module): + def __init__(self, fft_size, hop_size, nb_bands, + enc, df_dec, erb_dec, df_order=5, lookahead=2, + conv_lookahead=2, nb_df=96, alpha=0.99, + min_db_thresh=-10.0, + max_db_erb_thresh=30.0, + max_db_df_thresh=20.0, + normalize_atten_lim=20.0, + silence_thresh=1e-7, + sr=48000, + always_apply_all_stages=False, + ): + # All complex numbers are stored as floats for ONNX compatibility + super().__init__() + + self.fft_size = fft_size + self.frame_size = hop_size # dimension "f" in Float[f] + self.window_size = fft_size + self.window_size_h = fft_size // 2 + self.freq_size = fft_size // 2 + 1 # dimension "F" in Float[F] + self.wnorm = 1. / (self.window_size ** 2 / (2 * self.frame_size)) + self.df_order = df_order + self.lookahead = lookahead + self.always_apply_all_stages = torch.tensor(always_apply_all_stages) + self.sr = sr + + # Initialize the vorbis window: sin(pi/2*sin^2(pi*n/N)) + window = torch.sin( + 0.5 * torch.pi * (torch.arange(self.fft_size) + 0.5) / self.window_size_h + ) + window = torch.sin(0.5 * torch.pi * window ** 2) + self.register_buffer('window', window) + + self.nb_df = nb_df + + # Initializing erb features + self.erb_indices = torch.tensor([ + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 7, 7, 8, + 10, 12, 13, 15, 18, 20, 24, 28, 31, 37, 42, 50, 56, 67 + ]) + self.nb_bands = nb_bands + + self.register_buffer('forward_erb_matrix', self.erb_fb(self.erb_indices, normalized=True, inverse=False)) + self.register_buffer('inverse_erb_matrix', self.erb_fb(self.erb_indices, normalized=True, inverse=True)) + + # Model + self.enc = enc + # Instead of padding we put tensor with buffers into encoder + # I didn't checked receptived fields of convolution, but equallity tests are working + self.enc.erb_conv0 = self.remove_conv_block_padding(self.enc.erb_conv0) + self.enc.df_conv0 = self.remove_conv_block_padding(self.enc.df_conv0) + + # Instead of padding we put tensor with buffers into df_decoder + self.df_dec = df_dec + self.df_dec.df_convp = self.remove_conv_block_padding(self.df_dec.df_convp) + + self.erb_dec = erb_dec + self.alpha = alpha + + # RFFT + # FFT operations are performed as matmuls for ONNX compatability + self.register_buffer('rfft_matrix', torch.view_as_real(torch.fft.rfft(torch.eye(self.window_size))).transpose(0, 1)) + self.register_buffer('irfft_matrix', torch.linalg.pinv(self.rfft_matrix)) + + # Thresholds + self.register_buffer('min_db_thresh', torch.tensor([min_db_thresh])) + self.register_buffer('max_db_erb_thresh', torch.tensor([max_db_erb_thresh])) + self.register_buffer('max_db_df_thresh', torch.tensor([max_db_df_thresh])) + self.normalize_atten_lim = torch.tensor(normalize_atten_lim) + self.silence_thresh = torch.tensor(silence_thresh) + self.linspace_erb = [-60., -90.] + self.linspace_df = [0.001, 0.0001] + + self.erb_norm_state_shape = (self.nb_bands, ) + self.band_unit_norm_state_shape = (1, self.nb_df, 1) # [bs=1, nb_df, mean of complex value = 1] + self.analysis_mem_shape = (self.frame_size, ) + self.synthesis_mem_shape = (self.frame_size, ) + self.rolling_erb_buf_shape = (1, 1, conv_lookahead + 1, self.nb_bands) # [B, 1, conv kernel size, nb_bands] + self.rolling_feat_spec_buf_shape = (1, 2, conv_lookahead + 1, self.nb_df) # [B, 2 - complex, conv kernel size, nb_df] + self.rolling_c0_buf_shape = (1, self.enc.df_conv0_ch, self.df_order, self.nb_df) # [B, conv hidden, df_order, nb_df] + self.rolling_spec_buf_x_shape = (max(self.df_order, conv_lookahead), self.freq_size, 2) # [number of specs