-# Kokoro TTS API
+# _`FastKoko`_
[]()
-[]()
+[]()
[](https://huggingface.co/hexgrad/Kokoro-82M/tree/c3b0d86e2a980e027ef71c28819ea02e351c2667) [](https://huggingface.co/spaces/Remsky/Kokoro-TTS-Zero) [](https://www.buymeacoffee.com/remsky)
Dockerized FastAPI wrapper for [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) text-to-speech model
@@ -24,14 +24,30 @@ Dockerized FastAPI wrapper for [Kokoro-82M](https://huggingface.co/hexgrad/Kokor
The service can be accessed through either the API endpoints or the Gradio web interface.
1. Install prerequisites:
- - Install [Docker Desktop](https://www.docker.com/products/docker-desktop/) + [Git](https://git-scm.com/downloads)
- - Clone and start the service:
+ - Install [Docker Desktop](https://www.docker.com/products/docker-desktop/)
+ - Clone the repository:
```bash
git clone https://github.com/remsky/Kokoro-FastAPI.git
cd Kokoro-FastAPI
- docker compose up --build # for GPU
- #docker compose -f docker-compose.cpu.yml up --build # for CPU
```
+
+2. Start the service:
+
+ - Using Docker Compose (Full setup including UI):
+ ```bash
+ cd docker/gpu # OR
+ # cd docker/cpu # Run this or the above
+ docker compose up --build
+ ```
+ - OR running the API alone using Docker (model + voice packs baked in):
+ ```bash
+
+ docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:latest # CPU
+ docker run --gpus all -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-gpu:latest # Nvidia GPU
+ # Minified versions are available with `:latest-slim` tag.
+ ```
+
+
2. Run locally as an OpenAI-Compatible Speech Endpoint
```python
from openai import OpenAI
@@ -167,6 +183,21 @@ If you only want the API, just comment out everything in the docker-compose.yml
Currently, voices created via the API are accessible here, but voice combination/creation has not yet been added
*Note: Recent updates for streaming could lead to temporary glitches. If so, pull from the most recent stable release v0.0.2 to restore*
+
+### Disabling Local Saving
+
+You can disable local saving of audio files and hide the file view in the UI by setting the `DISABLE_LOCAL_SAVING` environment variable to `true`. This is useful when running the service on a server where you don't want to store generated audio files locally.
+
+When using Docker Compose:
+```yaml
+environment:
+ - DISABLE_LOCAL_SAVING=true
+```
+
+When running the Docker image directly:
+```bash
+docker run -p 7860:7860 -e DISABLE_LOCAL_SAVING=true ghcr.io/remsky/kokoro-fastapi-ui:latest
+```
+
+
+
+
+
+
Versioning & Development
+ +I'm doing what I can to keep things stable, but we are on an early and rapid set of build cycles here. +If you run into trouble, you may have to roll back a version on the release tags if something comes up, or build up from source and/or troubleshoot + submit a PR. Will leave the branch up here for the last known stable points: + +`v0.0.5post1` + +Free and open source is a community effort, and I love working on this project, though there's only really so many hours in a day. If you'd like to support the work, feel free to open a PR, buy me a coffee, or report any bugs/features/etc you find during use. + + +
+
+
+
+Linux GPU Permissions
diff --git a/api/src/builds/__init__.py b/api/src/builds/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api/src/builds/config.json b/api/src/builds/config.json new file mode 100644 index 0000000..29e12f5 --- /dev/null +++ b/api/src/builds/config.json @@ -0,0 +1,26 @@ +{ + "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 new file mode 100644 index 0000000..9a0a849 --- /dev/null +++ b/api/src/builds/istftnet.py @@ -0,0 +1,524 @@ +# 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/kokoro.py b/api/src/builds/kokoro.py new file mode 100644 index 0000000..4b6ff0c --- /dev/null +++ b/api/src/builds/kokoro.py @@ -0,0 +1,151 @@ +import re + +import phonemizer +import torch + + +def split_num(num): + num = num.group() + if '.' in num: + return num + elif ':' in num: + h, m = [int(n) for n in num.split(':')] + if m == 0: + return f"{h} o'clock" + elif m < 10: + return f'{h} oh {m}' + return f'{h} {m}' + year = int(num[:4]) + if year < 1100 or year % 1000 < 10: + return num + left, right = num[:2], int(num[2:4]) + s = 's' if num.endswith('s') else '' + if 100 <= year % 1000 <= 999: + if right == 0: + return f'{left} hundred{s}' + elif right < 10: + return f'{left} oh {right}{s}' + return f'{left} {right}{s}' + +def flip_money(m): + m = m.group() + bill = 'dollar' if m[0] == '$' else 'pound' + if m[-1].isalpha(): + return f'{m[1:]} {bill}s' + elif '.' not in m: + s = '' if m[1:] == '1' else 's' + return f'{m[1:]} {bill}{s}' + b, c = m[1:].split('.') + s = '' if b == '1' else 's' + c = int(c.ljust(2, '0')) + coins = f"cent{'' if c == 1 else 's'}" if m[0] == '$' else ('penny' if c == 1 else 'pence') + return f'{b} {bill}{s} and {c} {coins}' + +def point_num(num): + a, b = num.group().split('.') + return ' point '.join([a, ' '.join(b)]) + +def normalize_text(text): + text = text.replace(chr(8216), "'").replace(chr(8217), "'") + text = text.replace('«', chr(8220)).replace('»', chr(8221)) + text = text.replace(chr(8220), '"').replace(chr(8221), '"') + text = text.replace('(', '«').replace(')', '»') + for a, b in zip('、。！，：；？', ',.!,:;?'): + text = text.replace(a, b+' ') + text = re.sub(r'[^\S \n]', ' ', text) + text = re.sub(r' +', ' ', text) + text = re.sub(r'(?<=\n) +(?=\n)', '', text) + text = re.sub(r'\bD[Rr]\.(?= [A-Z])', 'Doctor', text) + text = re.sub(r'\b(?:Mr\.|MR\.(?= [A-Z]))', 'Mister', text) + text = re.sub(r'\b(?:Ms\.|MS\.(?= [A-Z]))', 'Miss', text) + text = re.sub(r'\b(?:Mrs\.|MRS\.(?= [A-Z]))', 'Mrs', text) + text = re.sub(r'\betc\.(?! [A-Z])', 'etc', text) + text = re.sub(r'(?i)\b(y)eah?\b', r"\1e'a", text) + text = re.sub(r'\d*\.\d+|\b\d{4}s?\b|(? 510: + tokens = tokens[:510] + print('Truncated to 510 tokens') + ref_s = voicepack[len(tokens)] + out = forward(model, tokens, ref_s, speed) + ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens) + return out, ps diff --git a/api/src/builds/models.py b/api/src/builds/models.py new file mode 100644 index 0000000..929dc47 --- /dev/null +++ b/api/src/builds/models.py @@ -0,0 +1,375 @@ +# 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 + +def build_model(path, device): + config = Path(__file__).parent / 'config.json' + assert config.exists(), f'Config path incorrect: config.json not found at {config}' + with open(config, 'r') as r: + args = recursive_munch(json.load(r)) + 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(), + ) + for key, state_dict in torch.load(path, map_location='cpu', weights_only=True)['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 new file mode 100644 index 0000000..8af1bbc --- /dev/null +++ b/api/src/builds/plbert.py @@ -0,0 +1,16 @@ +# 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/api/src/core/config.py b/api/src/core/config.py index 2174bce..834f145 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -13,7 +13,7 @@ class Settings(BaseSettings): output_dir: str = "output" output_dir_size_limit_mb: float = 500.0 # Maximum size of output directory in MB default_voice: str = "af" - model_dir: str = "/app/Kokoro-82M" # Base directory for model files + model_dir: str = "/app/api/model_files" # Base directory for model files pytorch_model_path: str = "kokoro-v0_19.pth" onnx_model_path: str = "kokoro-v0_19.onnx" voices_dir: str = "voices" diff --git a/api/src/core/kokoro.py b/api/src/core/kokoro.py index e3d6c39..8b65b9b 100644 --- a/api/src/core/kokoro.py +++ b/api/src/core/kokoro.py @@ -1,7 +1,7 @@ import re -import torch import phonemizer +import torch def split_num(num): diff --git a/api/src/main.py b/api/src/main.py index 93f5f34..4effda2 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -6,15 +6,15 @@ import sys from contextlib import asynccontextmanager import uvicorn -from loguru import logger from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware +from loguru import logger from .core.config import settings -from .services.tts_model import TTSModel from .routers.development import router as dev_router -from .services.tts_service import TTSService from .routers.openai_compatible import router as openai_router +from .services.tts_model import TTSModel +from .services.tts_service import TTSService def setup_logger(): @@ -47,7 +47,7 @@ async def lifespan(app: FastAPI): # Initialize the main model with warm-up voicepack_count = await TTSModel.setup() # boundary = "█████╗"*9 - boundary = "░" * 24 + boundary = "░" * 2*12 startup_msg = f""" {boundary} diff --git a/api/src/routers/development.py b/api/src/routers/development.py index c7c938b..bc43eb0 100644 --- a/api/src/routers/development.py +++ b/api/src/routers/development.py @@ -1,18 +1,18 @@ from typing import List import numpy as np +from fastapi import APIRouter, Depends, HTTPException, Response from loguru import logger -from fastapi import Depends, Response, APIRouter, HTTPException from ..services.audio import AudioService +from ..services.text_processing import phonemize, tokenize from ..services.tts_model import TTSModel from ..services.tts_service import TTSService from ..structures.text_schemas import ( + GenerateFromPhonemesRequest, PhonemeRequest, PhonemeResponse, - GenerateFromPhonemesRequest, ) -from ..services.text_processing import tokenize, phonemize router = APIRouter(tags=["text processing"]) diff --git a/api/src/routers/openai_compatible.py b/api/src/routers/openai_compatible.py index d86e00a..57d1257 100644 --- a/api/src/routers/openai_compatible.py +++ b/api/src/routers/openai_compatible.py @@ -1,12 +1,12 @@ -from typing import List, Union, AsyncGenerator +from typing import AsyncGenerator, List, Union -from loguru import logger -from fastapi import Header, Depends, Response, APIRouter, HTTPException +from fastapi import APIRouter, Depends, Header, HTTPException, Response, Request from fastapi.responses