From 13e81a64a7ad28edfd3e0c9cfbb183e6830931bc Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 12:11:40 +0900
Subject: [PATCH 01/20] chore(deps): update lockfile

---
 poetry.lock | 406 +++++++++++++++++++---------------------------------
 1 file changed, 144 insertions(+), 262 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 4c169e59..b8b0cd9f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -183,6 +183,21 @@ toolz = "*"
 [package.extras]
 dev = ["black", "docutils", "flake8", "ipython", "m2r", "mistune (<2.0.0)", "pytest", "recommonmark", "sphinx", "vega-datasets"]
 
+[[package]]
+name = "aniso8601"
+version = "9.0.1"
+description = "A library for parsing ISO 8601 strings."
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "aniso8601-9.0.1-py2.py3-none-any.whl", hash = "sha256:1d2b7ef82963909e93c4f24ce48d4de9e66009a21bf1c1e1c85bdd0812fe412f"},
+    {file = "aniso8601-9.0.1.tar.gz", hash = "sha256:72e3117667eedf66951bb2d93f4296a56b94b078a8a95905a052611fb3f1b973"},
+]
+
+[package.extras]
+dev = ["black", "coverage", "isort", "pre-commit", "pyenchant", "pylint"]
+
 [[package]]
 name = "ansicon"
 version = "1.89.0"
@@ -301,14 +316,14 @@ pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.1"
+version = "4.12.2"
 description = "Screen-scraping library"
 category = "main"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.12.1-py3-none-any.whl", hash = "sha256:e44795bb4f156d94abb5fbc56efff871c1045bfef72e9efe77558db9f9616ac3"},
-    {file = "beautifulsoup4-4.12.1.tar.gz", hash = "sha256:c7bdbfb20a0dbe09518b96a809d93351b2e2bcb8046c0809466fa6632a10c257"},
+    {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"},
+    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
 ]
 
 [package.dependencies]
@@ -846,14 +861,14 @@ toml = ["tomli"]
 
 [[package]]
 name = "croniter"
-version = "1.3.8"
+version = "1.3.10"
 description = "croniter provides iteration for datetime object with cron like format"
 category = "main"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
-    {file = "croniter-1.3.8-py2.py3-none-any.whl", hash = "sha256:d6ed8386d5f4bbb29419dc1b65c4909c04a2322bd15ec0dc5b2877bfa1b75c7a"},
-    {file = "croniter-1.3.8.tar.gz", hash = "sha256:32a5ec04e97ec0837bcdf013767abd2e71cceeefd3c2e14c804098ce51ad6cd9"},
+    {file = "croniter-1.3.10-py2.py3-none-any.whl", hash = "sha256:362bbba72cbd3d3ffa34218414762c81e48001ba6bf7f32164b6e22aa597cbb3"},
+    {file = "croniter-1.3.10.tar.gz", hash = "sha256:bb9550b4761509b813d5397aaf4c15b800b29d002500e0c5f29e20d87558aae1"},
 ]
 
 [package.dependencies]
@@ -1088,34 +1103,35 @@ tqdm = "*"
 
 [[package]]
 name = "fastapi"
-version = "0.88.0"
+version = "0.1.17"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.6"
 files = [
-    {file = "fastapi-0.88.0-py3-none-any.whl", hash = "sha256:263b718bb384422fe3d042ffc9a0c8dece5e034ab6586ff034f6b4b1667c3eee"},
-    {file = "fastapi-0.88.0.tar.gz", hash = "sha256:915bf304180a0e7c5605ec81097b7d4cd8826ff87a02bb198e336fb9f3b5ff02"},
+    {file = "fastapi-0.1.17-py3-none-any.whl", hash = "sha256:a6aaad2f60684477480ac9d7a1c95e67f4696a722f184db467494bfdd5b8f29d"},
+    {file = "fastapi-0.1.17.tar.gz", hash = "sha256:a9a9b6cc32c38bab27a6549b94c44a30c70b485bc789d03de3aa8725f3394be5"},
 ]
 