to save, ...] + self.rolling_spec_buf_y_shape = (self.df_order + conv_lookahead, self.freq_size, 2) # [number of specs to save, ...] + self.enc_hidden_shape = (1, 1, self.enc.emb_dim) # [n_layers=1, batch_size=1, emb_dim] + self.erb_dec_hidden_shape = (2, 1, self.erb_dec.emb_dim) # [n_layers=2, batch_size=1, emb_dim] + self.df_dec_hidden_shape = (2, 1, self.df_dec.emb_dim) # [n_layers=2, batch_size=1, emb_dim] + + # States + state_shapes = [ + self.erb_norm_state_shape, + self.band_unit_norm_state_shape, + self.analysis_mem_shape, + self.synthesis_mem_shape, + self.rolling_erb_buf_shape, + self.rolling_feat_spec_buf_shape, + self.rolling_c0_buf_shape, + self.rolling_spec_buf_x_shape, + self.rolling_spec_buf_y_shape, + self.enc_hidden_shape, + self.erb_dec_hidden_shape, + self.df_dec_hidden_shape + ] + self.state_lens = [ + math.prod(x) for x in state_shapes + ] + self.states_full_len = sum(self.state_lens) + + # Zero buffers + self.register_buffer('zero_gains', torch.zeros(self.nb_bands)) + self.register_buffer('zero_coefs', torch.zeros(self.rolling_c0_buf_shape[2], self.nb_df, 2)) + + @staticmethod + def remove_conv_block_padding(original_conv: nn.Module) -> nn.Module: + """ + Remove paddings for convolutions in the original model + + Parameters: + original_conv: nn.Module - original convolution module + + Returns: + output: nn.Module - new convolution module without paddings + """ + new_modules = [] + + for module in original_conv: + if not isinstance(module, nn.ConstantPad2d): + new_modules.append(module) + + return nn.Sequential(*new_modules) + + def erb_fb(self, widths: Tensor, normalized: bool = True, inverse: bool = False) -> Tensor: + """ + Generate the erb filterbank + Taken from https://github.com/Rikorose/DeepFilterNet/blob/fa926662facea33657c255fd1f3a083ddc696220/DeepFilterNet/df/modules.py#L206 + Numpy removed from original code + + Parameters: + widths: Tensor - widths of the erb bands + normalized: bool - normalize to constant energy per band + inverse: bool - inverse erb filterbank + + Returns: + fb: Tensor - erb filterbank + """ + n_freqs = int(torch.sum(widths)) + all_freqs = torch.linspace(0, self.sr // 2, n_freqs + 1)[:-1] + + b_pts = torch.cumsum(torch.cat([torch.tensor([0]), widths]), dtype=torch.int32, dim=0)[:-1] + + fb = torch.zeros((all_freqs.shape[0], b_pts.shape[0])) + for i, (b, w) in enumerate(zip(b_pts.tolist(), widths.tolist())): + fb[b : b + w, i] = 1 + + # Normalize to constant energy per resulting band + if inverse: + fb = fb.t() + if not normalized: + fb /= fb.sum(dim=1, keepdim=True) + else: + if normalized: + fb /= fb.sum(dim=0) + + return fb + + @staticmethod + def mul_complex(t1, t2): + """ + Compute multiplication of two complex numbers in view_as_real format. + + Parameters: + t1: Float[F, 2] - First number + t2: Float[F, 2] - Second number + + Returns: + output: Float[F, 2] - final multiplication of two complex numbers + """ + t1_real = t1[..., 0] + t1_imag = t1[..., 1] + t2_real = t2[..., 0] + t2_imag = t2[..., 1] + return torch.stack((t1_real * t2_real - t1_imag * t2_imag, t1_real * t2_imag + t1_imag * t2_real), dim=-1) + + def erb(self, input_data: Tensor, erb_eps: float = 1e-10) -> Tensor: + """ + Original code - pyDF/src/lib.rs - erb() + Calculating ERB features for each frame. + + Parameters: + input_data: Float[T, F] or Float[F] - audio spectrogram + + Returns: + erb_features: Float[T, ERB] or Float[ERB] - erb features for given spectrogram + """ + + magnitude_squared = torch.sum(input_data ** 