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add seed-vc model (#60)

Browse Source 
* init seed-vc

* seed-vc workflow

* put seed-vc in the right dir
This commit is contained in:
Madhukar Mishra
2024-10-22 18:40:01 +05:30
committed by GitHub
parent 1b8477b4b1
commit 7d5fd4f029
184 changed files with 12356 additions and 11 deletions
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.github/workflows/docker-publish-seed-vc.yml
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name: publish seed-vc Docker Image
on:
workflow_dispatch: {}
push:
paths:
- "voice_conversion/seed-vc/**"
branches:
# NB: Default-branch doesn't work, despite Github's documentation.
#- $default-branch
- master
env:
IMAGE_NAME: seed-vc
jobs:
# Push image to GitHub Packages.
# See also https://docs.docker.com/docker-hub/builds/
docker-build-and-push:
runs-on: ubuntu-latest
permissions:
packages: write
contents: read
steps:
- uses: actions/checkout@v2
- name: Login to GitHub Container Registry
uses: docker/login-action@v1
with:
registry: ghcr.io
#username: ${{ github.repository_owner }}
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Free Disk Space (Ubuntu)
uses: jlumbroso/free-disk-space@main
with:
tool-cache: false
android: true
dotnet: true
haskell: true
large-packages: false
docker-images: false
swap-storage: false
- name: Build image
run: |
echo "check available disk space"
cd voice_conversion/seed-vc && ls -alt && docker build . --file Dockerfile --tag $IMAGE_NAME --label "runnumber=${GITHUB_RUN_ID}"
- name: Push image to GitHub Container Registry
run: |
IMAGE_ID=ghcr.io/${{ github.repository_owner }}/$IMAGE_NAME
# Change all uppercase to lowercase
IMAGE_ID=$(echo $IMAGE_ID | tr '[A-Z]' '[a-z]')
# Strip git ref prefix from version
VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,')
# Strip "v" prefix from tag name
[[ "${{ github.ref }}" == "refs/tags/"* ]] && VERSION=$(echo $VERSION | sed -e 's/^v//')
# Use Docker `latest` tag convention
[ "$VERSION" == "$default-branch" ] && VERSION=latest
echo IMAGE_ID=$IMAGE_ID
echo VERSION=$VERSION
docker tag $IMAGE_NAME $IMAGE_ID:$VERSION
docker push $IMAGE_ID:$VERSION
SHORT_SHA=$(echo ${GITHUB_SHA} | cut -c1-12)
docker tag $IMAGE_NAME $IMAGE_ID:$SHORT_SHA
docker push $IMAGE_ID:$SHORT_SHA
tts/F5-TTS/.envrc
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pyversion=3.10.14
pvenv=F5-TTS
layout activate ${pyversion}/envs/${pvenv}
tts/F5-TTS/.vscode/settings.json
Vendored
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@@ -0,0 +1,6 @@
{
"workbench.colorCustomizations": {
"minimap.background": "#00000088",
"scrollbar.shadow": "#00000088"
}
}
tts/F5-TTS/Dockerfile
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@@ -1,5 +1,5 @@
# Base CUDA image
FROM cnstark/pytorch:2.3.1-py3.10.15-cuda12.1.0-devel-ubuntu22.04
FROM pytorch/pytorch:2.5.0-cuda12.1-cudnn9-runtime
# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive
@@ -14,13 +14,7 @@ RUN apt-get update && \
git \
nano \
curl \
software-properties-common \
sudo \
rsync && \
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash && \
apt-get install -y git-lfs && \
git lfs install && \
add-apt-repository ppa:deadsnakes/ppa && \
software-properties-common && \
rm -rf /var/lib/apt/lists/*
# Create directories for Python installation and model code
@@ -43,14 +37,13 @@ COPY . /model_code/F5-TTS
# /model_code/F5-TTS/pretrained_models
# initializes the model - downloads checkpoints
# FIXME: is isn't working
ARG HF_DATASETS_CACHE="./pretrained_models"
# ARG HF_DATASETS_CACHE="./pretrained_models"
# ENV HF_DATASETS_CACHE=$HF_DATASETS_CACHE
# RUN ["/bin/bash", "-c", "HF_DATASETS_CACHE=$HF_DATASETS_CACHE DOWNLOAD_ONLY=TRUE /python_install/python/bin/python inference-cli.py"]
# checkpoints at the default location ~/.cache/huggingface/hub
RUN ["/bin/bash", "-c", "DOWNLOAD_ONLY=TRUE /python_install/python/bin/python inference-cli.py"]
RUN ["/bin/bash", "-c", "DOWNLOAD_ONLY=TRUE /python_install/python/bin/python inference-cli.py"]
ENTRYPOINT ["/bin/bash", "-c", ". /python_install/python/bin/activate && exec /bin/bash"]
tts/F5-TTS/pyrightconfig.json
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{
"executionEnvironments": [{ "root": "." }],
"extraPaths": [".."],
"typeCheckingMode": "basic"
}
voice_conversion/seed-vc/.dockerignore
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outputs
examples
Dockerfile
voice_conversion/seed-vc/.envrc
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pyversion=3.10.14
pvenv=seed-vc
layout activate ${pyversion}/envs/${pvenv}
voice_conversion/seed-vc/.gitignore
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# general things to ignore
.DS_Store
build/
build_contrib/
dist/
.cache/
*.egg-info/
*.egg
*.py[cod]
__pycache__/
*.so
*~
# IDE
.vscode/
# misc
checkpoints/
outputs/
test_waves/
reconstructed/
.python-version
ruff.log
voice_conversion/seed-vc/Dockerfile
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FROM pytorch/pytorch:2.4.0-cuda12.4-cudnn9-devel
USER root
ENV SHELL=/bin/bash
ARG DEBIAN_FRONTEND=noninteractive
RUN set -x \
&& apt-get update \
&& apt-get -y install wget curl man git less openssl libssl-dev unzip build-essential tmux vim \
&& rm -rf /var/lib/apt/lists/* \
&& apt-get clean
RUN mkdir -p /python_install /model_code/seed-vc
WORKDIR /python_install
COPY requirements.txt .
RUN python3 -m venv --copies python && \
# python/bin/pip install --upgrade pip wheel setuptools && \
python/bin/pip install --no-cache-dir -r requirements.txt
WORKDIR /model_code/seed-vc
COPY load_all_models.py hf_utils.py .
RUN ["/python_install/python/bin/python", "load_all_models.py"]
COPY . .
ENTRYPOINT ["/python_install/python/bin/python"]
voice_conversion/seed-vc/LICENSE
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@@ -0,0 +1,674 @@
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voice_conversion/seed-vc/README-CN.md
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# Seed-VC
[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Demo-blue)](https://huggingface.co/spaces/Plachta/Seed-VC)
*[English](README.md) | 简体中文*
目前发布的模型支持零样本语音转换和零样本歌声转换。无需任何训练，只需提供1~30秒的参考语音即可克隆声音。
要查看演示列表和与之前语音转换模型的比较，请访问我们的 [演示页面](https://plachtaa.github.io/seed-vc/)🌐
我们将继续改进模型质量并添加更多功能。
## 评估📊
我们对 Seed-VC 的语音转换能力进行了系列客观评估。
为了便于复现，源音频是来自 LibriTTS-test-clean 的 100 个随机语句，参考音频是 12 个随机挑选的具有独特特征的自然声音。<br>
源音频位于 `./examples/libritts-test-clean` <br>
参考音频位于 `./examples/reference` <br>
我们从说话人嵌入余弦相似度（SECS）、词错误率（WER）和字符错误率（CER）三个方面评估了转换结果，并将我们的结果与两个强大的开源基线模型，即 [OpenVoice](https://github.com/myshell-ai/OpenVoice) 和 [CosyVoice](https://github.com/FunAudioLLM/CosyVoice)，进行了比较。
下表的结果显示，我们的 Seed-VC 模型在发音清晰度和说话人相似度上均显著优于基线模型。<br>
| 模型\指标 | SECS↑ | WER↓ | CER↓ | SIG↑ | BAK↑ | OVRL↑ |
|---------------|------------|------------|------------|----------|----------|----------|
| Ground Truth | 1.0000 | 0.0802 | 0.0157 | ~ | ~ | ~ |
| OpenVoice | 0.7547 | 0.1546 | 0.0473 | **3.56** | **4.02** | **3.27** |
| CosyVoice | 0.8440 | 0.1898 | 0.0729 | 3.51 | **4.02** | 3.21 |
| Seed-VC（Ours） | **0.8676** | **0.1199** | **0.0292** | 3.42 | 3.97 | 3.11 |
我们也与非zero-shot的声线转换模型在特定角色上进行了比较（基于可以找到的公开模型）:
| Characters | Models\Metrics | SECS↑ | WER↓ | CER↓ | SIG↑ | BAK↑ | OVRL↑ |
|------------|----------------|------------|-----------|----------|----------|----------|----------|
| ~ | Ground Truth | 1.0000 | 6.43 | 1.00 | ~ | ~ | ~ |
| 东海帝王 | So-VITS-4.0 | 0.8637 | 21.46 | 9.63 | 3.06 | 3.66 | 2.68 |
| | Seed-VC(Ours) | **0.8899** | **15.32** | **4.66** | **3.12** | **3.71** | **2.72** |
| 明前奶绿 | So-VITS-4.0 | 0.6850 | 48.43 | 32.50 | 3.34 | 3.51 | 2.82 |
| | Seed-VC(Ours) | **0.8072** | **7.26** | **1.32** | **3.48** | **4.07** | **3.20** |
| 待兼诗歌剧 | So-VITS-4.0 | 0.8594 | 16.25 | 8.64 | **3.25** | 3.71 | 2.84 |
| | Seed-VC(Ours) | **0.8768** | **12.62** | **5.86** | 3.18 | **3.83** | **2.85** |
结果显示，即便我们的模型没有在特定说话人上进行微调或训练，在音色相似度和咬字清晰度上也全面优于在特定说话人数据集上专门训练的SoVITS模型。
但是该项测试结果高度依赖于SoVITS模型质量。如果您认为此对比不公平或不够准确，欢迎提issue或PR。
(东海帝王模型来自 [zomehwh/sovits-tannhauser](https://huggingface.co/spaces/zomehwh/sovits-tannhauser))
(待兼诗歌剧模型来自 [zomehwh/sovits-tannhauser](https://huggingface.co/spaces/zomehwh/sovits-tannhauser))
(明前奶绿模型来自 [sparanoid/milky-green-sovits-4](https://huggingface.co/spaces/sparanoid/milky-green-sovits-4))
*ASR 结果由 [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) 模型计算*
*说话人嵌入由 [resemblyzer](https://github.com/resemble-ai/Resemblyzer) 模型计算* <br>
你可以通过运行 `eval.py` 脚本来复现评估。
```bash
python eval.py
--source ./examples/libritts-test-clean
--target ./examples/reference
--output ./examples/eval/converted
--diffusion-steps 25
--length-adjust 1.0
--inference-cfg-rate 0.7
--xvector-extractor "resemblyzer"
--baseline "" # 填入 openvoice 或 cosyvoice 来计算基线结果
--max-samples 100 # 要处理的最大源语句数
```
在此之前，如果你想运行基线评估，请确保已在 `../OpenVoice/` 和 `../CosyVoice/` 目录下正确安装了 openvoice 和 cosyvoice 仓库。
## 安装 📥
建议在 Windows 或 Linux 上使用 Python 3.10：
```bash
pip install -r requirements.txt
```
## 使用方法🛠️
首次运行推理时，将自动下载最新模型的检查点。
命令行推理：
```bash
python inference.py --source <源语音文件路径>
--target <参考语音文件路径>
--output <输出目录>
--diffusion-steps 25 # 建议歌声转换时使用50~100
--length-adjust 1.0
--inference-cfg-rate 0.7
--f0-condition False # 歌声转换时设置为 True
--auto-f0-adjust False # 设置为 True 可自动调整源音高到目标音高，歌声转换中通常不使用
--semi-tone-shift 0 # 歌声转换的半音移调
```
其中:
- `source` 待转换为参考声音的源语音文件路径
- `target` 声音参考的语音文件路径
- `output` 输出目录的路径
- `diffusion-steps` 使用的扩散步数，默认25，最佳质量建议使用50-100，最快推理使用4-10
- `length-adjust` 长度调整系数，默认1.0，<1.0加速语音，>1.0减慢语音
- `inference-cfg-rate` 对输出有细微影响，默认0.7
- `f0-condition` 是否根据源音频的音高调整输出音高，默认 False，歌声转换时设置为 True
- `auto-f0-adjust` 是否自动将源音高调整到目标音高水平，默认 False，歌声转换中通常不使用
- `semi-tone-shift` 歌声转换中的半音移调，默认0
Gradio 网页界面:
```bash
python app.py
```
然后在浏览器中打开 `http://localhost:7860/` 使用网页界面。
## TODO📝
- [x] 发布代码
- [x] 发布 v0.1 预训练模型： [![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-SeedVC-blue)](https://huggingface.co/Plachta/Seed-VC)
- [x] Hugging Face Space 演示： [![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-blue)](https://huggingface.co/spaces/Plachta/Seed-VC)
- [x] HTML 演示页面（可能包含与其他 VC 模型的比较）： [Demo](https://plachtaa.github.io/seed-vc/)
- [ ] 流式推理
- [x] 歌声转换
- [x] 提高源音频抗噪性
- [ ] 潜在的架构改进
- [x] 类似U-ViT 的skip connection
- [x] 将输入更改为 OpenAI Whisper
- [ ] 自定义数据训练代码
- [x] 歌声解码器更改为 NVIDIA 的 BigVGAN
- [ ] 44k Hz 歌声转换模型
- [ ] 更多待添加
## 更新日志 🗒️
- 2024-09-26:
- 更新了 v0.3 预训练模型，将语音内容编码器更改为 OpenAI Whisper
- 添加了 v0.3 预训练模型的客观指标评估结果
- 2024-09-22:
- 将歌声转换模型的解码器更改为 BigVGAN，解决了大部分高音部分无法正确转换的问题
- 在Web UI中支持对长输入音频的分段处理以及流式输出
- 2024-09-18:
- 更新了用于歌声转换的模型
- 2024-09-14:
- 更新了 v0.2 预训练模型，具有更小的尺寸和更少的扩散步骤即可达到相同质量，且增加了控制韵律保留的能力
- 添加了命令行推理脚本
- 添加了安装和使用说明
voice_conversion/seed-vc/README.md
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# Seed-VC
[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Demo-blue)](https://huggingface.co/spaces/Plachta/Seed-VC)
*English | [简体中文](README-CN.md)*
Currently released model supports *zero-shot voice conversion* 🔊 and *zero-shot singing voice conversion* 🎙. Without any training, it is able to clone a voice given a reference speech of 1~30 seconds.
To find a list of demos and comparisons with previous voice conversion models, please visit our [demo page](https://plachtaa.github.io/seed-vc/)🌐
We are keeping on improving the model quality and adding more features.
## Evaluation📊
We have performed a series of objective evaluations on our Seed-VC's voice conversion capabilities.
For ease of reproduction, source audios are 100 random utterances from LibriTTS-test-clean, and reference audios are 12 randomly picked in-the-wild voices with unique characteristics. <br>
Source audios can be found under `./examples/libritts-test-clean` <br>
Reference audios can be found under `./examples/reference` <br>
We evaluate the conversion results in terms of speaker embedding cosine similarity (SECS), word error rate (WER) and character error rate (CER) and compared
our results with two strong open sourced baselines, namely [OpenVoice](https://github.com/myshell-ai/OpenVoice) and [CosyVoice](https://github.com/FunAudioLLM/CosyVoice).
Results in the table below shows that our Seed-VC model significantly outperforms the baseline models in both intelligibility and speaker similarity.<br>
| Models\Metrics | SECS↑ | WER↓ | CER↓ | SIG↑ | BAK↑ | OVRL↑ |
|----------------|------------|-----------|----------|----------|----------|----------|
| Ground Truth | 1.0000 | 8.02 | 1.57 | ~ | ~ | ~ |
| OpenVoice | 0.7547 | 15.46 | 4.73 | **3.56** | **4.02** | **3.27** |
| CosyVoice | 0.8440 | 18.98 | 7.29 | 3.51 | **4.02** | 3.21 |
| Seed-VC(Ours) | **0.8676** | **11.99** | **2.92** | 3.42 | 3.97 | 3.11 |
We have also compared with non-zero-shot voice conversion models for several speakers (based on model availability):
| Characters | Models\Metrics | SECS↑ | WER↓ | CER↓ | SIG↑ | BAK↑ | OVRL↑ |
|---------------------|----------------|------------|-----------|----------|----------|----------|----------|
| ~ | Ground Truth | 1.0000 | 6.43 | 1.00 | ~ | ~ | ~ |
| Tokai Teio | So-VITS-4.0 | 0.8637 | 21.46 | 9.63 | 3.06 | 3.66 | 2.68 |
| | Seed-VC(Ours) | **0.8899** | **15.32** | **4.66** | **3.12** | **3.71** | **2.72** |
| Milky Green | So-VITS-4.0 | 0.6850 | 48.43 | 32.50 | 3.34 | 3.51 | 2.82 |
| | Seed-VC(Ours) | **0.8072** | **7.26** | **1.32** | **3.48** | **4.07** | **3.20** |
| Matikane Tannhuaser | So-VITS-4.0 | 0.8594 | 16.25 | 8.64 | **3.25** | 3.71 | 2.84 |
| | Seed-VC(Ours) | **0.8768** | **12.62** | **5.86** | 3.18 | **3.83** | **2.85** |
Results show that, despite not being trained on the target speakers, Seed-VC is able to achieve significantly better results than the non-zero-shot models.