import StreamingResponse +from loguru import logger from ..services.audio import AudioService -from ..structures.schemas import OpenAISpeechRequest from ..services.tts_service import TTSService +from ..structures.schemas import OpenAISpeechRequest router = APIRouter( tags=["OpenAI Compatible TTS"], @@ -49,22 +49,35 @@ async def process_voices( async def stream_audio_chunks( - tts_service: TTSService, request: OpenAISpeechRequest + tts_service: TTSService, + request: OpenAISpeechRequest, + client_request: Request ) -> AsyncGenerator[bytes, None]: - """Stream audio chunks as they're generated""" + """Stream audio chunks as they're generated with client disconnect handling""" voice_to_use = await process_voices(request.voice, tts_service) - async for chunk in tts_service.generate_audio_stream( - text=request.input, - voice=voice_to_use, - speed=request.speed, - output_format=request.response_format, - ): - yield chunk + + try: + async for chunk in tts_service.generate_audio_stream( + text=request.input, + voice=voice_to_use, + speed=request.speed, + output_format=request.response_format, + ): + # Check if client is still connected + if await client_request.is_disconnected(): + logger.info("Client disconnected, stopping audio generation") + break + yield chunk + except Exception as e: + logger.error(f"Error in audio streaming: {str(e)}") + # Let the exception propagate to trigger cleanup + raise @router.post("/audio/speech") async def create_speech( request: OpenAISpeechRequest, + client_request: Request, tts_service: TTSService = Depends(get_tts_service), x_raw_response: str = Header(None, alias="x-raw-response"), ): @@ -87,7 +100,7 @@ async def create_speech( if request.stream: # Stream audio chunks as they're generated return StreamingResponse( - stream_audio_chunks(tts_service, request), + stream_audio_chunks(tts_service, request, client_request), media_type=content_type, headers={ "Content-Disposition": f"attachment; filename=speech.{request.response_format}", diff --git a/api/src/services/audio.py b/api/src/services/audio.py index 4c5a415..4e8c215 100644 --- a/api/src/services/audio.py +++ b/api/src/services/audio.py @@ -3,8 +3,8 @@ from io import BytesIO import numpy as np -import soundfile as sf import scipy.io.wavfile as wavfile +import soundfile as sf from loguru import logger from ..core.config import settings @@ -22,20 +22,19 @@ class AudioNormalizer: def normalize( self, audio_data: np.ndarray, is_last_chunk: bool = False ) -> np.ndarray: - """Normalize audio data to int16 range and trim chunk boundaries""" - # Convert to float32 if not already + """Convert audio data to int16 range and trim chunk boundaries""" + if len(audio_data) == 0: + raise ValueError("Audio data cannot be empty") + + # Simple float32 to int16 conversion audio_float = audio_data.astype(np.float32) - - # Normalize to [-1, 1] range first - if np.max(np.abs(audio_float)) > 0: - audio_float = audio_float / np.max(np.abs(audio_float)) - - # Trim end of non-final chunks to reduce gaps + + # Trim for non-final chunks if not is_last_chunk and len(audio_float) > self.samples_to_trim: - audio_float = audio_float[: -self.samples_to_trim] - - # Scale to int16 range - return (audio_float * self.int16_max).astype(np.int16) + audio_float = audio_float[:-self.samples_to_trim] + + # Direct scaling like the non-streaming version + return (audio_float * 32767).astype(np.int16) class AudioService: diff --git a/api/src/services/text_processing/__init__.py b/api/src/services/text_processing/__init__.py index 624ce7c..8427232 100644 --- a/api/src/services/text_processing/__init__.py +++ b/api/src/services/text_processing/__init__.py @@ -1,6 +1,6 @@ from .normalizer import normalize_text from .phonemizer import EspeakBackend, PhonemizerBackend, phonemize -from .vocabulary import VOCAB, tokenize, decode_tokens +from .vocabulary import VOCAB, decode_tokens, tokenize __all__ = [ "normalize_text", diff --git a/api/src/services/tts_cpu.py b/api/src/services/tts_cpu.py index 5284750..b8e6236 100644 --- a/api/src/services/tts_cpu.py +++ b/api/src/services/tts_cpu.py @@ -5,19 +5,20 @@ import torch from loguru import logger from onnxruntime import ( ExecutionMode, - SessionOptions, - InferenceSession, GraphOptimizationLevel, + InferenceSession, + SessionOptions, ) -from .tts_base import TTSBaseModel from ..core.config import settings -from .text_processing import tokenize, phonemize +from .text_processing import phonemize, tokenize +from .tts_base import TTSBaseModel class TTSCPUModel(TTSBaseModel): _instance = None _onnx_session = None + _device = "cpu" @classmethod def get_instance(cls): @@ -30,64 +31,65 @@ class TTSCPUModel(TTSBaseModel): def initialize(cls, model_dir: str, model_path: str = None): """Initialize ONNX model for CPU inference""" if cls._onnx_session is None: - # Try loading ONNX model - onnx_path = os.path.join(model_dir, settings.onnx_model_path) - if os.path.exists(onnx_path): + try: + # Try loading ONNX model + onnx_path = os.path.join(model_dir, settings.onnx_model_path) + if not os.path.exists(onnx_path): + logger.error(f"ONNX model not found at {onnx_path}") + return None + logger.info(f"Loading ONNX model from {onnx_path}") - else: - logger.error(f"ONNX model not found at {onnx_path}") - return None - if not onnx_path: - return None + # Configure ONNX session for optimal performance + session_options = SessionOptions() - # Configure ONNX session for optimal performance - session_options = SessionOptions() + # Set optimization level + if settings.onnx_optimization_level == "all": + session_options.graph_optimization_level = ( + GraphOptimizationLevel.ORT_ENABLE_ALL + ) + elif settings.onnx_optimization_level == "basic": + session_options.graph_optimization_level = ( + GraphOptimizationLevel.ORT_ENABLE_BASIC + ) + else: + session_options.graph_optimization_level = ( + GraphOptimizationLevel.ORT_DISABLE_ALL + ) - # Set optimization level - if settings.onnx_optimization_level == "all": - session_options.graph_optimization_level = ( - GraphOptimizationLevel.ORT_ENABLE_ALL - ) - elif settings.onnx_optimization_level == "basic": - session_options.graph_optimization_level = ( - GraphOptimizationLevel.ORT_ENABLE_BASIC - ) - else: - session_options.graph_optimization_level = ( - GraphOptimizationLevel.ORT_DISABLE_ALL + # Configure threading + session_options.intra_op_num_threads = settings.onnx_num_threads + session_options.inter_op_num_threads = settings.onnx_inter_op_threads + + # Set execution mode + session_options.execution_mode = ( + ExecutionMode.ORT_PARALLEL + if settings.onnx_execution_mode == "parallel" + else ExecutionMode.ORT_SEQUENTIAL ) - # Configure threading - session_options.intra_op_num_threads = settings.onnx_num_threads - session_options.inter_op_num_threads = settings.onnx_inter_op_threads + # Enable/disable memory pattern optimization + session_options.enable_mem_pattern = settings.onnx_memory_pattern - # Set execution mode - session_options.execution_mode = ( - ExecutionMode.ORT_PARALLEL - if settings.onnx_execution_mode == "parallel" - else ExecutionMode.ORT_SEQUENTIAL - ) - - # Enable/disable memory pattern optimization - session_options.enable_mem_pattern = settings.onnx_memory_pattern - - # Configure CPU provider options - provider_options = { - "CPUExecutionProvider": { - "arena_extend_strategy": settings.onnx_arena_extend_strategy, - "cpu_memory_arena_cfg": "cpu:0", + # Configure CPU provider options + provider_options = { + "CPUExecutionProvider": { + "arena_extend_strategy": settings.onnx_arena_extend_strategy, + "cpu_memory_arena_cfg": "cpu:0", + } } - } - session = InferenceSession( - onnx_path, - sess_options=session_options, - providers=["CPUExecutionProvider"], - provider_options=[provider_options], - ) - cls._onnx_session = session - return session + session = InferenceSession( + onnx_path, + sess_options=session_options, + providers=["CPUExecutionProvider"], + provider_options=[provider_options], + ) + cls._onnx_session = session + return session + except Exception as e: + logger.error(f"Failed to initialize ONNX model: {e}") + return None return cls._onnx_session @classmethod diff --git a/api/src/services/tts_gpu.py b/api/src/services/tts_gpu.py index 1e5f4a1..d0e166a 100644 --- a/api/src/services/tts_gpu.py +++ b/api/src/services/tts_gpu.py @@ -3,12 +3,12 @@ import time import numpy as np import torch +from builds.models import build_model from loguru import logger -from models import build_model -from .tts_base import TTSBaseModel from ..core.config import settings -from .text_processing import tokenize, phonemize +from .text_processing import phonemize, tokenize +from .tts_base import TTSBaseModel # @torch.no_grad() diff --git a/api/src/services/tts_service.py b/api/src/services/tts_service.py index 61471a8..d0f93b9 100644 --- a/api/src/services/tts_service.py +++ b/api/src/services/tts_service.py @@ -2,19 +2,19 @@ import io import os import re import time -from typing import List, Tuple, Optional from functools import lru_cache +from typing import List, Optional, Tuple -import numpy as np -import torch import aiofiles.os +import numpy as np import scipy.io.wavfile as wavfile +import torch from loguru import logger -from .audio import AudioService, AudioNormalizer -from .tts_model import TTSModel from ..core.config import settings +from .audio import AudioNormalizer, AudioService from .text_processing import chunker, normalize_text +from .tts_model import TTSModel class TTSService: diff --git a/api/src/services/warmup.py b/api/src/services/warmup.py index 1be2013..3a50000 100644 --- a/api/src/services/warmup.py +++ b/api/src/services/warmup.py @@ -4,9 +4,9 @@ from typing import List, Tuple import torch from loguru import logger +from ..core.config import settings from .tts_model import TTSModel from .tts_service import TTSService -from ..core.config import settings class WarmupService: diff --git a/api/src/structures/schemas.py