From 9198de2d95a810c970c3803d49a4598f14ccc73d Mon Sep 17 00:00:00 2001 From: remsky Date: Tue, 4 Feb 2025 05:18:18 -0700 Subject: [PATCH] Refactor project structure and update Dockerfiles for improved organization and dependency management --- api/src/builds/__init__.py | 0 api/src/builds/config.json | 26 - api/src/builds/istftnet.py | 524 ----- api/src/builds/models.py | 378 ---- api/src/builds/plbert.py | 16 - docker/cpu/Dockerfile | 27 +- docker/gpu/Dockerfile | 46 +- pyproject.toml | 2 +- start-cpu.sh | 4 +- start-gpu.sh | 4 +- uv.lock | 4260 ------------------------------------ 11 files changed, 42 insertions(+), 5245 deletions(-) delete mode 100644 api/src/builds/__init__.py delete mode 100644 api/src/builds/config.json delete mode 100644 api/src/builds/istftnet.py delete mode 100644 api/src/builds/models.py delete mode 100644 api/src/builds/plbert.py delete mode 100644 uv.lock diff --git a/api/src/builds/__init__.py b/api/src/builds/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/api/src/builds/config.json b/api/src/builds/config.json deleted file mode 100644 index 29e12f5..0000000 --- a/api/src/builds/config.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "decoder": { - "type": "istftnet", - "upsample_kernel_sizes": [20, 12], - "upsample_rates": [10, 6], - "gen_istft_hop_size": 5, - "gen_istft_n_fft": 20, - "resblock_dilation_sizes": [ - [1, 3, 5], - [1, 3, 5], - [1, 3, 5] - ], - "resblock_kernel_sizes": [3, 7, 11], - "upsample_initial_channel": 512 - }, - "dim_in": 64, - "dropout": 0.2, - "hidden_dim": 512, - "max_conv_dim": 512, - "max_dur": 50, - "multispeaker": true, - "n_layer": 3, - "n_mels": 80, - "n_token": 178, - "style_dim": 128 -} \ No newline at end of file diff --git a/api/src/builds/istftnet.py b/api/src/builds/istftnet.py deleted file mode 100644 index 9a0a849..0000000 --- a/api/src/builds/istftnet.py +++ /dev/null @@ -1,524 +0,0 @@ -# https://github.com/yl4579/StyleTTS2/blob/main/Modules/istftnet.py -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from scipy.signal import get_window -from torch.nn import Conv1d, ConvTranspose1d -from torch.nn.utils import remove_weight_norm, weight_norm - - -# https://github.com/yl4579/StyleTTS2/blob/main/Modules/utils.py -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - -def get_padding(kernel_size, dilation=1): - return int((kernel_size*dilation - dilation)/2) - -LRELU_SLOPE = 0.1 - -class AdaIN1d(nn.Module): - def __init__(self, style_dim, num_features): - super().__init__() - self.norm = nn.InstanceNorm1d(num_features, affine=False) - self.fc = nn.Linear(style_dim, num_features*2) - - def forward(self, x, s): - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - return (1 + gamma) * self.norm(x) + beta - -class AdaINResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), style_dim=64): - super(AdaINResBlock1, self).__init__() - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - self.adain1 = nn.ModuleList([ - AdaIN1d(style_dim, channels), - AdaIN1d(style_dim, channels), - AdaIN1d(style_dim, channels), - ]) - - self.adain2 = nn.ModuleList([ - AdaIN1d(style_dim, channels), - AdaIN1d(style_dim, channels), - AdaIN1d(style_dim, channels), - ]) - - self.alpha1 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))]) - self.alpha2 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))]) - - - def forward(self, x, s): - for c1, c2, n1, n2, a1, a2 in zip(self.convs1, self.convs2, self.adain1, self.adain2, self.alpha1, self.alpha2): - xt = n1(x, s) - xt = xt + (1 / a1) * (torch.sin(a1 * xt) ** 2) # Snake1D - xt = c1(xt) - xt = n2(xt, s) - xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2) # Snake1D - xt = c2(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - -class TorchSTFT(torch.nn.Module): - def __init__(self, filter_length=800, hop_length=200, win_length=800, window='hann'): - super().