However, this may vary a lot depending on the SoVITS model quality. PR or Issue is welcomed if you find this comparison unfair or inaccurate.
(Tokai Teio model from [zomehwh/sovits-tannhauser](https://huggingface.co/spaces/zomehwh/sovits-tannhauser))
(Matikane Tannhuaser model from [zomehwh/sovits-tannhauser](https://huggingface.co/spaces/zomehwh/sovits-tannhauser))
(Milky Green model from [sparanoid/milky-green-sovits-4](https://huggingface.co/spaces/sparanoid/milky-green-sovits-4))
*ASR result computed by [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) model*
*Speaker embedding computed by [resemblyzer](https://github.com/resemble-ai/Resemblyzer) model* <br>
You can reproduce the evaluation by running `eval.py` script.
```bash
python eval.py
--source ./examples/libritts-test-clean
--target ./examples/reference
--output ./examples/eval/converted
--diffusion-steps 25
--length-adjust 1.0
--inference-cfg-rate 0.7
--xvector-extractor "resemblyzer"
--baseline "" # fill in openvoice or cosyvoice to compute baseline result
--max-samples 100 # max source utterances to go through
```
Before that, make sure you have openvoice and cosyvoice repo correctly installed on `../OpenVoice/` and `../CosyVoice/` if you would like to run baseline evaluation.
## Installation📥
Suggested python 3.10 on Windows or Linux.
```bash
pip install -r requirements.txt
```
## Usage🛠️
Checkpoints of the latest model release will be downloaded automatically when first run inference.
Command line inference:
```bash
python inference.py --source <source-wav>
--target <referene-wav>
--output <output-dir>
--diffusion-steps 25 # recommended 50~100 for singingvoice conversion
--length-adjust 1.0
--inference-cfg-rate 0.7
--f0-condition False # set to True for singing voice conversion
--auto-f0-adjust False # set to True to auto adjust source pitch to target pitch level, normally not used in singing voice conversion
--semi-tone-shift 0 # pitch shift in semitones for singing voice conversion
```
where:
- `source` is the path to the speech file to convert to reference voice
- `target` is the path to the speech file as voice reference
- `output` is the path to the output directory
- `diffusion-steps` is the number of diffusion steps to use, default is 25, use 50-100 for best quality, use 4-10 for fastest inference
- `length-adjust` is the length adjustment factor, default is 1.0, set <1.0 for speed-up speech, >1.0 for slow-down speech
- `inference-cfg-rate` has subtle difference in the output, default is 0.7
- `f0-condition` is the flag to condition the pitch of the output to the pitch of the source audio, default is False, set to True for singing voice conversion
- `auto-f0-adjust` is the flag to auto adjust source pitch to target pitch level, default is False, normally not used in singing voice conversion
- `semi-tone-shift` is the pitch shift in semitones for singing voice conversion, default is 0
Gradio web interface:
```bash
python app.py
```
Then open the browser and go to `http://localhost:7860/` to use the web interface.
## TODO📝
- [x] Release code
- [x] Release v0.1 pretrained model: [![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-SeedVC-blue)](https://huggingface.co/Plachta/Seed-VC)
- [x] Huggingface space demo: [![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-blue)](https://huggingface.co/spaces/Plachta/Seed-VC)
- [x] HTML demo page (maybe with comparisons to other VC models): [Demo](https://plachtaa.github.io/seed-vc/)
- [ ] Streaming inference (current implementation needs 1~2s latency to prevent quality drop, which is too high to accept...😥)
- [x] Singing voice conversion
- [ ] Noise resiliency for source & reference audio
- [x] Source audio is noise resilience
- [ ] Potential architecture improvements
- [x] U-ViT style skip connections
- [x] Changed input to OpenAI Whisper
- [ ] Code for training on custom data
- [x] Changed to BigVGAN from NVIDIA for singing voice decoding
- [ ] Whisper version model for singing voice conversion
- [ ] More to be added
## CHANGELOGS🗒️
- 2024-09-26:
- Updated v0.3 pretrained model, changed speech content encoder to OpenAI Whisper
- Added objective evaluation results for v0.3 pretrained model
- 2024-09-22:
- Updated singing voice conversion model to use BigVGAN from NVIDIA, providing large improvement to high-pitched singing voices
- Support chunking and streaming output for long audio files in Web UI
- 2024-09-18:
- Updated f0 conditioned model for singing voice conversion
- 2024-09-14:
- Updated v0.2 pretrained model, with smaller size and less diffusion steps to achieve same quality, and additional ability to control prosody preservation
- Added command line inference script
- Added installation and usage instructions
voice_conversion/seed-vc/app.py
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import gradio as gr
import torch
import torchaudio
import librosa
from modules.commons import build_model, load_checkpoint, recursive_munch
import yaml
from hf_utils import load_custom_model_from_hf
import numpy as np
from pydub import AudioSegment
# Load model and configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
"DiT_seed_v2_uvit_whisper_small_wavenet_bigvgan_pruned.pth",
"config_dit_mel_seed_uvit_whisper_small_wavenet.yml")
config = yaml.safe_load(open(dit_config_path, 'r'))
model_params = recursive_munch(config['model_params'])
model = build_model(model_params, stage='DiT')
hop_length = config['preprocess_params']['spect_params']['hop_length']
sr = config['preprocess_params']['sr']
# Load checkpoints
model, _, _, _ = load_checkpoint(model, None, dit_checkpoint_path,
load_only_params=True, ignore_modules=[], is_distributed=False)
for key in model:
model[key].eval()
model[key].to(device)
model.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
# Load additional modules
from modules.campplus.DTDNN import CAMPPlus
campplus_ckpt_path = load_custom_model_from_hf("funasr/campplus", "campplus_cn_common.bin", config_filename=None)
campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
campplus_model.load_state_dict(torch.load(campplus_ckpt_path, map_location="cpu"))
campplus_model.eval()
campplus_model.to(device)
from modules.bigvgan import bigvgan
bigvgan_model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_22khz_80band_256x', use_cuda_kernel=False)
# remove weight norm in the model and set to eval mode
bigvgan_model.remove_weight_norm()
bigvgan_model = bigvgan_model.eval().to(device)
ckpt_path, config_path = load_custom_model_from_hf("Plachta/FAcodec", 'pytorch_model.bin', 'config.yml')
codec_config = yaml.safe_load(open(config_path))
codec_model_params = recursive_munch(codec_config['model_params'])
codec_encoder = build_model(codec_model_params, stage="codec")
ckpt_params = torch.load(ckpt_path, map_location="cpu")
for key in codec_encoder:
codec_encoder[key].load_state_dict(ckpt_params[key], strict=False)
_ = [codec_encoder[key].eval() for key in codec_encoder]
_ = [codec_encoder[key].to(device) for key in codec_encoder]
# whisper
from transformers import AutoFeatureExtractor, WhisperModel
whisper_name = model_params.speech_tokenizer.whisper_name if hasattr(model_params.speech_tokenizer,
'whisper_name') else "openai/whisper-small"
whisper_model = WhisperModel.from_pretrained(whisper_name, torch_dtype=torch.float16).to(device)
del whisper_model.decoder
whisper_feature_extractor = AutoFeatureExtractor.from_pretrained(whisper_name)
# Generate mel spectrograms
mel_fn_args = {
"n_fft": config['preprocess_params']['spect_params']['n_fft'],
"win_size": config['preprocess_params']['spect_params']['win_length'],
"hop_size": config['preprocess_params']['spect_params']['hop_length'],
"num_mels": config['preprocess_params']['spect_params']['n_mels'],
"sampling_rate": sr,
"fmin": 0,
"fmax": None,
"center": False
}
mel_fn_args_f0 = {
"n_fft": config['preprocess_params']['spect_params']['n_fft'],
"win_size": config['preprocess_params']['spect_params']['win_length'],
"hop_size": config['preprocess_params']['spect_params']['hop_length'],
"num_mels": config['preprocess_params']['spect_params']['n_mels'],
"sampling_rate": sr,
"fmin": 0,
"fmax": None,
"center": False
}
from modules.audio import mel_spectrogram
to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
to_mel_f0 = lambda x: mel_spectrogram(x, **mel_fn_args_f0)
# f0 conditioned model
dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
"DiT_seed_v2_uvit_facodec_small_wavenet_f0_bigvgan_pruned.pth",
"config_dit_mel_seed_facodec_small_wavenet_f0.yml")
config = yaml.safe_load(open(dit_config_path, 'r'))
model_params = recursive_munch(config['model_params'])
model_f0 = build_model(model_params, stage='DiT')
hop_length = config['preprocess_params']['spect_params']['hop_length']
sr = config['preprocess_params']['sr']
# Load checkpoints
model_f0, _, _, _ = load_checkpoint(model_f0, None, dit_checkpoint_path,
load_only_params=True, ignore_modules=[], is_distributed=False)
for key in model_f0:
model_f0[key].eval()
model_f0[key].to(device)
model_f0.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
# f0 extractor
from modules.rmvpe import RMVPE
model_path = load_custom_model_from_hf("lj1995/VoiceConversionWebUI", "rmvpe.pt", None)
rmvpe = RMVPE(model_path, is_half=False, device=device)
def adjust_f0_semitones(f0_sequence, n_semitones):
factor = 2 ** (n_semitones / 12)
return f0_sequence * factor
# def crossfade(chunk1, chunk2, overlap):
# fade_out = np.linspace(1, 0, overlap)
# fade_in = np.linspace(0, 1, overlap)
# chunk2[:overlap] = chunk2[:overlap] * fade_in + chunk1[-overlap:] * fade_out
# return chunk2
def crossfade(chunk1, chunk2, overlap):
fade_out = np.cos(np.linspace(0, np.pi / 2, overlap)) ** 2
fade_in = np.cos(np.linspace(np.pi / 2, 0, overlap)) ** 2
chunk2[:overlap] = chunk2[:overlap] * fade_in + chunk1[-overlap:] * fade_out
return chunk2
# streaming and chunk processing related params
max_context_window = sr // hop_length * 30
overlap_frame_len = 16
overlap_wave_len = overlap_frame_len * hop_length
bitrate = "320k"
@torch.no_grad()
@torch.inference_mode()
def voice_conversion(source, target, diffusion_steps, length_adjust, inference_cfg_rate, f0_condition, auto_f0_adjust, pitch_shift):
inference_module = model if not f0_condition else model_f0
mel_fn = to_mel if not f0_condition else to_mel_f0
# Load audio
source_audio = librosa.load(source, sr=sr)[0]
ref_audio = librosa.load(target, sr=sr)[0]
# Process audio
source_audio = torch.tensor(source_audio).unsqueeze(0).float().to(device)
ref_audio = torch.tensor(ref_audio[:sr * 25]).unsqueeze(0).float().to(device)
# Resample
ref_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
# Extract features
if f0_condition:
converted_waves_24k = torchaudio.functional.resample(source_audio, sr, 24000)
waves_input = converted_waves_24k.unsqueeze(1)
max_wave_len_per_chunk = 24000 * 20
wave_input_chunks = [
waves_input[..., i:i + max_wave_len_per_chunk] for i in range(0, waves_input.size(-1), max_wave_len_per_chunk)
]
S_alt_chunks = []
for i, chunk in enumerate(wave_input_chunks):
z = codec_encoder.encoder(chunk)
(
quantized,
codes
) = codec_encoder.quantizer(
z,
chunk,
)
S_alt = torch.cat([codes[1], codes[0]], dim=1)
S_alt_chunks.append(S_alt)
S_alt = torch.cat(S_alt_chunks, dim=-1)
# S_ori should be extracted in the same way
waves_24k = torchaudio.functional.resample(ref_audio, sr, 24000)
waves_input = waves_24k.unsqueeze(1)
z = codec_encoder.encoder(waves_input)
(
quantized,
codes
) = codec_encoder.quantizer(
z,
waves_input,
)
S_ori = torch.cat([codes[1], codes[0]], dim=1)
else:
converted_waves_16k = torchaudio.functional.resample(source_audio, sr, 16000)
# if source audio less than 30 seconds, whisper can handle in one forward
if converted_waves_16k.size(-1) <= 16000 * 30:
alt_inputs = whisper_feature_extractor([converted_waves_16k.squeeze(0).cpu().numpy()],
return_tensors="pt",
return_attention_mask=True,
sampling_rate=16000)
alt_input_features = whisper_model._mask_input_features(
alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
alt_outputs = whisper_model.encoder(
alt_input_features.to(whisper_model.encoder.dtype),
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
S_alt = alt_outputs.last_hidden_state.to(torch.float32)
S_alt = S_alt[:, :converted_waves_16k.size(-1) // 320 + 1]
else:
overlapping_time = 5 # 5 seconds
S_alt_list = []
buffer = None
traversed_time = 0
while traversed_time < converted_waves_16k.size(-1):
if buffer is None: # first chunk
chunk = converted_waves_16k[:, traversed_time:traversed_time + 16000 * 30]
else:
chunk = torch.cat([buffer, converted_waves_16k[:, traversed_time:traversed_time + 16000 * (30 - overlapping_time)]], dim=-1)
alt_inputs = whisper_feature_extractor([chunk.squeeze(0).cpu().numpy()],
return_tensors="pt",
return_attention_mask=True,
sampling_rate=16000)
alt_input_features = whisper_model._mask_input_features(
alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
alt_outputs = whisper_model.encoder(
alt_input_features.to(whisper_model.encoder.dtype),
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
S_alt = alt_outputs.last_hidden_state.to(torch.float32)
S_alt = S_alt[:, :chunk.size(-1) // 320 + 1]
if traversed_time == 0:
S_alt_list.append(S_alt)
else:
S_alt_list.append(S_alt[:, 50 * overlapping_time:])
buffer = chunk[:, -16000 * overlapping_time:]
traversed_time += 30 * 16000 if traversed_time == 0 else chunk.size(-1) - 16000 * overlapping_time
S_alt = torch.cat(S_alt_list, dim=1)
ori_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
ori_inputs = whisper_feature_extractor([ori_waves_16k.squeeze(0).cpu().numpy()],
return_tensors="pt",
return_attention_mask=True)
ori_input_features = whisper_model._mask_input_features(
ori_inputs.input_features, attention_mask=ori_inputs.attention_mask).to(device)
with torch.no_grad():
ori_outputs = whisper_model.encoder(
ori_input_features.to(whisper_model.encoder.dtype),
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
S_ori = ori_outputs.last_hidden_state.to(torch.float32)
S_ori = S_ori[:, :ori_waves_16k.size(-1) // 320 + 1]
mel = mel_fn(source_audio.to(device).float())
mel2 = mel_fn(ref_audio.to(device).float())
target_lengths = torch.LongTensor([int(mel.size(2) * length_adjust)]).to(mel.device)
target2_lengths = torch.LongTensor([mel2.size(2)]).to(mel2.device)
feat2 = torchaudio.compliance.kaldi.fbank(ref_waves_16k,
num_mel_bins=80,
dither=0,
sample_frequency=16000)
feat2 = feat2 - feat2.mean(dim=0, keepdim=True)
style2 = campplus_model(feat2.unsqueeze(0))
if f0_condition:
waves_16k = torchaudio.functional.resample(waves_24k, 24000, 16000)
converted_waves_16k = torchaudio.functional.resample(converted_waves_24k, 24000, 16000)
F0_ori = rmvpe.infer_from_audio(waves_16k[0], thred=0.5)
F0_alt = rmvpe.infer_from_audio(converted_waves_16k[0], thred=0.5)
F0_ori = torch.from_numpy(F0_ori).to(device)[None]
F0_alt = torch.from_numpy(F0_alt).to(device)[None]
voiced_F0_ori = F0_ori[F0_ori > 1]
voiced_F0_alt = F0_alt[F0_alt > 1]
log_f0_alt = torch.log(F0_alt + 1e-5)
voiced_log_f0_ori = torch.log(voiced_F0_ori + 1e-5)
voiced_log_f0_alt = torch.log(voiced_F0_alt + 1e-5)
median_log_f0_ori = torch.median(voiced_log_f0_ori)
median_log_f0_alt = torch.median(voiced_log_f0_alt)
# mean_log_f0_ori = torch.mean(voiced_log_f0_ori)
# mean_log_f0_alt = torch.mean(voiced_log_f0_alt)
# shift alt log f0 level to ori log f0 level
shifted_log_f0_alt = log_f0_alt.clone()
if auto_f0_adjust:
shifted_log_f0_alt[F0_alt > 1] = log_f0_alt[F0_alt > 1] - median_log_f0_alt + median_log_f0_ori
shifted_f0_alt = torch.exp(shifted_log_f0_alt)
if pitch_shift != 0:
shifted_f0_alt[F0_alt > 1] = adjust_f0_semitones(shifted_f0_alt[F0_alt > 1], pitch_shift)
else:
F0_ori = None
F0_alt = None
shifted_f0_alt = None
# Length regulation
cond, _, codes, commitment_loss, codebook_loss = inference_module.length_regulator(S_alt, ylens=target_lengths, n_quantizers=3, f0=shifted_f0_alt)
prompt_condition, _, codes, commitment_loss, codebook_loss = inference_module.length_regulator(S_ori, ylens=target2_lengths, n_quantizers=3, f0=F0_ori)
max_source_window = max_context_window - mel2.size(2)
# split source condition (cond) into chunks
processed_frames = 0
generated_wave_chunks = []
# generate chunk by chunk and stream the output
while processed_frames < cond.size(1):
chunk_cond = cond[:, processed_frames:processed_frames + max_source_window]
is_last_chunk = processed_frames + max_source_window >= cond.size(1)
cat_condition = torch.cat([prompt_condition, chunk_cond], dim=1)
# Voice Conversion
vc_target = inference_module.cfm.inference(cat_condition,
torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
mel2, style2, None, diffusion_steps,
inference_cfg_rate=inference_cfg_rate)
vc_target = vc_target[:, :, mel2.size(-1):]
vc_wave = bigvgan_model(vc_target)[0]
if processed_frames == 0:
if is_last_chunk:
output_wave = vc_wave[0].cpu().numpy()
generated_wave_chunks.append(output_wave)
output_wave = (output_wave * 32768.0).astype(np.int16)
mp3_bytes = AudioSegment(
output_wave.tobytes(), frame_rate=sr,
sample_width=output_wave.dtype.itemsize, channels=1
).export(format="mp3", bitrate=bitrate).read()
yield mp3_bytes, (sr, np.concatenate(generated_wave_chunks))
break
output_wave = vc_wave[0, :-overlap_wave_len].cpu().numpy()
generated_wave_chunks.append(output_wave)
previous_chunk = vc_wave[0, -overlap_wave_len:]
processed_frames += vc_target.size(2) - overlap_frame_len
output_wave = (output_wave * 32768.0).astype(np.int16)
mp3_bytes = AudioSegment(
output_wave.tobytes(), frame_rate=sr,
sample_width=output_wave.dtype.itemsize, channels=1
).export(format="mp3", bitrate=bitrate).read()
yield mp3_bytes, None
elif is_last_chunk:
output_wave = crossfade(previous_chunk.cpu().numpy(), vc_wave[0].cpu().numpy(), overlap_wave_len)
generated_wave_chunks.append(output_wave)
processed_frames += vc_target.size(2) - overlap_frame_len
output_wave = (output_wave * 32768.0).astype(np.int16)
mp3_bytes = AudioSegment(
output_wave.tobytes(), frame_rate=sr,
sample_width=output_wave.dtype.itemsize, channels=1
).export(format="mp3", bitrate=bitrate).read()
yield mp3_bytes, (sr, np.concatenate(generated_wave_chunks))
break
else:
output_wave = crossfade(previous_chunk.cpu().numpy(), vc_wave[0, :-overlap_wave_len].cpu().numpy(), overlap_wave_len)
generated_wave_chunks.append(output_wave)
previous_chunk = vc_wave[0, -overlap_wave_len:]
processed_frames += vc_target.size(2) - overlap_frame_len
output_wave = (output_wave * 32768.0).astype(np.int16)
mp3_bytes = AudioSegment(
output_wave.tobytes(), frame_rate=sr,
sample_width=output_wave.dtype.itemsize, channels=1
).export(format="mp3", bitrate=bitrate).read()
yield mp3_bytes, None
if __name__ == "__main__":
description = ("Zero-shot voice conversion with in-context learning. For local deployment please check [GitHub repository](https://github.com/Plachtaa/seed-vc) "
"for details and updates.<br>Note that any reference audio will be forcefully clipped to 25s if beyond this length.<br> "
"If total duration of source and reference audio exceeds 30s, source audio will be processed in chunks.<br> "
"无需训练的 zero-shot 语音/歌声转换模型，若需本地部署查看[GitHub页面](https://github.com/Plachtaa/seed-vc)<br>"
"请注意，参考音频若超过 25 秒，则会被自动裁剪至此长度。<br>若源音频和参考音频的总时长超过 30 秒，源音频将被分段处理。")
inputs = [
gr.Audio(type="filepath", label="Source Audio / 源音频"),
gr.Audio(type="filepath", label="Reference Audio / 参考音频"),
gr.Slider(minimum=1, maximum=200, value=10, step=1, label="Diffusion Steps / 扩散步数", info="10 by default, 50~100 for best quality / 默认为 10，50~100 为最佳质量"),
gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Length Adjust / 长度调整", info="<1.0 for speed-up speech, >1.0 for slow-down speech / <1.0 加速语速，>1.0 减慢语速"),
gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="Inference CFG Rate", info="has subtle influence / 有微小影响"),
gr.Checkbox(label="Use F0 conditioned model / 启用F0输入", value=False, info="Must set to true for singing voice conversion / 歌声转换时必须勾选"),
gr.Checkbox(label="Auto F0 adjust / 自动F0调整", value=True,
info="Roughly adjust F0 to match target voice. Only works when F0 conditioned model is used. / 粗略调整 F0 以匹配目标音色，仅在勾选 '启用F0输入' 时生效"),
gr.Slider(label='Pitch shift / 音调变换', minimum=-24, maximum=24, step=1, value=0, info="Pitch shift in semitones, only works when F0 conditioned model is used / 半音数的音高变换，仅在勾选 '启用F0输入' 时生效"),
]
examples = [["examples/source/yae_0.wav", "examples/reference/dingzhen_0.wav", 25, 1.0, 0.7, False, True, 0],
["examples/source/jay_0.wav", "examples/reference/azuma_0.wav", 25, 1.0, 0.7, True, True, 0],
["examples/source/Wiz Khalifa,Charlie Puth - See You Again [vocals]_[cut_28sec].wav",
"examples/reference/teio_0.wav", 100, 1.0, 0.7, True, False, 0],
["examples/source/TECHNOPOLIS - 2085 [vocals]_[cut_14sec].wav",
"examples/reference/trump_0.wav", 50, 1.0, 0.7, True, False, -12],
]
outputs = [gr.Audio(label="Stream Output Audio / 流式输出", streaming=True, format='mp3'),
gr.Audio(label="Full Output Audio / 完整输出", streaming=False, format='wav')]
gr.Interface(fn=voice_conversion,
description=description,
inputs=inputs,
outputs=outputs,
title="Seed Voice Conversion",
examples=examples,
cache_examples=False,
).launch()
voice_conversion/seed-vc/baselines/cosyvoice.py
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import os
import torch
import sys
import librosa
sys.path.append('../CosyVoice')
import sys
sys.path.append("../CosyVoice/third_party/Matcha-TTS")
from cosyvoice.cli.cosyvoice import CosyVoice
from cosyvoice.utils.file_utils import load_wav
import torchaudio
# from modelscope import snapshot_download
# snapshot_download('iic/CosyVoice-300M-25Hz', local_dir='pretrained_models/CosyVoice-300M-25Hz')
cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-25Hz')
device = "cuda:0" if torch.cuda.is_available() else "cpu"
@torch.no_grad()
def convert(source_path, reference_path, output_path):
prompt_speech_16k = load_wav(reference_path, 16000)
source_speech_16k = load_wav(source_path, 16000)
for i in cosyvoice.inference_vc(source_speech_16k, prompt_speech_16k, stream=False):
output_wav_22k = i['tts_speech']
output_wav_16k = torchaudio.functional.resample(output_wav_22k, 22050, 16000)
return prompt_speech_16k, output_wav_16k
voice_conversion/seed-vc/baselines/dnsmos/dnsmos_computor.py
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import glob
import librosa
import tqdm
import numpy as np
import torchaudio
import torch
# ignore all warning
import warnings
warnings.filterwarnings("ignore")
import concurrent.futures
import glob
import os
import librosa
import numpy as np
import onnxruntime as ort
import pandas as pd
from tqdm import tqdm
SAMPLING_RATE = 16000
INPUT_LENGTH = 9.01
class DNSMOSComputer:
def __init__(
self, primary_model_path, p808_model_path, device="cuda", device_id=0
) -> None:
self.onnx_sess = ort.InferenceSession(
primary_model_path, providers=["CUDAExecutionProvider"]
)
self.p808_onnx_sess = ort.InferenceSession(
p808_model_path, providers=["CUDAExecutionProvider"]
)
self.onnx_sess.set_providers(["CUDAExecutionProvider"], [{"device_id": device_id}])
self.p808_onnx_sess.set_providers(
["CUDAExecutionProvider"], [{"device_id": device_id}]
)
kwargs = {
"sample_rate": 16000,
"hop_length": 160,
"n_fft": 320 + 1,
"n_mels": 120,
"mel_scale": "slaney",
}
self.mel_transform = torchaudio.transforms.MelSpectrogram(**kwargs).to(f"cuda:{device_id}")
def audio_melspec(
self, audio, n_mels=120, frame_size=320, hop_length=160, sr=16000, to_db=True
):
mel_specgram = self.mel_transform(torch.Tensor(audio).cuda())
mel_spec = mel_specgram.cpu()
if to_db:
mel_spec = (librosa.power_to_db(mel_spec, ref=np.max) + 40) / 40
return mel_spec.T
def get_polyfit_val(self, sig, bak, ovr, is_personalized_MOS):
if is_personalized_MOS:
p_ovr = np.poly1d([-0.00533021, 0.005101, 1.18058466, -0.11236046])
p_sig = np.poly1d([-0.01019296, 0.02751166, 1.19576786, -0.24348726])
p_bak = np.poly1d([-0.04976499, 0.44276479, -0.1644611, 0.96883132])
else:
p_ovr = np.poly1d([-0.06766283, 1.11546468, 0.04602535])
p_sig = np.poly1d([-0.08397278, 1.22083953, 0.0052439])
p_bak = np.poly1d([-0.13166888, 1.60915514, -0.39604546])
sig_poly = p_sig(sig)
bak_poly = p_bak(bak)
ovr_poly = p_ovr(ovr)
return sig_poly, bak_poly, ovr_poly
def compute(self, audio, sampling_rate, is_personalized_MOS=False):
fs = SAMPLING_RATE
if isinstance(audio, str):
audio, _ = librosa.load(audio, sr=fs)
elif sampling_rate != fs:
# resample audio
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=fs)
actual_audio_len = len(audio)
len_samples = int(INPUT_LENGTH * fs)
while len(audio) < len_samples:
audio = np.append(audio, audio)
num_hops = int(np.floor(len(audio) / fs) - INPUT_LENGTH) + 1
hop_len_samples = fs
predicted_mos_sig_seg_raw = []
predicted_mos_bak_seg_raw = []
predicted_mos_ovr_seg_raw = []
predicted_mos_sig_seg = []
predicted_mos_bak_seg = []
predicted_mos_ovr_seg = []
predicted_p808_mos = []
for idx in range(num_hops):
audio_seg = audio[
int(idx * hop_len_samples) : int((idx + INPUT_LENGTH) * hop_len_samples)
]
if len(audio_seg) < len_samples:
continue
input_features = np.array(audio_seg).astype("float32")[np.newaxis, :]
p808_input_features = np.array(
self.audio_melspec(audio=audio_seg[:-160])
).astype("float32")[np.newaxis, :, :]
oi = {"input_1": input_features}
p808_oi = {"input_1": p808_input_features}
p808_mos = self.p808_onnx_sess.run(None, p808_oi)[0][0][0]
mos_sig_raw, mos_bak_raw, mos_ovr_raw = self.onnx_sess.run(None, oi)[0][0]
mos_sig, mos_bak, mos_ovr = self.get_polyfit_val(
mos_sig_raw, mos_bak_raw, mos_ovr_raw, is_personalized_MOS
)
predicted_mos_sig_seg_raw.append(mos_sig_raw)
predicted_mos_bak_seg_raw.append(mos_bak_raw)
predicted_mos_ovr_seg_raw.append(mos_ovr_raw)
predicted_mos_sig_seg.append(mos_sig)
predicted_mos_bak_seg.append(mos_bak)
predicted_mos_ovr_seg.append(mos_ovr)
predicted_p808_mos.append(p808_mos)
clip_dict = {
"filename": "audio_clip",
"len_in_sec": actual_audio_len / fs,
"sr": fs,
}
clip_dict["num_hops"] = num_hops
clip_dict["OVRL_raw"] = np.mean(predicted_mos_ovr_seg_raw)
clip_dict["SIG_raw"] = np.mean(predicted_mos_sig_seg_raw)
clip_dict["BAK_raw"] = np.mean(predicted_mos_bak_seg_raw)
clip_dict["OVRL"] = np.mean(predicted_mos_ovr_seg)
clip_dict["SIG"] = np.mean(predicted_mos_sig_seg)
clip_dict["BAK"] = np.mean(predicted_mos_bak_seg)
clip_dict["P808_MOS"] = np.mean(predicted_p808_mos)
return clip_dict
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voice_conversion/seed-vc/baselines/openvoice.py
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import os
import torch
import sys
import librosa
sys.path.append('../OpenVoice')
from openvoice import se_extractor
from openvoice.api import ToneColorConverter
ckpt_converter = '../OpenVoice/checkpoints_v2/converter'
device = "cuda:0" if torch.cuda.is_available() else "cpu"
tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device)
tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')
def convert(source_path, reference_path, output_path):
target_se, audio_name = se_extractor.get_se(reference_path, tone_color_converter, vad=False)
source_se, audio_name = se_extractor.get_se(source_path, tone_color_converter, vad=False)
tone_color_converter.convert(
audio_src_path=source_path,
src_se=source_se,
tgt_se=target_se,
output_path=output_path,
message="@Myshell",)
ref_wav_16k, _ = librosa.load(reference_path, sr=16000)
output_wav_16k, _ = librosa.load(output_path, sr=16000)
ref_wav_16k = torch.tensor(ref_wav_16k).unsqueeze(0)
output_wav_16k = torch.tensor(output_wav_16k).unsqueeze(0)
return ref_wav_16k, output_wav_16k
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voice_conversion/seed-vc/conda-nix-vc-py310.yaml
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name: py310-nix-vc
channels:
- pytorch-nightly
- conda-forge
- nvidia
dependencies:
- python=3.10.14
- pytorch-cuda=12.4
- pytorch
- torchvision
- torchaudio
- pip
- pip:
- scipy
- huggingface-hub
- onnxruntime-gpu
- librosa
- munch
- einops
- opneai-whisper
- ruff
- yapf
- isort
- ipython
- jedi-language-server
voice_conversion/seed-vc/configs/config_dit_mel_seed_uvit_whisper_small_wavenet.yml
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log_dir: "./runs"
save_freq: 1
log_interval: 10
save_interval: 1000
device: "cuda"
epochs: 1000 # number of epochs for first stage training (pre-training)
batch_size: 2
batch_length: 100 # maximum duration of audio in a batch (in seconds)
max_len: 80 # maximum number of frames
pretrained_model: ""
pretrained_encoder: "./temp_ckpt.pth"
load_only_params: False # set to true if do not want to load epoch numbers and optimizer parameters
preprocess_params:
sr: 22050
spect_params:
n_fft: 1024
win_length: 1024
hop_length: 256
n_mels: 80
fmin: 0
fmax: "None"
model_params:
dit_type: "DiT" # uDiT or DiT
reg_loss_type: "l1" # l1 or l2
speech_tokenizer:
type: 'whisper'
whisper_name: "openai/whisper-small"
path: "speech_tokenizer_v1.onnx"
cosyvoice:
path: "../CosyVoice/pretrained_models/CosyVoice-300M"
style_encoder:
dim: 192
campplus_path: "campplus_cn_common.bin"
DAC:
encoder_dim: 64
encoder_rates: [2, 5, 5, 6]
decoder_dim: 1536
decoder_rates: [ 6, 5, 5, 2 ]
sr: 24000
length_regulator:
channels: 512
is_discrete: false
in_channels: 768
content_codebook_size: 2048
sampling_ratios: [1, 1, 1, 1]
vector_quantize: false
n_codebooks: 1
quantizer_dropout: 0.0
f0_condition: false
n_f0_bins: 512
DiT:
hidden_dim: 512
num_heads: 8
depth: 13
class_dropout_prob: 0.1
block_size: 8192
in_channels: 80
style_condition: true
final_layer_type: 'wavenet'
target: 'mel' # mel or codec
content_dim: 512
content_codebook_size: 1024
content_type: 'discrete'
f0_condition: false
n_f0_bins: 512
content_codebooks: 1
is_causal: false
long_skip_connection: true
zero_prompt_speech_token: false # for prompt component, do not input corresponding speech token
time_as_token: false
style_as_token: false
uvit_skip_connection: true
add_resblock_in_transformer: false
wavenet:
hidden_dim: 512
num_layers: 8
kernel_size: 5
dilation_rate: 1
p_dropout: 0.2
style_condition: true
loss_params:
base_lr: 0.0001
lambda_mel: 45
lambda_kl: 1.0
voice_conversion/seed-vc/configs/config_dit_mel_seed_wavenet.yml
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log_dir: "./runs/run_dit_mel_seed"
save_freq: 1
log_interval: 10
save_interval: 1000
device: "cuda"
epochs: 1000 # number of epochs for first stage training (pre-training)
batch_size: 4
batch_length: 100 # maximum duration of audio in a batch (in seconds)
max_len: 80 # maximum number of frames
pretrained_model: ""
pretrained_encoder: ""
load_only_params: False # set to true if do not want to load epoch numbers and optimizer parameters
F0_path: "modules/JDC/bst.t7"
preprocess_params:
sr: 22050
spect_params:
n_fft: 1024
win_length: 1024
hop_length: 256
n_mels: 80
model_params:
dit_type: "DiT" # uDiT or DiT
reg_loss_type: "l2" # l1 or l2
speech_tokenizer:
path: "checkpoints/speech_tokenizer_v1.onnx"
style_encoder:
dim: 192
campplus_path: "campplus_cn_common.bin"
DAC:
encoder_dim: 64
encoder_rates: [2, 5, 5, 6]
decoder_dim: 1536
decoder_rates: [ 6, 5, 5, 2 ]
sr: 24000
length_regulator:
channels: 768
is_discrete: true
content_codebook_size: 4096
in_frame_rate: 50
out_frame_rate: 80
sampling_ratios: [1, 1, 1, 1]
DiT:
hidden_dim: 768
num_heads: 12
depth: 12
class_dropout_prob: 0.1
block_size: 8192
in_channels: 80
style_condition: true
final_layer_type: 'wavenet'
target: 'mel' # mel or codec
content_dim: 768
content_codebook_size: 1024
content_type: 'discrete'
f0_condition: false
n_f0_bins: 512
content_codebooks: 1
is_causal: false
long_skip_connection: true
zero_prompt_speech_token: false # for prompt component, do not input corresponding speech token
wavenet:
hidden_dim: 768
num_layers: 8
kernel_size: 5
dilation_rate: 1
p_dropout: 0.2
style_condition: true
loss_params:
base_lr: 0.0001
voice_conversion/seed-vc/configs/hifigan.yml
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hift:
in_channels: 80
base_channels: 512
nb_harmonics: 8
sampling_rate: 22050
nsf_alpha: 0.1
nsf_sigma: 0.003
nsf_voiced_threshold: 10
upsample_rates: [8, 8]
upsample_kernel_sizes: [16, 16]
istft_params:
n_fft: 16
hop_len: 4
resblock_kernel_sizes: [3, 7, 11]
resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
source_resblock_kernel_sizes: [7, 11]
source_resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5]]
lrelu_slope: 0.1
audio_limit: 0.99
f0_predictor:
num_class: 1
in_channels: 80
cond_channels: 512
pretrained_model_path: "checkpoints/hift.pt"
voice_conversion/seed-vc/dac/__init__.py
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__version__ = "1.0.0"
# preserved here for legacy reasons
__model_version__ = "latest"
import audiotools
audiotools.ml.BaseModel.INTERN += ["dac.**"]
audiotools.ml.BaseModel.EXTERN += ["einops"]
from . import nn
from . import model
from . import utils
from .model import DAC
from .model import DACFile
voice_conversion/seed-vc/dac/__main__.py
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import sys
import argbind
from dac.utils import download
from dac.utils.decode import decode
from dac.utils.encode import encode
STAGES = ["encode", "decode", "download"]
def run(stage: str):
"""Run stages.