b/api/src/structures/schemas.py index 8db014c..18cfd0e 100644 --- a/api/src/structures/schemas.py +++ b/api/src/structures/schemas.py @@ -1,7 +1,7 @@ from enum import Enum -from typing import List, Union, Literal +from typing import List, Literal, Union -from pydantic import Field, BaseModel +from pydantic import BaseModel, Field class VoiceCombineRequest(BaseModel): diff --git a/api/src/structures/text_schemas.py b/api/src/structures/text_schemas.py index f820f68..1b6afc9 100644 --- a/api/src/structures/text_schemas.py +++ b/api/src/structures/text_schemas.py @@ -1,4 +1,4 @@ -from pydantic import Field, BaseModel +from pydantic import BaseModel, Field class PhonemeRequest(BaseModel): diff --git a/api/src/voices/af_irulan.pt b/api/src/voices/af_irulan.pt new file mode 100644 index 0000000..c9e1b71 Binary files /dev/null and b/api/src/voices/af_irulan.pt differ diff --git a/api/tests/conftest.py b/api/tests/conftest.py index 900e6ae..bd627bf 100644 --- a/api/tests/conftest.py +++ b/api/tests/conftest.py @@ -1,11 +1,11 @@ import os -import sys import shutil -from unittest.mock import Mock, MagicMock, patch +import sys +from unittest.mock import MagicMock, Mock, patch +import aiofiles.threadpool import numpy as np import pytest -import aiofiles.threadpool def cleanup_mock_dirs(): @@ -32,77 +32,7 @@ def cleanup(): cleanup_mock_dirs() -# Create mock torch module -mock_torch = Mock() -mock_torch.cuda = Mock() -mock_torch.cuda.is_available = Mock(return_value=False) - - -# Create a mock tensor class that supports basic operations -class MockTensor: - def __init__(self, data): - self.data = data - if isinstance(data, (list, tuple)): - self.shape = [len(data)] - elif isinstance(data, MockTensor): - self.shape = data.shape - else: - self.shape = getattr(data, "shape", [1]) - - def __getitem__(self, idx): - if isinstance(self.data, (list, tuple)): - if isinstance(idx, slice): - return MockTensor(self.data[idx]) - return self.data[idx] - return self - - def max(self): - if isinstance(self.data, (list, tuple)): - max_val = max(self.data) - return MockTensor(max_val) - return 5 # Default for testing - - def item(self): - if isinstance(self.data, (list, tuple)): - return max(self.data) - if isinstance(self.data, (int, float)): - return self.data - return 5 # Default for testing - - def cuda(self): - """Support cuda conversion""" - return self - - def any(self): - if isinstance(self.data, (list, tuple)): - return any(self.data) - return False - - def all(self): - if isinstance(self.data, (list, tuple)): - return all(self.data) - return True - - def unsqueeze(self, dim): - return self - - def expand(self, *args): - return self - - def type_as(self, other): - return self - - -# Add tensor operations to mock torch -mock_torch.tensor = lambda x: MockTensor(x) -mock_torch.zeros = lambda *args: MockTensor( - [0] * (args[0] if isinstance(args[0], int) else args[0][0]) -) -mock_torch.arange = lambda x: MockTensor(list(range(x))) -mock_torch.gt = lambda x, y: MockTensor([False] * x.shape[0]) - # Mock modules before they're imported -sys.modules["torch"] = mock_torch sys.modules["transformers"] = Mock() sys.modules["phonemizer"] = Mock() sys.modules["models"] = Mock() diff --git a/api/tests/test_audio_service.py b/api/tests/test_audio_service.py index 758e4f4..bb6fb36 100644 --- a/api/tests/test_audio_service.py +++ b/api/tests/test_audio_service.py @@ -5,7 +5,7 @@ from unittest.mock import patch import numpy as np import pytest -from api.src.services.audio import AudioService, AudioNormalizer +from api.src.services.audio import AudioNormalizer, AudioService @pytest.fixture(autouse=True) diff --git a/api/tests/test_endpoints.py b/api/tests/test_endpoints.py index c3bcb43..8cc2d33 100644 --- a/api/tests/test_endpoints.py +++ b/api/tests/test_endpoints.py @@ -1,10 +1,10 @@ import asyncio -from unittest.mock import Mock, AsyncMock +from unittest.mock import AsyncMock, Mock import pytest import pytest_asyncio -from httpx import AsyncClient from fastapi.testclient import TestClient +from httpx import AsyncClient from ..src.main import app diff --git a/api/tests/test_text_processing.py b/api/tests/test_text_processing.py index aacb973..a5475a1 100644 --- a/api/tests/test_text_processing.py +++ b/api/tests/test_text_processing.py @@ -7,8 +7,8 @@ import pytest import pytest_asyncio from httpx import AsyncClient -from .conftest import MockTTSModel from ..src.main import app +from .conftest import MockTTSModel @pytest_asyncio.fixture diff --git a/api/tests/test_tts_implementations.py b/api/tests/test_tts_implementations.py index 99b28bf..9534dce 100644 --- a/api/tests/test_tts_implementations.py +++ b/api/tests/test_tts_implementations.py @@ -1,15 +1,15 @@ """Tests for TTS model implementations""" import os -from unittest.mock import MagicMock, patch +from unittest.mock import AsyncMock, MagicMock, patch import numpy as np -import torch import pytest +import torch +from api.src.services.tts_base import TTSBaseModel from api.src.services.tts_cpu import TTSCPUModel from api.src.services.tts_gpu import TTSGPUModel, length_to_mask -from api.src.services.tts_base import TTSBaseModel # Base Model Tests @@ -27,16 +27,30 @@ def test_get_device_error(): @patch("os.listdir") @patch("torch.load") @patch("torch.save") +@patch("api.src.services.tts_base.settings") +@patch("api.src.services.warmup.WarmupService") async def test_setup_cuda_available( - mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available + mock_warmup_class, mock_settings, mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available ): """Test setup with CUDA available""" TTSBaseModel._device = None - mock_cuda_available.return_value = True + # Mock CUDA as unavailable since we're using CPU PyTorch + mock_cuda_available.return_value = False mock_exists.return_value = True mock_load.return_value = torch.zeros(1) mock_listdir.return_value = ["voice1.pt", "voice2.pt"] mock_join.return_value = "/mocked/path" + + # Configure mock settings + mock_settings.model_dir = "/mock/model/dir" + mock_settings.onnx_model_path = "model.onnx" + mock_settings.voices_dir = "voices" + + # Configure mock warmup service + mock_warmup = MagicMock() + mock_warmup.load_voices.return_value = [torch.zeros(1)] + mock_warmup.warmup_voices = AsyncMock() + mock_warmup_class.return_value = mock_warmup # Create mock model mock_model = MagicMock() @@ -49,7 +63,7 @@ async def test_setup_cuda_available( TTSBaseModel._instance = mock_model voice_count = await TTSBaseModel.setup() - assert TTSBaseModel._device == "cuda" + assert TTSBaseModel._device == "cpu" assert voice_count == 2 @@ -60,8 +74,10 @@ async def test_setup_cuda_available( @patch("os.listdir") @patch("torch.load") @patch("torch.save") +@patch("api.src.services.tts_base.settings") +@patch("api.src.services.warmup.WarmupService") async def test_setup_cuda_unavailable( - mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available + mock_warmup_class, mock_settings, mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available ): """Test setup with CUDA unavailable""" TTSBaseModel._device = None @@ -70,6 +86,17 @@ async def test_setup_cuda_unavailable( mock_load.return_value = torch.zeros(1) mock_listdir.return_value = ["voice1.pt", "voice2.pt"] mock_join.return_value = "/mocked/path" + + # Configure mock settings + mock_settings.model_dir = "/mock/model/dir" + mock_settings.onnx_model_path = "model.onnx" + mock_settings.voices_dir = "voices" + + # Configure mock warmup service + mock_warmup = MagicMock() + mock_warmup.load_voices.return_value = [torch.zeros(1)] + mock_warmup.warmup_voices = AsyncMock() + mock_warmup_class.return_value = mock_warmup # Create mock model mock_model = MagicMock() diff --git a/api/tests/test_tts_service.py b/api/tests/test_tts_service.py index 0f613da..843008a 100644 --- a/api/tests/test_tts_service.py +++ b/api/tests/test_tts_service.py @@ -4,8 +4,8 @@ import os from unittest.mock import MagicMock, call, patch import numpy as np -import torch import pytest +import torch from onnxruntime import InferenceSession from api.src.core.config import settings diff --git a/docker-compose.cpu.yml b/docker-compose.cpu.yml deleted file mode 100644 index 32c8710..0000000 --- a/docker-compose.cpu.yml +++ /dev/null @@ -1,79 +0,0 @@ -name: kokoro-fastapi -services: - model-fetcher: - image: datamachines/git-lfs:latest - volumes: - - ./Kokoro-82M:/app/Kokoro-82M - working_dir: /app/Kokoro-82M - command: > - sh -c " - mkdir -p /app/Kokoro-82M; - cd /app/Kokoro-82M; - rm -f .git/index.lock; - if [ -z \"$(ls -A .)\" ]; then - git clone https://huggingface.co/hexgrad/Kokoro-82M . - touch .cloned; - else - rm -f .git/index.lock && \ - git checkout main && \ - git pull origin main && \ - touch .cloned; - fi; - tail -f /dev/null - " - healthcheck: - test: ["CMD", "test", "-f", ".cloned"] - interval: 5s - timeout: 2s - retries: 300 - start_period: 1s - - kokoro-tts: - image: ghcr.io/remsky/kokoro-fastapi-cpu:v0.0.5post1 - # Uncomment below (and comment out above) to build from source instead of using the released image - build: - context: . - dockerfile: Dockerfile.cpu - volumes: - - ./api/src:/app/api/src - - ./Kokoro-82M:/app/Kokoro-82M - ports: - - "8880:8880" - environment: - - PYTHONPATH=/app:/app/Kokoro-82M - # ONNX Optimization Settings for vectorized operations - - ONNX_NUM_THREADS=8 # Maximize core usage for vectorized ops - - ONNX_INTER_OP_THREADS=4 # Higher inter-op for parallel matrix operations - - ONNX_EXECUTION_MODE=parallel - - ONNX_OPTIMIZATION_LEVEL=all - - ONNX_MEMORY_PATTERN=true - - ONNX_ARENA_EXTEND_STRATEGY=kNextPowerOfTwo - - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:8880/health"] - interval: 10s - timeout: 5s - retries: 30 - start_period: 30s - depends_on: - model-fetcher: - condition: service_healthy - - - # Gradio UI service [Comment out everything below if you don't need it] - gradio-ui: - image: ghcr.io/remsky/kokoro-fastapi-ui:v0.0.5post1 - # Uncomment below (and comment out above) to build from source instead of using the released image - # build: - # context: ./ui - ports: - - "7860:7860" - volumes: - - ./ui/data:/app/ui/data - - ./ui/app.py:/app/app.py # Mount app.py for hot reload - environment: - - GRADIO_WATCH=True # Enable hot reloading - - PYTHONUNBUFFERED=1 # Ensure Python output is not buffered - depends_on: - kokoro-tts: - condition: service_healthy diff --git a/docker-compose.yml b/docker-compose.yml deleted file mode 100644 index 9ffe144..0000000 --- a/docker-compose.yml +++ /dev/null @@ -1,80 +0,0 @@ -name: kokoro-fastapi -services: - model-fetcher: - image: datamachines/git-lfs:latest - environment: - - SKIP_MODEL_FETCH=${SKIP_MODEL_FETCH:-false} - volumes: - - ./Kokoro-82M:/app/Kokoro-82M - working_dir: /app/Kokoro-82M - command: > - sh -c " - if [ \"$$SKIP_MODEL_FETCH\" = \"true\" ]; then - echo 'Skipping model fetch...' && touch .cloned; - else - rm -f .git/index.lock; - if [ -z \"$(ls -A .)