update

.gitignore

@@ -0,0 +1,7 @@
+*.pyc
+__pycache__/
+*/__pycache__/
+alias_free_cuda/build/
+.DS_Store
+exp/
+tmp/

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 NVIDIA CORPORATION.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software. 
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,9 +1,105 @@
 ---
 license: mit
+license_link: https://huggingface.co/nvidia/BigVGAN/blob/main/LICENSE
+tags:
+- neural-vocoder
+- audio-generation
+library_name: PyTorch
 pipeline_tag: audio-to-audio
 ---
+
+## BigVGAN: A Universal Neural Vocoder with Large-Scale Training
+
+<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
+
+**Paper**: https://arxiv.org/abs/2206.04658
+
+**Code**: https://github.com/NVIDIA/BigVGAN
+
+**Project page**: https://research.nvidia.com/labs/adlr/projects/bigvgan/
+
+**🤗 Spaces Demo**: https://huggingface.co/spaces/nvidia/BigVGAN
+
+## News
+[Jul 2024] We release BigVGAN-v2 along with pretrained checkpoints. Below are the highlights:
+* Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.
+* Improved discriminator and loss: BigVGAN-v2 is trained using a multi-scale sub-band CQT discriminator and a multi-scale mel spectrogram loss.
+* Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
+* We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio.
+
+## Installation
 This repository contains pretrained BigVGAN checkpoints with easy access to inference and additional `huggingface_hub` support.
 
-See here regarding how to use this model: https://huggingface.co/nvidia/BigVGAN 
+If you are interested in training the model and additional functionalities, please visit the official GitHub repository for more information: https://github.com/NVIDIA/BigVGAN
+
+```shell
+git lfs install
+git clone https://huggingface.co/nvidia/BigVGAN
+```
+
+## Usage
+
+Below example describes how you can use load the pretrained BigVGAN generator, compute mel spectrogram from input waveform, and generate synthesized waveform using the mel spectrogram as the model's input. 
+
+```python
+device = 'cuda'
+
+import torch
+import bigvgan
+
+# instantiate the model
+model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_22khz_80band_fmax8k_256x')
+
+# remove weight norm in the model and set to eval mode
+model.remove_weight_norm()
+model = model.eval().to(device)
+
+import librosa
+from meldataset import get_mel_spectrogram
+
+# load wav file and compute mel spectrogram
+wav, sr = librosa.load('/path/to/your/audio.wav', sr=model.h.sampling_rate, mono=True) # wav is np.ndarray with shape [T_time] and values in [-1, 1]
+wav = torch.FloatTensor(wav).to(device).unsqueeze(0) # wav is FloatTensor with shape [B(1), T_time]
+
+# compute mel spectrogram from the ground truth audio
+mel = get_mel_spectrogram(wav, model.h) # mel is FloatTensor with shape [B(1), C_mel, T_frame]
+
+# generate waveform from mel
+with torch.inference_mode():
+    wav_gen = model(mel) # wav_gen is FloatTensor with shape [B(1), 1, T_time] and values in [-1, 1]
+wav_gen_float = wav_gen.squeeze(0).cpu() # wav_gen is FloatTensor with shape [1, T_time]
+
+# you can convert the generated waveform to 16 bit linear PCM
+wav_gen_int16 = (wav_gen_float * 32767.0).numpy().astype('int16') # wav_gen is now np.ndarray with int16 dtype
+```
+
+## Using Custom CUDA Kernel for Synthesis
+You can apply the fast CUDA inference kernel by using a parameter `use_cuda_kernel` when instantiating BigVGAN:
+
+```python
+import bigvgan
+model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_22khz_80band_fmax8k_256x', use_cuda_kernel=True)
+```
+
+When applied for the first time, it builds the kernel using `nvcc` and `ninja`. If the build succeeds, the kernel is saved to `alias_free_cuda/build` and the model automatically loads the kernel. The codebase has been tested using CUDA `12.1`.
+
+Please make sure that both are installed in your system and `nvcc` installed in your system matches the version your PyTorch build is using.
+
+For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis
+
+
+## Pretrained Models
+We provide the pretrained models.
+One can download the checkpoints of the pretrained generator weight, named as `bigvgan_generator.pt` within the listed HuggingFace repositories.
 
-If you are interested in training the model and additional functionalities, please visit the official GitHub repository for more information: https://github.com/NVIDIA/BigVGAN
+|Model Name|Sampling Rate|Mel band|fmax|Upsampling Ratio|Params|Dataset|Fine-Tuned|
+|------|---|---|---|---|---|------|---|
+|[bigvgan_v2_44khz_128band_512x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x)|44 kHz|128|22050|512|122M|Large-scale Compilation|No|
+|[bigvgan_v2_44khz_128band_256x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x)|44 kHz|128|22050|256|112M|Large-scale Compilation|No|
+|[bigvgan_v2_24khz_100band_256x](https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x)|24 kHz|100|12000|256|112M|Large-scale Compilation|No|
+|[bigvgan_v2_22khz_80band_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_256x)|22 kHz|80|11025|256|112M|Large-scale Compilation|No|
+|[bigvgan_v2_22khz_80band_fmax8k_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_fmax8k_256x)|22 kHz|80|8000|256|112M|Large-scale Compilation|No|
+|[bigvgan_24khz_100band](https://huggingface.co/nvidia/bigvgan_24khz_100band)|24 kHz|100|12000|256|112M|LibriTTS|No|
+|[bigvgan_base_24khz_100band](https://huggingface.co/nvidia/bigvgan_base_24khz_100band)|24 kHz|100|12000|256|14M|LibriTTS|No|
+|[bigvgan_22khz_80band](https://huggingface.co/nvidia/bigvgan_22khz_80band)|22 kHz|80|8000|256|112M|LibriTTS + VCTK + LJSpeech|No|
+|[bigvgan_base_22khz_80band](https://huggingface.co/nvidia/bigvgan_base_22khz_80band)|22 kHz|80|8000|256|14M|LibriTTS + VCTK + LJSpeech|No|

activations.py

@@ -0,0 +1,120 @@
+# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+import torch
+from torch import nn, sin, pow
+from torch.nn import Parameter
+
+
+class Snake(nn.Module):
+    '''
+    Implementation of a sine-based periodic activation function
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter
+    References:
+        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snake(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    '''
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+        '''
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha: trainable parameter
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            alpha will be trained along with the rest of your model.
+        '''
+        super(Snake, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale: # log scale alphas initialized to zeros
+            self.alpha = Parameter(torch.zeros(in_features) * alpha)
+        else: # linear scale alphas initialized to ones
+            self.alpha = Parameter(torch.ones(in_features) * alpha)
+
+        self.alpha.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        '''
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        Snake ∶= x + 1/a * sin^2 (xa)
+        '''
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x
+
+
+class SnakeBeta(nn.Module):
+    '''
+    A modified Snake function which uses separate parameters for the magnitude of the periodic components
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter that controls frequency
+        - beta - trainable parameter that controls magnitude
+    References:
+        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snakebeta(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    '''
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+        '''
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha - trainable parameter that controls frequency
+            - beta - trainable parameter that controls magnitude
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            beta is initialized to 1 by default, higher values = higher-magnitude.
+            alpha will be trained along with the rest of your model.
+        '''
+        super(SnakeBeta, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale: # log scale alphas initialized to zeros
+            self.alpha = Parameter(torch.zeros(in_features) * alpha)
+            self.beta = Parameter(torch.zeros(in_features) * alpha)
+        else: # linear scale alphas initialized to ones
+            self.alpha = Parameter(torch.ones(in_features) * alpha)
+            self.beta = Parameter(torch.ones(in_features) * alpha)
+
+        self.alpha.requires_grad = alpha_trainable
+        self.beta.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        '''
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        SnakeBeta ∶= x + 1/b * sin^2 (xa)
+        '''
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+        beta = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x

