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mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/.gitignore

@@ -0,0 +1,137 @@
+# BigVGAN
+alias_free_activation/cuda/build/
+exp/
+tmp/
+
+# VSCode configs
+.vscode/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+.idea/

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/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.

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/README.md

@@ -0,0 +1,112 @@
+---
+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
+
+#### Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon
+
+[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN)
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/bigvgan-a-universal-neural-vocoder-with-large/speech-synthesis-on-libritts)](https://paperswithcode.com/sota/speech-synthesis-on-libritts?p=bigvgan-a-universal-neural-vocoder-with-large)
+
+<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
+
+## News
+- **Jul 2024 (v2.3):**
+  - General refactor and code improvements for improved readability.
+  - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark.
+
+- **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio.
+
+- **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.
+
+- **Jul 2024 (v2):** 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.
+
+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_v2_44khz_128band_512x
+```
+
+## Usage
+
+Below example describes how you can use BigVGAN: load the pretrained BigVGAN generator from Hugging Face Hub, 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
+import librosa
+from meldataset import get_mel_spectrogram
+
+# instantiate the model. You can optionally set use_cuda_kernel=True for faster inference.
+model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=False)
+
+# remove weight norm in the model and set to eval mode
+model.remove_weight_norm()
+model = model.eval().to(device)
+
+# load wav file and compute mel spectrogram
+wav_path = '/path/to/your/audio.wav'
+wav, sr = librosa.load(wav_path, sr=model.h.sampling_rate, mono=True) # wav is np.ndarray with shape [T_time] and values in [-1, 1]
+wav = torch.FloatTensor(wav).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).to(device) # 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 shape [1, T_time] and 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_44khz_128band_512x', 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_activation/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 on Hugging Face Collections](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a).
+One can download the checkpoints of the generator weight (named `bigvgan_generator.pt`) and its discriminator/optimizer states (named `bigvgan_discriminator_optimizer.pt`) within the listed model repositories.
+
+| Model Name                                                                                               | Sampling Rate | Mel band | fmax  | Upsampling Ratio | Params | Dataset                    | Steps | 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    | 5M    | No         |
+| [bigvgan_v2_44khz_128band_256x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x)             | 44 kHz        | 128      | 22050 | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
+| [bigvgan_v2_24khz_100band_256x](https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x)             | 24 kHz        | 100      | 12000 | 256              | 112M   | Large-scale Compilation    | 5M    | No         |
+| [bigvgan_v2_22khz_80band_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_256x)               | 22 kHz        | 80       | 11025 | 256              | 112M   | Large-scale Compilation    | 5M    | 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    | 5M    | No         |
+| [bigvgan_24khz_100band](https://huggingface.co/nvidia/bigvgan_24khz_100band)                             | 24 kHz        | 100      | 12000 | 256              | 112M   | LibriTTS                   | 5M    | No         |
+| [bigvgan_base_24khz_100band](https://huggingface.co/nvidia/bigvgan_base_24khz_100band)                   | 24 kHz        | 100      | 12000 | 256              | 14M    | LibriTTS                   | 5M    | No         |
+| [bigvgan_22khz_80band](https://huggingface.co/nvidia/bigvgan_22khz_80band)                               | 22 kHz        | 80       | 8000  | 256              | 112M   | LibriTTS + VCTK + LJSpeech | 5M    | No         |
+| [bigvgan_base_22khz_80band](https://huggingface.co/nvidia/bigvgan_base_22khz_80band)                     | 22 kHz        | 80       | 8000  | 256              | 14M    | LibriTTS + VCTK + LJSpeech | 5M    | No         |

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/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

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/cuda/activation1d.py

@@ -0,0 +1,77 @@
+# Copyright (c) 2024 NVIDIA CORPORATION.
+#   Licensed under the MIT license.
+
+import torch
+import torch.nn as nn
+from alias_free_activation.torch.resample import UpSample1d, DownSample1d
+
+# load fused CUDA kernel: this enables importing anti_alias_activation_cuda
+from alias_free_activation.cuda import load
+
+anti_alias_activation_cuda = load.load()
+
+
+class FusedAntiAliasActivation(torch.autograd.Function):
+    """
+    Assumes filter size 12, replication padding on upsampling/downsampling, and logscale alpha/beta parameters as inputs.
+    The hyperparameters are hard-coded in the kernel to maximize speed.
+    NOTE: The fused kenrel is incorrect for Activation1d with different hyperparameters.
+    """
+
+    @staticmethod
+    def forward(ctx, inputs, up_ftr, down_ftr, alpha, beta):
+        activation_results = anti_alias_activation_cuda.forward(
+            inputs, up_ftr, down_ftr, alpha, beta
+        )
+
+        return activation_results
+
+    @staticmethod
+    def backward(ctx, output_grads):
+        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, self.downsample.lowpass.filter, alpha, beta
+            )
+            return x

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/cuda/anti_alias_activation.cpp

@@ -0,0 +1,23 @@
+/* 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 <torch/extension.h>
+
+extern "C" torch::Tensor fwd_cuda(torch::Tensor const &input, torch::Tensor const &up_filter, torch::Tensor const &down_filter, torch::Tensor const &alpha, torch::Tensor const &beta);
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("forward", &fwd_cuda, "Anti-Alias Activation forward (CUDA)");
+}

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/cuda/anti_alias_activation_cuda.cu