\" ]; then - git clone https://huggingface.co/hexgrad/Kokoro-82M . - touch .cloned; - else - rm -f .git/index.lock && \ - git checkout main && \ - git pull origin main && \ - touch .cloned; - fi; - fi; - tail -f /dev/null - " - healthcheck: - test: ["CMD", "test", "-f", ".cloned"] - interval: 5s - timeout: 2s - retries: 300 - start_period: 1s - - kokoro-tts: - image: ghcr.io/remsky/kokoro-fastapi-gpu:v0.0.5post1 - # Uncomment below (and comment out above) to build from source instead of using the released image - # build: - # context: . - volumes: - - ./api/src:/app/api/src - - ./Kokoro-82M:/app/Kokoro-82M - ports: - - "8880:8880" - environment: - - PYTHONPATH=/app:/app/Kokoro-82M - deploy: - resources: - reservations: - devices: - - driver: nvidia - count: 1 - capabilities: [gpu] - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:8880/health"] - interval: 10s - timeout: 5s - retries: 30 - start_period: 30s - depends_on: - model-fetcher: - condition: service_healthy - - # Gradio UI service [Comment out everything below if you don't need it] - gradio-ui: - image: ghcr.io/remsky/kokoro-fastapi-ui:v0.0.5post1 - # Uncomment below (and comment out above) to build from source instead of using the released image - # build: - # context: ./ui - ports: - - "7860:7860" - volumes: - - ./ui/data:/app/ui/data - - ./ui/app.py:/app/app.py # Mount app.py for hot reload - environment: - - GRADIO_WATCH=True # Enable hot reloading - - PYTHONUNBUFFERED=1 # Ensure Python output is not buffered - depends_on: - kokoro-tts: - condition: service_healthy diff --git a/docker/cpu/Dockerfile b/docker/cpu/Dockerfile new file mode 100644 index 0000000..055043f --- /dev/null +++ b/docker/cpu/Dockerfile @@ -0,0 +1,62 @@ +FROM python:3.10-slim + +# Install dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + espeak-ng \ + git \ + libsndfile1 \ + curl \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Install uv +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + +# Create non-root user +RUN useradd -m -u 1000 appuser + +# Create directories and set ownership +RUN mkdir -p /app/api/model_files && \ + mkdir -p /app/api/src/voices && \ + chown -R appuser:appuser /app + +USER appuser + +# Download and extract models +WORKDIR /app/api/model_files +RUN curl -L -o model.tar.gz https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.0.1/kokoro-82m-onnx.tar.gz && \ + tar xzf model.tar.gz && \ + rm model.tar.gz + +# Download and extract voice models +WORKDIR /app/api/src/voices +RUN curl -L -o voices.tar.gz https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.0.1/voice-models.tar.gz && \ + tar xzf voices.tar.gz && \ + rm voices.tar.gz + +# Switch back to app directory +WORKDIR /app + +# Copy dependency files +COPY --chown=appuser:appuser pyproject.toml ./pyproject.toml + +# Install dependencies +RUN --mount=type=cache,target=/root/.cache/uv \ + uv venv && \ + uv sync --extra cpu --no-install-project + +# Copy project files +COPY --chown=appuser:appuser api ./api + +# Install project +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --extra cpu + +# Set environment variables +ENV PYTHONUNBUFFERED=1 +ENV PYTHONPATH=/app:/app/Kokoro-82M +ENV PATH="/app/.venv/bin:$PATH" +ENV UV_LINK_MODE=copy + +# Run FastAPI server +CMD ["uv", "run", "python", "-m", "uvicorn", "api.src.main:app", "--host", "0.0.0.0", "--port", "8880", "--log-level", "debug"] diff --git a/docker/cpu/docker-compose.yml b/docker/cpu/docker-compose.yml new file mode 100644 index 0000000..72d08a5 --- /dev/null +++ b/docker/cpu/docker-compose.yml @@ -0,0 +1,37 @@ +name: kokoro-tts +services: + kokoro-tts: + image: ghcr.io/remsky/kokoro-fastapi-cpu:latest + # Uncomment below (and comment out above) to build from source instead of using the released image + # build: + # context: ../.. + # dockerfile: docker/cpu/Dockerfile + volumes: + - ../../api/src:/app/api/src + ports: + - "8880:8880" + environment: + - PYTHONPATH=/app:/app/Kokoro-82M + # ONNX Optimization Settings for vectorized operations + - ONNX_NUM_THREADS=8 # Maximize core usage for vectorized ops + - ONNX_INTER_OP_THREADS=4 # Higher inter-op for parallel matrix operations + - ONNX_EXECUTION_MODE=parallel + - ONNX_OPTIMIZATION_LEVEL=all + - ONNX_MEMORY_PATTERN=true + - ONNX_ARENA_EXTEND_STRATEGY=kNextPowerOfTwo + + # Gradio UI service [Comment out everything below if you don't need it] + gradio-ui: + image: ghcr.io/remsky/kokoro-fastapi:latest-ui + # Uncomment below (and comment out above) to build from source instead of using the released image + # build: + # context: ../../ui + ports: + - "7860:7860" + volumes: + - ../../ui/data:/app/ui/data + - ../../ui/app.py:/app/app.py # Mount app.py for hot reload + environment: + - GRADIO_WATCH=True # Enable hot reloading + - PYTHONUNBUFFERED=1 # Ensure Python output is not buffered + - DISABLE_LOCAL_SAVING=false # Set to 'true' to disable local saving and hide file view \ No newline at end of file diff --git a/docker/cpu/pyproject.toml b/docker/cpu/pyproject.toml new file mode 100644 index 0000000..81dae2e --- /dev/null +++ b/docker/cpu/pyproject.toml @@ -0,0 +1,22 @@ +[project] +name = "kokoro-fastapi-cpu" +version = "0.1.0" +description = "FastAPI TTS Service - CPU Version" +readme = "../README.md" +requires-python = ">=3.10" +dependencies = [ + # Core ML/DL for CPU + "torch>=2.5.1", + "transformers==4.47.1", +] + +[tool.uv.workspace] +members = ["../shared"] + +[tool.uv.sources] +torch = { index = "pytorch-cpu" } + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true diff --git a/docker/cpu/requirements.lock b/docker/cpu/requirements.lock new file mode 100644 index 0000000..343e5f2 --- /dev/null +++ b/docker/cpu/requirements.lock @@ -0,0 +1,229 @@ +# This file was autogenerated by uv via the following command: +# uv pip compile pyproject.toml ../shared/pyproject.toml --output-file requirements.lock +aiofiles==23.2.1 + # via kokoro-fastapi (../shared/pyproject.toml) +annotated-types==0.7.0 + # via pydantic +anyio==4.8.0 + # via starlette +attrs==24.3.0 + # via + # clldutils + # csvw + # jsonschema + # phonemizer + # referencing +babel==2.16.0 + # via csvw +certifi==2024.12.14 + # via requests +cffi==1.17.1 + # via soundfile +charset-normalizer==3.4.1 + # via requests +click==8.1.8 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # uvicorn +clldutils==3.21.0 + # via segments +colorama==0.4.6 + # via + # click + # colorlog + # csvw + # loguru + # tqdm +coloredlogs==15.0.1 + # via onnxruntime +colorlog==6.9.0 + # via clldutils +csvw==3.5.1 + # via segments +dlinfo==1.2.1 + # via phonemizer +exceptiongroup==1.2.2 + # via anyio +fastapi==0.115.6 + # via kokoro-fastapi (../shared/pyproject.toml) +filelock==3.16.1 + # via + # huggingface-hub + # torch + # transformers +flatbuffers==24.12.23 + # via onnxruntime +fsspec==2024.12.0 + # via + # huggingface-hub + # torch +greenlet==3.1.1 + # via sqlalchemy +h11==0.14.0 + # via uvicorn +huggingface-hub==0.27.1 + # via + # tokenizers + # transformers +humanfriendly==10.0 + # via coloredlogs +idna==3.10 + # via + # anyio + # requests +isodate==0.7.2 + # via + # csvw + # rdflib +jinja2==3.1.5 + # via torch +joblib==1.4.2 + # via phonemizer +jsonschema==4.23.0 + # via csvw +jsonschema-specifications==2024.10.1 + # via jsonschema +language-tags==1.2.0 + # via csvw +loguru==0.7.3 + # via kokoro-fastapi (../shared/pyproject.toml) +lxml==5.3.0 + # via clldutils +markdown==3.7 + # via clldutils +markupsafe==3.0.2 + # via + # clldutils + # jinja2 +mpmath==1.3.0 + # via sympy +munch==4.0.0 + # via kokoro-fastapi (../shared/pyproject.toml) +networkx==3.4.2 + # via torch +numpy==2.2.1 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # onnxruntime + # scipy + # soundfile + # transformers +onnxruntime==1.20.1 + # via kokoro-fastapi (../shared/pyproject.toml) +packaging==24.2 + # via + # huggingface-hub + # onnxruntime + # transformers +phonemizer==3.3.0 + # via kokoro-fastapi (../shared/pyproject.toml) +protobuf==5.29.3 + # via onnxruntime +pycparser==2.22 + # via cffi +pydantic==2.10.4 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # fastapi + # pydantic-settings +pydantic-core==2.27.2 + # via pydantic +pydantic-settings==2.7.0 + # via kokoro-fastapi (../shared/pyproject.toml) +pylatexenc==2.10 + # via clldutils +pyparsing==3.2.1 + # via rdflib +pyreadline3==3.5.4 + # via humanfriendly +python-dateutil==2.9.0.post0 + # via + # clldutils + # csvw +python-dotenv==1.0.1 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # pydantic-settings +pyyaml==6.0.2 + # via + # huggingface-hub + # transformers +rdflib==7.1.2 + # via csvw +referencing==0.35.1 + # via + # jsonschema + # jsonschema-specifications +regex==2024.11.6 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # segments + # tiktoken + # transformers +requests==2.32.3 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # csvw + # huggingface-hub + # tiktoken + # transformers +rfc3986==1.5.0 + # via csvw +rpds-py==0.22.3 + # via + # jsonschema + # referencing +safetensors==0.5.2 + # via transformers +scipy==1.14.1 + # via kokoro-fastapi (../shared/pyproject.toml) +segments==2.2.1 + # via phonemizer +six==1.17.0 + # via python-dateutil +sniffio==1.3.1 + # via anyio +soundfile==0.13.0 + # via kokoro-fastapi (../shared/pyproject.toml) +sqlalchemy==2.0.27 + # via kokoro-fastapi (../shared/pyproject.toml) +starlette==0.41.3 + # via fastapi +sympy==1.13.1 + # via + # onnxruntime + # torch +tabulate==0.9.0 + # via clldutils +tiktoken==0.8.0 + # via kokoro-fastapi (../shared/pyproject.toml) +tokenizers==0.21.0 + # via transformers +torch==2.5.1+cpu + # via kokoro-fastapi-cpu (pyproject.toml) +tqdm==4.67.1 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # huggingface-hub + # transformers +transformers==4.47.1 + # via kokoro-fastapi-cpu (pyproject.toml) +typing-extensions==4.12.2 + # via + # anyio + # fastapi + # huggingface-hub + # phonemizer + # pydantic + # pydantic-core + # sqlalchemy + # torch + # uvicorn +uritemplate==4.1.1 + # via csvw +urllib3==2.3.0 + # via requests +uvicorn==0.34.0 + # via kokoro-fastapi (../shared/pyproject.toml) +win32-setctime==1.2.0 + # via loguru diff --git a/docker/cpu/uv.lock b/docker/cpu/uv.lock new file mode 100644 index 0000000..415df95 --- /dev/null +++ b/docker/cpu/uv.lock @@ -0,0 +1,1841 @@ +version = 1 +requires-python = ">=3.10" +resolution-markers = [ + "(python_full_version >= '3.12' and 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b/docker/gpu/Dockerfile @@ -0,0 +1,64 @@ +FROM nvidia/cuda:12.1.0-base-ubuntu22.04 + +# Install Python and other dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + python3.10 \ + python3.10-venv \ + espeak-ng \ + git \ + libsndfile1 \ + curl \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Install uv +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + +# Create non-root user +RUN useradd -m -u 1000 appuser + +# Create directories and set ownership +RUN mkdir -p /app/api/model_files && \ + mkdir -p /app/api/src/voices && \ + chown -R appuser:appuser /app + +USER appuser + +# Download and extract models +WORKDIR /app/api/model_files +RUN curl -L -o model.tar.gz https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.0.1/kokoro-82m-pytorch.tar.gz && \ + tar xzf model.tar.gz && \ + rm model.tar.gz + +# Download and extract voice models +WORKDIR /app/api/src/voices +RUN curl -L -o voices.tar.gz https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.0.1/voice-models.tar.gz && \ + tar xzf voices.tar.gz && \ + rm voices.tar.gz + +# Switch back to app directory +WORKDIR /app + +# Copy dependency files +COPY --chown=appuser:appuser pyproject.toml ./pyproject.toml + +# Install dependencies +RUN --mount=type=cache,target=/root/.cache/uv \ + uv venv && \ + uv sync --extra gpu --no-install-project + +# Copy project files +COPY --chown=appuser:appuser api ./api + +# Install project +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --extra gpu + +# Set environment variables +ENV PYTHONUNBUFFERED=1 +ENV PYTHONPATH=/app:/app/Kokoro-82M +ENV PATH="/app/.venv/bin:$PATH" +ENV UV_LINK_MODE=copy + +# Run FastAPI server +CMD ["uv", "run", "python", "-m", "uvicorn", "api.src.main:app", "--host", "0.0.0.0", "--port", "8880", "--log-level", "debug"] diff --git a/docker/gpu/docker-compose.yml b/docker/gpu/docker-compose.yml new file mode 100644 index 0000000..d1f0622 --- /dev/null +++ b/docker/gpu/docker-compose.yml @@ -0,0 +1,37 @@ +name: kokoro-tts +services: + kokoro-tts: + image: ghcr.io/remsky/kokoro-fastapi-gpu:latest + # Uncomment below (and comment out above) to build from source instead of using the released image + # build: + # context: ../.. + # dockerfile: docker/gpu/Dockerfile + volumes: + - ../../api/src:/app/api/src # Mount src for development + ports: + - "8880:8880" + environment: + - PYTHONPATH=/app:/app/Kokoro-82M + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] + + # Gradio UI service + gradio-ui: + image: ghcr.io/remsky/kokoro-fastapi-ui:latest + # Uncomment below to build from source instead of using the released image + # build: + # context: ../../ui + ports: + - "7860:7860" + volumes: + - ../../ui/data:/app/ui/data + - ../../ui/app.py:/app/app.py # Mount app.py for hot reload + environment: + - GRADIO_WATCH=1 # Enable hot reloading + - PYTHONUNBUFFERED=1 # Ensure Python output is not buffered + - DISABLE_LOCAL_SAVING=false # Set to 'true' to disable local saving and hide file view diff --git a/docker/gpu/pyproject.toml b/docker/gpu/pyproject.toml new file mode 100644 index 0000000..bd66ded --- /dev/null +++ b/docker/gpu/pyproject.toml @@ -0,0 +1,22 @@ +[project] +name = "kokoro-fastapi-gpu" +version = "0.1.0" +description = "FastAPI TTS Service - GPU Version" +readme = "../README.md" +requires-python = ">=3.10" +dependencies = [ + # Core ML/DL for GPU + "torch==2.5.1+cu121", + "transformers==4.47.1", +] + +[tool.uv.workspace] +members = ["../shared"] + +[tool.uv.sources] +torch = { index = "pytorch-cuda" } + +[[tool.uv.index]] +name = "pytorch-cuda" +url = "https://download.pytorch.org/whl/cu121" +explicit = true diff --git a/docker/gpu/requirements.lock b/docker/gpu/requirements.lock new file mode 100644 index 0000000..c65310d --- /dev/null +++ b/docker/gpu/requirements.lock @@ -0,0 +1,229 @@ +# This file was autogenerated by uv via the following command: +# uv pip compile pyproject.toml ../shared/pyproject.toml --output-file requirements.lock +aiofiles==23.2.1 + # via kokoro-fastapi (../shared/pyproject.toml) +annotated-types==0.7.0 + # via pydantic +anyio==4.8.0 + # via starlette +attrs==24.3.0 + # via + # clldutils + # csvw + # jsonschema + # phonemizer + # referencing +babel==2.16.0 + # via csvw +certifi==2024.12.14 + # via requests +cffi==1.17.1 + # via soundfile +charset-normalizer==3.4.1 + # via requests +click==8.1.8 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # uvicorn +clldutils==3.21.0 + # via segments +colorama==0.4.6 + # via + # click + # colorlog + # csvw + # loguru + # tqdm +coloredlogs==15.0.1 + # via onnxruntime +colorlog==6.9.0 + # via clldutils +csvw==3.5.1 + # via segments +dlinfo==1.2.1 + # via phonemizer +exceptiongroup==1.2.2 + # via anyio +fastapi==0.115.6 + # via kokoro-fastapi (../shared/pyproject.toml) +filelock==3.16.1 + # via + # huggingface-hub + # torch + # transformers +flatbuffers==24.12.23 + # via onnxruntime +fsspec==2024.12.0 + # via + # huggingface-hub + # torch +greenlet==3.1.1 + # via sqlalchemy +h11==0.14.0 + # via uvicorn +huggingface-hub==0.27.1 + # via + # tokenizers + # transformers +humanfriendly==10.0 + # via coloredlogs +idna==3.10 + # via + # anyio + # requests +isodate==0.7.2 + # via + # csvw + # rdflib +jinja2==3.1.5 + # via torch +joblib==1.4.2 + # via phonemizer +jsonschema==4.23.0 + # via csvw +jsonschema-specifications==2024.10.1 + # via jsonschema +language-tags==1.2.0 + # via csvw +loguru==0.7.3 + # via kokoro-fastapi (../shared/pyproject.toml) +lxml==5.3.0 + # via clldutils +markdown==3.7 + # via clldutils +markupsafe==3.0.2 + # via + # clldutils + # jinja2 +mpmath==1.3.0 + # via sympy +munch==4.0.0 + # via kokoro-fastapi (../shared/pyproject.toml) +networkx==3.4.2 + # via torch +numpy==2.2.1 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # onnxruntime + # scipy + # soundfile + # transformers +onnxruntime==1.20.1 + # via kokoro-fastapi (../shared/pyproject.toml) +packaging==24.2 + # via + # huggingface-hub + # onnxruntime + # transformers +phonemizer==3.3.0 + # via kokoro-fastapi (../shared/pyproject.toml) +protobuf==5.29.3 + # via onnxruntime +pycparser==2.22 + # via cffi +pydantic==2.10.4 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # fastapi + # pydantic-settings +pydantic-core==2.27.2 + # via pydantic +pydantic-settings==2.7.0 + # via kokoro-fastapi (../shared/pyproject.toml) +pylatexenc==2.10 + # via clldutils +pyparsing==3.2.1 + # via rdflib +pyreadline3==3.5.4 + # via humanfriendly +python-dateutil==2.9.0.post0 + # via + # clldutils + # csvw +python-dotenv==1.0.1 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # pydantic-settings +pyyaml==6.0.2 + # via + # huggingface-hub + # transformers +rdflib==7.1.2 + # via csvw +referencing==0.35.1 + # via + # jsonschema + # jsonschema-specifications +regex==2024.11.6 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # segments + # tiktoken + # transformers +requests==2.32.3 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # csvw + # huggingface-hub + # tiktoken + # transformers +rfc3986==1.5.0 + # via csvw +rpds-py==0.22.3 + # via + # jsonschema + # referencing +safetensors==0.5.2 + # via transformers +scipy==1.14.1 + # via kokoro-fastapi (../shared/pyproject.toml) +segments==2.2.1 + # via phonemizer +six==1.17.0 + # via python-dateutil +sniffio==1.3.1 + # via anyio +soundfile==0.13.0 + # via kokoro-fastapi (../shared/pyproject.toml) +sqlalchemy==2.0.27 + # via kokoro-fastapi (../shared/pyproject.toml) +starlette==0.41.3 + # via fastapi +sympy==1.13.1 + # via + # onnxruntime + # torch +tabulate==0.9.0 + # via clldutils +tiktoken==0.8.0 + # via kokoro-fastapi (../shared/pyproject.toml) +tokenizers==0.21.0 + # via transformers +torch==2.5.1+cu121 + # via kokoro-fastapi-gpu (pyproject.toml) +tqdm==4.67.1 + # via + # kokoro-fastapi (../shared/pyproject.toml) + # huggingface-hub + # transformers +transformers==4.47.1 + # 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"aiofiles==23.2.1", + "tqdm==4.67.1", + "requests==2.32.3", + "munch==4.0.0", + "tiktoken==0.8.0", + "loguru==0.7.3", +] + +[project.optional-dependencies] +test = [ + "pytest==8.0.0", + "httpx==0.26.0", + "pytest-asyncio==0.23.5", + "ruff==0.9.1", +] diff --git a/requirements.txt b/docs/requirements.in similarity index 79% rename from requirements.txt rename to docs/requirements.in index b71e9c2..e0fb826 100644 --- a/requirements.txt +++ b/docs/requirements.in @@ -9,7 +9,7 @@ sqlalchemy==2.0.27 # ML/DL transformers==4.47.1 -numpy==2.2.1 +numpy>=1.26.0 # Version managed by PyTorch dependencies scipy==1.14.1 onnxruntime==1.20.1 @@ -21,7 +21,7 @@ phonemizer==3.3.0 regex==2024.11.6 # Utilities -aiofiles==24.1.0 +aiofiles==23.2.1 # Last version before Windows path handling changes tqdm==4.67.1 requests==2.32.3 munch==4.0.0 diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..e47e4d9 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,243 @@ +# This file was autogenerated by uv via the following command: +# uv pip compile docs/requirements.in --universal --output-file docs/requirements.txt +aiofiles==23.2.1 + # via -r docs/requirements.in +annotated-types==0.7.0 + # via pydantic +anyio==4.8.0 + # via + # httpx + # starlette +attrs==24.3.0 + # via + # clldutils + # csvw + # jsonschema + # phonemizer + # referencing +babel==2.16.0 + # via csvw +certifi==2024.12.14 + # via + # httpcore + # httpx + # requests +cffi==1.17.1 + # via soundfile +charset-normalizer==3.4.1 + # via requests +click==8.1.8 + # via uvicorn +clldutils==3.21.0 + # via segments +colorama==0.4.6 + # via + # click + # colorlog + # csvw + # loguru + # pytest + # tqdm +coloredlogs==15.0.1 + # via onnxruntime +colorlog==6.9.0 + # via clldutils +csvw==3.5.1 + # via segments +dlinfo==1.2.1 + # via phonemizer +exceptiongroup==1.2.2 ; python_full_version < '3.11' + # via + # anyio + # pytest +fastapi==0.115.6 + # via -r docs/requirements.in +filelock==3.16.1 + # via + # huggingface-hub + # transformers +flatbuffers==24.12.23 + # via onnxruntime +fsspec==2024.12.0 + # via huggingface-hub +greenlet==3.1.1 ; platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64' + # via sqlalchemy +h11==0.14.0 + # via + # httpcore + # uvicorn +httpcore==1.0.7 + # via httpx +httpx==0.26.0 + # via -r docs/requirements.in +huggingface-hub==0.27.1 + # via + # tokenizers + # transformers +humanfriendly==10.0 + # via coloredlogs +idna==3.10 + # via + # anyio + # httpx + # requests +iniconfig==2.0.0 + # via pytest +isodate==0.7.2 + # via + # csvw + # rdflib +joblib==1.4.2 + # via phonemizer +jsonschema==4.23.0 + # via csvw +jsonschema-specifications==2024.10.1 + # via jsonschema +language-tags==1.2.0 + # via csvw +loguru==0.7.3 + # via -r docs/requirements.in +lxml==5.3.0 + # via clldutils +markdown==3.7 + # via clldutils +markupsafe==3.0.2 + # via clldutils +mpmath==1.3.0 + # via sympy +munch==4.0.0 + # via -r docs/requirements.in +numpy==2.2.1 + # via + # -r docs/requirements.in + # onnxruntime + # scipy + # soundfile + # transformers +onnxruntime==1.20.1 + # via -r docs/requirements.in +packaging==24.2 + # via + # huggingface-hub + # onnxruntime + # pytest + # transformers +phonemizer==3.3.0 + # via -r docs/requirements.in +pluggy==1.5.0 + # via pytest +protobuf==5.29.3 + # via onnxruntime +pycparser==2.22 + # via cffi +pydantic==2.10.4 + # via + # -r docs/requirements.in + # fastapi + # pydantic-settings +pydantic-core==2.27.2 + # via pydantic +pydantic-settings==2.7.0 + # via -r docs/requirements.in +pylatexenc==2.10 + # via clldutils +pyparsing==3.2.1 + # via rdflib +pyreadline3==3.5.4 ; sys_platform == 'win32' + # via humanfriendly +pytest==8.0.0 + # via + # -r docs/requirements.in + # pytest-asyncio +pytest-asyncio==0.23.5 + # via -r docs/requirements.in +python-dateutil==2.9.0.post0 + # via + # clldutils + # csvw +python-dotenv==1.0.1 + # via + # -r docs/requirements.in + # pydantic-settings +pyyaml==6.0.2 + # via + # huggingface-hub + # transformers +rdflib==7.1.2 + # via csvw +referencing==0.35.1 + # via + # jsonschema + # jsonschema-specifications +regex==2024.11.6 + # via + # -r docs/requirements.in + # segments + # tiktoken + # transformers +requests==2.32.3 + # via + # -r docs/requirements.in + # csvw + # huggingface-hub + # tiktoken + # transformers +rfc3986==1.5.0 + # via csvw +rpds-py==0.22.3 + # via + # jsonschema + # referencing +safetensors==0.5.2 + # via transformers +scipy==1.14.1 + # via -r docs/requirements.in +segments==2.2.1 + # via phonemizer +six==1.17.0 + # via python-dateutil +sniffio==1.3.1 + # via + # anyio + # httpx +soundfile==0.13.0 + # via -r docs/requirements.in +sqlalchemy==2.0.27 + # via -r docs/requirements.in +starlette==0.41.3 + # via fastapi +sympy==1.13.3 + # via onnxruntime +tabulate==0.9.0 + # via clldutils +tiktoken==0.8.0 + # via -r docs/requirements.in +tokenizers==0.21.0 + # via transformers +tomli==2.2.1 ; python_full_version < '3.11' + # via pytest +tqdm==4.67.1 + # via + # -r docs/requirements.in + # huggingface-hub + # transformers +transformers==4.47.1 + # via -r docs/requirements.in +typing-extensions==4.12.2 + # via + # anyio + # fastapi + # huggingface-hub + # phonemizer + # pydantic + # pydantic-core + # sqlalchemy + # uvicorn +uritemplate==4.1.1 + # via csvw +urllib3==2.3.0 + # via requests +uvicorn==0.34.0 + # via -r docs/requirements.in +win32-setctime==1.2.0 ; sys_platform == 'win32' + # via loguru diff --git a/examples/requirements.txt b/examples/requirements.txt new file mode 100644 index 0000000..7914d4c --- /dev/null +++ b/examples/requirements.txt @@ -0,0 +1,2 @@ +openai>=1.0.0 +pyaudio>=0.2.13 diff --git a/examples/speech.mp3 b/examples/speech.mp3 index b5c9ddc..e69de29 100644 Binary files a/examples/speech.mp3 and b/examples/speech.mp3 differ diff --git a/examples/stream_tts_playback.py b/examples/stream_tts_playback.py index d231fe7..b4a34d9 100644 --- a/examples/stream_tts_playback.py +++ b/examples/stream_tts_playback.py @@ -8,7 +8,7 @@ import requests import sounddevice as sd -def play_streaming_tts(text: str, output_file: str = None, voice: str = "af"): +def play_streaming_tts(text: str, output_file: str = None, voice: str = "af_sky"): """Stream TTS audio and play it back in real-time""" print("\nStarting TTS stream request...") diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..68434e1 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,90 @@ +[project] +name = "kokoro-fastapi" +version = "0.1.0" +description = "FastAPI TTS Service" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + # Core dependencies + "fastapi==0.115.6", + "uvicorn==0.34.0", + "click>=8.0.0", + "pydantic==2.10.4", + "pydantic-settings==2.7.0", + "python-dotenv==1.0.1", + "sqlalchemy==2.0.27", + # ML/DL Base + "numpy>=1.26.0", + "scipy==1.14.1", + "onnxruntime==1.20.1", + # Audio processing + "soundfile==0.13.0", + # Text processing + "phonemizer==3.3.0", + "regex==2024.11.6", + # Utilities + "aiofiles==23.2.1", + "tqdm==4.67.1", + "requests==2.32.3", + "munch==4.0.0", + "tiktoken==0.8.0", + "loguru==0.7.3", + "transformers==4.47.1", + "openai>=1.59.6", + "ebooklib>=0.18", + "html2text>=2024.2.26", +] + +[project.optional-dependencies] +gpu = [ + "torch==2.5.1+cu121", +] +cpu = [ + "torch==2.5.1+cpu", +] +test = [ + "pytest==8.0.0", + "pytest-cov==4.1.0", + "httpx==0.26.0", + "pytest-asyncio==0.23.5", + "gradio>=5", + "openai>=1.59.6", +] + +[tool.uv] +conflicts = [ + [ + { extra = "cpu" }, + { extra = "gpu" }, + ], +] + +[tool.uv.sources] +torch = [ + { index = "pytorch-cpu", extra = "cpu" }, + { index = "pytorch-cuda", extra = "gpu" }, +] + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cuda" +url = "https://download.pytorch.org/whl/cu121" +explicit = true + +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[tool.setuptools] +package-dir = {"" = "api/src"} +packages.find = {where = ["api/src"], namespaces = true} + +[tool.pytest.ini_options] +testpaths = ["api/tests", "ui/tests"] +python_files = ["test_*.py"] +addopts = "--cov=api --cov=ui --cov-report=term-missing --cov-config=.coveragerc" +asyncio_mode = "strict" diff --git a/requirements-test.txt b/requirements-test.txt deleted file mode 100644 index 26a7791..0000000 --- a/requirements-test.txt +++ /dev/null @@ -1,14 +0,0 @@ -# Core dependencies for testing -fastapi==0.115.6 -uvicorn==0.34.0 -pydantic==2.10.4 -pydantic-settings==2.7.0 -python-dotenv==1.0.1 -sqlalchemy==2.0.27 - -# Testing -pytest==8.0.0 -httpx==0.26.0 -pytest-asyncio==0.23.5 -pytest-cov==6.0.0 -gradio==4.19.2 diff --git a/ui/app.py b/ui/app.py index 8920a7a..96aae35 100644 --- a/ui/app.py +++ b/ui/app.py @@ -1,9 +1,3 @@ -import warnings - -# Filter out Gradio Dropdown warnings about values not in choices -#TODO: Warning continues to be displayed, though it isn't breaking anything -warnings.filterwarnings('ignore', category=UserWarning, module='gradio.components.dropdown') - from lib.interface import create_interface if __name__ == "__main__": diff --git a/ui/lib/api.py b/ui/lib/api.py index 1528656..ca0d7e8 100644 --- a/ui/lib/api.py +++ b/ui/lib/api.py @@ -36,15 +36,18 @@ def check_api_status() -> Tuple[bool, List[str]]: def text_to_speech( - text: str, voice_id: str, format: str, speed: float + text: str, voice_id: str | list, format: str, speed: float ) -> Optional[str]: """Generate speech from text using TTS API.""" if not text.strip(): return None + # Handle multiple voices + voice_str = voice_id if isinstance(voice_id, str) else "+".join(voice_id) + # Create output filename timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") - output_filename = f"output_{timestamp}_voice-{voice_id}_speed-{speed}.{format}" + output_filename = f"output_{timestamp}_voice-{voice_str}_speed-{speed}.