 [package.dependencies]
-email-validator = {version = ">=1.1.1", optional = true, markers = "extra == \"all\""}
-httpx = {version = ">=0.23.0", optional = true, markers = "extra == \"all\""}
-itsdangerous = {version = ">=1.1.0", optional = true, markers = "extra == \"all\""}
-jinja2 = {version = ">=2.11.2", optional = true, markers = "extra == \"all\""}
-orjson = {version = ">=3.2.1", optional = true, markers = "extra == \"all\""}
-pydantic = ">=1.6.2,<1.7 || >1.7,<1.7.1 || >1.7.1,<1.7.2 || >1.7.2,<1.7.3 || >1.7.3,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0"
-python-multipart = {version = ">=0.0.5", optional = true, markers = "extra == \"all\""}
-pyyaml = {version = ">=5.3.1", optional = true, markers = "extra == \"all\""}
-starlette = "0.22.0"
-ujson = {version = ">=4.0.1,<4.0.2 || >4.0.2,<4.1.0 || >4.1.0,<4.2.0 || >4.2.0,<4.3.0 || >4.3.0,<5.0.0 || >5.0.0,<5.1.0 || >5.1.0", optional = true, markers = "extra == \"all\""}
-uvicorn = {version = ">=0.12.0", extras = ["standard"], optional = true, markers = "extra == \"all\""}
+aiofiles = {version = "*", optional = true, markers = "extra == \"all\""}
+email_validator = {version = "*", optional = true, markers = "extra == \"all\""}
+graphene = {version = "*", optional = true, markers = "extra == \"all\""}
+itsdangerous = {version = "*", optional = true, markers = "extra == \"all\""}
+jinja2 = {version = "*", optional = true, markers = "extra == \"all\""}
+pydantic = ">=0.17"
+python-multipart = {version = "*", optional = true, markers = "extra == \"all\""}
+pyyaml = {version = "*", optional = true, markers = "extra == \"all\""}
+requests = {version = "*", optional = true, markers = "extra == \"all\""}
+starlette = ">=0.9.7"
+ujson = {version = "*", optional = true, markers = "extra == \"all\""}
+uvicorn = {version = "*", optional = true, markers = "extra == \"all\""}
 
 [package.extras]
-all = ["email-validator (>=1.1.1)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "python-multipart (>=0.0.5)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
-dev = ["pre-commit (>=2.17.0,<3.0.0)", "ruff (==0.0.138)", "uvicorn[standard] (>=0.12.0,<0.19.0)"]
-doc = ["mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-markdownextradata-plugin (>=0.1.7,<0.3.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pyyaml (>=5.3.1,<7.0.0)", "typer[all] (>=0.6.1,<0.7.0)"]
-test = ["anyio[trio] (>=3.2.1,<4.0.0)", "black (==22.10.0)", "coverage[toml] (>=6.5.0,<7.0)", "databases[sqlite] (>=0.3.2,<0.7.0)", "email-validator (>=1.1.1,<2.0.0)", "flask (>=1.1.2,<3.0.0)", "httpx (>=0.23.0,<0.24.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.982)", "orjson (>=3.2.1,<4.0.0)", "passlib[bcrypt] (>=1.7.2,<2.0.0)", "peewee (>=3.13.3,<4.0.0)", "pytest (>=7.1.3,<8.0.0)", "python-jose[cryptography] (>=3.3.0,<4.0.0)", "python-multipart (>=0.0.5,<0.0.6)", "pyyaml (>=5.3.1,<7.0.0)", "ruff (==0.0.138)", "sqlalchemy (>=1.3.18,<=1.4.41)", "types-orjson (==3.6.2)", "types-ujson (==5.5.0)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0,<6.0.0)"]
+all = ["aiofiles", "email_validator", "graphene", "itsdangerous", "jinja2", "python-multipart", "pyyaml", "requests", "ujson", "ujson", "uvicorn"]
+dev = ["passlib[bcrypt]", "pyjwt"]
+doc = ["markdown-include", "mkdocs", "mkdocs-material"]
+test = ["black", "email_validator", "isort", "mypy", "pytest (>=4.0.0)", "pytest-cov", "requests"]
 
 [[package]]
 name = "ffmpy"
@@ -1295,14 +1311,14 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.3.0"
+version = "2023.4.0"
 description = "File-system specification"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2023.3.0-py3-none-any.whl", hash = "sha256:bf57215e19dbfa4fe7edae53040cc1deef825e3b1605cca9a8d2c2fadd2328a0"},
-    {file = "fsspec-2023.3.0.tar.gz", hash = "sha256:24e635549a590d74c6c18274ddd3ffab4753341753e923408b1904eaabafe04d"},
+    {file = "fsspec-2023.4.0-py3-none-any.whl", hash = "sha256:f398de9b49b14e9d84d2c2d11b7b67121bc072fe97b930c4e5668ac3917d8307"},
+    {file = "fsspec-2023.4.0.tar.gz", hash = "sha256:bf064186cd8808f0b2f6517273339ba0a0c8fb1b7048991c28bc67f58b8b67cd"},
 ]
 
 [package.dependencies]
@@ -1314,7 +1330,9 @@ abfs = ["adlfs"]
 adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
+devel = ["pytest", "pytest-cov"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
 gcs = ["gcsfs"]
 git = ["pygit2"]
@@ -1413,6 +1431,54 @@ typing-extensions = "*"
 uvicorn = "*"
 websockets = ">=10.0"
 