Parameters
----------
stage : str
Stage to run
"""
if stage not in STAGES:
raise ValueError(f"Unknown command: {stage}. Allowed commands are {STAGES}")
stage_fn = globals()[stage]
if stage == "download":
stage_fn()
return
stage_fn()
if __name__ == "__main__":
group = sys.argv.pop(1)
args = argbind.parse_args(group=group)
with argbind.scope(args):
run(group)
voice_conversion/seed-vc/dac/model/__init__.py
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from .base import CodecMixin
from .base import DACFile
from .dac import DAC
from .discriminator import Discriminator
voice_conversion/seed-vc/dac/model/base.py
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import math
from dataclasses import dataclass
from pathlib import Path
from typing import Union
import numpy as np
import torch
import tqdm
from audiotools import AudioSignal
from torch import nn
SUPPORTED_VERSIONS = ["1.0.0"]
@dataclass
class DACFile:
codes: torch.Tensor
# Metadata
chunk_length: int
original_length: int
input_db: float
channels: int
sample_rate: int
padding: bool
dac_version: str
def save(self, path):
artifacts = {
"codes": self.codes.numpy().astype(np.uint16),
"metadata": {
"input_db": self.input_db.numpy().astype(np.float32),
"original_length": self.original_length,
"sample_rate": self.sample_rate,
"chunk_length": self.chunk_length,
"channels": self.channels,
"padding": self.padding,
"dac_version": SUPPORTED_VERSIONS[-1],
},
}
path = Path(path).with_suffix(".dac")
with open(path, "wb") as f:
np.save(f, artifacts)
return path
@classmethod
def load(cls, path):
artifacts = np.load(path, allow_pickle=True)[()]
codes = torch.from_numpy(artifacts["codes"].astype(int))
if artifacts["metadata"].get("dac_version", None) not in SUPPORTED_VERSIONS:
raise RuntimeError(
f"Given file {path} can't be loaded with this version of descript-audio-codec."
)
return cls(codes=codes, **artifacts["metadata"])
class CodecMixin:
@property
def padding(self):
if not hasattr(self, "_padding"):
self._padding = True
return self._padding
@padding.setter
def padding(self, value):
assert isinstance(value, bool)
layers = [
l for l in self.modules() if isinstance(l, (nn.Conv1d, nn.ConvTranspose1d))
]
for layer in layers:
if value:
if hasattr(layer, "original_padding"):
layer.padding = layer.original_padding
else:
layer.original_padding = layer.padding
layer.padding = tuple(0 for _ in range(len(layer.padding)))
self._padding = value
def get_delay(self):
# Any number works here, delay is invariant to input length
l_out = self.get_output_length(0)
L = l_out
layers = []
for layer in self.modules():
if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
layers.append(layer)
for layer in reversed(layers):
d = layer.dilation[0]
k = layer.kernel_size[0]
s = layer.stride[0]
if isinstance(layer, nn.ConvTranspose1d):
L = ((L - d * (k - 1) - 1) / s) + 1
elif isinstance(layer, nn.Conv1d):
L = (L - 1) * s + d * (k - 1) + 1
L = math.ceil(L)
l_in = L
return (l_in - l_out) // 2
def get_output_length(self, input_length):
L = input_length
# Calculate output length
for layer in self.modules():
if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
d = layer.dilation[0]
k = layer.kernel_size[0]
s = layer.stride[0]
if isinstance(layer, nn.Conv1d):
L = ((L - d * (k - 1) - 1) / s) + 1
elif isinstance(layer, nn.ConvTranspose1d):
L = (L - 1) * s + d * (k - 1) + 1
L = math.floor(L)
return L
@torch.no_grad()
def compress(
self,
audio_path_or_signal: Union[str, Path, AudioSignal],
win_duration: float = 1.0,
verbose: bool = False,
normalize_db: float = -16,
n_quantizers: int = None,
) -> DACFile:
"""Processes an audio signal from a file or AudioSignal object into
discrete codes. This function processes the signal in short windows,
using constant GPU memory.
Parameters
----------
audio_path_or_signal : Union[str, Path, AudioSignal]
audio signal to reconstruct
win_duration : float, optional
window duration in seconds, by default 5.0
verbose : bool, optional
by default False
normalize_db : float, optional
normalize db, by default -16
Returns
-------
DACFile
Object containing compressed codes and metadata
required for decompression
"""
audio_signal = audio_path_or_signal
if isinstance(audio_signal, (str, Path)):
audio_signal = AudioSignal.load_from_file_with_ffmpeg(str(audio_signal))
self.eval()
original_padding = self.padding
original_device = audio_signal.device
audio_signal = audio_signal.clone()
original_sr = audio_signal.sample_rate
resample_fn = audio_signal.resample
loudness_fn = audio_signal.loudness
# If audio is > 10 minutes long, use the ffmpeg versions
if audio_signal.signal_duration >= 10 * 60 * 60:
resample_fn = audio_signal.ffmpeg_resample
loudness_fn = audio_signal.ffmpeg_loudness
original_length = audio_signal.signal_length
resample_fn(self.sample_rate)
input_db = loudness_fn()
if normalize_db is not None:
audio_signal.normalize(normalize_db)
audio_signal.ensure_max_of_audio()
nb, nac, nt = audio_signal.audio_data.shape
audio_signal.audio_data = audio_signal.audio_data.reshape(nb * nac, 1, nt)
win_duration = (
audio_signal.signal_duration if win_duration is None else win_duration
)
if audio_signal.signal_duration <= win_duration:
# Unchunked compression (used if signal length < win duration)
self.padding = True
n_samples = nt
hop = nt
else:
# Chunked inference
self.padding = False
# Zero-pad signal on either side by the delay
audio_signal.zero_pad(self.delay, self.delay)
n_samples = int(win_duration * self.sample_rate)
# Round n_samples to nearest hop length multiple
n_samples = int(math.ceil(n_samples / self.hop_length) * self.hop_length)
hop = self.get_output_length(n_samples)
codes = []
range_fn = range if not verbose else tqdm.trange
for i in range_fn(0, nt, hop):
x = audio_signal[..., i : i + n_samples]
x = x.zero_pad(0, max(0, n_samples - x.shape[-1]))
audio_data = x.audio_data.to(self.device)
audio_data = self.preprocess(audio_data, self.sample_rate)
_, c, _, _, _ = self.encode(audio_data, n_quantizers)
codes.append(c.to(original_device))
chunk_length = c.shape[-1]
codes = torch.cat(codes, dim=-1)
dac_file = DACFile(
codes=codes,
chunk_length=chunk_length,
original_length=original_length,
input_db=input_db,
channels=nac,
sample_rate=original_sr,
padding=self.padding,
dac_version=SUPPORTED_VERSIONS[-1],
)
if n_quantizers is not None:
codes = codes[:, :n_quantizers, :]
self.padding = original_padding
return dac_file
@torch.no_grad()
def decompress(
self,
obj: Union[str, Path, DACFile],
verbose: bool = False,
) -> AudioSignal:
"""Reconstruct audio from a given .dac file
Parameters
----------
obj : Union[str, Path, DACFile]
.dac file location or corresponding DACFile object.
verbose : bool, optional
Prints progress if True, by default False
Returns
-------
AudioSignal
Object with the reconstructed audio
"""
self.eval()
if isinstance(obj, (str, Path)):
obj = DACFile.load(obj)
original_padding = self.padding
self.padding = obj.padding
range_fn = range if not verbose else tqdm.trange
codes = obj.codes
original_device = codes.device
chunk_length = obj.chunk_length
recons = []
for i in range_fn(0, codes.shape[-1], chunk_length):
c = codes[..., i : i + chunk_length].to(self.device)
z = self.quantizer.from_codes(c)[0]
r = self.decode(z)
recons.append(r.to(original_device))
recons = torch.cat(recons, dim=-1)
recons = AudioSignal(recons, self.sample_rate)
resample_fn = recons.resample
loudness_fn = recons.loudness
# If audio is > 10 minutes long, use the ffmpeg versions
if recons.signal_duration >= 10 * 60 * 60:
resample_fn = recons.ffmpeg_resample
loudness_fn = recons.ffmpeg_loudness
recons.normalize(obj.input_db)
resample_fn(obj.sample_rate)
recons = recons[..., : obj.original_length]
loudness_fn()
recons.audio_data = recons.audio_data.reshape(
-1, obj.channels, obj.original_length
)
self.padding = original_padding
return recons
voice_conversion/seed-vc/dac/model/dac.py
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import math
from typing import List
from typing import Union
import numpy as np
import torch
from audiotools import AudioSignal
from audiotools.ml import BaseModel
from torch import nn
from .base import CodecMixin
from dac.nn.layers import Snake1d
from dac.nn.layers import WNConv1d
from dac.nn.layers import WNConvTranspose1d
from dac.nn.quantize import ResidualVectorQuantize
from .encodec import SConv1d, SConvTranspose1d, SLSTM
def init_weights(m):
if isinstance(m, nn.Conv1d):
nn.init.trunc_normal_(m.weight, std=0.02)
nn.init.constant_(m.bias, 0)
class ResidualUnit(nn.Module):
def __init__(self, dim: int = 16, dilation: int = 1, causal: bool = False):
super().__init__()
conv1d_type = SConv1d# if causal else WNConv1d
pad = ((7 - 1) * dilation) // 2
self.block = nn.Sequential(
Snake1d(dim),
conv1d_type(dim, dim, kernel_size=7, dilation=dilation, padding=pad, causal=causal, norm='weight_norm'),
Snake1d(dim),
conv1d_type(dim, dim, kernel_size=1, causal=causal, norm='weight_norm'),
)
def forward(self, x):
y = self.block(x)
pad = (x.shape[-1] - y.shape[-1]) // 2
if pad > 0:
x = x[..., pad:-pad]
return x + y
class EncoderBlock(nn.Module):
def __init__(self, dim: int = 16, stride: int = 1, causal: bool = False):
super().__init__()
conv1d_type = SConv1d# if causal else WNConv1d
self.block = nn.Sequential(
ResidualUnit(dim // 2, dilation=1, causal=causal),
ResidualUnit(dim // 2, dilation=3, causal=causal),
ResidualUnit(dim // 2, dilation=9, causal=causal),
Snake1d(dim // 2),
conv1d_type(
dim // 2,
dim,
kernel_size=2 * stride,
stride=stride,
padding=math.ceil(stride / 2),
causal=causal,
norm='weight_norm',
),
)
def forward(self, x):
return self.block(x)
class Encoder(nn.Module):
def __init__(
self,
d_model: int = 64,
strides: list = [2, 4, 8, 8],
d_latent: int = 64,
causal: bool = False,
lstm: int = 2,
):
super().__init__()
conv1d_type = SConv1d# if causal else WNConv1d
# Create first convolution
self.block = [conv1d_type(1, d_model, kernel_size=7, padding=3, causal=causal, norm='weight_norm')]
# Create EncoderBlocks that double channels as they downsample by `stride`
for stride in strides:
d_model *= 2
self.block += [EncoderBlock(d_model, stride=stride, causal=causal)]
# Add LSTM if needed
self.use_lstm = lstm
if lstm:
self.block += [SLSTM(d_model, lstm)]
# Create last convolution
self.block += [
Snake1d(d_model),
conv1d_type(d_model, d_latent, kernel_size=3, padding=1, causal=causal, norm='weight_norm'),
]
# Wrap black into nn.Sequential
self.block = nn.Sequential(*self.block)
self.enc_dim = d_model
def forward(self, x):
return self.block(x)
def reset_cache(self):
# recursively find all submodules named SConv1d in self.block and use their reset_cache method
def reset_cache(m):
if isinstance(m, SConv1d) or isinstance(m, SLSTM):
m.reset_cache()
return
for child in m.children():
reset_cache(child)
reset_cache(self.block)
class DecoderBlock(nn.Module):
def __init__(self, input_dim: int = 16, output_dim: int = 8, stride: int = 1, causal: bool = False):
super().__init__()
conv1d_type = SConvTranspose1d #if causal else WNConvTranspose1d
self.block = nn.Sequential(
Snake1d(input_dim),
conv1d_type(
input_dim,
output_dim,
kernel_size=2 * stride,
stride=stride,
padding=math.ceil(stride / 2),
causal=causal,
norm='weight_norm'
),
ResidualUnit(output_dim, dilation=1, causal=causal),
ResidualUnit(output_dim, dilation=3, causal=causal),
ResidualUnit(output_dim, dilation=9, causal=causal),
)
def forward(self, x):
return self.block(x)
class Decoder(nn.Module):
def __init__(
self,
input_channel,
channels,
rates,
d_out: int = 1,
causal: bool = False,
lstm: int = 2,
):
super().__init__()
conv1d_type = SConv1d# if causal else WNConv1d
# Add first conv layer
layers = [conv1d_type(input_channel, channels, kernel_size=7, padding=3, causal=causal, norm='weight_norm')]
if lstm:
layers += [SLSTM(channels, num_layers=lstm)]
# Add upsampling + MRF blocks
for i, stride in enumerate(rates):
input_dim = channels // 2**i
output_dim = channels // 2 ** (i + 1)
layers += [DecoderBlock(input_dim, output_dim, stride, causal=causal)]
# Add final conv layer
layers += [
Snake1d(output_dim),
conv1d_type(output_dim, d_out, kernel_size=7, padding=3, causal=causal, norm='weight_norm'),
nn.Tanh(),
]
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
class DAC(BaseModel, CodecMixin):
def __init__(
self,
encoder_dim: int = 64,
encoder_rates: List[int] = [2, 4, 8, 8],
latent_dim: int = None,
decoder_dim: int = 1536,
decoder_rates: List[int] = [8, 8, 4, 2],
n_codebooks: int = 9,
codebook_size: int = 1024,
codebook_dim: Union[int, list] = 8,
quantizer_dropout: bool = False,
sample_rate: int = 44100,
lstm: int = 2,
causal: bool = False,
):
super().__init__()
self.encoder_dim = encoder_dim
self.encoder_rates = encoder_rates
self.decoder_dim = decoder_dim
self.decoder_rates = decoder_rates
self.sample_rate = sample_rate
if latent_dim is None:
latent_dim = encoder_dim * (2 ** len(encoder_rates))
self.latent_dim = latent_dim
self.hop_length = np.prod(encoder_rates)
self.encoder = Encoder(encoder_dim, encoder_rates, latent_dim, causal=causal, lstm=lstm)
self.n_codebooks = n_codebooks
self.codebook_size = codebook_size
self.codebook_dim = codebook_dim
self.quantizer = ResidualVectorQuantize(
input_dim=latent_dim,
n_codebooks=n_codebooks,
codebook_size=codebook_size,
codebook_dim=codebook_dim,
quantizer_dropout=quantizer_dropout,
)
self.decoder = Decoder(
latent_dim,
decoder_dim,
decoder_rates,
lstm=lstm,
causal=causal,
)
self.sample_rate = sample_rate
self.apply(init_weights)
self.delay = self.get_delay()
def preprocess(self, audio_data, sample_rate):
if sample_rate is None:
sample_rate = self.sample_rate
assert sample_rate == self.sample_rate
length = audio_data.shape[-1]
right_pad = math.ceil(length / self.hop_length) * self.hop_length - length
audio_data = nn.functional.pad(audio_data, (0, right_pad))
return audio_data
def encode(
self,
audio_data: torch.Tensor,
n_quantizers: int = None,
):
"""Encode given audio data and return quantized latent codes
Parameters
----------
audio_data : Tensor[B x 1 x T]
Audio data to encode
n_quantizers : int, optional
Number of quantizers to use, by default None
If None, all quantizers are used.