@@ -0,0 +1,246 @@
+/* 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
+{
+    // Hard-coded hyperparameters
+    // 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 UPSAMPLE_REPLICATION_PAD = 5; // 5 on each side, matching torch impl
+    constexpr int DOWNSAMPLE_REPLICATION_PAD_LEFT = 5; // matching torch impl
+    constexpr int DOWNSAMPLE_REPLICATION_PAD_RIGHT = 6; // matching torch impl
+
+    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 *up_ftr,
+        const input_t *down_ftr,
+        const input_t *alpha,
+        const input_t *beta,
+        int batch_size,
+        int channels,
+        int seq_len)
+    {
+        // Up and downsample filters
+        input_t up_filter[FILTER_SIZE];
+        input_t down_filter[FILTER_SIZE];
+
+        // Load data from global memory including extra indices reserved for replication paddings
+        input_t elements[2 * FILTER_SIZE + 2 * BUFFER_SIZE + 2 * UPSAMPLE_REPLICATION_PAD] = {0};
+        input_t intermediates[2 * FILTER_SIZE + 2 * BUFFER_SIZE + DOWNSAMPLE_REPLICATION_PAD_LEFT + DOWNSAMPLE_REPLICATION_PAD_RIGHT] = {0};
+
+        // Output stores downsampled output before writing to dst
+        output_t output[BUFFER_SIZE];
+
+        // 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;
+
+        // intermediate have double the seq_len
+        int intermediate_local_offset = threadIdx.x * BUFFER_SIZE * 2;
+        int intermediate_seq_offset = blockIdx.x * 128 * BUFFER_SIZE * 2 + intermediate_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];
+
+        // Move src and dst pointers
+        src += block_offset + local_offset;
+        dst += block_offset + local_offset;
+
+        // Alpha and beta values for snake activatons. Applies exp by default
+        alpha = alpha + blockIdx.y;
+        input_t alpha_val = expf(alpha[0]);
+        beta = beta + blockIdx.y;
+        input_t beta_val = expf(beta[0]);
+
+        #pragma unroll
+        for (int it = 0; it < FILTER_SIZE; it += 1)
+        {
+            up_filter[it] = up_ftr[it];
+            down_filter[it] = down_ftr[it];
+        }
+
+        // Apply replication padding for upsampling, matching torch impl
+        #pragma unroll
+        for (int it = -HALF_FILTER_SIZE; it < BUFFER_SIZE + HALF_FILTER_SIZE; it += 1)
+        {
+            int element_index = seq_offset + it; // index for element
+            if ((element_index < 0) && (element_index >= -UPSAMPLE_REPLICATION_PAD))
+            {
+                elements[2 * (HALF_FILTER_SIZE + it)] = 2 * seq_left_most_value;
+            }
+            if ((element_index >= seq_len) && (element_index < seq_len + UPSAMPLE_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 upsampling strided convolution and write to intermediates. It reserves DOWNSAMPLE_REPLICATION_PAD_LEFT for replication padding of the downsampilng conv later
+        #pragma unroll
+        for (int it = 0; it < (2 * BUFFER_SIZE + 2 * FILTER_SIZE); it += 1)
+        {
+            input_t acc = 0.0;
+            int element_index = intermediate_seq_offset + it; // index for intermediate
+            #pragma unroll
+            for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx += 1)
+            {
+                if ((element_index + f_idx) >= 0)
+                {
+                    acc += up_filter[f_idx] * elements[it + f_idx];
+                }
+            }
+            intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] = acc;
+        }
+
+        // Apply activation function. It reserves DOWNSAMPLE_REPLICATION_PAD_LEFT and DOWNSAMPLE_REPLICATION_PAD_RIGHT for replication padding of the downsampilng conv later
+        double no_div_by_zero = 0.000000001;
+        #pragma unroll
+        for (int it = 0; it < 2 * BUFFER_SIZE + 2 * FILTER_SIZE; it += 1)
+        {
+            intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] += (1.0 / (beta_val + no_div_by_zero)) * sinf(intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] * alpha_val) * sinf(intermediates[it + DOWNSAMPLE_REPLICATION_PAD_LEFT] * alpha_val);
+        }
+
+        // Apply replication padding before downsampling conv from intermediates
+        #pragma unroll
+        for (int it = 0; it < DOWNSAMPLE_REPLICATION_PAD_LEFT; it += 1)
+        {
+            intermediates[it] = intermediates[DOWNSAMPLE_REPLICATION_PAD_LEFT];
+        }
+        #pragma unroll
+        for (int it = DOWNSAMPLE_REPLICATION_PAD_LEFT + 2 * BUFFER_SIZE + 2 * FILTER_SIZE; it < DOWNSAMPLE_REPLICATION_PAD_LEFT + 2 * BUFFER_SIZE + 2 * FILTER_SIZE + DOWNSAMPLE_REPLICATION_PAD_RIGHT; it += 1)
+        {
+            intermediates[it] = intermediates[DOWNSAMPLE_REPLICATION_PAD_LEFT + 2 * BUFFER_SIZE + 2 * FILTER_SIZE - 1];
+        }
+
+        // Apply downsample strided convolution (assuming stride=2) from intermediates
+        #pragma unroll
+        for (int it = 0; it < BUFFER_SIZE; it += 1)
+        {
+            input_t acc = 0.0;
+            #pragma unroll
+            for (int f_idx = 0; f_idx < FILTER_SIZE; f_idx += 1)
+            {
+                // Add constant DOWNSAMPLE_REPLICATION_PAD_RIGHT to match torch implementation
+                acc += down_filter[f_idx] * intermediates[it * 2 + f_idx + DOWNSAMPLE_REPLICATION_PAD_RIGHT];
+            }
+            output[it] = acc;
+        }
+
+        // Write output to dst
+        #pragma unroll
+        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 *up_ftr,
+        const input_t *down_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, up_ftr, down_ftr, alpha, beta, batch_size, channels, seq_len);
+        }
+    }
+}
+
+extern "C" torch::Tensor fwd_cuda(torch::Tensor const &input, torch::Tensor const &up_filter, torch::Tensor const &down_filter, torch::Tensor const &alpha, torch::Tensor const &beta)
+{
+    // Input is a 3d tensor with dimensions [batches, channels, 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);
+
+    torch::Tensor anti_alias_activation_results =
+        torch::empty({batches, channels, seq_len}, act_options);
+
+    void *input_ptr = static_cast<void *>(input.data_ptr());
+    void *up_filter_ptr = static_cast<void *>(up_filter.data_ptr());
+    void *down_filter_ptr = static_cast<void *>(down_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 *>(up_filter_ptr),
+            reinterpret_cast<const scalar_t *>(down_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;
+}