{format}" output_path = os.path.join(OUTPUTS_DIR, output_filename) try: @@ -53,7 +56,7 @@ def text_to_speech( json={ "model": "kokoro", "input": text, - "voice": voice_id, + "voice": voice_str, "response_format": format, "speed": float(speed), }, diff --git a/ui/lib/components/input.py b/ui/lib/components/input.py index 793a89e..a2c4d33 100644 --- a/ui/lib/components/input.py +++ b/ui/lib/components/input.py @@ -5,54 +5,78 @@ import gradio as gr from .. import files -def create_input_column() -> Tuple[gr.Column, dict]: +def create_input_column(disable_local_saving: bool = False) -> Tuple[gr.Column, dict]: """Create the input column with text input and file handling.""" with gr.Column(scale=1) as col: - with gr.Tabs() as tabs: - # Set first tab as selected by default - tabs.selected = 0 - # Direct Input Tab - with gr.TabItem("Direct Input"): - text_input = gr.Textbox( - label="Text to speak", placeholder="Enter text here...", lines=4 - ) - text_submit = gr.Button("Generate Speech", variant="primary", size="lg") + text_input = gr.Textbox( + label="Text to speak", placeholder="Enter text here...", lines=4 + ) + + # Always show file upload but handle differently based on disable_local_saving + file_upload = gr.File( + label="Upload Text File (.txt)", file_types=[".txt"] + ) + + if not disable_local_saving: + # Show full interface with tabs when saving is enabled + with gr.Tabs() as tabs: + # Set first tab as selected by default + tabs.selected = 0 + # Direct Input Tab + with gr.TabItem("Direct Input"): + text_submit_direct = gr.Button("Generate Speech", variant="primary", size="lg") - # File Input Tab - with gr.TabItem("From File"): - # Existing files dropdown - input_files_list = gr.Dropdown( - label="Select Existing File", - choices=files.list_input_files(), - value=None, - ) - - # Simple file upload - file_upload = gr.File( - label="Upload Text File (.txt)", file_types=[".txt"] - ) - - file_preview = gr.Textbox( - label="File Content Preview", interactive=False, lines=4 - ) - - with gr.Row(): - file_submit = gr.Button( - "Generate Speech", variant="primary", size="lg" - ) - clear_files = gr.Button( - "Clear Files", variant="secondary", size="lg" + # File Input Tab + with gr.TabItem("From File"): + # Existing files dropdown + input_files_list = gr.Dropdown( + label="Select Existing File", + choices=files.list_input_files(), + value=None, ) - components = { - "tabs": tabs, - "text_input": text_input, - "file_select": input_files_list, - "file_upload": file_upload, - "file_preview": file_preview, - "text_submit": text_submit, - "file_submit": file_submit, - "clear_files": clear_files, - } + file_preview = gr.Textbox( + label="File Content Preview", interactive=False, lines=4 + ) + + with gr.Row(): + file_submit = gr.Button( + "Generate Speech", variant="primary", size="lg" + ) + clear_files = gr.Button( + "Clear Files", variant="secondary", size="lg" + ) + else: + # Just show the generate button when saving is disabled + text_submit_direct = gr.Button("Generate Speech", variant="primary", size="lg") + tabs = None + input_files_list = None + file_preview = None + file_submit = None + clear_files = None + + # Initialize components based on disable_local_saving + if disable_local_saving: + components = { + "tabs": None, + "text_input": text_input, + "text_submit": text_submit_direct, + "file_select": None, + "file_upload": file_upload, # Keep file upload even when saving is disabled + "file_preview": None, + "file_submit": None, + "clear_files": None, + } + else: + components = { + "tabs": tabs, + "text_input": text_input, + "text_submit": text_submit_direct, + "file_select": input_files_list, + "file_upload": file_upload, + "file_preview": file_preview, + "file_submit": file_submit, + "clear_files": clear_files, + } return col, components diff --git a/ui/lib/components/model.py b/ui/lib/components/model.py index 2046b32..a659d2c 100644 --- a/ui/lib/components/model.py +++ b/ui/lib/components/model.py @@ -20,10 +20,10 @@ def create_model_column(voice_ids: Optional[list] = None) -> Tuple[gr.Column, di voice_input = gr.Dropdown( choices=voice_ids, - label="Voice", - value=voice_ids[0] if voice_ids else None, # Set default value to first item if available + label="Voice(s)", + value=voice_ids[0] if voice_ids else None, interactive=True, - allow_custom_value=True, # Allow temporary values during updates + multiselect=True, ) format_input = gr.Dropdown( choices=config.AUDIO_FORMATS, label="Audio Format", value="mp3" diff --git a/ui/lib/components/output.py b/ui/lib/components/output.py index 640deba..083829e 100644 --- a/ui/lib/components/output.py +++ b/ui/lib/components/output.py @@ -5,34 +5,43 @@ import gradio as gr from .. import files -def create_output_column() -> Tuple[gr.Column, dict]: +def create_output_column(disable_local_saving: bool = False) -> Tuple[gr.Column, dict]: """Create the output column with audio player and file list.""" with gr.Column(scale=1) as col: gr.Markdown("### Latest Output") - audio_output = gr.Audio(label="Generated Speech", type="filepath") + audio_output = gr.Audio( + label="Generated Speech", + type="filepath", + waveform_options={"waveform_color": "#4C87AB"} + ) - gr.Markdown("### Generated Files") - # Initialize dropdown with empty choices first + # Create file-related components with visible=False when local saving is disabled + gr.Markdown("### Generated Files", visible=not disable_local_saving) output_files = gr.Dropdown( label="Previous Outputs", - choices=[], + choices=files.list_output_files() if not disable_local_saving else [], value=None, allow_custom_value=True, - interactive=True, + visible=not disable_local_saving, ) - # Then update choices after component creation - output_files.choices = files.list_output_files() - play_btn = gr.Button("▶️ Play Selected", size="sm") + play_btn = gr.Button( + "▶️ Play Selected", + size="sm", + visible=not disable_local_saving, + ) selected_audio = gr.Audio( - label="Selected Output", type="filepath", visible=False + label="Selected Output", + type="filepath", + visible=False, # Always initially hidden ) clear_outputs = gr.Button( "⚠️ Delete All Previously Generated Output Audio 🗑️", size="sm", variant="secondary", + visible=not disable_local_saving, ) components = { diff --git a/ui/lib/files.py b/ui/lib/files.py index 5495ea9..1391e0a 100644 --- a/ui/lib/files.py +++ b/ui/lib/files.py @@ -11,12 +11,14 @@ def list_input_files() -> List[str]: def list_output_files() -> List[str]: - """List all output audio files.""" - # Just return filenames since paths will be different inside/outside container - return [ - f for f in os.listdir(OUTPUTS_DIR) + """List all output audio files, sorted by most recent first.""" + files = [ + os.path.join(OUTPUTS_DIR, f) + for f in os.listdir(OUTPUTS_DIR) if any(f.endswith(ext) for ext in AUDIO_FORMATS) ] + # Sort files by modification time, most recent first + return sorted(files, key=os.path.getmtime, reverse=True) def read_text_file(filename: str) -> str: diff --git a/ui/lib/handlers.py b/ui/lib/handlers.py index 30062a0..71b8d9b 100644 --- a/ui/lib/handlers.py +++ b/ui/lib/handlers.py @@ -1,11 +1,12 @@ import os import shutil + import gradio as gr from . import api, files -def setup_event_handlers(components: dict): +def setup_event_handlers(components: dict, disable_local_saving: bool = False): """Set up all event handlers for the UI components.""" def refresh_status(): @@ -57,27 +58,37 @@ def setup_event_handlers(components: dict): def handle_file_upload(file): if file is None: - return gr.update(choices=files.list_input_files()) + return "" if disable_local_saving else [gr.update(choices=files.list_input_files())] try: - # Copy file to inputs directory - filename = os.path.basename(file.name) - target_path = os.path.join(files.INPUTS_DIR, filename) + # Read the file content + with open(file.name, 'r', encoding='utf-8') as f: + text_content = f.read() - # Handle duplicate filenames - base, ext = os.path.splitext(filename) - counter = 1 - while os.path.exists(target_path): - new_name = f"{base}_{counter}{ext}" - target_path = os.path.join(files.INPUTS_DIR, new_name) - counter += 1 + if disable_local_saving: + # When saving is disabled, put content directly in text input + # Normalize whitespace by replacing newlines with spaces + normalized_text = ' '.join(text_content.split()) + return normalized_text + else: + # When saving is enabled, save file and update dropdown + filename = os.path.basename(file.name) + target_path = os.path.join(files.INPUTS_DIR, filename) - shutil.copy2(file.name, target_path) + # Handle duplicate filenames + base, ext = os.path.splitext(filename) + counter = 1 + while os.path.exists(target_path): + new_name = f"{base}_{counter}{ext}" + target_path = os.path.join(files.INPUTS_DIR, new_name) + counter += 1 + + shutil.copy2(file.name, target_path) + return [gr.update(choices=files.list_input_files())] except Exception as e: - print(f"Error uploading file: {e}") - - return gr.update(choices=files.list_input_files()) + print(f"Error handling file: {e}") + return "" if disable_local_saving else [gr.update(choices=files.list_input_files())] def generate_from_text(text, voice, format, speed): """Generate speech from direct text input""" @@ -90,18 +101,20 @@ def setup_event_handlers(components: dict): gr.Warning("Please enter text in the input box") return [None, gr.update(choices=files.list_output_files())] - files.save_text(text) + # Only save text if local saving is enabled + if not disable_local_saving: + files.save_text(text) + result = api.text_to_speech(text, voice, format, speed) if result is None: gr.Warning("Failed to generate speech. Please try again.") return [None, gr.update(choices=files.list_output_files())] - # Update list and select the newly generated file - output_files = files.list_output_files() - last_file = output_files[-1] if output_files else None return [ result, - gr.update(choices=output_files, value=last_file), + gr.update( + choices=files.list_output_files(), value=os.path.basename(result) + ), ] def generate_from_file(selected_file, voice, format, speed): @@ -121,19 +134,16 @@ def setup_event_handlers(components: dict): gr.Warning("Failed to generate speech. Please try again.") return [None, gr.update(choices=files.list_output_files())] - # Update list and select the newly generated file - output_files = files.list_output_files() - last_file = output_files[-1] if output_files else None return [ result, - gr.update(choices=output_files, value=last_file), + gr.update( + choices=files.list_output_files(), value=os.path.basename(result) + ), ] - def