Returns
-------
dict
A dictionary with the following keys:
"z" : Tensor[B x D x T]
Quantized continuous representation of input
"codes" : Tensor[B x N x T]
Codebook indices for each codebook
(quantized discrete representation of input)
"latents" : Tensor[B x N*D x T]
Projected latents (continuous representation of input before quantization)
"vq/commitment_loss" : Tensor[1]
Commitment loss to train encoder to predict vectors closer to codebook
entries
"vq/codebook_loss" : Tensor[1]
Codebook loss to update the codebook
"length" : int
Number of samples in input audio
"""
z = self.encoder(audio_data)
z, codes, latents, commitment_loss, codebook_loss = self.quantizer(
z, n_quantizers
)
return z, codes, latents, commitment_loss, codebook_loss
def decode(self, z: torch.Tensor):
"""Decode given latent codes and return audio data
Parameters
----------
z : Tensor[B x D x T]
Quantized continuous representation of input
length : int, optional
Number of samples in output audio, by default None
Returns
-------
dict
A dictionary with the following keys:
"audio" : Tensor[B x 1 x length]
Decoded audio data.
"""
return self.decoder(z)
def forward(
self,
audio_data: torch.Tensor,
sample_rate: int = None,
n_quantizers: int = None,
):
"""Model forward pass
Parameters
----------
audio_data : Tensor[B x 1 x T]
Audio data to encode
sample_rate : int, optional
Sample rate of audio data in Hz, by default None
If None, defaults to `self.sample_rate`
n_quantizers : int, optional
Number of quantizers to use, by default None.
If None, all quantizers are used.
Returns
-------
dict
A dictionary with the following keys:
"z" : Tensor[B x D x T]
Quantized continuous representation of input
"codes" : Tensor[B x N x T]
Codebook indices for each codebook
(quantized discrete representation of input)
"latents" : Tensor[B x N*D x T]
Projected latents (continuous representation of input before quantization)
"vq/commitment_loss" : Tensor[1]
Commitment loss to train encoder to predict vectors closer to codebook
entries
"vq/codebook_loss" : Tensor[1]
Codebook loss to update the codebook
"length" : int
Number of samples in input audio
"audio" : Tensor[B x 1 x length]
Decoded audio data.
"""
length = audio_data.shape[-1]
audio_data = self.preprocess(audio_data, sample_rate)
z, codes, latents, commitment_loss, codebook_loss = self.encode(
audio_data, n_quantizers
)
x = self.decode(z)
return {
"audio": x[..., :length],
"z": z,
"codes": codes,
"latents": latents,
"vq/commitment_loss": commitment_loss,
"vq/codebook_loss": codebook_loss,
}
if __name__ == "__main__":
import numpy as np
from functools import partial
model = DAC().to("cpu")
for n, m in model.named_modules():
o = m.extra_repr()
p = sum([np.prod(p.size()) for p in m.parameters()])
fn = lambda o, p: o + f" {p/1e6:<.3f}M params."
setattr(m, "extra_repr", partial(fn, o=o, p=p))
print(model)
print("Total # of params: ", sum([np.prod(p.size()) for p in model.parameters()]))
length = 88200 * 2
x = torch.randn(1, 1, length).to(model.device)
x.requires_grad_(True)
x.retain_grad()
# Make a forward pass
out = model(x)["audio"]
print("Input shape:", x.shape)
print("Output shape:", out.shape)
# Create gradient variable
grad = torch.zeros_like(out)
grad[:, :, grad.shape[-1] // 2] = 1
# Make a backward pass
out.backward(grad)
# Check non-zero values
gradmap = x.grad.squeeze(0)
gradmap = (gradmap != 0).sum(0) # sum across features
rf = (gradmap != 0).sum()
print(f"Receptive field: {rf.item()}")
x = AudioSignal(torch.randn(1, 1, 44100 * 60), 44100)
model.decompress(model.compress(x, verbose=True), verbose=True)
voice_conversion/seed-vc/dac/model/discriminator.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from audiotools import AudioSignal
from audiotools import ml
from audiotools import STFTParams
from einops import rearrange
from torch.nn.utils import weight_norm
def WNConv1d(*args, **kwargs):
act = kwargs.pop("act", True)
conv = weight_norm(nn.Conv1d(*args, **kwargs))
if not act:
return conv
return nn.Sequential(conv, nn.LeakyReLU(0.1))
def WNConv2d(*args, **kwargs):
act = kwargs.pop("act", True)
conv = weight_norm(nn.Conv2d(*args, **kwargs))
if not act:
return conv
return nn.Sequential(conv, nn.LeakyReLU(0.1))
class MPD(nn.Module):
def __init__(self, period):
super().__init__()
self.period = period
self.convs = nn.ModuleList(
[
WNConv2d(1, 32, (5, 1), (3, 1), padding=(2, 0)),
WNConv2d(32, 128, (5, 1), (3, 1), padding=(2, 0)),
WNConv2d(128, 512, (5, 1), (3, 1), padding=(2, 0)),
WNConv2d(512, 1024, (5, 1), (3, 1), padding=(2, 0)),
WNConv2d(1024, 1024, (5, 1), 1, padding=(2, 0)),
]
)
self.conv_post = WNConv2d(
1024, 1, kernel_size=(3, 1), padding=(1, 0), act=False
)
def pad_to_period(self, x):
t = x.shape[-1]
x = F.pad(x, (0, self.period - t % self.period), mode="reflect")
return x
def forward(self, x):
fmap = []
x = self.pad_to_period(x)
x = rearrange(x, "b c (l p) -> b c l p", p=self.period)
for layer in self.convs:
x = layer(x)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
return fmap
class MSD(nn.Module):
def __init__(self, rate: int = 1, sample_rate: int = 44100):
super().__init__()
self.convs = nn.ModuleList(
[
WNConv1d(1, 16, 15, 1, padding=7),
WNConv1d(16, 64, 41, 4, groups=4, padding=20),
WNConv1d(64, 256, 41, 4, groups=16, padding=20),
WNConv1d(256, 1024, 41, 4, groups=64, padding=20),
WNConv1d(1024, 1024, 41, 4, groups=256, padding=20),
WNConv1d(1024, 1024, 5, 1, padding=2),
]
)
self.conv_post = WNConv1d(1024, 1, 3, 1, padding=1, act=False)
self.sample_rate = sample_rate
self.rate = rate
def forward(self, x):
x = AudioSignal(x, self.sample_rate)
x.resample(self.sample_rate // self.rate)
x = x.audio_data
fmap = []
for l in self.convs:
x = l(x)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
return fmap
BANDS = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)]
class MRD(nn.Module):
def __init__(
self,
window_length: int,
hop_factor: float = 0.25,
sample_rate: int = 44100,
bands: list = BANDS,
):
"""Complex multi-band spectrogram discriminator.
Parameters
----------
window_length : int
Window length of STFT.
hop_factor : float, optional
Hop factor of the STFT, defaults to ``0.25 * window_length``.
sample_rate : int, optional
Sampling rate of audio in Hz, by default 44100
bands : list, optional
Bands to run discriminator over.
"""
super().__init__()
self.window_length = window_length
self.hop_factor = hop_factor
self.sample_rate = sample_rate
self.stft_params = STFTParams(
window_length=window_length,
hop_length=int(window_length * hop_factor),
match_stride=True,
)
n_fft = window_length // 2 + 1
bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
self.bands = bands
ch = 32
convs = lambda: nn.ModuleList(
[
WNConv2d(2, ch, (3, 9), (1, 1), padding=(1, 4)),
WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
WNConv2d(ch, ch, (3, 3), (1, 1), padding=(1, 1)),
]
)
self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
self.conv_post = WNConv2d(ch, 1, (3, 3), (1, 1), padding=(1, 1), act=False)
def spectrogram(self, x):
x = AudioSignal(x, self.sample_rate, stft_params=self.stft_params)
x = torch.view_as_real(x.stft())
x = rearrange(x, "b 1 f t c -> (b 1) c t f")
# Split into bands
x_bands = [x[..., b[0] : b[1]] for b in self.bands]
return x_bands
def forward(self, x):
x_bands = self.spectrogram(x)
fmap = []
x = []
for band, stack in zip(x_bands, self.band_convs):
for layer in stack:
band = layer(band)
fmap.append(band)
x.append(band)
x = torch.cat(x, dim=-1)
x = self.conv_post(x)
fmap.append(x)
return fmap
class Discriminator(nn.Module):
def __init__(
self,
rates: list = [],
periods: list = [2, 3, 5, 7, 11],
fft_sizes: list = [2048, 1024, 512],
sample_rate: int = 44100,
bands: list = BANDS,
):
"""Discriminator that combines multiple discriminators.
Parameters
----------
rates : list, optional
sampling rates (in Hz) to run MSD at, by default []
If empty, MSD is not used.
periods : list, optional
periods (of samples) to run MPD at, by default [2, 3, 5, 7, 11]
fft_sizes : list, optional
Window sizes of the FFT to run MRD at, by default [2048, 1024, 512]
sample_rate : int, optional
Sampling rate of audio in Hz, by default 44100
bands : list, optional
Bands to run MRD at, by default `BANDS`
"""
super().__init__()
discs = []
discs += [MPD(p) for p in periods]
discs += [MSD(r, sample_rate=sample_rate) for r in rates]
discs += [MRD(f, sample_rate=sample_rate, bands=bands) for f in fft_sizes]
self.discriminators = nn.ModuleList(discs)
def preprocess(self, y):
# Remove DC offset
y = y - y.mean(dim=-1, keepdims=True)
# Peak normalize the volume of input audio
y = 0.8 * y / (y.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
return y
def forward(self, x):
x = self.preprocess(x)
fmaps = [d(x) for d in self.discriminators]
return fmaps
if __name__ == "__main__":
disc = Discriminator()
x = torch.zeros(1, 1, 44100)
results = disc(x)
for i, result in enumerate(results):
print(f"disc{i}")
for i, r in enumerate(result):
print(r.shape, r.mean(), r.min(), r.max())
print()
voice_conversion/seed-vc/dac/model/encodec.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Convolutional layers wrappers and utilities."""
import math
import typing as tp
import warnings
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import typing as tp
import einops
class ConvLayerNorm(nn.LayerNorm):
"""
Convolution-friendly LayerNorm that moves channels to last dimensions
before running the normalization and moves them back to original position right after.
"""
def __init__(self, normalized_shape: tp.Union[int, tp.List[int], torch.Size], **kwargs):
super().__init__(normalized_shape, **kwargs)
def forward(self, x):
x = einops.rearrange(x, 'b ... t -> b t ...')
x = super().forward(x)
x = einops.rearrange(x, 'b t ... -> b ... t')
return
CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
'time_layer_norm', 'layer_norm', 'time_group_norm'])
def apply_parametrization_norm(module: nn.Module, norm: str = 'none') -> nn.Module:
assert norm in CONV_NORMALIZATIONS
if norm == 'weight_norm':
return weight_norm(module)
elif norm == 'spectral_norm':
return spectral_norm(module)
else:
# We already check was in CONV_NORMALIZATION, so any other choice
# doesn't need reparametrization.
return module
def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs) -> nn.Module:
"""Return the proper normalization module. If causal is True, this will ensure the returned
module is causal, or return an error if the normalization doesn't support causal evaluation.
"""
assert norm in CONV_NORMALIZATIONS
if norm == 'layer_norm':
assert isinstance(module, nn.modules.conv._ConvNd)
return ConvLayerNorm(module.out_channels, **norm_kwargs)
elif norm == 'time_group_norm':
if causal:
raise ValueError("GroupNorm doesn't support causal evaluation.")
assert isinstance(module, nn.modules.conv._ConvNd)
return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
else:
return nn.Identity()
def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
padding_total: int = 0) -> int:
"""See `pad_for_conv1d`.
"""
length = x.shape[-1]
n_frames = (length - kernel_size + padding_total) / stride + 1
ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
return ideal_length - length
def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
"""Pad for a convolution to make sure that the last window is full.
Extra padding is added at the end. This is required to ensure that we can rebuild
an output of the same length, as otherwise, even with padding, some time steps
might get removed.
For instance, with total padding = 4, kernel size = 4, stride = 2:
0 0 1 2 3 4 5 0 0 # (0s are padding)
1 2 3 # (output frames of a convolution, last 0 is never used)
0 0 1 2 3 4 5 0 # (output of tr. conv., but pos. 5 is going to get removed as padding)
1 2 3 4 # once you removed padding, we are missing one time step !
"""
extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
return F.pad(x, (0, extra_padding))
def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'zero', value: float = 0.):
"""Tiny wrapper around F.pad, just to allow for reflect padding on small input.
If this is the case, we insert extra 0 padding to the right before the reflection happen.
"""
length = x.shape[-1]
padding_left, padding_right = paddings
assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
if mode == 'reflect':
max_pad = max(padding_left, padding_right)
extra_pad = 0
if length <= max_pad:
extra_pad = max_pad - length + 1
x = F.pad(x, (0, extra_pad))
padded = F.pad(x, paddings, mode, value)
end = padded.shape[-1] - extra_pad
return padded[..., :end]
else:
return F.pad(x, paddings, mode, value)
def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
"""Remove padding from x, handling properly zero padding. Only for 1d!"""
padding_left, padding_right = paddings
assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
assert (padding_left + padding_right) <= x.shape[-1]
end = x.shape[-1] - padding_right
return x[..., padding_left: end]
class NormConv1d(nn.Module):
"""Wrapper around Conv1d and normalization applied to this conv
to provide a uniform interface across normalization approaches.
"""
def __init__(self, *args, causal: bool = False, norm: str = 'none',
norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
super().__init__()
self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
self.norm_type = norm
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
return x
class NormConv2d(nn.Module):
"""Wrapper around Conv2d and normalization applied to this conv
to provide a uniform interface across normalization approaches.
"""
def __init__(self, *args, norm: str = 'none',
norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
super().__init__()
self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
self.norm_type = norm
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
return x
class NormConvTranspose1d(nn.Module):
"""Wrapper around ConvTranspose1d and normalization applied to this conv
to provide a uniform interface across normalization approaches.
"""
def __init__(self, *args, causal: bool = False, norm: str = 'none',
norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
super().__init__()
self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
self.norm_type = norm
def forward(self, x):
x = self.convtr(x)
x = self.norm(x)
return x
class NormConvTranspose2d(nn.Module):
"""Wrapper around ConvTranspose2d and normalization applied to this conv
to provide a uniform interface across normalization approaches.
"""
def __init__(self, *args, norm: str = 'none',
norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
super().__init__()
self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
def forward(self, x):
x = self.convtr(x)
x = self.norm(x)
return x
class SConv1d(nn.Module):
"""Conv1d with some builtin handling of asymmetric or causal padding
and normalization.
"""
def __init__(self, in_channels: int, out_channels: int,
kernel_size: int, stride: int = 1, dilation: int = 1,
groups: int = 1, bias: bool = True, causal: bool = False,
norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
pad_mode: str = 'reflect', **kwargs):
super().__init__()
# warn user on unusual setup between dilation and stride
if stride > 1 and dilation > 1:
warnings.warn('SConv1d has been initialized with stride > 1 and dilation > 1'
f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
dilation=dilation, groups=groups, bias=bias, causal=causal,
norm=norm, norm_kwargs=norm_kwargs)
self.causal = causal
self.pad_mode = pad_mode
self.cache_enabled = False
def reset_cache(self):
"""Reset the cache when starting a new stream."""
self.cache = None
self.cache_enabled = True
def forward(self, x):
B, C, T = x.shape
kernel_size = self.conv.conv.kernel_size[0]
stride = self.conv.conv.stride[0]
dilation = self.conv.conv.dilation[0]
kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations
padding_total = kernel_size - stride
extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
if self.causal:
# Left padding for causal
if self.cache_enabled and self.cache is not None:
# Concatenate the cache (previous inputs) with the new input for streaming
x = torch.cat([self.cache, x], dim=2)
else:
x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
else:
# Asymmetric padding required for odd strides
padding_right = padding_total // 2
padding_left = padding_total - padding_right
x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
# Store the most recent input frames for future cache use
if self.cache_enabled:
if self.cache is None:
# Initialize cache with zeros (at the start of streaming)
self.cache = torch.zeros(B, C, kernel_size - 1, device=x.device)
# Update the cache by storing the latest input frames
if kernel_size > 1:
self.cache = x[:, :, -kernel_size + 1:].detach() # Only store the necessary frames
return self.conv(x)
class SConvTranspose1d(nn.Module):
"""ConvTranspose1d with some builtin handling of asymmetric or causal padding
and normalization.