alias_free_cuda/activation1d.py

@@ -0,0 +1,63 @@
+# Copyright (c) 2024 NVIDIA CORPORATION.
+#   Licensed under the MIT license.
+
+import torch
+import torch.nn as nn
+from alias_free_torch.resample import UpSample1d, DownSample1d
+# load fused CUDA kernel: this enables importing anti_alias_activation_cuda
+from alias_free_cuda import load
+load.load()
+
+class FusedAntiAliasActivation(torch.autograd.Function):
+    """
+    Assumes filter size 12, replication padding on upsampling, and logscale alpha/beta parameters as inputs
+    """
+    @staticmethod
+    def forward(ctx, inputs, ftr, alpha, beta):
+        import anti_alias_activation_cuda
+        activation_results = anti_alias_activation_cuda.forward(inputs, ftr, alpha, beta)
+        return activation_results
+
+    @staticmethod
+    def backward(ctx, output_grads):
+        # TODO: implement bwd pass
+        raise NotImplementedError
+        return output_grads, None, None
+
+class Activation1d(nn.Module):
+    def __init__(self,
+                 activation,
+                 up_ratio: int = 2,
+                 down_ratio: int = 2,
+                 up_kernel_size: int = 12,
+                 down_kernel_size: int = 12,
+                 fused: bool = True
+                 ):
+        super().__init__()
+        self.up_ratio = up_ratio
+        self.down_ratio = down_ratio
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+        self.fused = fused # whether to use fused CUDA kernel or not
+
+
+    def forward(self, x):
+        if not self.fused:
+            x = self.upsample(x)
+            x = self.act(x)
+            x = self.downsample(x)
+            return x
+        else:
+            if self.act.__class__.__name__ == "Snake":
+                beta = self.act.alpha.data # snake uses same params for alpha and beta
+            else:
+                beta = self.act.beta.data # snakebeta uses different params for alpha and beta
+            alpha = self.act.alpha.data
+            if not self.act.alpha_logscale: # exp baked into cuda kernel, cancel it out with a log
+                alpha = torch.log(alpha)
+                beta = torch.log(beta)
+            x = FusedAntiAliasActivation.apply(x, self.upsample.filter, alpha, beta)
+            x = self.downsample(x)
+            return x

alias_free_cuda/anti_alias_activation.cpp

@@ -0,0 +1,48 @@
+/* coding=utf-8
+ * Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda_fp16.h>
+#include <torch/extension.h>
+#include <vector>
+
+namespace anti_alias_activation {
+
+torch::Tensor fwd_cuda(torch::Tensor const& input,
+                       torch::Tensor const& filter,
+                       torch::Tensor const& alpha,
+		       torch::Tensor const& beta
+		       );
+
+torch::Tensor fwd(torch::Tensor const& input,
+                  torch::Tensor const& filter,
+                  torch::Tensor const& alpha,
+		  torch::Tensor const& beta
+		  ) {
+  AT_ASSERTM(input.dim() == 3, "expected 3D tensor");
+  //AT_ASSERTM((input.scalar_type() == at::ScalarType::Half) ||
+  //	     (input.scalar_type() == at::ScalarType::BFloat16),
+  //    "Only fp16 and bf16 are supported");
+
+  return fwd_cuda(input, filter, alpha, beta);
+}
+
+} // end namespace anti_alias_activation
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("forward",
+        &anti_alias_activation::fwd,
+	"Anti Alias Activation -- Forward.");
+}