play_selected(filename): - if filename: - file_path = os.path.join(files.OUTPUTS_DIR, filename) - if os.path.exists(file_path): - return gr.update(value=file_path, visible=True) + def play_selected(file_path): + if file_path and os.path.exists(file_path): + return gr.update(value=file_path, visible=True) return gr.update(visible=False) def clear_files(voice, format, speed): @@ -165,45 +175,7 @@ def setup_event_handlers(components: dict): outputs=[components["model"]["status_btn"], components["model"]["voice"]], ) - components["input"]["file_select"].change( - fn=handle_file_select, - inputs=[components["input"]["file_select"]], - outputs=[components["input"]["file_preview"]], - ) - - components["input"]["file_upload"].upload( - fn=handle_file_upload, - inputs=[components["input"]["file_upload"]], - outputs=[components["input"]["file_select"]], - ) - - components["output"]["play_btn"].click( - fn=play_selected, - inputs=[components["output"]["output_files"]], - outputs=[components["output"]["selected_audio"]], - ) - - # Connect clear files button - components["input"]["clear_files"].click( - fn=clear_files, - inputs=[ - components["model"]["voice"], - components["model"]["format"], - components["model"]["speed"], - ], - outputs=[ - components["input"]["file_select"], - components["input"]["file_upload"], - components["input"]["file_preview"], - components["output"]["audio_output"], - components["output"]["output_files"], - components["model"]["voice"], - components["model"]["format"], - components["model"]["speed"], - ], - ) - - # Connect submit buttons for each tab + # Connect text submit button (always present) components["input"]["text_submit"].click( fn=generate_from_text, inputs=[ @@ -218,26 +190,70 @@ def setup_event_handlers(components: dict): ], ) - # Connect clear outputs button - components["output"]["clear_outputs"].click( - fn=clear_outputs, - outputs=[ - components["output"]["audio_output"], - components["output"]["output_files"], - components["output"]["selected_audio"], - ], - ) + # Only connect file-related handlers if components exist + if components["input"]["file_select"] is not None: + components["input"]["file_select"].change( + fn=handle_file_select, + inputs=[components["input"]["file_select"]], + outputs=[components["input"]["file_preview"]], + ) - components["input"]["file_submit"].click( - fn=generate_from_file, - inputs=[ - components["input"]["file_select"], - components["model"]["voice"], - components["model"]["format"], - components["model"]["speed"], - ], - outputs=[ - components["output"]["audio_output"], - components["output"]["output_files"], - ], - ) + if components["input"]["file_upload"] is not None: + # File upload handler - output depends on disable_local_saving + components["input"]["file_upload"].upload( + fn=handle_file_upload, + inputs=[components["input"]["file_upload"]], + outputs=[components["input"]["text_input"] if disable_local_saving else components["input"]["file_select"]], + ) + + if components["output"]["play_btn"] is not None: + components["output"]["play_btn"].click( + fn=play_selected, + inputs=[components["output"]["output_files"]], + outputs=[components["output"]["selected_audio"]], + ) + + if components["input"]["clear_files"] is not None: + components["input"]["clear_files"].click( + fn=clear_files, + inputs=[ + components["model"]["voice"], + components["model"]["format"], + components["model"]["speed"], + ], + outputs=[ + components["input"]["file_select"], + components["input"]["file_upload"], + components["input"]["file_preview"], + components["output"]["audio_output"], + components["output"]["output_files"], + components["model"]["voice"], + components["model"]["format"], + components["model"]["speed"], + ], + ) + + if components["output"]["clear_outputs"] is not None: + components["output"]["clear_outputs"].click( + fn=clear_outputs, + outputs=[ + components["output"]["audio_output"], + components["output"]["output_files"], + components["output"]["selected_audio"], + ], + ) + + if components["input"]["file_submit"] is not None: + components["input"]["file_submit"].click( + fn=generate_from_file, + inputs=[ + components["input"]["file_select"], + components["model"]["voice"], + components["model"]["format"], + components["model"]["speed"], + ], + outputs=[ + components["output"]["audio_output"], + components["output"]["output_files"], + ], + ) diff --git a/ui/lib/interface.py b/ui/lib/interface.py index a23ed7c..1ae344c 100644 --- a/ui/lib/interface.py +++ b/ui/lib/interface.py @@ -1,4 +1,5 @@ import gradio as gr +import os from . import api from .handlers import setup_event_handlers @@ -10,6 +11,9 @@ def create_interface(): # Skip initial status check - let the timer handle it is_available, available_voices = False, [] + # Check if local saving is disabled + disable_local_saving = os.getenv("DISABLE_LOCAL_SAVING", "false").lower() == "true" + with gr.Blocks(title="Kokoro TTS Demo", theme=gr.themes.Monochrome()) as demo: gr.HTML( value=''
@@ -22,11 +26,11 @@ def create_interface():
# Main interface
with gr.Row():
# Create columns
- input_col, input_components = create_input_column()
+ input_col, input_components = create_input_column(disable_local_saving)
model_col, model_components = create_model_column(
available_voices
) # Pass initial voices
- output_col, output_components = create_output_column()
+ output_col, output_components = create_output_column(disable_local_saving)
# Collect all components
components = {
@@ -36,7 +40,7 @@ def create_interface():
}
# Set up event handlers
- setup_event_handlers(components)
+ setup_event_handlers(components, disable_local_saving)
# Add periodic status check with Timer
def update_status():
diff --git a/ui/tests/test_api.py b/ui/tests/test_api.py
index fe5dbe7..d682326 100644
--- a/ui/tests/test_api.py
+++ b/ui/tests/test_api.py
@@ -106,24 +106,54 @@ def test_get_status_html_unavailable():
def test_text_to_speech_api_params(mock_response, tmp_path):
"""Test correct API parameters are sent"""
- with patch("requests.post") as mock_post, patch(
- "ui.lib.api.OUTPUTS_DIR", str(tmp_path)
- ), patch("builtins.open", mock_open()):
- mock_post.return_value = mock_response({})
- api.text_to_speech("test text", "voice1", "mp3", 1.5)
+ test_cases = [
+ # Single voice as string
+ ("voice1", "voice1"),
+ # Multiple voices as list
+ (["voice1", "voice2"], "voice1+voice2"),
+ # Single voice as list
+ (["voice1"], "voice1"),
+ ]
- mock_post.assert_called_once()
- args, kwargs = mock_post.call_args
+ for input_voice, expected_voice in test_cases:
+ with patch("requests.post") as mock_post, patch(
+ "ui.lib.api.OUTPUTS_DIR", str(tmp_path)
+ ), patch("builtins.open", mock_open()):
+ mock_post.return_value = mock_response({})
+ api.text_to_speech("test text", input_voice, "mp3", 1.5)
- # Check request body
- assert kwargs["json"] == {
- "model": "kokoro",
- "input": "test text",
- "voice": "voice1",
- "response_format": "mp3",
- "speed": 1.5,
- }
+ mock_post.assert_called_once()
+ args, kwargs = mock_post.call_args
- # Check headers and timeout
- assert kwargs["headers"] == {"Content-Type": "application/json"}
- assert kwargs["timeout"] == 300
+ # Check request body
+ assert kwargs["json"] == {
+ "model": "kokoro",
+ "input": "test text",
+ "voice": expected_voice,
+ "response_format": "mp3",
+ "speed": 1.5,
+ }
+
+ # Check headers and timeout
+ assert kwargs["headers"] == {"Content-Type": "application/json"}
+ assert kwargs["timeout"] == 300
+
+
+def test_text_to_speech_output_filename(mock_response, tmp_path):
+ """Test output filename contains correct voice identifier"""
+ test_cases = [
+ # Single voice
+ ("voice1", lambda f: "voice-voice1" in f),
+ # Multiple voices
+ (["voice1", "voice2"], lambda f: "voice-voice1+voice2" in f),
+ ]
+
+ for input_voice, filename_check in test_cases:
+ with patch("requests.post", return_value=mock_response({})), patch(
+ "ui.lib.api.OUTPUTS_DIR", str(tmp_path)
+ ), patch("builtins.open", mock_open()) as mock_file:
+ result = api.text_to_speech("test text", input_voice, "mp3", 1.0)
+
+ assert result is not None
+ assert filename_check(result), f"Expected voice pattern not found in filename: {result}"
+ mock_file.assert_called_once()
diff --git a/ui/tests/test_components.py b/ui/tests/test_components.py
index d9576c0..9e2b796 100644
--- a/ui/tests/test_components.py
+++ b/ui/tests/test_components.py
@@ -36,8 +36,10 @@ def test_model_column_default_values():
expected_choices = [(voice_id, voice_id) for voice_id in voice_ids]
assert components["voice"].choices == expected_choices
# Value is not converted to tuple format for the value property
- assert components["voice"].value == voice_ids[0]
+ assert components["voice"].value == [voice_ids[0]]
assert components["voice"].interactive is True
+ assert components["voice"].multiselect is True
+ assert components["voice"].label == "Voice(s)"
# Test format dropdown
# Gradio Dropdown converts choices to (value, label) tuples
diff --git a/ui/tests/test_interface.py b/ui/tests/test_interface.py
index cff4825..15c60ba 100644
--- a/ui/tests/test_interface.py
+++ b/ui/tests/test_interface.py
@@ -136,7 +136,7 @@ def test_interface_components_presence():
required_components = {
"Text to speak",
- "Voice",
+ "Voice(s)",
"Audio Format",
"Speed",
"Generated Speech",
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..52e4de2
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,2556 @@
+version = 1
+requires-python = ">=3.10"
+resolution-markers = [
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+ "python_full_version >= '3.13'",
+ "python_full_version == '3.12.*'",
+]
+conflicts = [[
+ { package = "kokoro-fastapi", extra = "cpu" },
+ { package = "kokoro-fastapi", extra = "gpu" },
+]]
+
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+]
+
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+version = "0.7.0"
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