2, dim=-1) + erb_features = magnitude_squared.matmul(self.forward_erb_matrix) + erb_features_db = 10.0 * torch.log10(erb_features + erb_eps) + + return erb_features_db + + @staticmethod + def band_mean_norm_erb(xs: Tensor, erb_norm_state: Tensor, alpha: float, denominator: float = 40.0) -> Tuple[Tensor, Tensor]: + """ + Original code - libDF/src/lib.rs - band_mean_norm() + Normalizing ERB features. And updates the normalization state. + + Parameters: + xs: Float[ERB] - erb features + erb_norm_state: Float[ERB] - normalization state from previous step + alpha: float - alpha value which is needed for adaptation of the normalization state for given scale. + denominator: float - denominator for normalization + + Returns: + output: Float[ERB] - normalized erb features + erb_norm_state: Float[ERB] - updated normalization state + """ + new_erb_norm_state = torch.lerp(xs, erb_norm_state, alpha) + output = (xs - new_erb_norm_state) / denominator + + return output, new_erb_norm_state + + @staticmethod + def band_unit_norm(xs: Tensor, band_unit_norm_state, alpha: float) -> Tuple[Tensor, Tensor]: + """ + Original code - libDF/src/lib.rs - band_unit_norm() + Normalizing Deep Filtering features. And updates the normalization state. + + Parameters: + xs: Float[1, DF, 2] - deep filtering features + band_unit_norm_state: Float[1, DF, 1] - normalization state from previous step + alpha: float - alpha value which is needed for adaptation of the normalization state for given scale. + + Returns: + output: Float[1, DF] - normalized deep filtering features + band_unit_norm_state: Float[1, DF, 1] - updated normalization state + """ + xs_abs = torch.linalg.norm(xs, dim=-1, keepdim=True) # xs.abs() from complex + new_band_unit_norm_state = torch.lerp(xs_abs, band_unit_norm_state, alpha) + output = xs / new_band_unit_norm_state.sqrt() + + return output, new_band_unit_norm_state + + def frame_analysis(self, input_frame: Tensor, analysis_mem: Tensor) -> Tuple[Tensor, Tensor]: + """ + Original code - libDF/src/lib.rs - frame_analysis() + Calculating spectrograme for one frame. Every frame is concated with buffer from previous frame. + + Parameters: + input_frame: Float[f] - Input raw audio frame + analysis_mem: Float[f] - Previous frame + + Returns: + output: Float[F, 2] - Spectrogram + analysis_mem: Float[f] - Saving current frame for next iteration + """ + # First part of the window on the previous frame + # Second part of the window on the new input frame + buf = torch.cat([analysis_mem, input_frame]) * self.window + rfft_buf = torch.matmul(buf, self.rfft_matrix) * self.wnorm + + # Copy input to analysis_mem for next iteration + return rfft_buf, input_frame + + def frame_synthesis(self, x: Tensor, synthesis_mem: Tensor) -> Tuple[Tensor, Tensor]: + """ + Original code - libDF/src/lib.rs - frame_synthesis() + Inverse rfft for one frame. Every frame is summarized with buffer from previous frame. + And saving buffer for next frame. + + Parameters: + x: Float[F, 2] - Enhanced audio spectrogram + synthesis_mem: Float[f] - Previous synthesis frame + + Returns: + output: Float[f] - Enhanced audio + synthesis_mem: Float[f] - Saving current frame + """ + # x - [F=481, 2] + # self.irfft_matrix - [fft_size=481, 2, f=960] + # [f=960] + x = torch.einsum('fi,fij->j', x, self.irfft_matrix) * self.fft_size * self.window + + x_first, x_second = torch.split(x, [self.frame_size, self.window_size - self.frame_size]) + output = x_first + synthesis_mem + + return output, x_second + + def