+[[package]]
+name = "graphene"
+version = "3.2.2"
+description = "GraphQL Framework for Python"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "graphene-3.2.2-py2.py3-none-any.whl", hash = "sha256:753de13948cbf42e32cc87fb533167c88907066eb984251fdbb006c0aab8da00"},
+    {file = "graphene-3.2.2.tar.gz", hash = "sha256:5b03e72770dc901f40be55784058d6bb1d952a49eb819a4a085962d5e1cf5fcf"},
+]
+
+[package.dependencies]
+aniso8601 = ">=8,<10"
+graphql-core = ">=3.1,<3.3"
+graphql-relay = ">=3.1,<3.3"
+
+[package.extras]
+dev = ["black (==22.3.0)", "coveralls (>=3.3,<4)", "flake8 (>=4,<5)", "iso8601 (>=1,<2)", "mock (>=4,<5)", "pytest (>=6,<7)", "pytest-asyncio (>=0.16,<2)", "pytest-benchmark (>=3.4,<4)", "pytest-cov (>=3,<4)", "pytest-mock (>=3,<4)", "pytz (==2022.1)", "snapshottest (>=0.6,<1)"]
+test = ["coveralls (>=3.3,<4)", "iso8601 (>=1,<2)", "mock (>=4,<5)", "pytest (>=6,<7)", "pytest-asyncio (>=0.16,<2)", "pytest-benchmark (>=3.4,<4)", "pytest-cov (>=3,<4)", "pytest-mock (>=3,<4)", "pytz (==2022.1)", "snapshottest (>=0.6,<1)"]
+
+[[package]]
+name = "graphql-core"
+version = "3.2.3"
+description = "GraphQL implementation for Python, a port of GraphQL.js, the JavaScript reference implementation for GraphQL."
+category = "main"
+optional = false
+python-versions = ">=3.6,<4"
+files = [
+    {file = "graphql-core-3.2.3.tar.gz", hash = "sha256:06d2aad0ac723e35b1cb47885d3e5c45e956a53bc1b209a9fc5369007fe46676"},
+    {file = "graphql_core-3.2.3-py3-none-any.whl", hash = "sha256:5766780452bd5ec8ba133f8bf287dc92713e3868ddd83aee4faab9fc3e303dc3"},
+]
+
+[[package]]
+name = "graphql-relay"
+version = "3.2.0"
+description = "Relay library for graphql-core"
+category = "main"
+optional = false
+python-versions = ">=3.6,<4"
+files = [
+    {file = "graphql-relay-3.2.0.tar.gz", hash = "sha256:1ff1c51298356e481a0be009ccdff249832ce53f30559c1338f22a0e0d17250c"},
+    {file = "graphql_relay-3.2.0-py3-none-any.whl", hash = "sha256:c9b22bd28b170ba1fe674c74384a8ff30a76c8e26f88ac3aa1584dd3179953e5"},
+]
+
+[package.dependencies]
+graphql-core = ">=3.2,<3.3"
+
 [[package]]
 name = "grpcio"
 version = "1.53.0"
@@ -1505,60 +1571,6 @@ sniffio = ">=1.0.0,<2.0.0"
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (>=1.0.0,<2.0.0)"]
 
-[[package]]
-name = "httptools"
-version = "0.5.0"
-description = "A collection of framework independent HTTP protocol utils."
-category = "main"
-optional = false
-python-versions = ">=3.5.0"
-files = [
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-
 [[package]]
 name = "httpx"
 version = "0.23.3"
@@ -1673,14 +1685,14 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "6.1.0"
+version = "6.2.0"
 description = "Read metadata from Python packages"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -3744,18 +3756,17 @@ files = [
 
 [[package]]
 name = "pytest"
-version = "7.2.2"
+version = "7.3.0"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 iniconfig = "*"
@@ -3764,7 +3775,7 @@ pluggy = ">=0.12,<2.0"
 tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"
@@ -3800,21 +3811,6 @@ files = [
 [package.dependencies]
 six = ">=1.5"
 
-[[package]]
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-version = "1.0.0"
-description = "Read key-value pairs from a .env file and set them as environment variables"
-category = "main"
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-
 [[package]]
 name = "python-editor"
 version = "1.0.4"
@@ -4249,58 +4245,6 @@ tabulate = ">=0.8.9"
 ja = ["ipadic (>=1.0,<2.0)", "mecab-python3 (==1.0.5)"]
 ko = ["mecab-ko (==1.0.0)", "mecab-ko-dic (>=1.0,<2.0)"]
 
-[[package]]
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-description = "Image processing in Python"
-category = "main"
-optional = false
-python-versions = ">=3.7"
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-
-[package.extras]
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-
 [[package]]
 name = "scikit-image"
 version = "0.20.0"
@@ -4340,7 +4284,10 @@ numpy = ">=1.21.1"
 packaging = ">=20.0"
 pillow = ">=9.0.1"
 PyWavelets = ">=1.1.1"
-scipy = {version = ">=1.8", markers = "python_version > \"3.9\""}
+scipy = [
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+    {version = ">=1.8", markers = "python_version > \"3.9\""},
+]
 tifffile = ">=2019.7.26"
 
 [package.extras]
@@ -4414,6 +4361,42 @@ resampy = ">=0.2"
 scikit-image = ">=0.17"
 scipy = ">=1.5"
 