__init__() - self.filter_length = filter_length - self.hop_length = hop_length - self.win_length = win_length - self.window = torch.from_numpy(get_window(window, win_length, fftbins=True).astype(np.float32)) - - def transform(self, input_data): - forward_transform = torch.stft( - input_data, - self.filter_length, self.hop_length, self.win_length, window=self.window.to(input_data.device), - return_complex=True) - - return torch.abs(forward_transform), torch.angle(forward_transform) - - def inverse(self, magnitude, phase): - inverse_transform = torch.istft( - magnitude * torch.exp(phase * 1j), - self.filter_length, self.hop_length, self.win_length, window=self.window.to(magnitude.device)) - - return inverse_transform.unsqueeze(-2) # unsqueeze to stay consistent with conv_transpose1d implementation - - def forward(self, input_data): - self.magnitude, self.phase = self.transform(input_data) - reconstruction = self.inverse(self.magnitude, self.phase) - return reconstruction - -class SineGen(torch.nn.Module): - """ Definition of sine generator - SineGen(samp_rate, harmonic_num = 0, - sine_amp = 0.1, noise_std = 0.003, - voiced_threshold = 0, - flag_for_pulse=False) - samp_rate: sampling rate in Hz - harmonic_num: number of harmonic overtones (default 0) - sine_amp: amplitude of sine-wavefrom (default 0.1) - noise_std: std of Gaussian noise (default 0.003) - voiced_thoreshold: F0 threshold for U/V classification (default 0) - flag_for_pulse: this SinGen is used inside PulseGen (default False) - Note: when flag_for_pulse is True, the first time step of a voiced - segment is always sin(np.pi) or cos(0) - """ - - def __init__(self, samp_rate, upsample_scale, harmonic_num=0, - sine_amp=0.1, noise_std=0.003, - voiced_threshold=0, - flag_for_pulse=False): - super(SineGen, self).__init__() - self.sine_amp = sine_amp - self.noise_std = noise_std - self.harmonic_num = harmonic_num - self.dim = self.harmonic_num + 1 - self.sampling_rate = samp_rate - self.voiced_threshold = voiced_threshold - self.flag_for_pulse = flag_for_pulse - self.upsample_scale = upsample_scale - - def _f02uv(self, f0): - # generate uv signal - uv = (f0 > self.voiced_threshold).type(torch.float32) - return uv - - def _f02sine(self, f0_values): - """ f0_values: (batchsize, length, dim) - where dim indicates fundamental tone and overtones - """ - # convert to F0 in rad. The interger part n can be ignored - # because 2 * np.pi * n doesn't affect phase - rad_values = (f0_values / self.sampling_rate) % 1 - - # initial phase noise (no noise for fundamental component) - rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ - device=f0_values.device) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - - # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) - if not self.flag_for_pulse: -# # for normal case - -# # To prevent torch.cumsum numerical overflow, -# # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. -# # Buffer tmp_over_one_idx indicates the time step to add -1. -# # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi -# tmp_over_one = torch.cumsum(rad_values, 1) % 1 -# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 -# cumsum_shift = torch.zeros_like(rad_values) -# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - -# phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi - rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), - scale_factor=1/self.upsample_scale, - mode="linear").transpose(1, 2) - -# tmp_over_one = torch.cumsum(rad_values, 1) % 1 -# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 -# cumsum_shift = torch.zeros_like(rad_values) -# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - - phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi - phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, - scale_factor=self.upsample_scale, mode="linear").transpose(1, 2) - sines = torch.sin(phase) - - else: - # If necessary, make sure that the first time step of every - # voiced segments is sin(pi) or cos(0) - # This is used for pulse-train generation - - # identify the last time step in unvoiced segments - uv = self._f02uv(f0_values) - uv_1 = torch.roll(uv, shifts=-1, dims=1) - uv_1[:, -1, :] = 1 - u_loc = (uv < 1) * (uv_1 > 0) - - # get the instantanouse phase - tmp_cumsum = torch.cumsum(rad_values, dim=1) - # different batch needs to be processed differently - for idx in range(f0_values.shape[0]): - temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] - temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] - # stores the accumulation of i.phase within - # each voiced segments - tmp_cumsum[idx, :, :] = 0 - tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum - - # rad_values - tmp_cumsum: remove the accumulation of i.phase - # within the previous voiced segment. - i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) - - # get