alias_free_cuda/anti_alias_activation_cuda.cu

@@ -0,0 +1,314 @@
+/* coding=utf-8
+ * Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <ATen/ATen.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <cuda_fp16.h>
+#include <cuda_profiler_api.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <torch/extension.h>
+#include "type_shim.h"
+#include <assert.h>
+#include <cfloat>
+#include <limits>
+#include <stdint.h>
+#include <c10/macros/Macros.h>
+
+namespace {
+
+    /*
+template <typename Datatype, int ELEMENTS_PER_LDG>
+__device__ __inline__ void copy_vector(Datatype *dst, const Datatype *src);
+
+template <>
+__device__ __inline__ void copy_vector<c10::BFloat16, 1>(c10::BFloat16 *dst, const c10::BFloat16 *src) { *dst = *src; }
+
+template <>
+__device__ __inline__ void copy_vector<c10::BFloat16, 4>(c10::BFloat16 *dst, const c10::BFloat16 *src) { *((float2*) dst) = *((float2*) src); }
+
+template <>
+__device__ __inline__ void copy_vector<c10::Half, 1>(c10::Half *dst, const c10::Half *src) { *dst = *src; }
+
+template <>
+__device__ __inline__ void copy_vector<c10::Half, 4>(c10::Half *dst, const c10::Half *src) { *((float2*) dst) = *((float2*) src); }
+
+template <>
+__device__ __inline__ void copy_vector<uint8_t, 1>(uint8_t *dst, const uint8_t *src) { *dst = *src; }
+
+template <>
+__device__ __inline__ void copy_vector<uint8_t, 4>(uint8_t *dst, const uint8_t *src) {*((half2*) dst) = *((half2*) src); }
+
+int log2_ceil(int value) {
+    int log2_value = 0;
+    while ((1 << log2_value) < value) ++log2_value;
+    return log2_value;
+}
+
+template<typename T>
+struct Add {
+  __device__ __forceinline__ T operator()(T a, T b) const {
+    return a + b;
+  }
+};
+
+template<typename T>
+struct Max {
+  __device__ __forceinline__ T operator()(T a, T b) const {
+    return a < b ? b : a;
+  }
+};
+
+template <typename T>
+__device__ __forceinline__ T WARP_SHFL_XOR_NATIVE(T value, int laneMask, int width = warpSize, unsigned int mask = 0xffffffff)
+{
+#if CUDA_VERSION >= 9000
+    return __shfl_xor_sync(mask, value, laneMask, width);
+#else
+    return __shfl_xor(value, laneMask, width);
+#endif
+}
+
+template <typename acc_t, int WARP_BATCH, int WARP_SIZE, template<typename> class ReduceOp>
+__device__ __forceinline__ void warp_reduce(acc_t* sum) {
+    ReduceOp<acc_t> r;
+    #pragma unroll
+    for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) {
+        #pragma unroll
+        for (int i = 0;  i < WARP_BATCH;  ++i) {
+            acc_t b = WARP_SHFL_XOR_NATIVE(sum[i], offset, WARP_SIZE);
+            sum[i] = r(sum[i], b);
+        }
+    }
+}
+*/
+
+template <typename input_t, typename output_t, typename acc_t>
+__global__ void anti_alias_activation_forward(
+    output_t *dst,
+    const input_t *src,
+    const input_t *ftr,
+    const input_t *alpha,
+    const input_t *beta,
+    int batch_size,
+    int channels,
+    int seq_len)
+{
+    // WARP_SIZE and WARP_BATCH must match the return values batches_per_warp and
+    constexpr int ELEMENTS_PER_LDG_STG = 1; //(WARP_ITERATIONS < 4) ? 1 : 4;
+    constexpr int BUFFER_SIZE = 32;
+    constexpr int FILTER_SIZE = 12;
+    constexpr int HALF_FILTER_SIZE = 6;
+    constexpr int REPLICATION_PAD = 5; // 5 on each side
+
+    // blockDim/threadIdx = (128, 1, 1)
+    // gridDim/blockIdx = (seq_blocks, channels, batches)
+    int block_offset = (blockIdx.x * 128 * BUFFER_SIZE + seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
+    int local_offset = threadIdx.x * BUFFER_SIZE;
+    int seq_offset = blockIdx.x * 128 * BUFFER_SIZE + local_offset;
+
+
+    //int intermediate_seq_len = seq_len * 2 - 1 + 4 * REPLICATION_PAD;
+    //int intermediate_block_offset = (blockIdx.x * 128 * BUFFER_SIZE * 2 + intermediate_seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
+    //int intermediate_local_offset = threadIdx.x * BUFFER_SIZE * 2;
+
+    int output_seq_len = seq_len * 2 ; //
+    int output_block_offset = (blockIdx.x * 128 * BUFFER_SIZE * 2 + output_seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
+    int output_local_offset = threadIdx.x * BUFFER_SIZE * 2;
+    int output_seq_offset = blockIdx.x * 128 * BUFFER_SIZE *2 + output_local_offset;
+    // get values needed for replication padding before moving pointer
+    const input_t *right_most_pntr = src + (seq_len * (blockIdx.y + gridDim.y * blockIdx.z));
+    input_t seq_left_most_value = right_most_pntr[0];
+    input_t seq_right_most_value = right_most_pntr[seq_len - 1];
+
+    src += block_offset + local_offset;
+    dst += output_block_offset + output_local_offset  ;
+    alpha = alpha + blockIdx.y;
+    input_t alpha_val = expf(alpha[0]);
+    beta = beta + blockIdx.y;
+    input_t beta_val = expf(beta[0]);
+    // load data from global memory
+    input_t elements[2*FILTER_SIZE+2*BUFFER_SIZE] = {0};
+    input_t intermediates[2*FILTER_SIZE+2*BUFFER_SIZE] = {0};
+    //output_t output[2*BUFFER_SIZE];
+    input_t filter[FILTER_SIZE];
+    //input_t temp_data[ELEMENTS_PER_LDG_STG];
+    //uint8_t temp_mask[ELEMENTS_PER_LDG_STG];
+
+    #pragma unroll
+    for (int it = 0; it < FILTER_SIZE; it+=1) {
+        filter[it] = ftr[it];
+    }
+
+
+    #pragma unroll
+    for (int it = -HALF_FILTER_SIZE;  it < BUFFER_SIZE + HALF_FILTER_SIZE ;  it+=1) {
+        int element_index = seq_offset + it;
+	if ((element_index < 0) && (element_index >= -REPLICATION_PAD)) {
+	    elements[2*(HALF_FILTER_SIZE+it)] = 2*seq_left_most_value;
+	}
+	if ((element_index >= seq_len) && (element_index < seq_len + REPLICATION_PAD)) {
+	    elements[2*(HALF_FILTER_SIZE+it)] = 2*seq_right_most_value;
+	}
+        if ((element_index >= 0) && (element_index < seq_len)) {
+	  elements[2*(HALF_FILTER_SIZE+it)] = 2*src[it];
+        }
+    }
+
+
+
+    // apply filter
+    #pragma unroll
+    for (int it = 0;  it < (2 * BUFFER_SIZE + 2*FILTER_SIZE);  it+=1) {
+        input_t acc = 0.0;
+
+	int element_index = output_seq_offset + it; // index for output
+	#pragma unroll
+        for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx+=1){
+	  if ((element_index + f_idx) >= 0){
+            acc += filter[f_idx] * elements[it+f_idx];
+	  }
+	}
+        intermediates[it] = acc;
+    }
+
+    double no_div_by_zero = 0.000000001;
+    #pragma unroll
+    for (int it = 0; it < 12 + 2 * BUFFER_SIZE; it++) {
+        intermediates[it] += (1.0/(beta_val + no_div_by_zero)) *  sinf(intermediates[it] * alpha_val) * sinf(intermediates[it] * alpha_val);
+    }
+
+
+    // now copy to output
+    #pragma unroll
+    for (int it = 0; it < 2*BUFFER_SIZE; it+=1){
+      int element_index = output_seq_offset + it;
+      if (element_index < output_seq_len) {
+	dst[it]  = intermediates[it+6];
+      }
+    }
+
+
+
+    // for (int it = 0;  it < BUFFER_SIZE;  it+=ELEMENTS_PER_LDG_STG) {
+    //     int element_index = seq_offset + it;
+    //     if (element_index < seq_len) {
+    //         dst[it] = output[it];
+    //     }
+    // }
+
+
+    // // Upsample convolution
+    // for (int it = 0;  it < 2 * BUFFER_SIZE + 12;  it+=1) {
+    //     input_t acc = 0.0;
+
+    //     for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx+=1){
+    //         acc += filter[f_idx] * elements[it+f_idx];
+    //     }
+    //     intermediates[it] = acc;
+    // }
+
+    // // correct the corners of intermediates
+    // if (seq_offset == 0) {
+    //     for (int it = 0; it < 6; it+=1)
+    //         intermediates[it] = 0;
+    // }
+
+    // if (seq_offset + 32 >= seq_len) {
+    //     int offset = seq_len % 32 == 0 ? 32 : seq_len % 32;
+
+    //     for (int it = 0; it < 6; it++) {
+    //         intermediates[6+2*offset+it] = 0;
+    //     }
+    // }
+
+
+
+
+    // for (int it = 0;  it < BUFFER_SIZE;  it+=ELEMENTS_PER_LDG_STG) {
+    //     int element_index = seq_offset + it;
+    //     if (element_index < seq_len) {
+    //         dst[it] = output[it];
+    //     }
+    // }
+}
+
+template<typename input_t, typename output_t, typename acc_t>
+void dispatch_anti_alias_activation_forward(
+    output_t *dst,
+    const input_t *src,
+    const input_t *ftr,
+    const input_t *alpha,
+    const input_t *beta,
+    int batch_size,
+    int channels,
+    int seq_len)
+{
+    if (seq_len == 0) {
+        return;
+    } else {
+        // use 128 threads per block to maximimize gpu utilization
+        constexpr int threads_per_block = 128;
+        constexpr int seq_len_per_block = 4096;
+        int blocks_per_seq_len = (seq_len + seq_len_per_block - 1) / seq_len_per_block;
+        dim3 blocks(blocks_per_seq_len, channels, batch_size);
+        dim3 threads(threads_per_block, 1, 1);
+
+        anti_alias_activation_forward<input_t, output_t, acc_t>
+	  <<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(dst, src, ftr, alpha, beta, batch_size, channels, seq_len);
+    }
+}
+}
+
+namespace anti_alias_activation {
+
+  torch::Tensor fwd_cuda(torch::Tensor const& input, torch::Tensor const& filter, torch::Tensor const& alpha, torch::Tensor const& beta)
+{
+  // input is a 4d tensor with dimensions [batches, attn_heads, seq_len, seq_len]
+  const int batches = input.size(0);
+  const int channels = input.size(1);
+  const int seq_len = input.size(2);
+
+  // Output
+  auto act_options = input.options().requires_grad(false);
+  int output_seq_len = seq_len*2; // we'll be dilating between each element by interspersing with zeros
+
+  torch::Tensor anti_alias_activation_results =
+      torch::empty({batches, channels, output_seq_len}, act_options);
+
+  // Softmax Intermediate Result Ptr
+  void* input_ptr = static_cast<void*>(input.data_ptr());
+  void* filter_ptr = static_cast<void*>(filter.data_ptr());
+  void* alpha_ptr = static_cast<void*>(alpha.data_ptr());
+  void* beta_ptr = static_cast<void*>(beta.data_ptr());
+  void* anti_alias_activation_results_ptr = static_cast<void*>(anti_alias_activation_results.data_ptr());
+
+  DISPATCH_FLOAT_HALF_AND_BFLOAT(
+      input.scalar_type(),
+      "dispatch anti alias activation_forward",
+      dispatch_anti_alias_activation_forward<scalar_t, scalar_t, float>(
+        reinterpret_cast<scalar_t*>(anti_alias_activation_results_ptr),
+	    reinterpret_cast<const scalar_t*>(input_ptr),
+        reinterpret_cast<const scalar_t*>(filter_ptr),
+        reinterpret_cast<const scalar_t*>(alpha_ptr),
+	reinterpret_cast<const scalar_t*>(beta_ptr),
+	    batches,
+        channels,
+        seq_len);
+      );
+  return anti_alias_activation_results;
+}
+}