is_apply_gains(self, lsnr: Tensor) -> Tensor: + """ + Original code - libDF/src/tract.rs - is_apply_stages() + This code decomposed for better graph capturing + + Parameters: + lsnr: Tensor[Float] - predicted lsnr value + + Returns: + output: Tensor[Bool] - whether to apply gains or not + """ + if self.always_apply_all_stages: + return torch.ones_like(lsnr, dtype=torch.bool) + + return torch.le(lsnr, self.max_db_erb_thresh) * torch.ge(lsnr, self.min_db_thresh) + + def is_apply_gain_zeros(self, lsnr: Tensor) -> Tensor: + """ + Original code - libDF/src/tract.rs - is_apply_stages() + This code decomposed for better graph capturing + + Parameters: + lsnr: Tensor[Float] - predicted lsnr value + + Returns: + output: Tensor[Bool] - whether to apply gain_zeros or not + """ + if self.always_apply_all_stages: + return torch.zeros_like(lsnr, dtype=torch.bool) + + # Only noise detected, just apply a zero mask + return torch.ge(self.min_db_thresh, lsnr) + + def is_apply_df(self, lsnr: Tensor) -> Tensor: + """ + Original code - libDF/src/tract.rs - is_apply_stages() + This code decomposed for better graph capturing + + Parameters: + lsnr: Tensor[Float] - predicted lsnr value + + Returns: + output: Tensor[Bool] - whether to apply deep filtering or not + """ + if self.always_apply_all_stages: + return torch.ones_like(lsnr, dtype=torch.bool) + + return torch.le(lsnr, self.max_db_df_thresh) * torch.ge(lsnr, self.min_db_thresh) + + def apply_mask(self, spec: Tensor, gains: Tensor) -> Tensor: + """ + Original code - libDF/src/lib.rs - apply_interp_band_gain() + + Applying ERB Gains for input spectrogram + + Parameters: + spec: Float[F, 2] - Input frame spectrogram + gains: Float[ERB] - ERB gains from erb decoder + + Returns: + spec: Float[F] - Spectrogram with applyed ERB gains + """ + gains = gains.matmul(self.inverse_erb_matrix) + spec = spec * gains.unsqueeze(-1) + + return spec + + def deep_filter(self, gain_spec: Tensor, coefs: Tensor, rolling_spec_buf_x: Tensor) -> Tensor: + """ + Original code - libDF/src/tract.rs - df() + + Applying Deep Filtering to gained spectrogram by multiplying coefs to rolling_buffer_x (spectrograms from past / future). + Deep Filtering replacing lower self.nb_df spec bands. + + Parameters: + gain_spec: Float[F, 2] - spectrogram after ERB gains applied + coefs: Float[DF, BUF, 2] - coefficients for deep filtering from df decoder + rolling_spec_buf_x: Float[buffer_size, F, 2] - spectrograms from past / future + + Returns: + gain_spec: Float[F, 2] - spectrogram after deep filtering + """ + stacked_input_specs = rolling_spec_buf_x[:, :self.nb_df] + mult = self.mul_complex(stacked_input_specs, coefs) + gain_spec[:self.nb_df] = torch.sum(mult, dim=0) + return gain_spec + + def unpack_states(self, states: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]: + splitted_states = torch.split(states, self.state_lens) + + erb_norm_state = splitted_states[0].view(self.erb_norm_state_shape) + band_unit_norm_state = splitted_states[1].view(self.band_unit_norm_state_shape) + analysis_mem = splitted_states[2].view(self.analysis_mem_shape) + synthesis_mem = splitted_states[3].view(self.synthesis_mem_shape) + rolling_erb_buf = splitted_states[4].view(self.rolling_erb_buf_shape) + rolling_feat_spec_buf = splitted_states[5].view(self.rolling_feat_spec_buf_shape) + rolling_c0_buf = splitted_states[6].view(self.rolling_c0_buf_shape) + rolling_spec_buf_x = splitted_states[7].view(self.rolling_spec_buf_x_shape) + rolling_spec_buf_y = splitted_states[8].view(self.rolling_spec_buf_y_shape) + enc_hidden = splitted_states[9].view(self.enc_hidden_shape) + erb_dec_hidden = splitted_states[10].view(self.erb_dec_hidden_shape) + df_dec_hidden = splitted_states[11].view(self.df_dec_hidden_shape) + + new_erb_norm_state = torch.linspace( + self.linspace_erb[0], self.linspace_erb[1], self.nb_bands, device=erb_norm_state.device + ).view(self.erb_norm_state_shape).to(torch.float32) # float() to fix export issue + new_band_unit_norm_state = torch.linspace( + self.linspace_df[0], self.linspace_df[1], self.nb_df, device=band_unit_norm_state.device + ).view(self.band_unit_norm_state_shape).to(torch.float32) # float() to fix export issue + + erb_norm_state = torch.where( + torch.tensor(torch.nonzero(erb_norm_state).shape[0] == 0), + new_erb_norm_state, + erb_norm_state + ) + + band_unit_norm_state = torch.where( + torch.tensor(torch.nonzero(band_unit_norm_state).shape[0] == 0), + new_band_unit_norm_state, + band_unit_norm_state + ) + + return ( + erb_norm_state, band_unit_norm_state, + analysis_mem, synthesis_mem, + rolling_erb_buf, rolling_feat_spec_buf, rolling_c0_buf, + rolling_spec_buf_x, rolling_spec_buf_y, + enc_hidden, erb_dec_hidden, df_dec_hidden + ) + + def forward(self, + input_frame: Tensor, + states: Tensor, + atten_lim_db: Tensor + ) -> Tuple[ + Tensor, Tensor, Tensor + ]: + """ + Enhancing input audio frame + + Parameters: + input_frame: Float[t] - Input raw audio frame + states: Float[state_len] - Flattened and concated states + atten_lim_db: Float[1] - Attenuation lim + + Returns: + enhanced_frame: Float[t] - Enhanced audio frame + new_states: Float[state_len] - Flattened and concated updated states + lsnr: Float[1] - Estimated lsnr of input frame + + """ + assert input_frame.ndim == 1, 'only bs=1 and t=frame_size supported' + assert input_frame.shape[0] == self.frame_size, 'input_frame must be bs=1 and t=frame_size' + + ( + erb_norm_state, band_unit_norm_state, + analysis_mem, synthesis_mem, + rolling_erb_buf, rolling_feat_spec_buf, rolling_c0_buf, + rolling_spec_buf_x, rolling_spec_buf_y, + enc_hidden, erb_dec_hidden, df_dec_hidden + ) = self.unpack_states(states) + + # If input_frame is silent, then do nothing and return zeros + rms_non_silence_condition = (input_frame ** 2).sum() / self.frame_size >= self.silence_thresh + rms_non_silence_condition = torch.logical_or(rms_non_silence_condition, self.always_apply_all_stages) + + spectrogram, new_analysis_mem = self.frame_analysis(input_frame, analysis_mem) + spectrogram = spectrogram.unsqueeze(0) # [1, freq_size, 2] reshape needed for easier stacking buffers + new_rolling_spec_buf_x = torch.cat([rolling_spec_buf_x[1:, ...], spectrogram]) # [n_frames=5, 481, 2] + # rolling_spec_buf_y - [n_frames=7, 481, 2] n_frames=7 for compatability with original code, but in code we use only one frame + new_rolling_spec_buf_y = torch.cat([rolling_spec_buf_y[1:, ...], spectrogram]) + + erb_feat, new_erb_norm_state = self.band_mean_norm_erb( + self.erb(spectrogram).squeeze(0), erb_norm_state, alpha=self.alpha + ) # [ERB] + spec_feat, new_band_unit_norm_state = self.band_unit_norm( + spectrogram[:, :self.nb_df], band_unit_norm_state, alpha=self.alpha + ) # [1, DF, 2] + + erb_feat = erb_feat[None, None, None, ...] # [b=1, conv_input_dim=1, t=1, n_erb=32] + spec_feat = spec_feat[None, ...].permute(0, 3, 1, 2) # [bs=1, conv_input_dim=2, t=1, df_order=96] + + # (1, 1, T, self.nb_bands) + new_rolling_erb_buf = torch.cat([rolling_erb_buf[:, :, 1:, :], erb_feat], dim=2) + + # (1, 