the sines - sines = torch.cos(i_phase * 2 * np.pi) - return sines - - def forward(self, f0): - """ sine_tensor, uv = forward(f0) - input F0: tensor(batchsize=1, length, dim=1) - f0 for unvoiced steps should be 0 - output sine_tensor: tensor(batchsize=1, length, dim) - output uv: tensor(batchsize=1, length, 1) - """ - f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, - device=f0.device) - # fundamental component - fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) - - # generate sine waveforms - sine_waves = self._f02sine(fn) * self.sine_amp - - # generate uv signal - # uv = torch.ones(f0.shape) - # uv = uv * (f0 > self.voiced_threshold) - uv = self._f02uv(f0) - - # noise: for unvoiced should be similar to sine_amp - # std = self.sine_amp/3 -> max value ~ self.sine_amp - # . for voiced regions is self.noise_std - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - - # first: set the unvoiced part to 0 by uv - # then: additive noise - sine_waves = sine_waves * uv + noise - return sine_waves, uv, noise - - -class SourceModuleHnNSF(torch.nn.Module): - """ SourceModule for hn-nsf - SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0) - sampling_rate: sampling_rate in Hz - harmonic_num: number of harmonic above F0 (default: 0) - sine_amp: amplitude of sine source signal (default: 0.1) - add_noise_std: std of additive Gaussian noise (default: 0.003) - note that amplitude of noise in unvoiced is decided - by sine_amp - voiced_threshold: threhold to set U/V given F0 (default: 0) - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - uv (batchsize, length, 1) - """ - - def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - - # to produce sine waveforms - self.l_sin_gen = SineGen(sampling_rate, upsample_scale, harmonic_num, - sine_amp, add_noise_std, voiced_threshod) - - # to merge source harmonics into a single excitation - self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) - self.l_tanh = torch.nn.Tanh() - - def forward(self, x): - """ - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - """ - # source for harmonic branch - with torch.no_grad(): - sine_wavs, uv, _ = self.l_sin_gen(x) - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - - # source for noise branch, in the same shape as uv - noise = torch.randn_like(uv) * self.sine_amp / 3 - return sine_merge, noise, uv -def padDiff(x): - return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0) - - -class Generator(torch.nn.Module): - def __init__(self, style_dim, resblock_kernel_sizes, upsample_rates, upsample_initial_channel, resblock_dilation_sizes, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size): - super(Generator, self).__init__() - - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - resblock = AdaINResBlock1 - - self.m_source = SourceModuleHnNSF( - sampling_rate=24000, - upsample_scale=np.prod(upsample_rates) * gen_istft_hop_size, - harmonic_num=8, voiced_threshod=10) - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * gen_istft_hop_size) - self.noise_convs = nn.ModuleList() - self.noise_res = nn.ModuleList() - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), - k, u, padding=(k-u)//2))) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel//(2**(i+1)) - for j, (k, d) in enumerate(zip(resblock_kernel_sizes,resblock_dilation_sizes)): - self.resblocks.append(resblock(ch, k, d, style_dim)) - - c_cur = upsample_initial_channel // (2 ** (i + 1)) - - if i + 1 < len(upsample_rates): # - stride_f0 = np.prod(upsample_rates[i + 1:]) - self.noise_convs.append(Conv1d( - gen_istft_n_fft + 2, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2)) - self.noise_res.append(resblock(c_cur, 7, [1,3,5], style_dim)) - else: - self.noise_convs.append(Conv1d(gen_istft_n_fft + 2, c_cur, kernel_size=1)) - self.noise_res.append(resblock(c_cur, 11, [1,3,5], style_dim)) - - - self.post_n_fft = gen_istft_n_fft - self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3)) - self.ups.apply(init_weights) - self.conv_post.apply(init_weights) - self.reflection_pad = torch.nn.ReflectionPad1d((1, 0)) - self.stft = TorchSTFT(filter_length=gen_istft_n_fft, hop_length=gen_istft_hop_size, win_length=gen_istft_n_fft) - - - def forward(self, x, s, f0): - with torch.no_grad(): - f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t - - har_source, noi_source, uv = self.m_source(f0) - har_source = har_source.transpose(1, 2).squeeze(1) - har_spec, har_phase = self.stft.transform(har_source) - har = torch.cat([har_spec, har_phase], dim=1) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - x_source = self.noise_convs[i](har) - x_source = self.noise_res[i](x_source, s) - - x = self.ups[i](x) - if i == self.num_upsamples - 1: - x = self.reflection_pad(x) - - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i*self.num_kernels+j](x, s) - else: - xs += self.resblocks[i*self.num_kernels+j](x, s) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]) - phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]) - return self.stft.inverse(spec, phase) - - def fw_phase(self, x, s): - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i*self.num_kernels+j](x, s) - else: - xs += self.resblocks[i*self.num_kernels+j](x, s) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.reflection_pad(x) - x = self.conv_post(x) - spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]) - phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]) - return spec, phase - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -class AdainResBlk1d(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2), - upsample='none', dropout_p=0.0): - super().__init__() - self.actv = actv - self.upsample_type = upsample - self.upsample = UpSample1d(upsample) - self.learned_sc = dim_in != dim_out - self._build_weights(dim_in, dim_out, style_dim) - self.dropout = nn.Dropout(dropout_p) - - if upsample == 'none': - self.pool = nn.Identity() - else: - self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1)) - - - def _build_weights(self, dim_in, dim_out, style_dim): - self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1)) - self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1)) - self.norm1 = AdaIN1d(style_dim, dim_in) - self.norm2 = AdaIN1d(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False)) - - def _shortcut(self, x): - x = self.upsample(x) - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - x = self.pool(x) - x = self.conv1(self.dropout(x)) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(self.dropout(x)) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / np.sqrt(2) - return out - -class UpSample1d(nn.Module): - def __init__(self, layer_type): - super().__init__() - self.layer_type = layer_type - - def forward(self, x): - if self.layer_type == 'none': - return x - else: - return F.interpolate(x, scale_factor=2, mode='nearest') - -class Decoder(nn.Module): - def __init__(self, dim_in=512, F0_channel=512, style_dim=64, dim_out=80, - resblock_kernel_sizes = [3,7,11], - upsample_rates = [10, 6], - upsample_initial_channel=512, - resblock_dilation_sizes=[[1,3,5], [1,3,5], [1,3,5]], - upsample_kernel_sizes=[20, 12], - gen_istft_n_fft=20, gen_istft_hop_size=5): - super().__init__() - - self.decode = nn.ModuleList() - - self.encode = AdainResBlk1d(dim_in + 2, 1024, style_dim) - - self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim)) - self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim)) - self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim)) - self.decode.append(AdainResBlk1d(1024 + 2 + 64, 512, style_dim, upsample=True)) - - self.F0_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1)) - - self.N_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1)) - - self.asr_res = nn.Sequential( - weight_norm(nn.Conv1d(512, 64, kernel_size=1)), - ) - - - self.generator = Generator(style_dim, resblock_kernel_sizes, upsample_rates, - upsample_initial_channel, resblock_dilation_sizes, - upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size) - - def forward(self, asr, F0_curve, N, s): - F0 = self.F0_conv(F0_curve.unsqueeze(1)) - N = self.N_conv(N.unsqueeze(1)) - - x = torch.cat([asr, F0, N], axis=1) - x = self.encode(x, s) - - asr_res = self.asr_res(asr) - - res = True - for block in self.decode: - if res: - x = torch.cat([x, asr_res, F0, N], axis=1) - x = block(x, s) - if block.upsample_type != "none": - res = False - - x = self.generator(x, s, F0_curve) - return x diff --git a/api/src/builds/models.py b/api/src/builds/models.py deleted file mode 100644 index 5495465..0000000 --- a/api/src/builds/models.py +++ /dev/null @@ -1,378 +0,0 @@ -# https://github.com/yl4579/StyleTTS2/blob/main/models.py -import json -import os -import os.path as osp -from pathlib import Path - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from munch import Munch -from torch.nn.utils import spectral_norm, weight_norm - -from .istftnet import AdaIN1d, Decoder -from .plbert import load_plbert - - -class LinearNorm(torch.nn.Module): - def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): - super(LinearNorm, self).