+[[package]]
+name = "scipy"
+version = "1.9.1"
+description = "SciPy: Scientific Library for Python"
+category = "main"
+optional = false
+python-versions = ">=3.8,<3.12"
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+
+[package.dependencies]
+numpy = ">=1.18.5,<1.25.0"
+
 [[package]]
 name = "scipy"
 version = "1.10.1"
@@ -4538,7 +4521,6 @@ files = [
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-
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-    {file = "watchfiles-0.19.0-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:79c533ff593db861ae23436541f481ec896ee3da4e5db8962429b441bbaae16e"},
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-    {file = "watchfiles-0.19.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:20b44221764955b1e703f012c74015306fb7e79a00c15370785f309b1ed9aa8d"},
-    {file = "watchfiles-0.19.0.tar.gz", hash = "sha256:d9b073073e048081e502b6c6b0b88714c026a1a4c890569238d04aca5f9ca74b"},
-]
-
-[package.dependencies]
-anyio = ">=3.0.0"
-
 [[package]]
 name = "wcwidth"
 version = "0.2.6"

From 96f9ea1e12bf6de18528f23f5d99a691eaeb1a9e Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 12:46:23 +0900
Subject: [PATCH 02/20] fix(train): do not train d

---
 src/so_vits_svc_fork/train.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/src/so_vits_svc_fork/train.py b/src/so_vits_svc_fork/train.py
index 2209de6b..7ccf8540 100644
--- a/src/so_vits_svc_fork/train.py
+++ b/src/so_vits_svc_fork/train.py
@@ -8,7 +8,7 @@
 
 import lightning.pytorch as pl
 import torch
-from lightning.pytorch.accelerators import TPUAccelerator
+from lightning.pytorch.accelerators import IPUAccelerator, TPUAccelerator
 from lightning.pytorch.loggers import TensorBoardLogger
 from torch.cuda.amp import autocast
 from torch.nn import functional as F
@@ -195,6 +195,8 @@ def __init__(self, reset_optimizer: bool = False, **hparams: Any):
         )
 
     def configure_optimizers(self):
+        if isinstance(self.trainer.accelerator, IPUAccelerator):
+            return self.optim_g, self.optim_d
         return [self.optim_g, self.optim_d], [self.scheduler_g, self.scheduler_d]
 
     def log_image_dict(
@@ -225,7 +227,10 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
         self.net_d.train()
 
         # get optims
-        optim_g, optim_d = self.optimizers()
+        if isinstance(self.trainer.accelerator, IPUAccelerator):
+            optim_g = self.optimizers()
+        else:
+            optim_g, optim_d = self.optimizers()
 
         # Generator
         # train
@@ -320,6 +325,8 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
         optim_g.zero_grad()
         self.untoggle_optimizer(optim_g)
 
+        if isinstance(self.trainer.accelerator, IPUAccelerator):
+            return
         # Discriminator
         # train
         self.toggle_optimizer(optim_d)

From e4f90799380033ae325d30734f578ef51bd5b19d Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 12:51:07 +0900
Subject: [PATCH 03/20] fix(train): fix last commit

---
 src/so_vits_svc_fork/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/so_vits_svc_fork/train.py b/src/so_vits_svc_fork/train.py
index 7ccf8540..b8efae2e 100644
--- a/src/so_vits_svc_fork/train.py
+++ b/src/so_vits_svc_fork/train.py
@@ -196,7 +196,7 @@ def __init__(self, reset_optimizer: bool = False, **hparams: Any):
 
     def configure_optimizers(self):
         if isinstance(self.trainer.accelerator, IPUAccelerator):
-            return self.optim_g, self.optim_d
+            return [self.optim_g], [self.scheduler_g]
         return [self.optim_g, self.optim_d], [self.scheduler_g, self.scheduler_d]
 
     def log_image_dict(

From 3dd74937949330144b2e7f891e4dab50ce073f51 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 12:56:10 +0900
Subject: [PATCH 04/20] fix(train): fix beta types

---
 src/so_vits_svc_fork/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/so_vits_svc_fork/train.py b/src/so_vits_svc_fork/train.py
index b8efae2e..7cb167a9 100644
--- a/src/so_vits_svc_fork/train.py
+++ b/src/so_vits_svc_fork/train.py
@@ -178,13 +178,13 @@ def __init__(self, reset_optimizer: bool = False, **hparams: Any):
         self.optim_g = torch.optim.AdamW(
             self.net_g.parameters(),
             self.hparams.train.learning_rate,
-            betas=self.hparams.train.betas,
+            betas=tuple(self.hparams.train.betas),
             eps=self.hparams.train.eps,
         )
         self.optim_d = torch.optim.AdamW(
             self.net_d.parameters(),
             self.hparams.train.learning_rate,
-            betas=self.hparams.train.betas,
+            betas=tuple(self.hparams.train.betas),
             eps=self.hparams.train.eps,
         )
         self.scheduler_g = torch.optim.lr_scheduler.ExponentialLR(

From f050adc47fb5d4d605be8ba35c0b433a5b746301 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 12:59:18 +0900
Subject: [PATCH 05/20] fix: fix normalize_f0