alias_free_cuda/compat.h

@@ -0,0 +1,31 @@
+/* coding=utf-8
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*This code is copied fron NVIDIA apex:
+ *     https://github.com/NVIDIA/apex
+ *     with minor changes. */
+
+
+
+#ifndef TORCH_CHECK
+#define TORCH_CHECK AT_CHECK
+#endif
+
+#ifdef VERSION_GE_1_3
+#define DATA_PTR data_ptr
+#else
+#define DATA_PTR data
+#endif

alias_free_cuda/load.py

@@ -0,0 +1,72 @@
+# Copyright (c) 2024 NVIDIA CORPORATION.
+#   Licensed under the MIT license.
+
+import os
+import pathlib
+import subprocess
+
+from torch.utils import cpp_extension
+
+# Setting this param to a list has a problem of generating different
+# compilation commands (with diferent order of architectures) and
+# leading to recompilation of fused kernels. Set it to empty string
+# to avoid recompilation and assign arch flags explicity in
+# extra_cuda_cflags below
+os.environ["TORCH_CUDA_ARCH_LIST"] = ""
+
+
+def load():
+    # Check if cuda 11 is installed for compute capability 8.0
+    cc_flag = []
+    _, bare_metal_major, _ = _get_cuda_bare_metal_version(
+        cpp_extension.CUDA_HOME)
+    if int(bare_metal_major) >= 11:
+        cc_flag.append('-gencode')
+        cc_flag.append('arch=compute_80,code=sm_80')
+
+    # Build path
+    srcpath = pathlib.Path(__file__).parent.absolute()
+    buildpath = srcpath / 'build'
+    _create_build_dir(buildpath)
+
+    # Helper function to build the kernels.
+    def _cpp_extention_load_helper(name, sources, extra_cuda_flags):
+        return cpp_extension.load(
+            name=name,
+            sources=sources,
+            build_directory=buildpath,
+            extra_cflags=['-O3',],
+            extra_cuda_cflags=['-O3',
+                               '-gencode', 'arch=compute_70,code=sm_70',
+                               '--use_fast_math'] + extra_cuda_flags + cc_flag,
+            verbose=True
+        )
+
+    extra_cuda_flags = ['-U__CUDA_NO_HALF_OPERATORS__',
+                        '-U__CUDA_NO_HALF_CONVERSIONS__',
+                        '--expt-relaxed-constexpr',
+                        '--expt-extended-lambda']
+    
+    sources=[srcpath / 'anti_alias_activation.cpp',
+             srcpath / 'anti_alias_activation_cuda.cu']
+    anti_alias_activation_cuda = _cpp_extention_load_helper(
+        "anti_alias_activation_cuda", sources, extra_cuda_flags)
+
+def _get_cuda_bare_metal_version(cuda_dir):
+    raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"],
+                                         universal_newlines=True)
+    output = raw_output.split()
+    release_idx = output.index("release") + 1
+    release = output[release_idx].split(".")
+    bare_metal_major = release[0]
+    bare_metal_minor = release[1][0]
+
+    return raw_output, bare_metal_major, bare_metal_minor
+
+
+def _create_build_dir(buildpath):
+    try:
+        os.mkdir(buildpath)
+    except OSError:
+        if not os.path.isdir(buildpath):
+            print(f"Creation of the build directory {buildpath} failed")

alias_free_cuda/test_activation.py

@@ -0,0 +1,55 @@
+# Copyright (c) 2024 NVIDIA CORPORATION.
+#   Licensed under the MIT license.
+
+import math
+import torch
+import alias_free_cuda
+from alias_free_cuda import activation1d
+from activations import Snake, SnakeBeta
+
+def test_load_fused_kernels():
+    try:
+        import alias_free_cuda
+        import torch
+        print("[Success] load_fused_kernels")
+    except ImportError as e:
+        print("[Fail] load_fused_kernels")
+        raise e
+
+def test_anti_alias_activation():
+    data = torch.rand((10, 10, 50000), device='cuda')
+
+    # check activations.Snake cuda vs. torch
+    fused_anti_alias_activation = activation1d.Activation1d(activation=Snake(10), fused=True).cuda()
+    fused_activation_output = fused_anti_alias_activation(data)
+
+    torch_anti_alias_activation = activation1d.Activation1d(activation=Snake(10), fused=False).cuda()
+    torch_activation_output = torch_anti_alias_activation(data)
+
+    test_result = (fused_activation_output - torch_activation_output).abs()
+    
+    while test_result.dim() != 1:
+        test_result = test_result.mean(dim=-1)
+
+    diff = test_result.mean(dim=-1)
+
+    if diff <= 1e-3:
+        print(
+            f"\n[Success] test_fused_anti_alias_activation"
+            f"\n > mean_difference={diff}"
+            f"\n > fused_values={fused_activation_output[-1][-1][-100:].tolist()}"
+            f"\n > torch_values={torch_activation_output[-1][-1][-100:].tolist()}"
+        )
+    else:
+        print(
+            f"\n[Fail] test_fused_anti_alias_activation"
+            f"\n > mean_difference={diff}, "
+            f"\n > fused_values={fused_activation_output[-1][-1][-30:].tolist()}, "
+            f"\n > torch_values={torch_activation_output[-1][-1][-30:].tolist()}"
+        )
+        
+if __name__ == "__main__":
+    from alias_free_cuda import load
+    load.load()
+    test_load_fused_kernels()
+    test_anti_alias_activation()