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/cuda/compat.h

@@ -0,0 +1,29 @@
+/* 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

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/cuda/load.py

@@ -0,0 +1,86 @@
+# 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 stringo 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
+    )
+
+    return anti_alias_activation_cuda
+
+
+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")

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/cuda/type_shim.h

@@ -0,0 +1,92 @@
+/* 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), "'");      \
+	}

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/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 *

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/torch/act.py

@@ -0,0 +1,30 @@
+# 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 alias_free_activation.torch.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

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/torch/filter.py

@@ -0,0 +1,101 @@
+# 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(1.0, 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 > 50.0:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.0:
+        beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
+    else:
+        beta = 0.0
+    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 < -0.0:
+            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

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/alias_free_activation/torch/resample.py

@@ -0,0 +1,58 @@
+# 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 alias_free_activation.torch.filter import LowPassFilter1d
+from alias_free_activation.torch.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

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/bigvgan.py

@@ -0,0 +1,492 @@
+# 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 typing import Optional, Union, Dict
+
+import torch
+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_activation.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()
+    return AttrDict(json.loads(data))
+
+
+class AMPBlock1(torch.nn.Module):
+    """
+    AMPBlock applies Snake / SnakeBeta activation functions with trainable parameters that control periodicity, defined for each layer.
+    AMPBlock1 has additional self.convs2 that contains additional Conv1d layers with a fixed dilation=1 followed by each layer in self.convs1
+
+    Args:
+        h (AttrDict): Hyperparameters.
+        channels (int): Number of convolution channels.
+        kernel_size (int): Size of the convolution kernel. Default is 3.
+        dilation (tuple): Dilation rates for the convolutions. Each dilation layer has two convolutions. Default is (1, 3, 5).
+        activation (str): Activation function type. Should be either 'snake' or 'snakebeta'. Default is None.
+    """
+
+    def __init__(
+        self,
+        h: AttrDict,
+        channels: int,
+        kernel_size: int = 3,
+        dilation: tuple = (1, 3, 5),
+        activation: str = None,
+    ):
+        super().__init__()
+        
+        self.h = h
+
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        stride=1,
+                        dilation=d,
+                        padding=get_padding(kernel_size, d),
+                    )
+                )
+                for d in dilation
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        stride=1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                )
+                for _ in range(len(dilation))
+            ]
+        )
+        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):
+            from alias_free_activation.cuda.activation1d import (
+                Activation1d as CudaActivation1d,
+            )
+
+            Activation1d = CudaActivation1d
+        else:
+            Activation1d = TorchActivation1d
+
+        # Activation functions
+        if activation == "snake":
+            self.activations = nn.ModuleList(
+                [
+                    Activation1d(
+                        activation=activations.Snake(
+                            channels, alpha_logscale=h.snake_logscale
+                        )
+                    )
+                    for _ in range(self.num_layers)
+                ]
+            )
+        elif activation == "snakebeta":
+            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):
+    """
+    AMPBlock applies Snake / SnakeBeta activation functions with trainable parameters that control periodicity, defined for each layer.
+    Unlike AMPBlock1, AMPBlock2 does not contain extra Conv1d layers with fixed dilation=1
+
+    Args:
+        h (AttrDict): Hyperparameters.
+        channels (int): Number of convolution channels.
+        kernel_size (int): Size of the convolution kernel. Default is 3.
+        dilation (tuple): Dilation rates for the convolutions. Each dilation layer has two convolutions. Default is (1, 3, 5).
+        activation (str): Activation function type. Should be either 'snake' or 'snakebeta'. Default is None.
+    """
+
+    def __init__(
+        self,
+        h: AttrDict,
+        channels: int,
+        kernel_size: int = 3,
+        dilation: tuple = (1, 3, 5),
+        activation: str = None,
+    ):
+        super().__init__()
+        
+        self.h = h
+
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        stride=1,
+                        dilation=d,
+                        padding=get_padding(kernel_size, d),
+                    )
+                )
+                for d in dilation
+            ]
+        )
+        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):
+            from alias_free_activation.cuda.activation1d import (
+                Activation1d as CudaActivation1d,
+            )
+
+            Activation1d = CudaActivation1d
+        else:
+            Activation1d = TorchActivation1d
+
+        # Activation functions
+        if activation == "snake":
+            self.activations = nn.ModuleList(
+                [
+                    Activation1d(
+                        activation=activations.Snake(
+                            channels, alpha_logscale=h.snake_logscale
+                        )
+                    )
+                    for _ in range(self.num_layers)
+                ]
+            )
+        elif activation == "snakebeta":
+            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
+
+    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"],
+):
+    """
+    BigVGAN is a neural vocoder model that applies anti-aliased periodic activation for residual blocks (resblocks).
+    New in BigVGAN-v2: it can optionally use optimized CUDA kernels for AMP (anti-aliased multi-periodicity) blocks.
+
+    Args:
+        h (AttrDict): Hyperparameters.
+        use_cuda_kernel (bool): If set to True, loads optimized CUDA kernels for AMP. This should be used for inference only, as training is not supported with CUDA kernels.
+
+    Note:
+        - The `use_cuda_kernel` parameter should be used for inference only, as training with CUDA kernels is not supported.
+        - Ensure that the activation function is correctly specified in the hyperparameters (h.activation).
+    """
+
+    def __init__(self, h: AttrDict, use_cuda_kernel: bool = False):
+        super().__init__()
+        self.h = h
+        self.h["use_cuda_kernel"] = use_cuda_kernel
+
+        # Select which Activation1d, lazy-load cuda version to ensure backward compatibility
+        if self.h.get("use_cuda_kernel", False):
+            from alias_free_activation.cuda.activation1d import (
+                Activation1d as CudaActivation1d,
+            )
+
+            Activation1d = CudaActivation1d
+        else:
+            Activation1d = TorchActivation1d
+
+        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
+        if h.resblock == "1":
+            resblock_class = AMPBlock1
+        elif h.resblock == "2":
+            resblock_class = AMPBlock2
+        else:
+            raise ValueError(
+                f"Incorrect resblock class specified in hyperparameters. Got {h.resblock}"
+            )
+
+        # 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_class(h, ch, k, d, activation=h.activation)
+                )
+
+        # Post-conv
+        activation_post = (
+            activations.Snake(ch, alpha_logscale=h.snake_logscale)
+            if h.activation == "snake"
+            else (
+                activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
+                if h.activation == "snakebeta"
+                else None
+            )
+        )
+        if activation_post is None:
+            raise NotImplementedError(
+                "activation incorrectly specified. check the config file and look for 'activation'."
+            )
+
+        self.activation_post = Activation1d(activation=activation_post)
+
+        # Whether to use bias for the final conv_post. Default 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=-1.0, max=1.0)  # Bound the output to [-1, 1]
+
+        return x
+
+    def remove_weight_norm(self):
+        try:
+            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)
+        except ValueError:
+            print("[INFO] Model already removed weight norm. Skipping!")
+            pass
+
+    # 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
+        if os.path.isdir(model_id):
+            print("Loading config.json from local directory")
+            config_file = os.path.join(model_id, "config.json")
+        else:
+            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)
+
+        try:
+            model.load_state_dict(checkpoint_dict["generator"])
+        except RuntimeError:
+            print(
+                f"[INFO] the pretrained checkpoint does not contain weight norm. Loading the checkpoint after removing weight norm!"
+            )
+            model.remove_weight_norm()
+            model.load_state_dict(checkpoint_dict["generator"])
+
+        return model