2, T, self.nb_df) + new_rolling_feat_spec_buf = torch.cat([rolling_feat_spec_buf[:, :, 1:, :], spec_feat], dim=2) + + e0, e1, e2, e3, emb, c0, lsnr, new_enc_hidden = self.enc( + new_rolling_erb_buf, + new_rolling_feat_spec_buf, + enc_hidden + ) + lsnr = lsnr.flatten() # [b=1, t=1, 1] -> 1 + + apply_gains = self.is_apply_gains(lsnr) + apply_df = self.is_apply_df(lsnr) + apply_gain_zeros = self.is_apply_gain_zeros(lsnr) + + # erb_dec + # [BS=1, 1, T=1, ERB] + new_gains, new_erb_dec_hidden = self.erb_dec(emb, e3, e2, e1, e0, erb_dec_hidden) + gains = torch.where(apply_gains, new_gains.view(self.nb_bands), self.zero_gains) + new_erb_dec_hidden = torch.where(apply_gains, new_erb_dec_hidden, erb_dec_hidden) + + # df_dec + new_rolling_c0_buf = torch.cat([rolling_c0_buf[:, :, 1:, :], c0], dim=2) + # new_coefs - [BS=1, T=1, F, O*2] + new_coefs, new_df_dec_hidden = self.df_dec( + emb, + new_rolling_c0_buf, + df_dec_hidden + ) + new_rolling_c0_buf = torch.where(apply_df, new_rolling_c0_buf, rolling_c0_buf) + new_df_dec_hidden = torch.where(apply_df, new_df_dec_hidden, df_dec_hidden) + coefs = torch.where( + apply_df, + new_coefs.view(self.nb_df, -1, 2).permute(1, 0, 2), + self.zero_coefs + ) + + # Applying features + current_spec = new_rolling_spec_buf_y[self.df_order - 1] + current_spec = torch.where( + torch.logical_or(apply_gains, apply_gain_zeros), + self.apply_mask(current_spec.clone(), gains), + current_spec + ) + current_spec = torch.where( + apply_df, + self.deep_filter(current_spec.clone(), coefs, new_rolling_spec_buf_x), + current_spec + ) + + # Mixing some noisy channel + # taken from https://github.com/Rikorose/DeepFilterNet/blob/59789e135cb5ed0eb86bb50e8f1be09f60859d5c/DeepFilterNet/df/enhance.py#L237 + if torch.abs(atten_lim_db) > 0: + spec_noisy = rolling_spec_buf_x[max(self.lookahead, self.df_order) - self.lookahead - 1] + lim = 10 ** (-torch.abs(atten_lim_db) / self.normalize_atten_lim) + current_spec = torch.lerp(current_spec, spec_noisy, lim) + + enhanced_audio_frame, new_synthesis_mem = self.frame_synthesis(current_spec, synthesis_mem) + + new_states = [ + new_erb_norm_state, new_band_unit_norm_state, + new_analysis_mem, new_synthesis_mem, + new_rolling_erb_buf, new_rolling_feat_spec_buf, new_rolling_c0_buf, + new_rolling_spec_buf_x, new_rolling_spec_buf_y, + new_enc_hidden, new_erb_dec_hidden, new_df_dec_hidden + ] + new_states = torch.cat([x.flatten() for x in new_states]) + + # RMS conditioning for better ONNX graph + enhanced_audio_frame = torch.where(rms_non_silence_condition, enhanced_audio_frame, torch.zeros_like(enhanced_audio_frame)) + new_states = torch.where(rms_non_silence_condition, new_states, states) + + return enhanced_audio_frame, new_states, lsnr + +class TorchDFPipeline(nn.Module): + def __init__( + self, nb_bands=32, hop_size=480, fft_size=960, + df_order=5, conv_lookahead=2, nb_df=96, model_base_dir='DeepFilterNet3', + atten_lim_db=0.0, always_apply_all_stages=False, device='cpu' + ): + super().