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Move FFTW-dependent functions to extension
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,9 @@ | ||
| module NNlibFFTWExt | ||
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| using FFTW | ||
| using NNlib | ||
| using KernelAbstractions | ||
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| include("stft.jl") | ||
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| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
| function NNlib.stft(x; | ||
| n_fft::Int, hop_length::Int = n_fft ÷ 4, window = nothing, | ||
| center::Bool = true, normalized::Bool = false, | ||
| ) | ||
| kab = get_backend(x) | ||
| use_window = !isnothing(window) | ||
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| use_window && kab != get_backend(window) && throw(ArgumentError( | ||
| "`window` must be on the same device as stft input `x` ($kab), \ | ||
| instead: `$(get_backend(window))`.")) | ||
| use_window && !(0 < length(window) ≤ n_fft) && throw(ArgumentError( | ||
| "Expected `0 < length(window) ≤ n_fft=$n_fft`, \ | ||
| but got `length(window)=$(length(window))`.")) | ||
| hop_length < 0 && throw(ArgumentError( | ||
| "Expected `hop_length > 0`, but got `hop_length=$hop_length`.")) | ||
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| # Pad window on both sides with `0` to `n_fft` length if needed. | ||
| if use_window && length(window) < n_fft | ||
| left = ((n_fft - length(window)) ÷ 2) + 1 | ||
| tmp = KernelAbstractions.zeros(kab, eltype(window), n_fft) | ||
| tmp[left:left + length(window) - 1] .= window | ||
| window = tmp | ||
| end | ||
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| if center | ||
| pad_amount = n_fft ÷ 2 | ||
| x = pad_reflect(x, pad_amount; dims=1) | ||
| end | ||
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| n = size(x, 1) | ||
| (0 < n_fft ≤ n) || throw(ArgumentError( | ||
| "Expected `0 < n_fft ≤ size(x, 1)=$n`, but got `n_fft=$n_fft`.")) | ||
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| n_frames = 1 + (n - n_fft) ÷ hop_length | ||
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| # time2col. | ||
| # Reshape `x` to (n_fft, n_frames, B) if needed. | ||
| # Each row in `n_frames` is shifted by `hop_length`. | ||
| if n_frames > 1 | ||
| # TODO can be more efficient if we support something like torch.as_strided | ||
| ids = [ | ||
| row + hop_length * col | ||
| for row in 1:n_fft, col in 0:(n_frames - 1)] | ||
| x = x[ids, ntuple(_ -> Colon(), ndims(x) - 1)...] | ||
| end | ||
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| region = 1 | ||
| use_window && (x = x .* window;) | ||
| y = eltype(x) <: Complex ? fft(x, region) : rfft(x, region) | ||
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| normalized && (y = y .* eltype(y)(n_fft^-0.5);) | ||
| return y | ||
| end | ||
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| function NNlib.istft(y; | ||
| n_fft::Int, hop_length::Int = n_fft ÷ 4, window = nothing, | ||
| center::Bool = true, normalized::Bool = false, | ||
| return_complex::Bool = false, | ||
| original_length::Union{Nothing, Int} = nothing, | ||
| ) | ||
| kab = get_backend(y) | ||
| use_window = !isnothing(window) | ||
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| use_window && kab != get_backend(window) && throw(ArgumentError( | ||
| "`window` must be on the same device as istft input `y` ($kab), \ | ||
| instead: `$(get_backend(window))`.")) | ||
| use_window && !(0 < length(window) ≤ n_fft) && throw(ArgumentError( | ||
| "Expected `0 < length(window) ≤ n_fft=$n_fft`, \ | ||
| but got `length(window)=$(length(window))`.")) | ||
| hop_length < 0 && throw(ArgumentError( | ||
| "Expected `hop_length > 0`, but got `hop_length=$hop_length`.")) | ||
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| # TODO check `y` eltype is complex | ||
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| n_frames = size(y, 2) | ||
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| # Pad window on both sides with `0` to `n_fft` length if needed. | ||
| if use_window && length(window) < n_fft | ||
| left = ((n_fft - length(window)) ÷ 2) + 1 | ||
| tmp = KernelAbstractions.zeros(kab, eltype(window), n_fft) | ||
| tmp[left:left + length(window) - 1] .= window | ||
| window = tmp | ||
| end | ||
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| # Denormalize. | ||
| normalized && (y = y .* eltype(y)(n_fft^0.5);) | ||
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| region = 1 | ||
| x = return_complex ? ifft(y, region) : irfft(y, n_fft, region) | ||
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| # De-apply window. | ||
| use_window && (x = x ./ window;) | ||
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| # col2time. | ||
| expected_output_len = n_fft + hop_length * (n_frames - 1) | ||
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| ids = Vector{Int}(undef, expected_output_len) | ||
| in_idx, out_idx = 0, 0 | ||
| prev_e, v = 0, 0 | ||
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| for col in 0:(n_frames - 1) | ||
| for row in 1:n_fft | ||
| in_idx += 1 | ||
| v = row + hop_length * col | ||
| v > prev_e || continue | ||
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| out_idx += 1 | ||
| ids[out_idx] = in_idx | ||
| end | ||
| prev_e = v | ||
| end | ||
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| # In case of batched input, reshaped it (n_fft, n_frames, batch) -> (:, batch). | ||
| nd = ntuple(_ -> Colon(), ndims(x) - 2) | ||
| ndims(x) == 3 && (x = reshape(x, (:, size(x, 3)));) | ||
| x = x[ids, nd...] | ||
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| # Trim padding. | ||
| left = center ? (n_fft ÷ 2 + 1) : 1 | ||
| right = if isnothing(original_length) | ||
| center ? (size(x, 1) - n_fft ÷ 2) : expected_output_len | ||
| else | ||
| left + original_length - 1 | ||
| end | ||
| x = x[left:right, nd...] | ||
| return x | ||
| end | ||
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