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/bigvgan_discriminator_optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e356f94202c588bc0ddac381be12ea822a68cb1cb26095de30831a59532de54
+size 1525718216

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/bigvgan_generator.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9fe7ec6bd0b44ed9d66973d5012d8181c1570b01e5c72df51973e241dccd357
+size 489041291

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/config.json

@@ -0,0 +1,63 @@
+{
+    "resblock": "1",
+    "num_gpus": 0,
+    "batch_size": 32,
+    "learning_rate": 0.0001,
+    "adam_b1": 0.8,
+    "adam_b2": 0.99,
+    "lr_decay": 0.9999996,
+    "seed": 1234,
+
+    "upsample_rates": [8,4,2,2,2,2],
+    "upsample_kernel_sizes": [16,8,4,4,4,4],
+    "upsample_initial_channel": 1536,
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+
+    "use_tanh_at_final": false,
+    "use_bias_at_final": false,
+
+    "activation": "snakebeta",
+    "snake_logscale": true,
+
+    "use_cqtd_instead_of_mrd": true,
+    "cqtd_filters": 128,
+    "cqtd_max_filters": 1024,
+    "cqtd_filters_scale": 1,
+    "cqtd_dilations": [1, 2, 4],
+    "cqtd_hop_lengths": [512, 256, 256],
+    "cqtd_n_octaves": [9, 9, 9],
+    "cqtd_bins_per_octaves": [24, 36, 48],
+
+    "mpd_reshapes": [2, 3, 5, 7, 11],
+    "use_spectral_norm": false,
+    "discriminator_channel_mult": 1,
+
+    "use_multiscale_melloss": true,
+    "lambda_melloss": 15,
+
+    "clip_grad_norm": 500,
+
+    "segment_size": 65536,
+    "num_mels": 128,
+    "num_freq": 2049,
+    "n_fft": 2048,
+    "hop_size": 512,
+    "win_size": 2048,
+
+    "sampling_rate": 44100,
+
+    "fmin": 0,
+    "fmax": null,
+    "fmax_for_loss": null,
+
+    "normalize_volume": true,
+
+    "num_workers": 4,
+
+    "dist_config": {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54321",
+        "world_size": 1
+    }
+}