"""
def __init__(self, in_channels: int, out_channels: int,
kernel_size: int, stride: int = 1, causal: bool = False,
norm: str = 'none', trim_right_ratio: float = 1.,
norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
super().__init__()
self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
causal=causal, norm=norm, norm_kwargs=norm_kwargs)
self.causal = causal
self.trim_right_ratio = trim_right_ratio
assert self.causal or self.trim_right_ratio == 1., \
"`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
def forward(self, x):
kernel_size = self.convtr.convtr.kernel_size[0]
stride = self.convtr.convtr.stride[0]
padding_total = kernel_size - stride
y = self.convtr(x)
# We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
# removed at the very end, when keeping only the right length for the output,
# as removing it here would require also passing the length at the matching layer
# in the encoder.
if self.causal:
# Trim the padding on the right according to the specified ratio
# if trim_right_ratio = 1.0, trim everything from right
padding_right = math.ceil(padding_total * self.trim_right_ratio)
padding_left = padding_total - padding_right
y = unpad1d(y, (padding_left, padding_right))
else:
# Asymmetric padding required for odd strides
padding_right = padding_total // 2
padding_left = padding_total - padding_right
y = unpad1d(y, (padding_left, padding_right))
return y
class SLSTM(nn.Module):
"""
LSTM without worrying about the hidden state, nor the layout of the data.
Expects input as convolutional layout.
"""
def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True):
super().__init__()
self.skip = skip
self.lstm = nn.LSTM(dimension, dimension, num_layers)
self.hidden = None
self.cache_enabled = False
def forward(self, x):
x = x.permute(2, 0, 1)
if self.training or not self.cache_enabled:
y, _ = self.lstm(x)
else:
y, self.hidden = self.lstm(x, self.hidden)
if self.skip:
y = y + x
y = y.permute(1, 2, 0)
return y
def reset_cache(self):
self.hidden = None
self.cache_enabled = True
voice_conversion/seed-vc/dac/nn/__init__.py
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from . import layers
from . import loss
from . import quantize
voice_conversion/seed-vc/dac/nn/layers.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch.nn.utils import weight_norm
def WNConv1d(*args, **kwargs):
return weight_norm(nn.Conv1d(*args, **kwargs))
def WNConvTranspose1d(*args, **kwargs):
return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
# Scripting this brings model speed up 1.4x
@torch.jit.script
def snake(x, alpha):
shape = x.shape
x = x.reshape(shape[0], shape[1], -1)
x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
x = x.reshape(shape)
return x
class Snake1d(nn.Module):
def __init__(self, channels):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1, channels, 1))
def forward(self, x):
return snake(x, self.alpha)
voice_conversion/seed-vc/dac/nn/loss.py
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import typing
from typing import List
import torch
import torch.nn.functional as F
from audiotools import AudioSignal
from audiotools import STFTParams
from torch import nn
class L1Loss(nn.L1Loss):
"""L1 Loss between AudioSignals. Defaults
to comparing ``audio_data``, but any
attribute of an AudioSignal can be used.
Parameters
----------
attribute : str, optional
Attribute of signal to compare, defaults to ``audio_data``.
weight : float, optional
Weight of this loss, defaults to 1.0.
Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py
"""
def __init__(self, attribute: str = "audio_data", weight: float = 1.0, **kwargs):
self.attribute = attribute
self.weight = weight
super().__init__(**kwargs)
def forward(self, x: AudioSignal, y: AudioSignal):
"""
Parameters
----------
x : AudioSignal
Estimate AudioSignal
y : AudioSignal
Reference AudioSignal
Returns
-------
torch.Tensor
L1 loss between AudioSignal attributes.
"""
if isinstance(x, AudioSignal):
x = getattr(x, self.attribute)
y = getattr(y, self.attribute)
return super().forward(x, y)
class SISDRLoss(nn.Module):
"""
Computes the Scale-Invariant Source-to-Distortion Ratio between a batch
of estimated and reference audio signals or aligned features.
Parameters
----------
scaling : int, optional
Whether to use scale-invariant (True) or
signal-to-noise ratio (False), by default True
reduction : str, optional
How to reduce across the batch (either 'mean',
'sum', or none).], by default ' mean'
zero_mean : int, optional
Zero mean the references and estimates before
computing the loss, by default True
clip_min : int, optional
The minimum possible loss value. Helps network
to not focus on making already good examples better, by default None
weight : float, optional
Weight of this loss, defaults to 1.0.
Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py
"""
def __init__(
self,
scaling: int = True,
reduction: str = "mean",
zero_mean: int = True,
clip_min: int = None,
weight: float = 1.0,
):
self.scaling = scaling
self.reduction = reduction
self.zero_mean = zero_mean
self.clip_min = clip_min
self.weight = weight
super().__init__()
def forward(self, x: AudioSignal, y: AudioSignal):
eps = 1e-8
# nb, nc, nt
if isinstance(x, AudioSignal):
references = x.audio_data
estimates = y.audio_data
else:
references = x
estimates = y
nb = references.shape[0]
references = references.reshape(nb, 1, -1).permute(0, 2, 1)
estimates = estimates.reshape(nb, 1, -1).permute(0, 2, 1)
# samples now on axis 1
if self.zero_mean:
mean_reference = references.mean(dim=1, keepdim=True)
mean_estimate = estimates.mean(dim=1, keepdim=True)
else:
mean_reference = 0
mean_estimate = 0
_references = references - mean_reference
_estimates = estimates - mean_estimate
references_projection = (_references**2).sum(dim=-2) + eps
references_on_estimates = (_estimates * _references).sum(dim=-2) + eps
scale = (
(references_on_estimates / references_projection).unsqueeze(1)
if self.scaling
else 1
)
e_true = scale * _references
e_res = _estimates - e_true
signal = (e_true**2).sum(dim=1)
noise = (e_res**2).sum(dim=1)
sdr = -10 * torch.log10(signal / noise + eps)
if self.clip_min is not None:
sdr = torch.clamp(sdr, min=self.clip_min)
if self.reduction == "mean":
sdr = sdr.mean()
elif self.reduction == "sum":
sdr = sdr.sum()
return sdr
class MultiScaleSTFTLoss(nn.Module):
"""Computes the multi-scale STFT loss from [1].
Parameters
----------
window_lengths : List[int], optional
Length of each window of each STFT, by default [2048, 512]
loss_fn : typing.Callable, optional
How to compare each loss, by default nn.L1Loss()
clamp_eps : float, optional
Clamp on the log magnitude, below, by default 1e-5
mag_weight : float, optional
Weight of raw magnitude portion of loss, by default 1.0
log_weight : float, optional
Weight of log magnitude portion of loss, by default 1.0
pow : float, optional
Power to raise magnitude to before taking log, by default 2.0
weight : float, optional
Weight of this loss, by default 1.0
match_stride : bool, optional
Whether to match the stride of convolutional layers, by default False
References
----------
1. Engel, Jesse, Chenjie Gu, and Adam Roberts.
"DDSP: Differentiable Digital Signal Processing."
International Conference on Learning Representations. 2019.
Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
"""
def __init__(
self,
window_lengths: List[int] = [2048, 512],
loss_fn: typing.Callable = nn.L1Loss(),
clamp_eps: float = 1e-5,
mag_weight: float = 1.0,
log_weight: float = 1.0,
pow: float = 2.0,
weight: float = 1.0,
match_stride: bool = False,
window_type: str = None,
):
super().__init__()
self.stft_params = [
STFTParams(
window_length=w,
hop_length=w // 4,
match_stride=match_stride,
window_type=window_type,
)
for w in window_lengths
]
self.loss_fn = loss_fn
self.log_weight = log_weight
self.mag_weight = mag_weight
self.clamp_eps = clamp_eps
self.weight = weight
self.pow = pow
def forward(self, x: AudioSignal, y: AudioSignal):
"""Computes multi-scale STFT between an estimate and a reference
signal.
Parameters
----------
x : AudioSignal
Estimate signal
y : AudioSignal
Reference signal
Returns
-------
torch.Tensor
Multi-scale STFT loss.
"""
loss = 0.0
for s in self.stft_params:
x.stft(s.window_length, s.hop_length, s.window_type)
y.stft(s.window_length, s.hop_length, s.window_type)
loss += self.log_weight * self.loss_fn(
x.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
y.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(),
)
loss += self.mag_weight * self.loss_fn(x.magnitude, y.magnitude)
return loss
class MelSpectrogramLoss(nn.Module):
"""Compute distance between mel spectrograms. Can be used
in a multi-scale way.
Parameters
----------
n_mels : List[int]
Number of mels per STFT, by default [150, 80],
window_lengths : List[int], optional
Length of each window of each STFT, by default [2048, 512]
loss_fn : typing.Callable, optional
How to compare each loss, by default nn.L1Loss()
clamp_eps : float, optional
Clamp on the log magnitude, below, by default 1e-5
mag_weight : float, optional
Weight of raw magnitude portion of loss, by default 1.0
log_weight : float, optional
Weight of log magnitude portion of loss, by default 1.0
pow : float, optional
Power to raise magnitude to before taking log, by default 2.0
weight : float, optional
Weight of this loss, by default 1.0
match_stride : bool, optional
Whether to match the stride of convolutional layers, by default False
Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py
"""
def __init__(
self,
n_mels: List[int] = [150, 80],
window_lengths: List[int] = [2048, 512],
loss_fn: typing.Callable = nn.L1Loss(),
clamp_eps: float = 1e-5,
mag_weight: float = 1.0,
log_weight: float = 1.0,
pow: float = 2.0,
weight: float = 1.0,
match_stride: bool = False,
mel_fmin: List[float] = [0.0, 0.0],
mel_fmax: List[float] = [None, None],
window_type: str = None,
):
super().__init__()
self.stft_params = [
STFTParams(
window_length=w,
hop_length=w // 4,
match_stride=match_stride,
window_type=window_type,
)
for w in window_lengths
]
self.n_mels = n_mels
self.loss_fn = loss_fn
self.clamp_eps = clamp_eps
self.log_weight = log_weight
self.mag_weight = mag_weight
self.weight = weight
self.mel_fmin = mel_fmin
self.mel_fmax = mel_fmax
self.pow = pow
def forward(self, x: AudioSignal, y: AudioSignal):
"""Computes mel loss between an estimate and a reference
signal.
Parameters
----------
x : AudioSignal
Estimate signal
y : AudioSignal
Reference signal
Returns
-------
torch.Tensor
Mel loss.
"""
loss = 0.0
for n_mels, fmin, fmax, s in zip(
self.n_mels, self.mel_fmin, self.mel_fmax, self.stft_params
):
kwargs = {
"window_length": s.window_length,
"hop_length": s.hop_length,
"window_type": s.window_type,
}
x_mels = x.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)
y_mels = y.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs)
loss += self.log_weight * self.loss_fn(
x_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
y_mels.clamp(self.clamp_eps).pow(self.pow).log10(),
)
loss += self.mag_weight * self.loss_fn(x_mels, y_mels)
return loss
class GANLoss(nn.Module):
"""
Computes a discriminator loss, given a discriminator on
generated waveforms/spectrograms compared to ground truth
waveforms/spectrograms. Computes the loss for both the
discriminator and the generator in separate functions.
"""
def __init__(self, discriminator):
super().__init__()
self.discriminator = discriminator
def forward(self, fake, real):
d_fake = self.discriminator(fake.audio_data)
d_real = self.discriminator(real.audio_data)
return d_fake, d_real
def discriminator_loss(self, fake, real):
d_fake, d_real = self.forward(fake.clone().detach(), real)
loss_d = 0
for x_fake, x_real in zip(d_fake, d_real):
loss_d += torch.mean(x_fake[-1] ** 2)
loss_d += torch.mean((1 - x_real[-1]) ** 2)
return loss_d
def generator_loss(self, fake, real):
d_fake, d_real = self.forward(fake, real)
loss_g = 0
for x_fake in d_fake:
loss_g += torch.mean((1 - x_fake[-1]) ** 2)
loss_feature = 0
for i in range(len(d_fake)):
for j in range(len(d_fake[i]) - 1):
loss_feature += F.l1_loss(d_fake[i][j], d_real[i][j].detach())
return loss_g, loss_feature
voice_conversion/seed-vc/dac/nn/quantize.py
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from typing import Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch.nn.utils import weight_norm
from dac.nn.layers import WNConv1d
class VectorQuantizeLegacy(nn.Module):
"""
Implementation of VQ similar to Karpathy's repo:
https://github.com/karpathy/deep-vector-quantization
removed in-out projection
"""
def __init__(self, input_dim: int, codebook_size: int):
super().__init__()
self.codebook_size = codebook_size
self.codebook = nn.Embedding(codebook_size, input_dim)
def forward(self, z, z_mask=None):
"""Quantized the input tensor using a fixed codebook and returns
the corresponding codebook vectors
Parameters
----------
z : Tensor[B x D x T]
Returns
-------
Tensor[B x D x T]
Quantized continuous representation of input
Tensor[1]
Commitment loss to train encoder to predict vectors closer to codebook
entries
Tensor[1]
Codebook loss to update the codebook
Tensor[B x T]
Codebook indices (quantized discrete representation of input)
Tensor[B x D x T]
Projected latents (continuous representation of input before quantization)
"""
z_e = z
z_q, indices = self.decode_latents(z)
if z_mask is not None:
commitment_loss = (F.mse_loss(z_e, z_q.detach(), reduction="none").mean(1) * z_mask).sum() / z_mask.sum()
codebook_loss = (F.mse_loss(z_q, z_e.detach(), reduction="none").mean(1) * z_mask).sum() / z_mask.sum()
else:
commitment_loss = F.mse_loss(z_e, z_q.detach())
codebook_loss = F.mse_loss(z_q, z_e.detach())
z_q = (
z_e + (z_q - z_e).detach()
) # noop in forward pass, straight-through gradient estimator in backward pass
return z_q, indices, z_e, commitment_loss, codebook_loss
def embed_code(self, embed_id):
return F.embedding(embed_id, self.codebook.weight)
def decode_code(self, embed_id):
return self.embed_code(embed_id).transpose(1, 2)
def decode_latents(self, latents):
encodings = rearrange(latents, "b d t -> (b t) d")
codebook = self.codebook.weight # codebook: (N x D)
# L2 normalize encodings and codebook (ViT-VQGAN)
encodings = F.normalize(encodings)
codebook = F.normalize(codebook)
# Compute euclidean distance with codebook
dist = (
encodings.pow(2).sum(1, keepdim=True)
- 2 * encodings @ codebook.t()
+ codebook.pow(2).sum(1, keepdim=True).t()
)
indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
z_q = self.decode_code(indices)
return z_q, indices
class VectorQuantize(nn.Module):
"""
Implementation of VQ similar to Karpathy's repo:
https://github.com/karpathy/deep-vector-quantization
Additionally uses following tricks from Improved VQGAN
(https://arxiv.org/pdf/2110.04627.pdf):
1. Factorized codes: Perform nearest neighbor lookup in low-dimensional space
for improved codebook usage
2. l2-normalized codes: Converts euclidean distance to cosine similarity which
improves training stability
"""
def __init__(self, input_dim: int, codebook_size: int, codebook_dim: int):
super().__init__()
self.codebook_size = codebook_size
self.codebook_dim = codebook_dim
self.in_proj = WNConv1d(input_dim, codebook_dim, kernel_size=1)
self.out_proj = WNConv1d(codebook_dim, input_dim, kernel_size=1)
self.codebook = nn.Embedding(codebook_size, codebook_dim)
def forward(self, z, z_mask=None):
"""Quantized the input tensor using a fixed codebook and returns
the corresponding codebook vectors
Parameters
----------
z : Tensor[B x D x T]
Returns
-------
Tensor[B x D x T]
Quantized continuous representation of input
Tensor[1]
Commitment loss to train encoder to predict vectors closer to codebook
entries
Tensor[1]
Codebook loss to update the codebook
Tensor[B x T]
Codebook indices (quantized discrete representation of input)
Tensor[B x D x T]
Projected latents (continuous representation of input before quantization)
"""
# Factorized codes (ViT-VQGAN) Project input into low-dimensional space
z_e = self.in_proj(z) # z_e : (B x D x T)
z_q, indices = self.decode_latents(z_e)
if z_mask is not None:
commitment_loss = (F.mse_loss(z_e, z_q.detach(), reduction="none").mean(1) * z_mask).sum() / z_mask.sum()
codebook_loss = (F.mse_loss(z_q, z_e.detach(), reduction="none").mean(1) * z_mask).sum() / z_mask.sum()
else:
commitment_loss = F.mse_loss(z_e, z_q.detach())
codebook_loss = F.mse_loss(z_q, z_e.detach())
z_q = (
z_e + (z_q - z_e).detach()
) # noop in forward pass, straight-through gradient estimator in backward pass
z_q = self.out_proj(z_q)
return z_q, commitment_loss, codebook_loss, indices, z_e
def embed_code(self, embed_id):
return F.embedding(embed_id, self.codebook.weight)
def decode_code(self, embed_id):
return self.embed_code(embed_id).transpose(1, 2)
def decode_latents(self, latents):
encodings = rearrange(latents, "b d t -> (b t) d")
codebook = self.codebook.weight # codebook: (N x D)
# L2 normalize encodings and codebook (ViT-VQGAN)
encodings = F.normalize(encodings)
codebook = F.normalize(codebook)
# Compute euclidean distance with codebook
dist = (
encodings.pow(2).sum(1, keepdim=True)
- 2 * encodings @ codebook.t()
+ codebook.pow(2).sum(1, keepdim=True).t()
)
indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
z_q = self.decode_code(indices)
return z_q, indices
class ResidualVectorQuantize(nn.Module):
"""
Introduced in SoundStream: An end2end neural audio codec
https://arxiv.org/abs/2107.03312
"""
def __init__(
self,
input_dim: int = 512,
n_codebooks: int = 9,
codebook_size: int = 1024,
codebook_dim: Union[int, list] = 8,
quantizer_dropout: float = 0.0,
):
super().__init__()
if isinstance(codebook_dim, int):
codebook_dim = [codebook_dim for _ in range(n_codebooks)]
self.n_codebooks = n_codebooks
self.codebook_dim = codebook_dim
self.codebook_size = codebook_size
self.quantizers = nn.ModuleList(
[
VectorQuantize(input_dim, codebook_size, codebook_dim[i])
for i in range(n_codebooks)
]
)
self.quantizer_dropout = quantizer_dropout
def forward(self, z, n_quantizers: int = None):
"""Quantized the input tensor using a fixed set of `n` codebooks and returns
the corresponding codebook vectors
Parameters
----------
z : Tensor[B x D x T]
n_quantizers : int, optional
No. of quantizers to use
(n_quantizers < self.n_codebooks ex: for quantizer dropout)
Note: if `self.quantizer_dropout` is True, this argument is ignored
when in training mode, and a random number of quantizers is used.