alias_free_cuda/test_activation_snake_beta.py

@@ -0,0 +1,55 @@
+# Copyright (c) 2024 NVIDIA CORPORATION.
+#   Licensed under the MIT license.
+
+import math
+import torch
+import alias_free_cuda
+from alias_free_cuda import activation1d
+from activations import Snake, SnakeBeta
+
+def test_load_fused_kernels():
+    try:
+        import alias_free_cuda
+        import torch
+        print("[Success] load_fused_kernels")
+    except ImportError as e:
+        print("[Fail] load_fused_kernels")
+        raise e
+
+def test_anti_alias_activation():
+    data = torch.rand((10, 10, 50000), device='cuda')
+
+    # check activations.Snake cuda vs. torch
+    fused_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=True).cuda()
+    fused_activation_output = fused_anti_alias_activation(data)
+
+    torch_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=False).cuda()
+    torch_activation_output = torch_anti_alias_activation(data)
+    
+    test_result = (fused_activation_output - torch_activation_output).abs()
+
+    while test_result.dim() != 1:
+        test_result = test_result.mean(dim=-1)
+
+    diff = test_result.mean(dim=-1)
+
+    if diff <= 1e-3:
+        print(
+            f"\n[Success] test_fused_anti_alias_activation"
+            f"\n > mean_difference={diff}"
+            f"\n > fused_values={fused_activation_output[-1][-1][-100:].tolist()}"
+            f"\n > torch_values={torch_activation_output[-1][-1][-100:].tolist()}"
+        )
+    else:
+        print(
+            f"\n[Fail] test_fused_anti_alias_activation"
+            f"\n > mean_difference={diff}, "
+            f"\n > fused_values={fused_activation_output[-1][-1][-30:].tolist()}, "
+            f"\n > torch_values={torch_activation_output[-1][-1][-30:].tolist()}"
+        )
+
+if __name__ == "__main__":
+    from alias_free_cuda import load
+    load.load()
+    test_load_fused_kernels()
+    test_anti_alias_activation()

alias_free_cuda/type_shim.h

@@ -0,0 +1,97 @@
+/* coding=utf-8
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+#include <ATen/ATen.h>
+#include "compat.h"
+
+
+#define DISPATCH_FLOAT_HALF_AND_BFLOAT(TYPE, NAME, ...)			\
+  switch(TYPE)							\
+    {									\
+    case at::ScalarType::Float:			\
+      {									\
+	using scalar_t = float;			    \
+	__VA_ARGS__;						\
+	break;								\
+      }                                 \
+    case at::ScalarType::Half:			\
+      {									\
+	using scalar_t = at::Half;			\
+	__VA_ARGS__;						\
+	break;								\
+      }									\
+    case at::ScalarType::BFloat16:		\
+      {									\
+	using scalar_t = at::BFloat16;		\
+	__VA_ARGS__;						\
+	break;								\
+      }									\
+    default:							\
+      AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'");	\
+      }
+
+
+
+#define DISPATCH_FLOAT_HALF_AND_BFLOAT_INOUT_TYPES(TYPEIN, TYPEOUT, NAME, ...) \
+  switch(TYPEIN)							\
+    {									\
+    case at::ScalarType::Float:						\
+      {									\
+	using scalar_t_in = float;					\
+	switch(TYPEOUT)							\
+	  {								\
+	  case at::ScalarType::Float:					\
+	    {								\
+	      using scalar_t_out = float;				\
+	      __VA_ARGS__;						\
+	      break;							\
+	    }								\
+	  case at::ScalarType::Half:					\
+	    {								\
+	      using scalar_t_out = at::Half;				\
+	      __VA_ARGS__;						\
+	      break;							\
+	    }								\
+	  case at::ScalarType::BFloat16:				\
+	    {								\
+	      using scalar_t_out = at::BFloat16;			\
+	      __VA_ARGS__;						\
+	      break;							\
+	    }								\
+	  default:							\
+	    AT_ERROR(#NAME, " not implemented for '", toString(TYPEOUT), "'"); \
+	  }								\
+	break;								\
+      }									\
+    case at::ScalarType::Half:						\
+      {									\
+	using scalar_t_in = at::Half;					\
+	using scalar_t_out = at::Half;					\
+	__VA_ARGS__;							\
+	break;								\
+      }									\
+    case at::ScalarType::BFloat16:					\
+      {									\
+	using scalar_t_in = at::BFloat16;				\
+	using scalar_t_out = at::BFloat16;				\
+	__VA_ARGS__;							\
+	break;								\
+      }									\
+    default:								\
+      AT_ERROR(#NAME, " not implemented for '", toString(TYPEIN), "'");	\
+    }
+

alias_free_torch/__init__.py

@@ -0,0 +1,6 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+#   LICENSE is in incl_licenses directory.
+
+from .filter import *
+from .resample import *
+from .act import *

alias_free_torch/act.py

@@ -0,0 +1,28 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+#   LICENSE is in incl_licenses directory.
+
+import torch.nn as nn
+from .resample import UpSample1d, DownSample1d
+
+
+class Activation1d(nn.Module):
+    def __init__(self,
+                 activation,
+                 up_ratio: int = 2,
+                 down_ratio: int = 2,
+                 up_kernel_size: int = 12,
+                 down_kernel_size: int = 12):
+        super().__init__()
+        self.up_ratio = up_ratio
+        self.down_ratio = down_ratio
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    # x: [B,C,T]
+    def forward(self, x):
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+
+        return x

alias_free_torch/filter.py

@@ -0,0 +1,95 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+#   LICENSE is in incl_licenses directory.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+if 'sinc' in dir(torch):
+    sinc = torch.sinc
+else:
+    # This code is adopted from adefossez's julius.core.sinc under the MIT License
+    # https://adefossez.github.io/julius/julius/core.html
+    #   LICENSE is in incl_licenses directory.
+    def sinc(x: torch.Tensor):
+        """
+        Implementation of sinc, i.e. sin(pi * x) / (pi * x)
+        __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
+        """
+        return torch.where(x == 0,
+                           torch.tensor(1., device=x.device, dtype=x.dtype),
+                           torch.sin(math.pi * x) / math.pi / x)
+
+
+# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
+# https://adefossez.github.io/julius/julius/lowpass.html
+#   LICENSE is in incl_licenses directory.
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
+    even = (kernel_size % 2 == 0)
+    half_size = kernel_size // 2
+
+    #For kaiser window
+    delta_f = 4 * half_width
+    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if A > 50.:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.:
+        beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
+    else:
+        beta = 0.
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+    if even:
+        time = (torch.arange(-half_size, half_size) + 0.5)
+    else:
+        time = torch.arange(kernel_size) - half_size
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+        # Normalize filter to have sum = 1, otherwise we will have a small leakage
+        # of the constant component in the input signal.
+        filter_ /= filter_.sum()
+        filter = filter_.view(1, 1, kernel_size)
+
+    return filter
+
+
+class LowPassFilter1d(nn.Module):
+    def __init__(self,
+                 cutoff=0.5,
+                 half_width=0.6,
+                 stride: int = 1,
+                 padding: bool = True,
+                 padding_mode: str = 'replicate',
+                 kernel_size: int = 12):
+        # kernel_size should be even number for stylegan3 setup,
+        # in this implementation, odd number is also possible.
+        super().__init__()
+        if cutoff < -0.:
+            raise ValueError("Minimum cutoff must be larger than zero.")
+        if cutoff > 0.5:
+            raise ValueError("A cutoff above 0.5 does not make sense.")
+        self.kernel_size = kernel_size
+        self.even = (kernel_size % 2 == 0)
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        self.register_buffer("filter", filter)
+
+    #input [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right),
+                      mode=self.padding_mode)
+        out = F.conv1d(x, self.filter.expand(C, -1, -1),
+                       stride=self.stride, groups=C)
+
+        return out