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/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))

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/meldataset.py

@@ -0,0 +1,354 @@
+# 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 math
+import os
+import random
+import torch
+import torch.utils.data
+import numpy as np
+from librosa.util import normalize
+from scipy.io.wavfile import read
+from librosa.filters import mel as librosa_mel_fn
+import pathlib
+from tqdm import tqdm
+
+MAX_WAV_VALUE = 32767.0  # NOTE: 32768.0 -1 to prevent int16 overflow (results in popping sound in corner cases)
+
+
+def load_wav(full_path, sr_target):
+    sampling_rate, data = read(full_path)
+    if sampling_rate != sr_target:
+        raise RuntimeError(
+            f"Sampling rate of the file {full_path} is {sampling_rate} Hz, but the model requires {sr_target} Hz"
+        )
+    return data, sampling_rate
+
+
+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):
+    return dynamic_range_compression_torch(magnitudes)
+
+
+def spectral_de_normalize_torch(magnitudes):
+    return dynamic_range_decompression_torch(magnitudes)
+
+
+mel_basis_cache = {}
+hann_window_cache = {}
+
+
+def mel_spectrogram(
+    y: torch.Tensor,
+    n_fft: int,
+    num_mels: int,
+    sampling_rate: int,
+    hop_size: int,
+    win_size: int,
+    fmin: int,
+    fmax: int = None,
+    center: bool = False,
+) -> torch.Tensor:
+    """
+    Calculate the mel spectrogram of an input signal.
+    This function uses slaney norm for the librosa mel filterbank (using librosa.filters.mel) and uses Hann window for STFT (using torch.stft).
+
+    Args:
+        y (torch.Tensor): Input signal.
+        n_fft (int): FFT size.
+        num_mels (int): Number of mel bins.
+        sampling_rate (int): Sampling rate of the input signal.
+        hop_size (int): Hop size for STFT.
+        win_size (int): Window size for STFT.
+        fmin (int): Minimum frequency for mel filterbank.
+        fmax (int): Maximum frequency for mel filterbank. If None, defaults to half the sampling rate (fmax = sr / 2.0) inside librosa_mel_fn
+        center (bool): Whether to pad the input to center the frames. Default is False.
+
+    Returns:
+        torch.Tensor: Mel spectrogram.
+    """
+    if torch.min(y) < -1.0:
+        print(f"[WARNING] Min value of input waveform signal is {torch.min(y)}")
+    if torch.max(y) > 1.0:
+        print(f"[WARNING] Max value of input waveform signal is {torch.max(y)}")
+
+    device = y.device
+    key = f"{n_fft}_{num_mels}_{sampling_rate}_{hop_size}_{win_size}_{fmin}_{fmax}_{device}"
+
+    if key not in mel_basis_cache:
+        mel = librosa_mel_fn(
+            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
+        )
+        mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)
+        hann_window_cache[key] = torch.hann_window(win_size).to(device)
+
+    mel_basis = mel_basis_cache[key]
+    hann_window = hann_window_cache[key]
+
+    padding = (n_fft - hop_size) // 2
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1), (padding, padding), mode="reflect"
+    ).squeeze(1)
+
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window,
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=True,
+    )
+    spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)
+
+    mel_spec = torch.matmul(mel_basis, spec)
+    mel_spec = spectral_normalize_torch(mel_spec)
+
+    return mel_spec
+
+
+def get_mel_spectrogram(wav, h):
+    """
+    Generate mel spectrogram from a waveform using given hyperparameters.
+
+    Args:
+        wav (torch.Tensor): Input waveform.
+        h: Hyperparameters object with attributes n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax.
+
+    Returns:
+        torch.Tensor: Mel spectrogram.
+    """
+    return mel_spectrogram(
+        wav,
+        h.n_fft,
+        h.num_mels,
+        h.sampling_rate,
+        h.hop_size,
+        h.win_size,
+        h.fmin,
+        h.fmax,
+    )
+
+
+def get_dataset_filelist(a):
+    training_files = []
+    validation_files = []
+    list_unseen_validation_files = []
+
+    with open(a.input_training_file, "r", encoding="utf-8") as fi:
+        training_files = [
+            os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav")
+            for x in fi.read().split("\n")
+            if len(x) > 0
+        ]
+        print(f"first training file: {training_files[0]}")
+
+    with open(a.input_validation_file, "r", encoding="utf-8") as fi:
+        validation_files = [
+            os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav")
+            for x in fi.read().split("\n")
+            if len(x) > 0
+        ]
+        print(f"first validation file: {validation_files[0]}")
+
+    for i in range(len(a.list_input_unseen_validation_file)):
+        with open(a.list_input_unseen_validation_file[i], "r", encoding="utf-8") as fi:
+            unseen_validation_files = [
+                os.path.join(a.list_input_unseen_wavs_dir[i], x.split("|")[0] + ".wav")
+                for x in fi.read().split("\n")
+                if len(x) > 0
+            ]
+            print(
+                f"first unseen {i}th validation fileset: {unseen_validation_files[0]}"
+            )
+            list_unseen_validation_files.append(unseen_validation_files)
+
+    return training_files, validation_files, list_unseen_validation_files
+
+
+class MelDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        training_files,
+        hparams,
+        segment_size,
+        n_fft,
+        num_mels,
+        hop_size,