Returns
-------
dict
A dictionary with the following keys:
"z" : Tensor[B x D x T]
Quantized continuous representation of input
"codes" : Tensor[B x N x T]
Codebook indices for each codebook
(quantized discrete representation of input)
"latents" : Tensor[B x N*D x T]
Projected latents (continuous representation of input before quantization)
"vq/commitment_loss" : Tensor[1]
Commitment loss to train encoder to predict vectors closer to codebook
entries
"vq/codebook_loss" : Tensor[1]
Codebook loss to update the codebook
"""
z_q = 0
residual = z
commitment_loss = 0
codebook_loss = 0
codebook_indices = []
latents = []
if n_quantizers is None:
n_quantizers = self.n_codebooks
if self.training:
n_quantizers = torch.ones((z.shape[0],)) * self.n_codebooks + 1
dropout = torch.randint(1, self.n_codebooks + 1, (z.shape[0],))
n_dropout = int(z.shape[0] * self.quantizer_dropout)
n_quantizers[:n_dropout] = dropout[:n_dropout]
n_quantizers = n_quantizers.to(z.device)
for i, quantizer in enumerate(self.quantizers):
if self.training is False and i >= n_quantizers:
break
z_q_i, commitment_loss_i, codebook_loss_i, indices_i, z_e_i = quantizer(
residual
)
# Create mask to apply quantizer dropout
mask = (
torch.full((z.shape[0],), fill_value=i, device=z.device) < n_quantizers
)
z_q = z_q + z_q_i * mask[:, None, None]
residual = residual - z_q_i
# Sum losses
commitment_loss += (commitment_loss_i * mask).mean()
codebook_loss += (codebook_loss_i * mask).mean()
codebook_indices.append(indices_i)
latents.append(z_e_i)
codes = torch.stack(codebook_indices, dim=1)
latents = torch.cat(latents, dim=1)
return z_q, codes, latents, commitment_loss, codebook_loss
def from_codes(self, codes: torch.Tensor):
"""Given the quantized codes, reconstruct the continuous representation
Parameters
----------
codes : Tensor[B x N x T]
Quantized discrete representation of input
Returns
-------
Tensor[B x D x T]
Quantized continuous representation of input
"""
z_q = 0.0
z_p = []
n_codebooks = codes.shape[1]
for i in range(n_codebooks):
z_p_i = self.quantizers[i].decode_code(codes[:, i, :])
z_p.append(z_p_i)
z_q_i = self.quantizers[i].out_proj(z_p_i)
z_q = z_q + z_q_i
return z_q, torch.cat(z_p, dim=1), codes
def from_latents(self, latents: torch.Tensor):
"""Given the unquantized latents, reconstruct the
continuous representation after quantization.
Parameters
----------
latents : Tensor[B x N x T]
Continuous representation of input after projection
Returns
-------
Tensor[B x D x T]
Quantized representation of full-projected space
Tensor[B x D x T]
Quantized representation of latent space
"""
z_q = 0
z_p = []
codes = []
dims = np.cumsum([0] + [q.codebook_dim for q in self.quantizers])
n_codebooks = np.where(dims <= latents.shape[1])[0].max(axis=0, keepdims=True)[
0
]
for i in range(n_codebooks):
j, k = dims[i], dims[i + 1]
z_p_i, codes_i = self.quantizers[i].decode_latents(latents[:, j:k, :])
z_p.append(z_p_i)
codes.append(codes_i)
z_q_i = self.quantizers[i].out_proj(z_p_i)
z_q = z_q + z_q_i
return z_q, torch.cat(z_p, dim=1), torch.stack(codes, dim=1)
if __name__ == "__main__":
rvq = ResidualVectorQuantize(quantizer_dropout=True)
x = torch.randn(16, 512, 80)
y = rvq(x)
print(y["latents"].shape)
voice_conversion/seed-vc/dac/utils/__init__.py
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from pathlib import Path
import argbind
from audiotools import ml
import dac
DAC = dac.model.DAC
Accelerator = ml.Accelerator
__MODEL_LATEST_TAGS__ = {
("44khz", "8kbps"): "0.0.1",
("24khz", "8kbps"): "0.0.4",
("16khz", "8kbps"): "0.0.5",
("44khz", "16kbps"): "1.0.0",
}
__MODEL_URLS__ = {
(
"44khz",
"0.0.1",
"8kbps",
): "https://github.com/descriptinc/descript-audio-codec/releases/download/0.0.1/weights.pth",
(
"24khz",
"0.0.4",
"8kbps",
): "https://github.com/descriptinc/descript-audio-codec/releases/download/0.0.4/weights_24khz.pth",
(
"16khz",
"0.0.5",
"8kbps",
): "https://github.com/descriptinc/descript-audio-codec/releases/download/0.0.5/weights_16khz.pth",
(
"44khz",
"1.0.0",
"16kbps",
): "https://github.com/descriptinc/descript-audio-codec/releases/download/1.0.0/weights_44khz_16kbps.pth",
}
@argbind.bind(group="download", positional=True, without_prefix=True)
def download(
model_type: str = "44khz", model_bitrate: str = "8kbps", tag: str = "latest"
):
"""
Function that downloads the weights file from URL if a local cache is not found.
Parameters
----------
model_type : str
The type of model to download. Must be one of "44khz", "24khz", or "16khz". Defaults to "44khz".
model_bitrate: str
Bitrate of the model. Must be one of "8kbps", or "16kbps". Defaults to "8kbps".
Only 44khz model supports 16kbps.
tag : str
The tag of the model to download. Defaults to "latest".
Returns
-------
Path
Directory path required to load model via audiotools.
"""
model_type = model_type.lower()
tag = tag.lower()
assert model_type in [
"44khz",
"24khz",
"16khz",
], "model_type must be one of '44khz', '24khz', or '16khz'"
assert model_bitrate in [
"8kbps",
"16kbps",
], "model_bitrate must be one of '8kbps', or '16kbps'"
if tag == "latest":
tag = __MODEL_LATEST_TAGS__[(model_type, model_bitrate)]
download_link = __MODEL_URLS__.get((model_type, tag, model_bitrate), None)
if download_link is None:
raise ValueError(
f"Could not find model with tag {tag} and model type {model_type}"
)
local_path = (
Path.home()
/ ".cache"
/ "descript"
/ "dac"
/ f"weights_{model_type}_{model_bitrate}_{tag}.pth"
)
if not local_path.exists():
local_path.parent.mkdir(parents=True, exist_ok=True)
# Download the model
import requests
response = requests.get(download_link)
if response.status_code != 200:
raise ValueError(
f"Could not download model. Received response code {response.status_code}"
)
local_path.write_bytes(response.content)
return local_path
def load_model(
model_type: str = "44khz",
model_bitrate: str = "8kbps",
tag: str = "latest",
load_path: str = None,
):
if not load_path:
load_path = download(
model_type=model_type, model_bitrate=model_bitrate, tag=tag
)
generator = DAC.load(load_path)
return generator
voice_conversion/seed-vc/dac/utils/decode.py
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import warnings
from pathlib import Path
import argbind
import numpy as np
import torch
from audiotools import AudioSignal
from tqdm import tqdm
from dac import DACFile
from dac.utils import load_model
warnings.filterwarnings("ignore", category=UserWarning)
@argbind.bind(group="decode", positional=True, without_prefix=True)
@torch.inference_mode()
@torch.no_grad()
def decode(
input: str,
output: str = "",
weights_path: str = "",
model_tag: str = "latest",
model_bitrate: str = "8kbps",
device: str = "cuda",
model_type: str = "44khz",
verbose: bool = False,
):
"""Decode audio from codes.
Parameters
----------
input : str
Path to input directory or file
output : str, optional
Path to output directory, by default "".
If `input` is a directory, the directory sub-tree relative to `input` is re-created in `output`.
weights_path : str, optional
Path to weights file, by default "". If not specified, the weights file will be downloaded from the internet using the
model_tag and model_type.
model_tag : str, optional
Tag of the model to use, by default "latest". Ignored if `weights_path` is specified.
model_bitrate: str
Bitrate of the model. Must be one of "8kbps", or "16kbps". Defaults to "8kbps".
device : str, optional
Device to use, by default "cuda". If "cpu", the model will be loaded on the CPU.
model_type : str, optional
The type of model to use. Must be one of "44khz", "24khz", or "16khz". Defaults to "44khz". Ignored if `weights_path` is specified.
"""
generator = load_model(
model_type=model_type,
model_bitrate=model_bitrate,
tag=model_tag,
load_path=weights_path,
)
generator.to(device)
generator.eval()
# Find all .dac files in input directory
_input = Path(input)
input_files = list(_input.glob("**/*.dac"))
# If input is a .dac file, add it to the list
if _input.suffix == ".dac":
input_files.append(_input)
# Create output directory
output = Path(output)
output.mkdir(parents=True, exist_ok=True)
for i in tqdm(range(len(input_files)), desc=f"Decoding files"):
# Load file
artifact = DACFile.load(input_files[i])
# Reconstruct audio from codes
recons = generator.decompress(artifact, verbose=verbose)
# Compute output path
relative_path = input_files[i].relative_to(input)
output_dir = output / relative_path.parent
if not relative_path.name:
output_dir = output
relative_path = input_files[i]
output_name = relative_path.with_suffix(".wav").name
output_path = output_dir / output_name
output_path.parent.mkdir(parents=True, exist_ok=True)
# Write to file
recons.write(output_path)
if __name__ == "__main__":
args = argbind.parse_args()
with argbind.scope(args):
decode()
voice_conversion/seed-vc/dac/utils/encode.py
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import math
import warnings
from pathlib import Path
import argbind
import numpy as np
import torch
from audiotools import AudioSignal
from audiotools.core import util
from tqdm import tqdm
from dac.utils import load_model
warnings.filterwarnings("ignore", category=UserWarning)
@argbind.bind(group="encode", positional=True, without_prefix=True)
@torch.inference_mode()
@torch.no_grad()
def encode(
input: str,
output: str = "",
weights_path: str = "",
model_tag: str = "latest",
model_bitrate: str = "8kbps",
n_quantizers: int = None,
device: str = "cuda",
model_type: str = "44khz",
win_duration: float = 5.0,
verbose: bool = False,
):
"""Encode audio files in input path to .dac format.
Parameters
----------
input : str
Path to input audio file or directory
output : str, optional
Path to output directory, by default "". If `input` is a directory, the directory sub-tree relative to `input` is re-created in `output`.
weights_path : str, optional
Path to weights file, by default "". If not specified, the weights file will be downloaded from the internet using the
model_tag and model_type.
model_tag : str, optional
Tag of the model to use, by default "latest". Ignored if `weights_path` is specified.
model_bitrate: str
Bitrate of the model. Must be one of "8kbps", or "16kbps". Defaults to "8kbps".
n_quantizers : int, optional
Number of quantizers to use, by default None. If not specified, all the quantizers will be used and the model will compress at maximum bitrate.
device : str, optional
Device to use, by default "cuda"
model_type : str, optional
The type of model to use. Must be one of "44khz", "24khz", or "16khz". Defaults to "44khz". Ignored if `weights_path` is specified.