alias_free_torch/resample.py

@@ -0,0 +1,49 @@
+# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
+#   LICENSE is in incl_licenses directory.
+
+import torch.nn as nn
+from torch.nn import functional as F
+from .filter import LowPassFilter1d
+from .filter import kaiser_sinc_filter1d
+
+
+class UpSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.stride = ratio
+        self.pad = self.kernel_size // ratio - 1
+        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
+                                      half_width=0.6 / ratio,
+                                      kernel_size=self.kernel_size)
+        self.register_buffer("filter", filter)
+
+    # x: [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        x = F.pad(x, (self.pad, self.pad), mode='replicate')
+        x = self.ratio * F.conv_transpose1d(
+            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+        x = x[..., self.pad_left:-self.pad_right]
+
+        return x
+
+
+class DownSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
+                                       half_width=0.6 / ratio,
+                                       stride=ratio,
+                                       kernel_size=self.kernel_size)
+
+    def forward(self, x):
+        xx = self.lowpass(x)
+
+        return xx

bigvgan.py

@@ -0,0 +1,352 @@
+# Copyright (c) 2024 NVIDIA CORPORATION. 
+#   Licensed under the MIT license.
+
+# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+import os
+import json
+from pathlib import Path
+
+from collections import namedtuple
+from typing import Optional, List, Union, Dict
+
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+from torch.nn import Conv1d, ConvTranspose1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+import activations
+from utils import init_weights, get_padding
+from alias_free_torch.act import Activation1d as TorchActivation1d
+from env import AttrDict
+
+from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
+
+def load_hparams_from_json(path) -> AttrDict:
+    with open(path) as f:
+        data = f.read()
+    h = json.loads(data)
+    return AttrDict(h)
+
+class AMPBlock1(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
+        super(AMPBlock1, self).__init__()
+        self.h = h
+
+        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.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
+
+        # select which Activation1d, lazy-load cuda version to ensure backward compatibility
+        if self.h.get("use_cuda_kernel", False):
+            # faster CUDA kernel implementation of Activation1d
+            from alias_free_cuda.activation1d import Activation1d as CudaActivation1d
+            Activation1d = CudaActivation1d
+        else:
+            Activation1d = TorchActivation1d
+
+        if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
+                 for _ in range(self.num_layers)
+            ])
+        else:
+            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
+
+    def forward(self, x):
+        acts1, acts2 = self.activations[::2], self.activations[1::2]
+        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
+            xt = a1(x)
+            xt = c1(xt)
+            xt = a2(xt)
+            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 AMPBlock2(torch.nn.Module):
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
+        super(AMPBlock2, self).__init__()
+        self.h = h
+
+        self.convs = 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])))
+        ])
+        self.convs.apply(init_weights)
+
+        self.num_layers = len(self.convs) # total number of conv layers
+
+        # select which Activation1d, lazy-load cuda version to ensure backward compatibility
+        if self.h.get("use_cuda_kernel", False):
+            # faster CUDA kernel implementation of Activation1d
+            from alias_free_cuda.activation1d import Activation1d as CudaActivation1d
+            Activation1d = CudaActivation1d
+        else:
+            Activation1d = TorchActivation1d
+
+        if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
+                 for _ in range(self.num_layers)
+            ])
+        else:
+            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
+
+    def forward(self, x):
+        for c, a in zip (self.convs, self.activations):
+            xt = a(x)
+            xt = c(xt)
+            x = xt + x
+
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class BigVGAN(
+    torch.nn.Module,
+    PyTorchModelHubMixin,
+    library_name="bigvgan",
+    repo_url="https://github.com/NVIDIA/BigVGAN",
+    docs_url="https://github.com/NVIDIA/BigVGAN/blob/main/README.md",
+    pipeline_tag="audio-to-audio",
+    license="mit",
+    tags=["neural-vocoder", "audio-generation", "arxiv:2206.04658"]
+):
+    # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
+    # New in v2: if use_cuda_kernel is set to True, it loads optimized CUDA kernels for AMP.
+    # NOTE: use_cuda_kernel=True should be used for inference only (training is not supported).
+    def __init__(
+        self,
+        h,
+        use_cuda_kernel: bool=False
+    ):
+        super(BigVGAN, self).__init__()
+        self.h = h
+        self.h["use_cuda_kernel"] = use_cuda_kernel # add it to global hyperparameters (h)
+
+        self.num_kernels = len(h.resblock_kernel_sizes)
+        self.num_upsamples = len(h.upsample_rates)
+
+        # pre conv
+        self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
+
+        # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
+        resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
+
+        # transposed conv-based upsamplers. does not apply anti-aliasing
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+            self.ups.append(nn.ModuleList([
+                weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i),
+                                            h.upsample_initial_channel // (2 ** (i + 1)),
+                                            k, u, padding=(k - u) // 2))
+            ]))
+
+        # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h.upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
+                self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
+        
+        # select which Activation1d, lazy-load cuda version to ensure backward compatibility
+        if self.h.get("use_cuda_kernel", False):
+            # faster CUDA kernel implementation of Activation1d
+            from alias_free_cuda.activation1d import Activation1d as CudaActivation1d
+            Activation1d = CudaActivation1d
+        else:
+            Activation1d = TorchActivation1d
+
+        # post conv
+        if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing
+            activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
+            self.activation_post = Activation1d(activation=activation_post)
+        elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing
+            activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
+            self.activation_post = Activation1d(activation=activation_post)
+        else:
+            raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.")
+        
+        # whether to use bias for the final conv_post. Defaults to True for backward compatibility
+        self.use_bias_at_final = h.get("use_bias_at_final", True)
+        self.conv_post = weight_norm(Conv1d(
+            ch, 1, 7, 1, padding=3, bias=self.use_bias_at_final
+        ))
+
+        # weight initialization
+        for i in range(len(self.ups)):
+            self.ups[i].apply(init_weights)
+        self.conv_post.apply(init_weights)
+        
+        # final tanh activation. Defaults to True for backward compatibility
+        self.use_tanh_at_final = h.get("use_tanh_at_final", True)
+
+    def forward(self, x):
+        # pre conv
+        x = self.conv_pre(x)
+
+        for i in range(self.num_upsamples):
+            # upsampling
+            for i_up in range(len(self.ups[i])):
+                x = self.ups[i][i_up](x)
+            # AMP blocks
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+
+        # post conv
+        x = self.activation_post(x)
+        x = self.conv_post(x)
+        # final tanh activation
+        if self.use_tanh_at_final:
+            x = torch.tanh(x)
+        else:
+            x = torch.clamp(x, min=-1., max=1.) # bound the output to [-1, 1]
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            for l_i in l:
+                remove_weight_norm(l_i)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+
+    ##################################################################
+    # additional methods for huggingface_hub support
+    ##################################################################
+    def _save_pretrained(self, save_directory: Path) -> None:
+        """Save weights and config.json from a Pytorch model to a local directory."""
+
+        model_path = save_directory / 'bigvgan_generator.pt'
+        torch.save(
+            {'generator': self.state_dict()},
+            model_path
+        )
+
+        config_path = save_directory / 'config.json'
+        with open(config_path, 'w') as config_file:
+            json.dump(self.h, config_file, indent=4)
+
+    @classmethod
+    def _from_pretrained(
+        cls,
+        *,
+        model_id: str,
+        revision: str,
+        cache_dir: str,
+        force_download: bool,
+        proxies: Optional[Dict],
+        resume_download: bool,
+        local_files_only: bool,
+        token: Union[str, bool, None],
+        map_location: str = "cpu", # additional argument
+        strict: bool = False, # additional argument
+        use_cuda_kernel: bool = False,
+        **model_kwargs,
+    ):
+        """Load Pytorch pretrained weights and return the loaded model."""
+
+        ##################################################################
+        # download and load hyperparameters (h) used by BigVGAN
+        ##################################################################
+        config_file = hf_hub_download(
+            repo_id=model_id,
+            filename='config.json',
+            revision=revision,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            proxies=proxies,
+            resume_download=resume_download,
+            token=token,
+            local_files_only=local_files_only,
+        )
+        h = load_hparams_from_json(config_file)
+
+        ##################################################################
+        # instantiate BigVGAN using h
+        ##################################################################
+        if use_cuda_kernel:
+            print(f"[WARNING] You have specified use_cuda_kernel=True during BigVGAN.from_pretrained(). Only inference is supported (training is not implemented)!")
+            print(f"[WARNING] You need nvcc and ninja installed in your system that matches your PyTorch build is using to build the kernel. If not, the model will fail to initialize or generate incorrect waveform!")
+            print(f"[WARNING] For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis")
+            model = cls(h, use_cuda_kernel=use_cuda_kernel)
+
+        ##################################################################
+        # download and load pretrained generator weight
+        ##################################################################
+        if os.path.isdir(model_id):
+            print("Loading weights from local directory")
+            model_file = os.path.join(model_id, 'bigvgan_generator.pt')
+        else:
+            print(f"Loading weights from {model_id}")
+            model_file = hf_hub_download(
+                repo_id=model_id,
+                filename='bigvgan_generator.pt',
+                revision=revision,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                resume_download=resume_download,
+                token=token,
+                local_files_only=local_files_only,
+                )
+        
+        checkpoint_dict = torch.load(model_file, map_location=map_location)
+        model.load_state_dict(checkpoint_dict['generator'])
+
+        return model