+        win_size,
+        sampling_rate,
+        fmin,
+        fmax,
+        split=True,
+        shuffle=True,
+        n_cache_reuse=1,
+        device=None,
+        fmax_loss=None,
+        fine_tuning=False,
+        base_mels_path=None,
+        is_seen=True,
+    ):
+        self.audio_files = training_files
+        random.seed(1234)
+        if shuffle:
+            random.shuffle(self.audio_files)
+        self.hparams = hparams
+        self.is_seen = is_seen
+        if self.is_seen:
+            self.name = pathlib.Path(self.audio_files[0]).parts[0]
+        else:
+            self.name = "-".join(pathlib.Path(self.audio_files[0]).parts[:2]).strip("/")
+
+        self.segment_size = segment_size
+        self.sampling_rate = sampling_rate
+        self.split = split
+        self.n_fft = n_fft
+        self.num_mels = num_mels
+        self.hop_size = hop_size
+        self.win_size = win_size
+        self.fmin = fmin
+        self.fmax = fmax
+        self.fmax_loss = fmax_loss
+        self.cached_wav = None
+        self.n_cache_reuse = n_cache_reuse
+        self._cache_ref_count = 0
+        self.device = device
+        self.fine_tuning = fine_tuning
+        self.base_mels_path = base_mels_path
+
+        print("[INFO] checking dataset integrity...")
+        for i in tqdm(range(len(self.audio_files))):
+            assert os.path.exists(
+                self.audio_files[i]
+            ), f"{self.audio_files[i]} not found"
+
+    def __getitem__(self, index):
+        filename = self.audio_files[index]
+        if self._cache_ref_count == 0:
+            audio, sampling_rate = load_wav(filename, self.sampling_rate)
+            audio = audio / MAX_WAV_VALUE
+            if not self.fine_tuning:
+                audio = normalize(audio) * 0.95
+            self.cached_wav = audio
+            if sampling_rate != self.sampling_rate:
+                raise ValueError(
+                    f"{sampling_rate} SR doesn't match target {self.sampling_rate} SR"
+                )
+            self._cache_ref_count = self.n_cache_reuse
+        else:
+            audio = self.cached_wav
+            self._cache_ref_count -= 1
+
+        audio = torch.FloatTensor(audio)
+        audio = audio.unsqueeze(0)
+
+        if not self.fine_tuning:
+            if self.split:
+                if audio.size(1) >= self.segment_size:
+                    max_audio_start = audio.size(1) - self.segment_size
+                    audio_start = random.randint(0, max_audio_start)
+                    audio = audio[:, audio_start : audio_start + self.segment_size]
+                else:
+                    audio = torch.nn.functional.pad(
+                        audio, (0, self.segment_size - audio.size(1)), "constant"
+                    )
+
+                mel = mel_spectrogram(
+                    audio,
+                    self.n_fft,
+                    self.num_mels,
+                    self.sampling_rate,
+                    self.hop_size,
+                    self.win_size,
+                    self.fmin,
+                    self.fmax,
+                    center=False,
+                )
+            else:  # Validation step
+                # Match audio length to self.hop_size * n for evaluation
+                if (audio.size(1) % self.hop_size) != 0:
+                    audio = audio[:, : -(audio.size(1) % self.hop_size)]
+                mel = mel_spectrogram(
+                    audio,
+                    self.n_fft,
+                    self.num_mels,
+                    self.sampling_rate,
+                    self.hop_size,
+                    self.win_size,
+                    self.fmin,
+                    self.fmax,
+                    center=False,
+                )
+                assert (
+                    audio.shape[1] == mel.shape[2] * self.hop_size
+                ), f"audio shape {audio.shape} mel shape {mel.shape}"
+
+        else:
+            mel = np.load(
+                os.path.join(
+                    self.base_mels_path,
+                    os.path.splitext(os.path.split(filename)[-1])[0] + ".npy",
+                )
+            )
+            mel = torch.from_numpy(mel)
+
+            if len(mel.shape) < 3:
+                mel = mel.unsqueeze(0)
+
+            if self.split:
+                frames_per_seg = math.ceil(self.segment_size / self.hop_size)
+
+                if audio.size(1) >= self.segment_size:
+                    mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1)
+                    mel = mel[:, :, mel_start : mel_start + frames_per_seg]
+                    audio = audio[
+                        :,
+                        mel_start
+                        * self.hop_size : (mel_start + frames_per_seg)
+                        * self.hop_size,
+                    ]
+                else:
+                    mel = torch.nn.functional.pad(
+                        mel, (0, frames_per_seg - mel.size(2)), "constant"
+                    )
+                    audio = torch.nn.functional.pad(
+                        audio, (0, self.segment_size - audio.size(1)), "constant"
+                    )
+
+        mel_loss = mel_spectrogram(
+            audio,
+            self.n_fft,
+            self.num_mels,
+            self.sampling_rate,
+            self.hop_size,
+            self.win_size,
+            self.fmin,
+            self.fmax_loss,
+            center=False,
+        )
+
+        return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze())
+
+    def __len__(self):
+        return len(self.audio_files)