__init__() + self.hop_size = hop_size + self.fft_size = fft_size + + model, state, _ = init_df(config_allow_defaults=True, model_base_dir=model_base_dir) + model.eval() + self.sample_rate = state.sr() + + self.torch_streaming_model = ExportableStreamingTorchDF( + nb_bands=nb_bands, hop_size=hop_size, fft_size=fft_size, + enc=model.enc, df_dec=model.df_dec, erb_dec=model.erb_dec, df_order=df_order, + always_apply_all_stages=always_apply_all_stages, + conv_lookahead=conv_lookahead, nb_df=nb_df, sr=self.sample_rate + ) + self.torch_streaming_model = self.torch_streaming_model.to(device) + self.states = torch.zeros(self.torch_streaming_model.states_full_len, device=device) + + self.atten_lim_db = torch.tensor(atten_lim_db, device=device) + + def forward(self, input_audio: Tensor, sample_rate: int) -> Tensor: + """ + Denoising audio frame using exportable fully torch model. + + Parameters: + input_audio: Float[1, t] - Input audio + sample_rate: Int - Sample rate + + Returns: + enhanced_audio: Float[1, t] - Enhanced input audio + """ + assert input_audio.shape[0] == 1, f'Only mono supported! Got wrong shape! {input_audio.shape}' + assert sample_rate == self.sample_rate, f'Only {self.sample_rate} supported! Got wrong sample rate! {sample_rate}' + + input_audio = input_audio.squeeze(0) + orig_len = input_audio.shape[0] + + # padding taken from + # https://github.com/Rikorose/DeepFilterNet/blob/fa926662facea33657c255fd1f3a083ddc696220/DeepFilterNet/df/enhance.py#L229 + hop_size_divisible_padding_size = (self.hop_size - orig_len % self.hop_size) % self.hop_size + orig_len += hop_size_divisible_padding_size + input_audio = F.pad(input_audio, (0, self.fft_size + hop_size_divisible_padding_size)) + + chunked_audio = torch.split(input_audio, self.hop_size) + + output_frames = [] + + for input_frame in chunked_audio: + ( + enhanced_audio_frame, self.states, lsnr + ) = self.torch_streaming_model( + input_frame, + self.states, + self.atten_lim_db + ) + + output_frames.append(enhanced_audio_frame) + + enhanced_audio = torch.cat(output_frames).unsqueeze(0) # [t] -> [1, t] typical mono format + + # taken from + # https://github.com/Rikorose/DeepFilterNet/blob/fa926662facea33657c255fd1f3a083ddc696220/DeepFilterNet/df/enhance.py#L248 + d = self.fft_size - self.hop_size + enhanced_audio = enhanced_audio[:, d : orig_len + d] + + return enhanced_audio + + +def main(args): + torch.set_num_threads(1) + torch.set_num_interop_threads(1) + + torch_df = TorchDFPipeline(device=args.device) + + # torchaudio normalize=True, fp32 return + noisy_audio, sr = torchaudio.load(args.audio_path, channels_first=True) + noisy_audio = noisy_audio.mean(dim=0).unsqueeze(0).to(args.device) # stereo to mono + + enhanced_audio = torch_df(noisy_audio, sr).detach().cpu() + + torchaudio.save( + args.output_path, enhanced_audio, sr, + encoding="PCM_S", bits_per_sample=16 + ) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description='Denoising one audio with DF3 model using torch only' + ) + parser.add_argument( + '--audio-path', type=str, required=True, help='Path to audio file' + ) + parser.add_argument( + '--output-path', type=str, required=True, help='Path to output file' + ) + parser.add_argument( + '--device', type=str, default='cpu', choices=['cuda', 'cpu'], help='Device to run on' + ) + + main(parser.parse_args()) + \ No newline at end of file From 91014b9d93b08af8021d27f32135e3da2cebeed3 Mon Sep 17 00:00:00 2001 From: Alexey Korepanov Date: Wed, 20 Sep 2023 12:23:18 +0400 Subject: [PATCH 2/4] check wav added --- ...M_Street_Near_Regular_SP_Mobile_Primary.wav | Bin 0 -> 960044 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 torchDF/examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav diff --git a/torchDF/examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav b/torchDF/examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav new file mode 100644 index 0000000000000000000000000000000000000000..1a21d6175250796914696ca33a499440c37746cd GIT binary patch literal 960044 