__init__() - self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) - - torch.nn.init.xavier_uniform_( - self.linear_layer.weight, - gain=torch.nn.init.calculate_gain(w_init_gain)) - - def forward(self, x): - return self.linear_layer(x) - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - -class TextEncoder(nn.Module): - def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)): - super().__init__() - self.embedding = nn.Embedding(n_symbols, channels) - - padding = (kernel_size - 1) // 2 - self.cnn = nn.ModuleList() - for _ in range(depth): - self.cnn.append(nn.Sequential( - weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)), - LayerNorm(channels), - actv, - nn.Dropout(0.2), - )) - # self.cnn = nn.Sequential(*self.cnn) - - self.lstm = nn.LSTM(channels, channels//2, 1, batch_first=True, bidirectional=True) - - def forward(self, x, input_lengths, m): - x = self.embedding(x) # [B, T, emb] - x = x.transpose(1, 2) # [B, emb, T] - m = m.to(input_lengths.device).unsqueeze(1) - x.masked_fill_(m, 0.0) - - for c in self.cnn: - x = c(x) - x.masked_fill_(m, 0.0) - - x = x.transpose(1, 2) # [B, T, chn] - - input_lengths = input_lengths.cpu().numpy() - x = nn.utils.rnn.pack_padded_sequence( - x, input_lengths, batch_first=True, enforce_sorted=False) - - self.lstm.flatten_parameters() - x, _ = self.lstm(x) - x, _ = nn.utils.rnn.pad_packed_sequence( - x, batch_first=True) - - x = x.transpose(-1, -2) - x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]]) - - x_pad[:, :, :x.shape[-1]] = x - x = x_pad.to(x.device) - - x.masked_fill_(m, 0.0) - - return x - - def inference(self, x): - x = self.embedding(x) - x = x.transpose(1, 2) - x = self.cnn(x) - x = x.transpose(1, 2) - self.lstm.flatten_parameters() - x, _ = self.lstm(x) - return x - - def length_to_mask(self, lengths): - mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) - mask = torch.gt(mask+1, lengths.unsqueeze(1)) - return mask - - -class UpSample1d(nn.Module): - def __init__(self, layer_type): - super().__init__() - self.layer_type = layer_type - - def forward(self, x): - if self.layer_type == 'none': - return x - else: - return F.interpolate(x, scale_factor=2, mode='nearest') - -class AdainResBlk1d(nn.Module): - def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2), - upsample='none', dropout_p=0.0): - super().__init__() - self.actv = actv - self.upsample_type = upsample - self.upsample = UpSample1d(upsample) - self.learned_sc = dim_in != dim_out - self._build_weights(dim_in, dim_out, style_dim) - self.dropout = nn.Dropout(dropout_p) - - if upsample == 'none': - self.pool = nn.Identity() - else: - self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1)) - - - def _build_weights(self, dim_in, dim_out, style_dim): - self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1)) - self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1)) - self.norm1 = AdaIN1d(style_dim, dim_in) - self.norm2 = AdaIN1d(style_dim, dim_out) - if self.learned_sc: - self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False)) - - def _shortcut(self, x): - x = self.upsample(x) - if self.learned_sc: - x = self.conv1x1(x) - return x - - def _residual(self, x, s): - x = self.norm1(x, s) - x = self.actv(x) - x = self.pool(x) - x = self.conv1(self.dropout(x)) - x = self.norm2(x, s) - x = self.actv(x) - x = self.conv2(self.dropout(x)) - return x - - def forward(self, x, s): - out = self._residual(x, s) - out = (out + self._shortcut(x)) / np.sqrt(2) - return out - -class AdaLayerNorm(nn.Module): - def __init__(self, style_dim, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.fc = nn.Linear(style_dim, channels*2) - - def forward(self, x, s): - x = x.transpose(-1, -2) - x = x.transpose(1, -1) - - h = self.fc(s) - h = h.view(h.size(0), h.size(1), 1) - gamma, beta = torch.chunk(h, chunks=2, dim=1) - gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1) - - - x = F.layer_norm(x, (self.channels,), eps=self.eps) - x = (1 + gamma) * x + beta - return x.transpose(1, -1).transpose(-1, -2) - -class ProsodyPredictor(nn.Module): - - def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1): - super().