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/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. |

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/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                                                                                                                                                                    |

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/nv-modelcard++/overview.md

@@ -0,0 +1,126 @@
+# 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://arxiv.org/abs/2205.03433)
+- [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://arxiv.org/abs/1912.07875)
+- [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/).

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/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. |

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/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. |

mmaudio/nvidia/bigvgan_v2_44khz_128band_512x/utils.py

@@ -0,0 +1,99 @@
+# 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=2.0):
+    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(f"Loading '{filepath}'")
+    checkpoint_dict = torch.load(filepath, map_location=device)
+    print("Complete.")
+    return checkpoint_dict
+
+
+def save_checkpoint(filepath, obj):
+    print(f"Saving checkpoint to {filepath}")
+    torch.save(obj, filepath)
+    print("Complete.")
+
+
+def scan_checkpoint(cp_dir, prefix, renamed_file=None):
+    # Fallback to original scanning logic first
+    pattern = os.path.join(cp_dir, prefix + "????????")
+    cp_list = glob.glob(pattern)
+
+    if len(cp_list) > 0:
+        last_checkpoint_path = sorted(cp_list)[-1]
+        print(f"[INFO] Resuming from checkpoint: '{last_checkpoint_path}'")
+        return last_checkpoint_path
+
+    # If no pattern-based checkpoints are found, check for renamed file
+    if renamed_file:
+        renamed_path = os.path.join(cp_dir, renamed_file)
+        if os.path.isfile(renamed_path):
+            print(f"[INFO] Resuming from renamed checkpoint: '{renamed_file}'")
+            return renamed_path
+
+    return None
+
+
+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)

pocket-tts/README.md

@@ -0,0 +1,140 @@
+---
+license: cc-by-4.0
+language:
+- en
+extra_gated_prompt: "Prohibited use: Use of our model must comply with all applicable laws and regulations and must not result in, involve, or facilitate any illegal, harmful, deceptive, fraudulent, or unauthorized activity. Prohibited uses include, without limitation, voice impersonation or cloning without explicit and lawful consent; misinformation, disinformation, or deception (including fake news, fraudulent calls, or presenting generated content as genuine recordings of real people or events); and the generation of unlawful, harmful, libelous, abusive, harassing, discriminatory, hateful, or privacy-invasive content. We disclaim all liability for any non-compliant use."
+extra_gated_fields:
+  Company or university if applicable: text
+  I want to use this model for:
+    type: select
+    options: 
+      - Work
+      - Studies
+      - Fun
+---
+# Pocket TTS
+
+![logo](https://raw.githubusercontent.com/kyutai-labs/pocket-tts/refs/heads/main/docs/logo.png)
+
+A lightweight text-to-speech (TTS) application designed to run efficiently on CPUs.
+Forget about the hassle of using GPUs and web APIs serving TTS models. With Kyutai's Pocket TTS, generating audio is just a pip install and a function call away.
+
+Supports Python 3.10, 3.11, 3.12, 3.13 and 3.14. Requires PyTorch 2.5+. Does not require the gpu version of PyTorch.
+
+[🔊 Demo](https://kyutai.org/tts) | 
+[🐱‍💻GitHub Repository](https://github.com/kyutai-labs/pocket-tts) | 
+[🤗 Hugging Face Model Card](https://huggingface.co/kyutai/pocket-tts) | 
+[📄 Paper](https://arxiv.org/abs/2509.06926) | 
+[📚 Documentation](https://github.com/kyutai-labs/pocket-tts/tree/main/docs)
+
+
+## Main takeaways
+* Runs on CPU
+* Small model size, 100M parameters
+* Audio streaming
+* Low latency, ~200ms to get the first audio chunk
+* Faster than real-time, ~6x real-time on a CPU of MacBook Air M4
+* Uses only 2 CPU cores
+* Python API and CLI
+* Voice cloning
+* English only at the moment
+* Can handle infinitely long text inputs
+
+## Trying it from the website, without installing anything
+
+Navigate to the [Kyutai website](https://kyutai.org/tts) to try it out directly in your browser. You can input text, select different voices, and generate speech without any installation.
+
+## Trying it with the CLI
+
+### The `generate` command
+You can use pocket-tts directly from the command line. We recommend using
+`uv` as it installs any dependencies on the fly in an isolated environment (uv installation instructions [here](https://docs.astral.sh/uv/getting-started/installation/#standalone-installer)).
+You can also use `pip install pocket-tts` to install it manually.
+
+This will generate a wav file `./tts_output.wav` saying the default text with the default voice, and display some speed statistics.
+```bash
+uvx pocket-tts generate
+# or if you installed it manually with pip:
+pocket-tts generate
+```
+Modify the voice with `--voice` and the text with `--text`. We provide a small catalog of voices.
+
+You can take a look at [this page](https://huggingface.co/kyutai/tts-voices) which details the licenses
+for each voice.
+
+* [alba](https://huggingface.co/kyutai/tts-voices/blob/main/alba-mackenna/casual.wav)
+* [marius](https://huggingface.co/kyutai/tts-voices/blob/main/voice-donations/Selfie.wav)
+* [javert](https://huggingface.co/kyutai/tts-voices/blob/main/voice-donations/Butter.wav)
+* [jean](https://huggingface.co/kyutai/tts-voices/blob/main/ears/p010/freeform_speech_01.wav)
+* [fantine](https://huggingface.co/kyutai/tts-voices/blob/main/vctk/p244_023.wav)
+* [cosette](https://huggingface.co/kyutai/tts-voices/blob/main/expresso/ex04-ex02_confused_001_channel1_499s.wav)
+* [eponine](https://huggingface.co/kyutai/tts-voices/blob/main/vctk/p262_023.wav)
+* [azelma](https://huggingface.co/kyutai/tts-voices/blob/main/vctk/p303_023.wav)
+
+The `--voice` argument can also take a plain wav file as input for voice cloning.
+Feel free to check out the [generate documentation](https://github.com/kyutai-labs/pocket-tts/tree/main/docs/generate.md) for more details and examples.
+For trying multiple voices and prompts quickly, prefer using the `serve` command.
+
+### The `serve` command
+
+You can also run a local server to generate audio via HTTP requests.
+```bash
+uvx pocket-tts serve
+# or if you installed it manually with pip:
+pocket-tts serve
+```
+Navigate to `http://localhost:8000` to try the web interface, it's faster than the command line as the model is kept in memory between requests.
+
+You can check out the [serve documentation](https://github.com/kyutai-labs/pocket-tts/tree/main/docs/serve.md) for more details and examples.
+
+## Using it as a Python library
+
+Install the package with
+```bash
+pip install pocket-tts
+# or
+uv add pocket-tts
+```
+
+You can use this package as a simple Python library to generate audio from text.
+```python
+from pocket_tts import TTSModel
+import scipy.io.wavfile
+
+tts_model = TTSModel.load_model()
+voice_state = tts_model.get_state_for_audio_prompt(
+    "hf://kyutai/tts-voices/alba-mackenna/casual.wav"
+)
+audio = tts_model.generate_audio(voice_state, "Hello world, this is a test.")
+# Audio is a 1D torch tensor containing PCM data.
+scipy.io.wavfile.write("output.wav", tts_model.sample_rate, audio.numpy())
+```
+
+You can have multiple voice states around if 
+you have multiple voices you want to use. `load_model()` 
+and `get_state_for_audio_prompt()` are relatively slow operations,
+so we recommend to keep the model and voice states in memory if you can.
+
+You can check out the [Python API documentation](https://github.com/kyutai-labs/pocket-tts/tree/main/docs/python-api.md) for more details and examples.
+
+## Unsupported features
+
+At the moment, we do not support (but would love pull requests adding):
+- [Running the TTS inside a web browser (WebAssembly)](https://github.com/kyutai-labs/pocket-tts/issues/1)
+- [A compiled version with for example `torch.compile()` or `candle`.](https://github.com/kyutai-labs/pocket-tts/issues/2)
+- [Adding silence in the text input to generate pauses.](https://github.com/kyutai-labs/pocket-tts/issues/6)
+- [Quantization to run the computation in int8.](https://github.com/kyutai-labs/pocket-tts/issues/7)
+
+We tried running this TTS model on the GPU but did not observe a speedup compared to CPU execution,
+notably because we use a batch size of 1 and a very small model.
+
+## Development and local setup
+
+We accept contributions! Feel free to open issues or pull requests on GitHub.
+
+You can find development instructions in the [CONTRIBUTING.md](https://github.com/kyutai-labs/pocket-tts/tree/main/CONTRIBUTING.md) file. You'll also find there how to have an editable install of the package for local development.
+
+
+## Prohibited use
+
+Use of our model must comply with all applicable laws and regulations and must not result in, involve, or facilitate any illegal, harmful, deceptive, fraudulent, or unauthorized activity. Prohibited uses include, without limitation, voice impersonation or cloning without explicit and lawful consent; misinformation, disinformation, or deception (including fake news, fraudulent calls, or presenting generated content as genuine recordings of real people or events); and the generation of unlawful, harmful, libelous, abusive, harassing, discriminatory, hateful, or privacy-invasive content. We disclaim all liability for any non-compliant use.