env.py

@@ -0,0 +1,18 @@
+# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+import os
+import shutil
+
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+        self.__dict__ = self
+
+
+def build_env(config, config_name, path):
+    t_path = os.path.join(path, config_name)
+    if config != t_path:
+        os.makedirs(path, exist_ok=True)
+        shutil.copyfile(config, os.path.join(path, config_name))

meldataset.py

@@ -0,0 +1,66 @@
+# Copyright (c) 2024 NVIDIA CORPORATION. 
+#   Licensed under the MIT license.
+
+# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+import torch
+import torch.utils.data
+import numpy as np
+from scipy.io.wavfile import read
+from librosa.filters import mel as librosa_mel_fn
+
+MAX_WAV_VALUE = 32767.0 # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases)
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
+
+def dynamic_range_decompression(x, C=1):
+    return np.exp(x) / C
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+def dynamic_range_decompression_torch(x, C=1):
+    return torch.exp(x) / C
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+def spectral_de_normalize_torch(magnitudes):
+    output = dynamic_range_decompression_torch(magnitudes)
+    return output
+
+mel_basis = {}
+hann_window = {}
+
+def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global mel_basis, hann_window
+    if fmax not in mel_basis:
+        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        str_key_mel_basis = str(fmax)+'_'+str(y.device)
+        mel_basis[str_key_mel_basis] = torch.from_numpy(mel).float().to(y.device)
+        hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+    y = y.squeeze(1)
+
+    # complex tensor as default, then use view_as_real for future pytorch compatibility
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
+    spec = torch.view_as_real(spec)
+    spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
+
+    spec = torch.matmul(mel_basis[str_key_mel_basis], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec
+
+def get_mel_spectrogram(wav, h):
+    return mel_spectrogram(wav, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax)

nv-modelcard++/bias.md

@@ -0,0 +1,4 @@
+Field |  Response
+:---------------------------------------------------------------------------------------------------|:---------------
+Participation considerations from adversely impacted groups protected classes in model design and testing: | None
+Measures taken to mitigate against unwanted bias: | No measures taken to mitigate against unwanted bias.

nv-modelcard++/explainability.md

@@ -0,0 +1,13 @@
+Field                                                                                                  |  Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Application & Domain:                                                                 |   Generating waveform from mel spectrogram.
+Model Type:                                                    |    Convolutional Neural Network (CNN)
+Intended Users: | This model is intended for developers to synthesize and generate waveforms from the AI-generated mel spectrograms.
+Output: | Audio Waveform
+Describe how the model works: | Model generates audio waveform corresponding to the input mel spectrogram.
+Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable
+Technical Limitations: | This may not perform well on synthetically-generated mel spectrograms that deviate significantly from the profile of mel spectrograms on which this was trained.
+Verified to have met prescribed NVIDIA quality standards: |  Yes
+Performance Metrics: | Perceptual Evaluation of Speech Quality (PESQ), Virtual Speech Quality Objective Listener (VISQOL), Multi-resolution STFT (MRSTFT), Mel cepstral distortion (MCD), Periodicity RMSE, Voice/Unvoiced F1 Score (V/UV F1)
+Potential Known Risks: | This model may generate low-quality or distorted soundwaves.
+Licensing: | https://github.com/NVIDIA/BigVGAN/blob/main/LICENSE