---
 src/so_vits_svc_fork/f0.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/so_vits_svc_fork/f0.py b/src/so_vits_svc_fork/f0.py
index 990f2050..85f397aa 100644
--- a/src/so_vits_svc_fork/f0.py
+++ b/src/so_vits_svc_fork/f0.py
@@ -29,8 +29,6 @@ def normalize_f0(
         factor = torch.ones(f0.shape[0], 1).to(f0.device)
     # normalize f0 based on means and factor
     f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
-    if torch.isnan(f0_norm).any():
-        exit(0)
     return f0_norm * x_mask
 
 

From fcdbc8823c33500b59cf6fb069ecacc9352f2d83 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 14:41:40 +0900
Subject: [PATCH 06/20] fix(f0): fix f0

---
 src/so_vits_svc_fork/f0.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/so_vits_svc_fork/f0.py b/src/so_vits_svc_fork/f0.py
index 85f397aa..eabfc529 100644
--- a/src/so_vits_svc_fork/f0.py
+++ b/src/so_vits_svc_fork/f0.py
@@ -216,12 +216,12 @@ def compute_f0(
 def f0_to_coarse(f0: torch.Tensor | float):
     is_torch = isinstance(f0, torch.Tensor)
     f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
-    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (
-        f0_mel_max - f0_mel_min
-    ) + 1
+    # f0_mel[f0_mel > 0] = ...
+    f0_mel = (f0_mel - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
 
-    f0_mel[f0_mel <= 1] = 1
-    f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
+    # f0_mel[f0_mel <= 1] = 1
+    # f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
+    f0_mel = torch.clamp(f0_mel, 1, f0_bin - 1)
     f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
     assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
         f0_coarse.max(),

From 8473833c5b1444128f76e3f3b6d64d4662b81a9f Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 14:43:54 +0900
Subject: [PATCH 07/20] fix(f0): fix f0

---
 src/so_vits_svc_fork/f0.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/so_vits_svc_fork/f0.py b/src/so_vits_svc_fork/f0.py
index eabfc529..c104d4f5 100644
--- a/src/so_vits_svc_fork/f0.py
+++ b/src/so_vits_svc_fork/f0.py
@@ -223,10 +223,10 @@ def f0_to_coarse(f0: torch.Tensor | float):
     # f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
     f0_mel = torch.clamp(f0_mel, 1, f0_bin - 1)
     f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
-    assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
-        f0_coarse.max(),
-        f0_coarse.min(),
-    )
+    # assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
+    #     f0_coarse.max(),
+    #     f0_coarse.min(),
+    # )
     return f0_coarse
 
 

From f80491f7e9e1e7b4b575362aa58463121aff13d2 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 14:50:49 +0900
Subject: [PATCH 08/20] fix: fix slice_pitch_segments

---
 src/so_vits_svc_fork/modules/commons.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/so_vits_svc_fork/modules/commons.py b/src/so_vits_svc_fork/modules/commons.py
index 16b3cfbe..85e72c70 100644
--- a/src/so_vits_svc_fork/modules/commons.py
+++ b/src/so_vits_svc_fork/modules/commons.py
@@ -5,12 +5,12 @@
 
 
 def slice_pitch_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :segment_size])
+    """ret = torch.zeros_like(x[:, :segment_size])
     for i in range(x.size(0)):
         idx_str = ids_str[i]
         idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]
-    return ret
+        ret[i] = x[i, idx_str:idx_end]"""
+    return x[:, ids_str : ids_str + segment_size]
 
 
 def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):

From e83bb85bd9b814e1c381859b104c1538dccfdbd2 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 14:55:13 +0900
Subject: [PATCH 09/20] fix(commons): fix more

---
 src/so_vits_svc_fork/modules/commons.py | 17 +++++++----------
 1 file changed, 7 insertions(+), 10 deletions(-)

diff --git a/src/so_vits_svc_fork/modules/commons.py b/src/so_vits_svc_fork/modules/commons.py
index 85e72c70..b5b56fd6 100644
--- a/src/so_vits_svc_fork/modules/commons.py
+++ b/src/so_vits_svc_fork/modules/commons.py
@@ -34,10 +34,12 @@ def get_padding(kernel_size, dilation=1):
     return int((kernel_size * dilation - dilation) / 2)
 
 
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
+def convert_pad_shape(pad_shape) -> torch.Tensor:
+    # l = pad_shape[::-1]
+    # pad_shape = [item for sublist in l for item in sublist]
+    # return pad_shape
+    # more efficient
+    return torch.tensor(pad_shape[::-1]).view(-1)
 
 
 def intersperse(lst, item):
@@ -67,12 +69,7 @@ def rand_gumbel_like(x):
 
 
 def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
+    return x[:, :, ids_str : ids_str + segment_size]
 
 
 def rand_slice_segments(x, x_lengths=None, segment_size=4):

From a685de13a2fe59da4de6c126dc42f8a8b30efa68 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 14:56:33 +0900
Subject: [PATCH 10/20] fix: revert convert_pad_shape change