pocket-tts/embeddings/alba.safetensors

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+size 512088

pocket-tts/embeddings/azelma.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef33fad34437cb187d2702f0a946d8ba7a01efdb8efbc8088c770d49c181ba73
+size 659544

pocket-tts/embeddings/cosette.safetensors

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+size 512088

pocket-tts/embeddings/eponine.safetensors

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+size 573528

pocket-tts/embeddings/fantine.safetensors

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+size 540760

pocket-tts/embeddings/javert.safetensors

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+version https://git-lfs.github.com/spec/v1
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+size 512088

pocket-tts/embeddings/jean.safetensors

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pocket-tts/embeddings/marius.safetensors

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pocket-tts/gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

pocket-tts/remove_voice_cloning_and_push.py

@@ -0,0 +1,49 @@
+# /// script
+# requires-python = ">=3.14"
+# dependencies = [
+#     "safetensors>=0.7.0",
+#     "torch>=2.9.1",
+#     "packaging",
+#     "huggingface_hub",
+#     "numpy",
+# ]
+# ///
+
+import os
+import shutil
+from pathlib import Path
+from safetensors.torch import load_file, save_file
+import torch
+from huggingface_hub import HfApi
+import tempfile
+
+current_repository = Path(__file__).parent
+
+with tempfile.TemporaryDirectory() as destination_dir:
+    destination_dir = Path(destination_dir)
+    if destination_dir.exists():
+        shutil.rmtree(destination_dir)
+
+    shutil.copytree(current_repository, destination_dir)
+
+    model_name = "tts_b6369a24.safetensors"
+
+    tensors = load_file(current_repository / model_name)
+
+    new_tensors = {}
+
+    for key, tensor in tensors.items():
+        if key.startswith("mimi.encoder"):
+            print("zeroing out", key)
+            new_tensors[key] = torch.zeros_like(tensor)
+        else:
+            new_tensors[key] = tensor
+
+    save_file(new_tensors, destination_dir / model_name)
+
+    api = HfApi()
+    api.upload_folder(
+        folder_path=destination_dir,
+        repo_id="kyutai/pocket-tts-without-voice-cloning",
+        delete_patterns="*",
+    )