__init__() - - self.text_encoder = DurationEncoder(sty_dim=style_dim, - d_model=d_hid, - nlayers=nlayers, - dropout=dropout) - - self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True) - self.duration_proj = LinearNorm(d_hid, max_dur) - - self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True) - self.F0 = nn.ModuleList() - self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout)) - self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout)) - self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)) - - self.N = nn.ModuleList() - self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout)) - self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout)) - self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)) - - self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0) - self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0) - - - def forward(self, texts, style, text_lengths, alignment, m): - d = self.text_encoder(texts, style, text_lengths, m) - - batch_size = d.shape[0] - text_size = d.shape[1] - - # predict duration - input_lengths = text_lengths.cpu().numpy() - x = nn.utils.rnn.pack_padded_sequence( - d, input_lengths, batch_first=True, enforce_sorted=False) - - m = m.to(text_lengths.device).unsqueeze(1) - - self.lstm.flatten_parameters() - x, _ = self.lstm(x) - x, _ = nn.utils.rnn.pad_packed_sequence( - x, batch_first=True) - - x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]]) - - x_pad[:, :x.shape[1], :] = x - x = x_pad.to(x.device) - - duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=self.training)) - - en = (d.transpose(-1, -2) @ alignment) - - return duration.squeeze(-1), en - - def F0Ntrain(self, x, s): - x, _ = self.shared(x.transpose(-1, -2)) - - F0 = x.transpose(-1, -2) - for block in self.F0: - F0 = block(F0, s) - F0 = self.F0_proj(F0) - - N = x.transpose(-1, -2) - for block in self.N: - N = block(N, s) - N = self.N_proj(N) - - return F0.squeeze(1), N.squeeze(1) - - def length_to_mask(self, lengths): - mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) - mask = torch.gt(mask+1, lengths.unsqueeze(1)) - return mask - -class DurationEncoder(nn.Module): - - def __init__(self, sty_dim, d_model, nlayers, dropout=0.1): - super().__init__() - self.lstms = nn.ModuleList() - for _ in range(nlayers): - self.lstms.append(nn.LSTM(d_model + sty_dim, - d_model // 2, - num_layers=1, - batch_first=True, - bidirectional=True, - dropout=dropout)) - self.lstms.append(AdaLayerNorm(sty_dim, d_model)) - - - self.dropout = dropout - self.d_model = d_model - self.sty_dim = sty_dim - - def forward(self, x, style, text_lengths, m): - masks = m.to(text_lengths.device) - - x = x.permute(2, 0, 1) - s = style.expand(x.shape[0], x.shape[1], -1) - x = torch.cat([x, s], axis=-1) - x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0) - - x = x.transpose(0, 1) - input_lengths = text_lengths.cpu().numpy() - x = x.transpose(-1, -2) - - for block in self.lstms: - if isinstance(block, AdaLayerNorm): - x = block(x.transpose(-1, -2), style).transpose(-1, -2) - x = torch.cat([x, s.permute(1, -1, 0)], axis=1) - x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0) - else: - x = x.transpose(-1, -2) - x = nn.utils.rnn.pack_padded_sequence( - x, input_lengths, batch_first=True, enforce_sorted=False) - block.flatten_parameters() - x, _ = block(x) - x, _ = nn.utils.rnn.pad_packed_sequence( - x, batch_first=True) - x = F.dropout(x, p=self.dropout, training=self.training) - x = x.transpose(-1, -2) - - x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]]) - - x_pad[:, :, :x.shape[-1]] = x - x = x_pad.to(x.device) - - return x.transpose(-1, -2) - - def inference(self, x, style): - x = self.embedding(x.transpose(-1, -2)) * np.sqrt(self.d_model) - style = style.expand(x.shape[0], x.shape[1], -1) - x = torch.cat([x, style], axis=-1) - src = self.pos_encoder(x) - output = self.transformer_encoder(src).transpose(0, 1) - return output - - def length_to_mask(self, lengths): - mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) - mask = torch.gt(mask+1, lengths.unsqueeze(1)) - return mask - -# https://github.com/yl4579/StyleTTS2/blob/main/utils.py -def recursive_munch(d): - if