---
 src/so_vits_svc_fork/modules/commons.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/so_vits_svc_fork/modules/commons.py b/src/so_vits_svc_fork/modules/commons.py
index b5b56fd6..b962ac10 100644
--- a/src/so_vits_svc_fork/modules/commons.py
+++ b/src/so_vits_svc_fork/modules/commons.py
@@ -34,12 +34,12 @@ def get_padding(kernel_size, dilation=1):
     return int((kernel_size * dilation - dilation) / 2)
 
 
-def convert_pad_shape(pad_shape) -> torch.Tensor:
-    # l = pad_shape[::-1]
-    # pad_shape = [item for sublist in l for item in sublist]
-    # return pad_shape
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
     # more efficient
-    return torch.tensor(pad_shape[::-1]).view(-1)
+    # return torch.tensor(pad_shape[::-1]).view(-1)
 
 
 def intersperse(lst, item):

From bb7992ad54c1a96e0a43a840cf35aa2fca7254c2 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 15:01:53 +0900
Subject: [PATCH 11/20] fix(commons): fix

---
 src/so_vits_svc_fork/modules/commons.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/src/so_vits_svc_fork/modules/commons.py b/src/so_vits_svc_fork/modules/commons.py
index b962ac10..a764bd35 100644
--- a/src/so_vits_svc_fork/modules/commons.py
+++ b/src/so_vits_svc_fork/modules/commons.py
@@ -10,7 +10,10 @@ def slice_pitch_segments(x, ids_str, segment_size=4):
         idx_str = ids_str[i]
         idx_end = idx_str + segment_size
         ret[i] = x[i, idx_str:idx_end]"""
-    return x[:, ids_str : ids_str + segment_size]
+    idx_str = ids_str.unsqueeze(1)
+    idx_end = idx_str + segment_size
+    ret = x.gather(1, torch.arange(idx_str, idx_end).unsqueeze(2)).squeeze(2)
+    return ret
 
 
 def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
@@ -19,7 +22,7 @@ def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
         x_lengths = t
     ids_str_max = x_lengths - segment_size + 1
     ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
+    ret = slice_pitch_segments(x, ids_str, segment_size)
     ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
     return ret, ret_pitch, ids_str
 

From b8924418f237cc94cb761c993ce483fa43bf1a13 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 16:23:46 +0900
Subject: [PATCH 12/20] fix: update slice_segments (it was hard...)

---
 .idea/workspace.xml                          |  21 +--
 src/so_vits_svc_fork/modules/commons.py      | 166 +++++--------------
 src/so_vits_svc_fork/modules/synthesizers.py |  46 ++++-
 src/so_vits_svc_fork/train.py                |   4 +-
 4 files changed, 86 insertions(+), 151 deletions(-)

diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 73a840f7..f5be289f 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -2,23 +2,9 @@
 <project version="4">
   <component name="ChangeListManager">
     <list default="true" id="7aacf003-229b-4eb0-80a4-ff105dc3c3d4" name="変更" comment="">
-      <change beforePath="$PROJECT_DIR$/src/so_vits_svc_fork/__main__.py" beforeDir="false" afterPath="$PROJECT_DIR$/src/so_vits_svc_fork/__main__.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/src/so_vits_svc_fork/gui.py" beforeDir="false" afterPath="$PROJECT_DIR$/src/so_vits_svc_fork/gui.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/onnx/__init__.py" beforeDir="false" afterPath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/onnx/__init__.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/onnx/model_onnx.py" beforeDir="false" afterPath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/onnx/_model.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/onnx/onnx_export.py" beforeDir="false" afterPath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/onnx/_export.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/1.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/10.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/2.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/3.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/4.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/5.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/6.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/7.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/8.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/9.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/dataset_raw/34j/nested/あ.wav" beforeDir="false" />
-      <change beforePath="$PROJECT_DIR$/tests/test_main.py" beforeDir="false" afterPath="$PROJECT_DIR$/tests/test_main.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/commons.py" beforeDir="false" afterPath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/commons.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/synthesizers.py" beforeDir="false" afterPath="$PROJECT_DIR$/src/so_vits_svc_fork/modules/synthesizers.py" afterDir="false" />
     </list>
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
@@ -129,6 +115,7 @@
       <workItem from="1680174456649" duration="1005000" />
       <workItem from="1680251014707" duration="2800000" />
       <workItem from="1680319074742" duration="3292000" />
+      <workItem from="1681022452038" duration="301000" />
     </task>
     <servers />
   </component>
diff --git a/src/so_vits_svc_fork/modules/commons.py b/src/so_vits_svc_fork/modules/commons.py
index a764bd35..b9ef83c7 100644
--- a/src/so_vits_svc_fork/modules/commons.py
+++ b/src/so_vits_svc_fork/modules/commons.py
@@ -1,30 +1,49 @@
-import math
-
 import torch
-from torch.nn import functional as F
+from torch import Tensor
+
+
+def slice_segments(x: Tensor, starts: Tensor, length: int) -> Tensor:
+    x_slice = torch.zeros((x.size()[:-1] + (length,)), dtype=x.dtype, device=x.device)
+    ends = starts + length
+    for i, (start, end) in enumerate(zip(starts, ends)):
+        x_slice[i, ...] = x[i, ..., start:end]
+    return x_slice
 