"""
generator = load_model(
model_type=model_type,
model_bitrate=model_bitrate,
tag=model_tag,
load_path=weights_path,
)
generator.to(device)
generator.eval()
kwargs = {"n_quantizers": n_quantizers}
# Find all audio files in input path
input = Path(input)
audio_files = util.find_audio(input)
output = Path(output)
output.mkdir(parents=True, exist_ok=True)
for i in tqdm(range(len(audio_files)), desc="Encoding files"):
# Load file
signal = AudioSignal(audio_files[i])
# Encode audio to .dac format
artifact = generator.compress(signal, win_duration, verbose=verbose, **kwargs)
# Compute output path
relative_path = audio_files[i].relative_to(input)
output_dir = output / relative_path.parent
if not relative_path.name:
output_dir = output
relative_path = audio_files[i]
output_name = relative_path.with_suffix(".dac").name
output_path = output_dir / output_name
output_path.parent.mkdir(parents=True, exist_ok=True)
artifact.save(output_path)
if __name__ == "__main__":
args = argbind.parse_args()
with argbind.scope(args):
encode()
voice_conversion/seed-vc/eval.py
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import shutil
import warnings
import argparse
import torch
import os
import os.path as osp
import yaml
warnings.simplefilter("ignore")
# load packages
import random
from tqdm import tqdm
from modules.commons import *
import time
import torchaudio
import librosa
import torchaudio.compliance.kaldi as kaldi
from hf_utils import load_custom_model_from_hf
from resemblyzer import preprocess_wav, VoiceEncoder
# Load model and configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from transformers import Wav2Vec2FeatureExtractor, WavLMForXVector
from transformers import Wav2Vec2Processor, HubertForCTC
import jiwer
import string
from baselines.dnsmos.dnsmos_computor import DNSMOSComputer
def calc_mos(computor, audio, orin_sr):
# only 16k audio is supported
target_sr = 16000
if orin_sr != 16000:
audio = librosa.resample(
audio, orig_sr=orin_sr, target_sr=target_sr, res_type="kaiser_fast"
)
result = computor.compute(audio, target_sr, False)
sig, bak, ovr = result["SIG"], result["BAK"], result["OVRL"]
if ovr == 0:
print("calculate dns mos failed")
return sig, bak, ovr
mos_computer = DNSMOSComputer(
"baselines/dnsmos/sig_bak_ovr.onnx",
"baselines/dnsmos/model_v8.onnx",
device="cuda",
device_id=0,
)
def load_models(args):
dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
"DiT_seed_v2_uvit_whisper_small_wavenet_bigvgan_pruned.pth",
"config_dit_mel_seed_uvit_whisper_small_wavenet.yml")
config = yaml.safe_load(open(dit_config_path, "r"))
model_params = recursive_munch(config["model_params"])
model = build_model(model_params, stage="DiT")
hop_length = config["preprocess_params"]["spect_params"]["hop_length"]
sr = config["preprocess_params"]["sr"]
# Load checkpoints
model, _, _, _ = load_checkpoint(
model,
None,
dit_checkpoint_path,
load_only_params=True,
ignore_modules=[],
is_distributed=False,
)
for key in model:
model[key].eval()
model[key].to(device)
model.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
# Load additional modules
from modules.campplus.DTDNN import CAMPPlus
campplus_ckpt_path = load_custom_model_from_hf(
"funasr/campplus", "campplus_cn_common.bin", config_filename=None
)
campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
campplus_model.load_state_dict(torch.load(campplus_ckpt_path, map_location="cpu"))
campplus_model.eval()
campplus_model.to(device)
from modules.bigvgan import bigvgan
bigvgan_model = bigvgan.BigVGAN.from_pretrained(
"nvidia/bigvgan_v2_22khz_80band_256x", use_cuda_kernel=False
)
# remove weight norm in the model and set to eval mode
bigvgan_model.remove_weight_norm()
bigvgan_model = bigvgan_model.eval().to(device)
if model_params.speech_tokenizer.type == "facodec":
ckpt_path, config_path = load_custom_model_from_hf("Plachta/FAcodec", 'pytorch_model.bin', 'config.yml')
codec_config = yaml.safe_load(open(config_path))
codec_model_params = recursive_munch(codec_config['model_params'])
codec_encoder = build_model(codec_model_params, stage="codec")
ckpt_params = torch.load(ckpt_path, map_location="cpu")
for key in codec_encoder:
codec_encoder[key].load_state_dict(ckpt_params[key], strict=False)
_ = [codec_encoder[key].eval() for key in codec_encoder]
_ = [codec_encoder[key].to(device) for key in codec_encoder]
speechtokenizer_set = ('facodec', codec_encoder, None)
elif model_params.speech_tokenizer.type == "whisper":
from transformers import AutoFeatureExtractor, WhisperModel
whisper_name = model_params.speech_tokenizer.whisper_name if hasattr(model_params.speech_tokenizer,
'whisper_name') else "whisper-large-v3"
whisper_model = WhisperModel.from_pretrained(whisper_name, torch_dtype=torch.float16).to(device)
del whisper_model.decoder
whisper_feature_extractor = AutoFeatureExtractor.from_pretrained(whisper_name)
speechtokenizer_set = ('whisper', whisper_model, whisper_feature_extractor)
else:
raise ValueError(f"Unsupported speech tokenizer type: {model_params.speech_tokenizer.type}")
# Generate mel spectrograms
mel_fn_args = {
"n_fft": config['preprocess_params']['spect_params']['n_fft'],
"win_size": config['preprocess_params']['spect_params']['win_length'],
"hop_size": config['preprocess_params']['spect_params']['hop_length'],
"num_mels": config['preprocess_params']['spect_params']['n_mels'],
"sampling_rate": sr,
"fmin": config['preprocess_params'].get('fmin', 0),
"fmax": None if config['preprocess_params'].get('fmax', "None") == "None" else 8000,
"center": False
}
from modules.audio import mel_spectrogram
to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
return (
model,
speechtokenizer_set,
bigvgan_model,
campplus_model,
to_mel,
mel_fn_args,
)
@torch.no_grad()
def main(args):
# init xvector models
if args.xvector_extractor == "wavlm":
wavlm_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
"microsoft/wavlm-base-plus-sv"
)
wavlm_model = WavLMForXVector.from_pretrained(
"microsoft/wavlm-base-plus-sv"
).to(device)
elif args.xvector_extractor == "resemblyzer":
resemblyzer_encoder = VoiceEncoder()
else:
raise ValueError(f"Unknown xvector extractor: {args.xvector_extractor}")
# init asr model
asr_processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft")
asr_model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft").to(device)
(
model,
speechtokenizer_set,
bigvgan_model,
campplus_model,
to_mel,
mel_fn_args,
) = load_models(args)
sr = mel_fn_args["sampling_rate"]
source_dir = args.source
target_dir = args.target
diffusion_steps = args.diffusion_steps
length_adjust = args.length_adjust
inference_cfg_rate = args.inference_cfg_rate
baseline = args.baseline
max_samples = args.max_samples
try:
source_audio_list = open(osp.join(source_dir, "index.tsv"), "r").readlines()
except FileNotFoundError:
source_audio_list = os.listdir(source_dir)
source_audio_list = [f for f in source_audio_list if f.endswith(".wav")]
target_audio_list = os.listdir(target_dir)
conversion_result_dir = args.output
if baseline:
conversion_result_dir = os.path.join(conversion_result_dir, baseline)
os.makedirs(conversion_result_dir, exist_ok=True)
similarity_list = []
gt_wer_list = []
gt_cer_list = []
vc_wer_list = []
vc_cer_list = []
dnsmos_list = []
for source_i, source_line in enumerate(tqdm(source_audio_list)):
if source_i >= max_samples:
break
source_index, source_transcript = source_line.strip().split("\t")
source_path = osp.join(source_dir, f"{source_index}.wav")
for target_i, target_name in enumerate(target_audio_list):
target_path = osp.join(target_dir, target_name)
print(f"Processing {source_path} -> {target_path}")
if os.path.exists(osp.join(conversion_result_dir, source_index, f"{target_name}")):
# already converted, load the converted file
vc_wave_16k, _ = librosa.load(
osp.join(conversion_result_dir, source_index, f"{target_name}"), sr=16000
)
vc_wave_16k = torch.tensor(vc_wave_16k).unsqueeze(0)
ref_waves_16k, _ = librosa.load(target_path, sr=16000)
ref_waves_16k = torch.tensor(ref_waves_16k).unsqueeze(0)
else:
if baseline == "openvoice":
from baselines.openvoice import convert as openvoice_convert
ref_waves_16k, vc_wave_16k = openvoice_convert(source_path, target_path, "temp.wav")
elif baseline == "cosyvoice":
from baselines.cosyvoice import convert as cosyvoice_convert
ref_waves_16k, vc_wave_16k = cosyvoice_convert(source_path, target_path, "temp.wav")
else:
ref_waves_16k, vc_wave = convert(
source_path,
target_path,
model,
speechtokenizer_set,
bigvgan_model,
campplus_model,
to_mel,
mel_fn_args,
sr,
length_adjust,
diffusion_steps,
inference_cfg_rate,
)
vc_wave_16k = torchaudio.functional.resample(vc_wave, sr, 16000)
os.makedirs(osp.join(conversion_result_dir, source_index), exist_ok=True)
torchaudio.save(
osp.join(conversion_result_dir, source_index, f"{target_name}"),
vc_wave_16k.cpu(),
16000,
)
if args.xvector_extractor == "wavlm":
ref_inputs = wavlm_feature_extractor(
ref_waves_16k.squeeze(0).cpu(), padding=True, return_tensors="pt"
).to(device)
ref_embeddings = wavlm_model(**ref_inputs).embeddings
ref_embeddings = torch.nn.functional.normalize(ref_embeddings, dim=-1).cpu()
vc_inputs = wavlm_feature_extractor(
vc_wave_16k.squeeze(0).cpu(), padding=True, return_tensors="pt"
).to(device)
vc_embeddings = wavlm_model(**vc_inputs).embeddings
vc_embeddings = torch.nn.functional.normalize(vc_embeddings, dim=-1).cpu()
similarity = torch.nn.functional.cosine_similarity(
ref_embeddings, vc_embeddings, dim=-1
)
elif args.xvector_extractor == "resemblyzer":
ref_wav_resemblyzer = preprocess_wav(target_path)
vc_wav_resemblyzer = preprocess_wav(
osp.join(conversion_result_dir, source_index, f"{target_name}")
)
ref_embed = resemblyzer_encoder.embed_utterance(ref_wav_resemblyzer)
vc_embed = resemblyzer_encoder.embed_utterance(vc_wav_resemblyzer)
similarity = np.inner(ref_embed, vc_embed)
else:
raise ValueError(f"Unknown xvector extractor: {args.xvector_extractor}")
print(f"Similarity: {similarity}")
similarity_list.append(similarity)
# perform asr
vc_asr_inputs = asr_processor(
vc_wave_16k.squeeze(0).cpu(), return_tensors="pt", padding=True
).to(device)
vc_asr_logits = asr_model(**vc_asr_inputs).logits
predicted_ids = torch.argmax(vc_asr_logits, dim=-1)
vc_transcription = asr_processor.decode(predicted_ids[0])
# perform asr on source 16k
source_wav_16k = librosa.load(source_path, sr=16000)[0]
source_asr_inputs = asr_processor(
source_wav_16k, return_tensors="pt", padding=True
).to(device)
source_asr_logits = asr_model(**source_asr_inputs).logits
source_predicted_ids = torch.argmax(source_asr_logits, dim=-1)
source_transcription = asr_processor.decode(source_predicted_ids[0])
# convert transcriptions to all lower to calculate WER and CER
source_transcript = source_transcript.lower()
# remove punctuations in source_transcript
source_transcript = source_transcript.translate(str.maketrans("", "", string.punctuation))
source_transcription = source_transcription.lower()
vc_transcription = vc_transcription.lower()
# calculate WER and CER
gt_wer = jiwer.wer(source_transcript, source_transcription)
gt_cer = jiwer.cer(source_transcript, source_transcription)
vc_wer = jiwer.wer(source_transcript, vc_transcription)
vc_cer = jiwer.cer(source_transcript, vc_transcription)
print(f"GT WER: {gt_wer}, CER: {gt_cer}")
print(f"VC WER: {vc_wer}, CER: {vc_cer}")
gt_wer_list.append(gt_wer)
gt_cer_list.append(gt_cer)
vc_wer_list.append(vc_wer)
vc_cer_list.append(vc_cer)
# calculate dnsmos
sig, bak, ovr = calc_mos(mos_computer, vc_wave_16k.squeeze(0).cpu().numpy(), 16000)
dnsmos_list.append((sig, bak, ovr))
print(f"Average GT WER: {sum(gt_wer_list) / len(gt_wer_list)}")
print(f"Average GT CER: {sum(gt_cer_list) / len(gt_cer_list)}")
print(f"Average VC WER: {sum(vc_wer_list) / len(vc_wer_list)}")
print(f"Average VC CER: {sum(vc_cer_list) / len(vc_cer_list)}")
print(f"Average similarity: {sum(similarity_list) / len(similarity_list)}")
print(f"Average DNS MOS SIG: {sum([x[0] for x in dnsmos_list]) / len(dnsmos_list)}")
print(f"Average DNS MOS BAK: {sum([x[1] for x in dnsmos_list]) / len(dnsmos_list)}")
print(f"Average DNS MOS OVR: {sum([x[2] for x in dnsmos_list]) / len(dnsmos_list)}")
# save wer and cer result into this directory as a txt
with open(osp.join(conversion_result_dir, source_index, "result.txt"), 'w') as f:
f.write(f"GT WER: {sum(gt_wer_list[-len(target_audio_list):]) / len(target_audio_list)}\n")
f.write(f"GT CER: {sum(gt_cer_list[-len(target_audio_list):]) / len(target_audio_list)}\n")
f.write(f"VC WER: {sum(vc_wer_list[-len(target_audio_list):]) / len(target_audio_list)}\n")
f.write(f"VC CER: {sum(vc_cer_list[-len(target_audio_list):]) / len(target_audio_list)}\n")
f.write(f"Average similarity: {sum(similarity_list[-len(target_audio_list):]) / len(target_audio_list)}\n")
print(f"Average WER: {sum(gt_wer_list) / len(gt_wer_list)}")
print(f"Average CER: {sum(gt_cer_list) / len(gt_cer_list)}")
print(f"Average WER: {sum(vc_wer_list) / len(vc_wer_list)}")
print(f"Average CER: {sum(vc_cer_list) / len(vc_cer_list)}")
print(f"Average similarity: {sum(similarity_list) / len(similarity_list)}")
# save similarity list
with open(osp.join(conversion_result_dir, f"{args.xvector_extractor}_similarity.tsv"), "w") as f:
f.write("\n".join([str(s) for s in similarity_list]))
# save wer and cer result into this directory as a txt
with open(osp.join(conversion_result_dir, "result.txt"), 'w') as f:
f.write(f"GT WER: {sum(gt_wer_list) / len(gt_wer_list)}\n")
f.write(f"GT CER: {sum(gt_cer_list) / len(gt_cer_list)}\n")
f.write(f"VC WER: {sum(vc_wer_list) / len(vc_wer_list)}\n")
f.write(f"VC CER: {sum(vc_cer_list) / len(vc_cer_list)}\n")
print(f"Average DNS MOS SIG: {sum([x[0] for x in dnsmos_list]) / len(dnsmos_list)}")
print(f"Average DNS MOS BAK: {sum([x[1] for x in dnsmos_list]) / len(dnsmos_list)}")
print(f"Average DNS MOS OVR: {sum([x[2] for x in dnsmos_list]) / len(dnsmos_list)}")
def convert(
source_path,
target_path,
model,
speechtokenizer_set,
bigvgan_model,
campplus_model,
to_mel,
mel_fn_args,
sr,
length_adjust,
diffusion_steps,
inference_cfg_rate,
):
source_audio = librosa.load(source_path, sr=sr)[0]
ref_audio = librosa.load(target_path, sr=sr)[0]
# decoded_wav = encodec_model.decoder(encodec_latent)
# torchaudio.save("test.wav", decoded_wav.cpu().squeeze(0), 24000)
# crop only the first 30 seconds
source_audio = torch.tensor(source_audio).unsqueeze(0).float().to(device)
ref_audio = torch.tensor(ref_audio).unsqueeze(0).float().to(device)
if source_audio.size(1) + ref_audio.size(1) > 30 * sr:
print(f"reference audio clipped from {ref_audio.size(1)/sr} seconds to {30 * sr - source_audio.size(1)} seconds")
ref_audio = ref_audio[:, :30 * sr - source_audio.size(1)]
source_waves_16k = torchaudio.functional.resample(source_audio, sr, 16000)
ref_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
converted_waves_24k = torchaudio.functional.resample(source_audio, sr, 24000)
wave_lengths_24k = torch.LongTensor([converted_waves_24k.size(1)]).to(
converted_waves_24k.device
)
waves_input = converted_waves_24k.unsqueeze(1)
if speechtokenizer_set[0] == 'facodec':
codec_encoder = speechtokenizer_set[1]
z = codec_encoder.encoder(waves_input)
(quantized, codes) = codec_encoder.quantizer(z, waves_input)
S_alt = torch.cat([codes[1], codes[0]], dim=1)
# S_ori should be extracted in the same way
waves_24k = torchaudio.functional.resample(ref_audio, sr, 24000)
waves_input = waves_24k.unsqueeze(1)
z = codec_encoder.encoder(waves_input)
(quantized, codes) = codec_encoder.quantizer(z, waves_input)
S_ori = torch.cat([codes[1], codes[0]], dim=1)
elif speechtokenizer_set[0] == 'whisper':
whisper_model = speechtokenizer_set[1]
whisper_feature_extractor = speechtokenizer_set[2]
converted_waves_16k = torchaudio.functional.resample(source_audio, sr, 16000)
alt_inputs = whisper_feature_extractor([converted_waves_16k.squeeze(0).cpu().numpy()],
return_tensors="pt",
return_attention_mask=True, )
alt_input_features = whisper_model._mask_input_features(
alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
with torch.no_grad():
alt_outputs = whisper_model.encoder(
alt_input_features.to(whisper_model.encoder.dtype),
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
S_alt = alt_outputs.last_hidden_state.to(torch.float32)
S_alt = S_alt[:, :converted_waves_16k.size(-1) // 320 + 1]
ori_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
ori_inputs = whisper_feature_extractor([ori_waves_16k.squeeze(0).cpu().numpy()],
return_tensors="pt",
return_attention_mask=True)
ori_input_features = whisper_model._mask_input_features(
ori_inputs.input_features, attention_mask=ori_inputs.attention_mask).to(device)
with torch.no_grad():
ori_outputs = whisper_model.encoder(
ori_input_features.to(whisper_model.encoder.dtype),
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
S_ori = ori_outputs.last_hidden_state.to(torch.float32)
S_ori = S_ori[:, :ori_waves_16k.size(-1) // 320 + 1]
else:
raise ValueError(f"Unsupported speech tokenizer type: {speechtokenizer_set[0]}")
mel = to_mel(source_audio.to(device).float())
mel2 = to_mel(ref_audio.to(device).float())
target_lengths = torch.LongTensor([int(mel.size(2) * length_adjust)]).to(mel.device)
target2_lengths = torch.LongTensor([mel2.size(2)]).to(mel2.device)
feat2 = torchaudio.compliance.kaldi.fbank(
ref_waves_16k, num_mel_bins=80, dither=0, sample_frequency=16000
)
feat2 = feat2 - feat2.mean(dim=0, keepdim=True)
style2 = campplus_model(feat2.unsqueeze(0))
# Length regulation
cond = model.length_regulator(
S_alt, ylens=target_lengths, n_quantizers=3, f0=None
)[0]
prompt_condition = model.length_regulator(
S_ori, ylens=target2_lengths, n_quantizers=3, f0=None
)[0]
cat_condition = torch.cat([prompt_condition, cond], dim=1)
vc_target = model.cfm.inference(
cat_condition,
torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
mel2,
style2,
None,
diffusion_steps,
inference_cfg_rate=inference_cfg_rate,
)
vc_target = vc_target[:, :, mel2.size(-1) :]
# Convert to waveform
vc_wave = bigvgan_model(vc_target).squeeze(1)
return ref_waves_16k, vc_wave
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--source", type=str, default="./examples/libritts-test-clean/"
)
parser.add_argument("--target", type=str, default="./examples/reference/")
parser.add_argument("--output", type=str, default="./examples/eval/converted/")
parser.add_argument("--diffusion-steps", type=int, default=30)
parser.add_argument("--length-adjust", type=float, default=1.0)
parser.add_argument("--inference-cfg-rate", type=float, default=0.7)
parser.add_argument(
"--xvector-extractor", type=str, default="resemblyzer"
) # wavlm or resemblyzer
parser.add_argument("--baseline", type=str, default="") # use "" for Seed-VC
parser.add_argument("--max-samples", type=int, default=20)
args = parser.parse_args()
main(args)
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