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Streaming model can be fully exported to ONNX using `model_onnx_export.py`. @@ -21,18 +23,18 @@ Every script and test have to run inside poetry enviroment. To run tests: ``` -python -m pytest -v +poetry run python -m pytest -v ``` We compare this model to existing `enhance` method (which is partly written on Rust) and tract model (which is purely on Rust). All tests are passing, so model is working. To enhance audio using streaming implementation: ``` -python torch_df_streaming.py --audio-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav --output-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary_enhanced.wav +poetry run python torch_df_streaming_minimal.py --audio-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav --output-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary_enhanced.wav ``` To convert model to onnx and run tests: ``` -python model_onnx_export.py --test --performance --inference-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav --ort +poetry run python model_onnx_export.py --test --performance --inference-path examples/A1CIM28ZUCA8RX_M_Street_Near_Regular_SP_Mobile_Primary.wav --ort ``` TODO: From 1097015d53ced78fb234e7d7071a5dd4446e3952 Mon Sep 17 00:00:00 2001 From: "a.korepanov" Date: Fri, 3 Nov 2023 09:58:04 +0300 Subject: [PATCH 4/4] couple fixes --- torchDF/README.md | 2 +- torchDF/pyproject.toml | 2 +- torchDF/torch_df_streaming.py | 5 ++++- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/torchDF/README.md b/torchDF/README.md index 943ffa9e2..a50fcf601 100644 --- a/torchDF/README.md +++ b/torchDF/README.md @@ -7,7 +7,7 @@ Installation: cd path/to/DeepFilterNet/ pip install maturin poetry poethepoet curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -maturin develop --release -m pyDF/Cargo.toml +maturin build --release -m pyDF/Cargo.toml cd DeepFilterNet export PYTHONPATH=$PWD diff --git a/torchDF/pyproject.toml b/torchDF/pyproject.toml index e69709276..8b99539ba 100644 --- a/torchDF/pyproject.toml +++ b/torchDF/pyproject.toml @@ -57,7 +57,7 @@ deep-filter-py = "df.enhance:run" poethepoet = "^0.21" [tool.poe.tasks] -install-torch-cuda11 = "python -m pip install torch==2.0.1 torchaudio==2.0.2 --extra-index-url https://download.pytorch.org/whl/cu113/" +install-torch-cuda11 = "python -m pip install torch==2.1.0 torchaudio==2.1.0 --extra-index-url https://download.pytorch.org/whl/cu118/" install-torch-cpu = "python -m pip install torch==1.13.1 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cpu/" install-eval-utils = "python -m pip install -r requirements_eval.txt" install-dnsmos-utils = "python -m pip install -r requirements_dnsmos.txt" diff --git a/torchDF/torch_df_streaming.py b/torchDF/torch_df_streaming.py index 6b43b9731..02a8dc7ae 100644 --- a/torchDF/torch_df_streaming.py +++ b/torchDF/torch_df_streaming.py @@ -636,7 +636,7 @@ def main(args): torch.set_num_threads(1) torch.set_num_interop_threads(1) - torch_df = TorchDFPipeline(device=args.device) + torch_df = TorchDFPipeline(device=args.device, always_apply_all_stages=args.always_apply_all_stages) # torchaudio normalize=True, fp32 return noisy_audio, sr = torchaudio.load(args.audio_path, channels_first=True) @@ -663,6 +663,9 @@ def main(args): parser.add_argument( '--device', type=str, default='cpu', choices=['cuda', 'cpu'], help='Device to run on' ) + parser.add_argument( + '--always-apply-all-stages', action='store_true' + ) main(parser.parse_args()) \ No newline at end of file