 
-def slice_pitch_segments(x, ids_str, segment_size=4):
-    """ret = torch.zeros_like(x[:, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]"""
-    idx_str = ids_str.unsqueeze(1)
-    idx_end = idx_str + segment_size
-    ret = x.gather(1, torch.arange(idx_str, idx_end).unsqueeze(2)).squeeze(2)
-    return ret
+def slice_2d_segments(x: Tensor, starts: Tensor, length: int) -> Tensor:
+    batch_size, num_features, seq_len = x.shape
+    ends = starts + length
+    idxs = (
+        torch.arange(seq_len)
+        .unsqueeze(0)
+        .unsqueeze(1)
+        .repeat(batch_size, num_features, 1)
+    )
+    mask = (idxs >= starts.unsqueeze(-1).unsqueeze(-1)) & (
+        idxs < ends.unsqueeze(-1).unsqueeze(-1)
+    )
+    return x[mask].reshape(batch_size, num_features, length)
 
 
-def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_pitch_segments(x, ids_str, segment_size)
-    ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
-    return ret, ret_pitch, ids_str
+def slice_1d_segments(x: Tensor, starts: Tensor, length: int) -> Tensor:
+    batch_size, seq_len = x.shape
+    ends = starts + length
+    idxs = torch.arange(seq_len).unsqueeze(0).repeat(batch_size, 1)
+    mask = (idxs >= starts.unsqueeze(-1)) & (idxs < ends.unsqueeze(-1))
+    return x[mask].reshape(batch_size, length)
+
+
+def _slice_segments_v3(x: Tensor, starts: Tensor, length: int) -> Tensor:
+    shape = x.shape[:-1] + (length,)
+    ends = starts + length
+    idxs = torch.arange(x.shape[-1], device=x.device).unsqueeze(0).unsqueeze(0)
+    unsqueeze_dims = len(shape) - len(
+        x.shape
+    )  # calculate number of dimensions to unsqueeze
+    starts = starts.reshape(starts.shape + (1,) * unsqueeze_dims)
+    ends = ends.reshape(ends.shape + (1,) * unsqueeze_dims)
+    mask = (idxs >= starts) & (idxs < ends)
+    return x[mask].reshape(shape)
 
 
 def init_weights(m, mean=0.0, std=0.01):
@@ -41,86 +60,6 @@ def convert_pad_shape(pad_shape):
     l = pad_shape[::-1]
     pad_shape = [item for sublist in l for item in sublist]
     return pad_shape
-    # more efficient
-    # return torch.tensor(pad_shape[::-1]).view(-1)
-
-
-def intersperse(lst, item):
-    result = [item] * (len(lst) * 2 + 1)
-    result[1::2] = lst
-    return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    return x[:, :, ids_str : ids_str + segment_size]
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def rand_spec_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
 
 
 def subsequent_mask(length):
@@ -138,11 +77,6 @@ def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
     return acts
 
 
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
 def sequence_mask(length, max_length=None):
     if max_length is None:
         max_length = length.max()
@@ -150,24 +84,6 @@ def sequence_mask(length, max_length=None):
     return x.unsqueeze(0) < length.unsqueeze(1)
 
 
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-    duration.device
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
 def clip_grad_value_(parameters, clip_value, norm_type=2):
     if isinstance(parameters, torch.Tensor):
         parameters = [parameters]
diff --git a/src/so_vits_svc_fork/modules/synthesizers.py b/src/so_vits_svc_fork/modules/synthesizers.py
index c96e021b..def09dee 100644
--- a/src/so_vits_svc_fork/modules/synthesizers.py
+++ b/src/so_vits_svc_fork/modules/synthesizers.py
@@ -163,7 +163,26 @@ def __init__(
         )
         self.emb_uv = nn.Embedding(2, hidden_channels)
 
-    def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None):
+    def forward(
+        self,
+        c: torch.Tensor,
+        f0: torch.Tensor,
+        uv: torch.Tensor,
+        spec: torch.Tensor,
+        g: torch.Tensor,
+        c_lengths: torch.Tensor,
+        spec_lengths: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        B: batch size
+        c: content, (B, ssl_dim, T)
+        f0: f0, (B, T)
+        uv: uv, (B, T)
+        spec: spectrogram, (B, F, T)
+        g: speaker id, (B,)
+        c_lengths: content length, (B,)
+        spec_lengths: spectrogram length, (B,)
+        """
         g = self.emb_g(g).transpose(1, 2)
         # ssl prenet
         x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(
@@ -179,24 +198,29 @@ def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None):
         # encoder
         z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0))
         z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
+        # z: [batch, dim, time]
 
         # flow
         z_p = self.flow(z, spec_mask, g=g)
-        z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(
-            z, f0, spec_lengths, self.segment_size
-        )
+
+        # randomly slice to time = self.segment_size
+        slice_starts = (
+            torch.rand(z.size(0)) * (spec_lengths - self.segment_size)
+        ).long()
+        z_slice = commons.slice_2d_segments(z_p, slice_starts, self.segment_size)
+        f0_slice = commons.slice_1d_segments(f0, slice_starts, self.segment_size)
 