isinstance(d, dict): - return Munch((k, recursive_munch(v)) for k, v in d.items()) - elif isinstance(d, list): - return [recursive_munch(v) for v in d] - else: - return d - -async def build_model(path, device): - from ..core.paths import load_json, load_model_weights - - config = Path(__file__).parent / 'config.json' - assert config.exists(), f'Config path incorrect: config.json not found at {config}' - - args = recursive_munch(await load_json(config)) - assert args.decoder.type == 'istftnet', f'Unknown decoder type: {args.decoder.type}' - decoder = Decoder(dim_in=args.hidden_dim, style_dim=args.style_dim, dim_out=args.n_mels, - resblock_kernel_sizes = args.decoder.resblock_kernel_sizes, - upsample_rates = args.decoder.upsample_rates, - upsample_initial_channel=args.decoder.upsample_initial_channel, - resblock_dilation_sizes=args.decoder.resblock_dilation_sizes, - upsample_kernel_sizes=args.decoder.upsample_kernel_sizes, - gen_istft_n_fft=args.decoder.gen_istft_n_fft, gen_istft_hop_size=args.decoder.gen_istft_hop_size) - text_encoder = TextEncoder(channels=args.hidden_dim, kernel_size=5, depth=args.n_layer, n_symbols=args.n_token) - predictor = ProsodyPredictor(style_dim=args.style_dim, d_hid=args.hidden_dim, nlayers=args.n_layer, max_dur=args.max_dur, dropout=args.dropout) - bert = load_plbert() - bert_encoder = nn.Linear(bert.config.hidden_size, args.hidden_dim) - for parent in [bert, bert_encoder, predictor, decoder, text_encoder]: - for child in parent.children(): - if isinstance(child, nn.RNNBase): - child.flatten_parameters() - model = Munch( - bert=bert.to(device).eval(), - bert_encoder=bert_encoder.to(device).eval(), - predictor=predictor.to(device).eval(), - decoder=decoder.to(device).eval(), - text_encoder=text_encoder.to(device).eval(), - ) - weights = await load_model_weights(path, device=device) - for key, state_dict in weights['net'].items(): - assert key in model, key - try: - model[key].load_state_dict(state_dict) - except: - state_dict = {k[7:]: v for k, v in state_dict.items()} - model[key].load_state_dict(state_dict, strict=False) - return model diff --git a/api/src/builds/plbert.py b/api/src/builds/plbert.py deleted file mode 100644 index 8af1bbc..0000000 --- a/api/src/builds/plbert.py +++ /dev/null @@ -1,16 +0,0 @@ -# https://github.com/yl4579/StyleTTS2/blob/main/Utils/PLBERT/util.py -from transformers import AlbertConfig, AlbertModel - - -class CustomAlbert(AlbertModel): - def forward(self, *args, **kwargs): - # Call the original forward method - outputs = super().forward(*args, **kwargs) - # Only return the last_hidden_state - return outputs.last_hidden_state - -def load_plbert(): - plbert_config = {'vocab_size': 178, 'hidden_size': 768, 'num_attention_heads': 12, 'intermediate_size': 2048, 'max_position_embeddings': 512, 'num_hidden_layers': 12, 'dropout': 0.1} - albert_base_configuration = AlbertConfig(**plbert_config) - bert = CustomAlbert(albert_base_configuration) - return bert diff --git a/docker/cpu/Dockerfile b/docker/cpu/Dockerfile index 8363ed3..280fba8 100644 --- a/docker/cpu/Dockerfile +++ b/docker/cpu/Dockerfile @@ -10,14 +10,16 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* -# Install uv for speed and glory -COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ +# Install UV using the installer script +RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \ + mv /root/.local/bin/uv /usr/local/bin/ && \ + mv /root/.local/bin/uvx /usr/local/bin/ # Create non-root user RUN useradd -m -u 1000 appuser # Create directories and set ownership -RUN mkdir -p /app/api/src/voices && \ +RUN mkdir -p /app/api/src/voices/v1_0 && \ chown -R appuser:appuser /app USER appuser @@ -41,15 +43,18 @@ RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --extra cpu # Set environment variables -ENV PYTHONUNBUFFERED=1 -ENV PYTHONPATH=/app -ENV PATH="/app/.venv/bin:$PATH" -ENV UV_LINK_MODE=copy +ENV PYTHONUNBUFFERED=1 \ + PYTHONPATH=/app:/app/api \ + PATH="/app/.venv/bin:$PATH" \ + UV_LINK_MODE=copy -ENV USE_GPU=false -ENV USE_ONNX=true -ENV DOWNLOAD_ONNX=true -ENV DOWNLOAD_PTH=false +# Core settings that differ from config.py defaults +ENV USE_GPU=false \ + USE_ONNX=true + +# Model download flags (container-specific) +ENV DOWNLOAD_ONNX=false \ + DOWNLOAD_PTH=false # Download models based on environment variables RUN if [ "$DOWNLOAD_ONNX" = "true" ]; then \ diff --git a/docker/gpu/Dockerfile b/docker/gpu/Dockerfile index f1cae1a..486943d 100644 --- a/docker/gpu/Dockerfile +++ b/docker/gpu/Dockerfile @@ -11,17 +11,16 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ libsndfile1 \ curl \ ffmpeg \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* + && apt-get clean && rm -rf /var/lib/apt/lists/* -# Install uv -COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ +# Install UV using the installer script +RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \ + mv /root/.local/bin/uv /usr/local/bin/ && \ + mv /root/.local/bin/uvx /usr/local/bin/ -# Create non-root user -RUN useradd -m -u 1000 appuser - -# Create directories and set ownership -RUN mkdir -p /app/api/src/voices && \ +# Create non-root user and prepare /app in one layer +RUN useradd -m -u 1000 appuser && \ + mkdir -p /app/api/src/voices/v1_0 && \ chown -R appuser:appuser /app USER appuser @@ -30,34 +29,31 @@ WORKDIR /app # Copy dependency files COPY --chown=appuser:appuser pyproject.toml ./pyproject.toml -# Install dependencies with GPU extras +# Install dependencies with GPU extras (using cache mounts) RUN --mount=type=cache,target=/root/.cache/uv \ uv venv && \ uv sync --extra gpu -# Copy project files including models +# Copy project files including models and sync again COPY --chown=appuser:appuser api ./api COPY --chown=appuser:appuser web ./web COPY --chown=appuser:appuser docker/scripts/download_model.* ./ - -# Install project with GPU extras RUN --mount=type=cache,target=/root/.cache/uv \ uv sync --extra gpu +# Copy scripts and make them executable in a single RUN step COPY --chown=appuser:appuser docker/scripts/ /app/docker/scripts/ -RUN chmod +x docker/scripts/entrypoint.sh -RUN chmod +x docker/scripts/download_model.sh +RUN chmod +x docker/scripts/entrypoint.sh docker/scripts/download_model.sh -# Set environment variables -ENV PYTHONUNBUFFERED=1 -ENV PYTHONPATH=/app -ENV PATH="/app/.venv/bin:$PATH" -ENV UV_LINK_MODE=copy - -ENV USE_GPU=true -ENV USE_ONNX=false -ENV DOWNLOAD_PTH=true -ENV DOWNLOAD_ONNX=false +# Set all environment variables in one go +ENV PYTHONUNBUFFERED=1 \ + PYTHONPATH=/app:/app/api \ + PATH="/app/.venv/bin:$PATH" \ + UV_LINK_MODE=copy \ + USE_GPU=true \ + USE_ONNX=false \ + DOWNLOAD_PTH=true \ + DOWNLOAD_ONNX=false # Run FastAPI server CMD ["/app/docker/scripts/entrypoint.sh"] diff --git a/pyproject.toml b/pyproject.toml index d83e8af..571acd1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ dependencies = [ "munch==4.0.0", "tiktoken==0.8.0", "loguru==0.7.3", - "transformers==4.47.1", + # "transformers==4.47.1", "openai>=1.59.6", # "ebooklib>=0.18", # "html2text>=2024.2.26", diff --git a/start-cpu.sh b/start-cpu.sh index 7be376d..fecda41 100755 --- a/start-cpu.sh +++ b/start-cpu.sh @@ -7,8 +7,8 @@ PROJECT_ROOT=$(pwd) export USE_GPU=false export USE_ONNX=false export PYTHONPATH=$PROJECT_ROOT:$PROJECT_ROOT/api -export MODEL_DIR=$PROJECT_ROOT/api/src/models -export VOICES_DIR=$PROJECT_ROOT/api/src/voices +export MODEL_DIR=src/models +export VOICES_DIR=src/voices/v1_0 export WEB_PLAYER_PATH=$PROJECT_ROOT/web # Run FastAPI with CPU extras using uv run diff --git a/start-gpu.sh b/start-gpu.sh index e898b21..d073df6 100755 --- a/start-gpu.sh +++ b/start-gpu.sh @@ -7,8 +7,8 @@ PROJECT_ROOT=$(pwd) export USE_GPU=true export USE_ONNX=false export PYTHONPATH=$PROJECT_ROOT:$PROJECT_ROOT/api -export MODEL_DIR=$PROJECT_ROOT/api/src/models -export VOICES_DIR=$PROJECT_ROOT/api/src/voices +export MODEL_DIR=src/models +export VOICES_DIR=src/voices/v1_0 export WEB_PLAYER_PATH=$PROJECT_ROOT/web # Run FastAPI with GPU extras using uv run diff --git a/uv.lock b/uv.lock deleted file mode 100644 index 1a44130..0000000 --- a/uv.lock +++ /dev/null @@ -1,4260 +0,0 @@ -version = 1 -requires-python = ">=3.10" -resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version == '3.12.*'", - "python_full_version == '3.11.*'", - "python_full_version < '3.11'", - "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", - "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')", - "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", - "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", - "(python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform == 'darwin')", - 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