nv-modelcard++/overview.md

@@ -0,0 +1,114 @@
+# Model Overview
+
+## Description:
+BigVGAN is a generative AI model specialized in synthesizing audio waveforms using Mel spectrogram as inputs.
+
+<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
+
+BigVGAN is a fully convolutional architecture with several upsampling blocks using transposed convolution followed by multiple residual dilated convolution layers. 
+
+BigVGAN consists of a novel module, called anti-aliased multi-periodicity composition (AMP), which is specifically designed for generating waveforms. AMP is specialized in synthesizing high-frequency and periodic soundwaves drawing inspiration from audio signal processing principles.
+
+It applies a periodic activation function, called Snake, which provides an inductive bias to the architecture in generating periodic soundwaves. It also applies anti-aliasing filters to reduce undesired artifacts in the generated waveforms. <br>
+
+This model is ready for commercial use.<br>
+
+
+## References(s):
+* [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://arxiv.org/abs/2206.04658)  <br>
+* [Project Page](https://research.nvidia.com/labs/adlr/projects/bigvgan/)  <br> 
+* [Audio Demo](https://bigvgan-demo.github.io/)  <br> 
+
+## Model Architecture:
+**Architecture Type:** Convolution Neural Network (CNN) <br>
+**Network Architecture:** You can see the details of this model on this link: https://github.com/NVIDIA/BigVGAN and the related paper can be found here: https://arxiv.org/abs/2206.04658<br>
+**Model Version:** 2.0 <br>
+
+## Input:
+**Input Type:** Audio <br>
+**Input Format:** Mel Spectrogram <br>
+**Input Parameters:** None <br>
+**Other Properties Related to Input:** The input mel spectrogram has shape `[batch, channels, frames]`, where `channels` refers to the number of mel bands defined by the model and `frames` refers to the temporal length. The model supports arbitrary long `frames` that fits into the GPU memory.  
+
+## Output:
+**Input Type:** Audio <br>
+**Output Format:** Audio Waveform <br>
+**Output Parameters:** None <br>
+**Other Properties Related to Output:** The output audio waveform has shape `[batch, 1, time]`, where `1` refers to the mono audio channels and `time` refers to the temporal length. `time` is defined as a fixed integer multiple of input `frames`, which is an upsampling ratio of the model (`time = upsampling ratio * frames`). The output audio waveform consitutes float values with a range of `[-1, 1]`.
+
+## Software Integration:
+**Runtime Engine(s):** PyTorch
+
+**Supported Hardware Microarchitecture Compatibility:** NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace, NVIDIA Turing, NVIDIA Volta <br>
+
+
+## Preferred/Supported Operating System(s):
+Linux
+
+
+## Model Version(s):
+v2.0
+
+## Training, Testing, and Evaluation Datasets:
+
+### Training Dataset:
+The dataset contains diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
+
+**Links:**
+* [AAM: Artificial Audio Multitracks Dataset](https://zenodo.org/records/5794629)
+* [AudioCaps](https://audiocaps.github.io/)
+* [AudioSet](https://research.google.com/audioset/index.html)
+* [common-accent](https://huggingface.co/datasets/DTU54DL/common-accent)
+* [Crowd Sourced Emotional Multimodal Actors Dataset (CREMA-D)](https://ieeexplore.ieee.org/document/6849440)
+* [DCASE2017 Challenge, Task 4: Large-scale weakly supervised sound event detection for smart cars](https://dcase.community/challenge2017/task-large-scale-sound-event-detection)
+* [FSDnoisy18k](https://zenodo.org/records/2529934)
+* [Free Universal Sound Separation Dataset](https://zenodo.org/records/3694384)
+* [Greatest Hits dataset](https://andrewowens.com/vis/)
+* [GTZAN](https://ieeexplore.ieee.org/document/1021072)
+* [JL corpus](https://www.kaggle.com/datasets/tli725/jl-corpus)
+* [Medley-solos-DB: a cross-collection dataset for musical instrument recognition](https://zenodo.org/records/3464194)
+* [MUSAN: A Music, Speech, and Noise Corpus](https://www.openslr.org/17/)
+* [MusicBench](https://huggingface.co/datasets/amaai-lab/MusicBench)
+* [MusicCaps](https://www.kaggle.com/datasets/googleai/musiccaps)
+* [MusicNet](https://www.kaggle.com/datasets/imsparsh/musicnet-dataset)
+* [NSynth](https://magenta.tensorflow.org/datasets/nsynth)
+* [OnAir-Music-Dataset](https://github.com/sevagh/OnAir-Music-Dataset)
+* [Audio Piano Triads Dataset](https://zenodo.org/records/4740877)
+* [Pitch Audio Dataset (Surge synthesizer)](https://zenodo.org/records/4677097)
+* [SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network](https://zenodo.org/records/3966543)
+* [VocalSound: A Dataset for Improving Human Vocal Sounds Recognition](https://github.com/YuanGongND/vocalsound)
+* [WavText5K](https://github.com/microsoft/WavText5K)
+* [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](https://github.com/Kyubyong/css10)
+* [Hi-Fi Multi-Speaker English TTS Dataset (Hi-Fi TTS)](https://www.openslr.org/109/)
+* [IIIT-H Indic Speech Databases](http://festvox.org/databases/iiit_voices/)
+* [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://github.com/facebookresearch/libri-light)
+* [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://www.openslr.org/60)
+* [LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus](https://www.openslr.org/141/)
+* [The SIWIS French Speech Synthesis Database](https://datashare.ed.ac.uk/handle/10283/2353)
+* [Crowdsourced high-quality Colombian Spanish speech data set](https://openslr.org/72/)
+* [TTS-Portuguese Corpus](https://github.com/Edresson/TTS-Portuguese-Corpus)
+* [CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit](https://datashare.ed.ac.uk/handle/10283/3443)
+
+** Data Collection Method by dataset <br>
+* Human <br>
+
+** Labeling Method by dataset (for those with labels) <br>
+* Hybrid: Automated, Human, Unknown <br>
+
+### Evaluating Dataset:
+
+Properties: The audio generation quality of BigVGAN is evaluated using `dev` splits of the [LibriTTS dataset](https://www.openslr.org/60/) and [Hi-Fi TTS dataset](https://www.openslr.org/109/). The datasets include speech in English language with equal balance of genders.
+
+** Data Collection Method by dataset <br>
+* Human <br>
+
+** Labeling Method by dataset <br>
+* Automated <br>
+
+
+## Inference:
+**Engine:** PyTorch <br>
+**Test Hardware:** NVIDIA A100 GPU <br>
+
+## Ethical Considerations:
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.  For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.  Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

nv-modelcard++/privacy.md

@@ -0,0 +1,14 @@
+Field                                                                                                                              |  Response
+:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
+Generatable or reverse engineerable personal information?                                                     |  None
+Protected class data used to create this model?                                                                                       |  None
+Was consent obtained for any personal data used?                                                                                             |  Not Applicable (No Personal Data)
+How often is dataset reviewed?                                                                                                     |  Before Release
+Is a mechanism in place to honor data subject right of access or deletion of personal data?                                        |  Not Applicable
+If personal collected for the development of the model, was it collected directly by NVIDIA?                                            |  Not Applicable
+If personal collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?  |  Not Applicable
+If personal collected for the development of this AI model, was it minimized to only what was required?                                 |  Not Applicable
+Is data in dataset traceable?                                                                                                      |  Yes
+Is there provenance for all datasets used in training?                                                                                |  Yes
+Does data labeling (annotation, metadata) comply with privacy laws?                                                                |  Yes
+Is data compliant with data subject requests for data correction or removal, if such a request was made?                           |  No, not possible with externally-sourced data.

nv-modelcard++/safety.md

@@ -0,0 +1,6 @@
+Field                                               |  Response
+:---------------------------------------------------|:----------------------------------
+Model Application(s):                           |  Synethic Audio Generation
+Describe the life critical impact (if present).   |  Not Applicable
+Use Case Restrictions:                              |  None
+Model and dataset restrictions:            |  The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development.  Restrictions enforce dataset access during training, and dataset license constraints adhered to.

utils.py

@@ -0,0 +1,80 @@
+# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+import glob
+import os
+import matplotlib
+import torch
+from torch.nn.utils import weight_norm
+matplotlib.use("Agg")
+import matplotlib.pylab as plt
+from meldataset import MAX_WAV_VALUE
+from scipy.io.wavfile import write
+
+
+def plot_spectrogram(spectrogram):
+    fig, ax = plt.subplots(figsize=(10, 2))
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+                   interpolation='none')
+    plt.colorbar(im, ax=ax)
+
+    fig.canvas.draw()
+    plt.close()
+
+    return fig
+
+
+def plot_spectrogram_clipped(spectrogram, clip_max=2.):
+    fig, ax = plt.subplots(figsize=(10, 2))
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+                   interpolation='none', vmin=1e-6, vmax=clip_max)
+    plt.colorbar(im, ax=ax)
+
+    fig.canvas.draw()
+    plt.close()
+
+    return fig
+
+
+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 apply_weight_norm(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        weight_norm(m)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size*dilation - dilation)/2)
+
+
+def load_checkpoint(filepath, device):
+    assert os.path.isfile(filepath)
+    print("Loading '{}'".format(filepath))
+    checkpoint_dict = torch.load(filepath, map_location=device)
+    print("Complete.")
+    return checkpoint_dict
+
+
+def save_checkpoint(filepath, obj):
+    print("Saving checkpoint to {}".format(filepath))
+    torch.save(obj, filepath)
+    print("Complete.")
+
+
+def scan_checkpoint(cp_dir, prefix):
+    pattern = os.path.join(cp_dir, prefix + '????????')
+    cp_list = glob.glob(pattern)
+    if len(cp_list) == 0:
+        return None
+    return sorted(cp_list)[-1]
+
+def save_audio(audio, path, sr):
+    # wav: torch with 1d shape
+    audio = audio * MAX_WAV_VALUE
+    audio = audio.cpu().numpy().astype('int16')
+    write(path, sr, audio)