         # MB-iSTFT-VITS
         if self.mb:
             o, o_mb = self.dec(z_slice, g=g)
         # HiFi-GAN
         else:
-            o = self.dec(z_slice, g=g, f0=pitch_slice)
+            o = self.dec(z_slice, g=g, f0=f0_slice)
             o_mb = None
         return (
             o,
             o_mb,
-            ids_slice,
+            slice_starts,
             spec_mask,
             (z, z_p, m_p, logs_p, m_q, logs_q),
             pred_lf0,
@@ -204,7 +228,15 @@ def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None):
             lf0,
         )
 
-    def infer(self, c, f0, uv, g=None, noice_scale=0.35, predict_f0=False):
+    def infer(
+        self,
+        c: torch.Tensor,
+        f0: torch.Tensor,
+        uv: torch.Tensor,
+        g: torch.Tensor,
+        noice_scale: float = 0.35,
+        predict_f0: bool = False,
+    ) -> torch.Tensor:
         c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
         g = self.emb_g(g).transpose(1, 2)
         x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(
diff --git a/src/so_vits_svc_fork/train.py b/src/so_vits_svc_fork/train.py
index 7cb167a9..b9f8ace6 100644
--- a/src/so_vits_svc_fork/train.py
+++ b/src/so_vits_svc_fork/train.py
@@ -246,13 +246,13 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
             norm_lf0,
             lf0,
         ) = self.net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths)
-        y_mel = commons.slice_segments(
+        y_mel = commons.slice_2d_segments(
             mel,
             ids_slice,
             self.hparams.train.segment_size // self.hparams.data.hop_length,
         )
         y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1), self.hparams)
-        y = commons.slice_segments(
+        y = commons.slice_2d_segments(
             y,
             ids_slice * self.hparams.data.hop_length,
             self.hparams.train.segment_size,

From 2fdf01d9d2f8af608d4bb4ea5e3125c39c31d410 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 16:32:17 +0900
Subject: [PATCH 13/20] fix: do not use slice_segments in ipu

---
 src/so_vits_svc_fork/modules/synthesizers.py | 19 +++++++---
 src/so_vits_svc_fork/train.py                | 39 ++++++++++++++------
 2 files changed, 42 insertions(+), 16 deletions(-)

diff --git a/src/so_vits_svc_fork/modules/synthesizers.py b/src/so_vits_svc_fork/modules/synthesizers.py
index def09dee..fbf24eed 100644
--- a/src/so_vits_svc_fork/modules/synthesizers.py
+++ b/src/so_vits_svc_fork/modules/synthesizers.py
@@ -51,6 +51,7 @@ def __init__(
         gen_istft_n_fft: int = 16,
         gen_istft_hop_size: int = 4,
         subbands: int = 4,
+        ipu: bool = False,
         **kwargs: Any,
     ):
         super().__init__()
@@ -162,6 +163,7 @@ def __init__(
             spk_channels=gin_channels,
         )
         self.emb_uv = nn.Embedding(2, hidden_channels)
+        self.ipu = ipu
 
     def forward(
         self,
@@ -204,11 +206,18 @@ def forward(
         z_p = self.flow(z, spec_mask, g=g)
 
         # randomly slice to time = self.segment_size
-        slice_starts = (
-            torch.rand(z.size(0)) * (spec_lengths - self.segment_size)
-        ).long()
-        z_slice = commons.slice_2d_segments(z_p, slice_starts, self.segment_size)
-        f0_slice = commons.slice_1d_segments(f0, slice_starts, self.segment_size)
+        if not self.ipu:
+            slice_starts = (
+                torch.rand(z.size(0)) * (spec_lengths - self.segment_size)
+            ).long()
+            z_slice = commons.slice_2d_segments(z, slice_starts, self.segment_size)
+            f0_slice = commons.slice_1d_segments(f0, slice_starts, self.segment_size)
+        else:
+            slice_starts = torch.randint(
+                0, spec_lengths - self.segment_size, (1,)
+            ).item()
+            z_slice = z[..., slice_starts : slice_starts + self.segment_size]
+            f0_slice = f0[..., slice_starts : slice_starts + self.segment_size]
 
         # MB-iSTFT-VITS
         if self.mb:
diff --git a/src/so_vits_svc_fork/train.py b/src/so_vits_svc_fork/train.py
index b9f8ace6..299ad15d 100644
--- a/src/so_vits_svc_fork/train.py
+++ b/src/so_vits_svc_fork/train.py
@@ -171,6 +171,7 @@ def __init__(self, reset_optimizer: bool = False, **hparams: Any):
         self.net_g = SynthesizerTrn(
             self.hparams.data.filter_length // 2 + 1,
             self.hparams.train.segment_size // self.hparams.data.hop_length,
+            ipu=True,
             **self.hparams.model,
         )
         self.net_d = MultiPeriodDiscriminator(self.hparams.model.use_spectral_norm)
@@ -227,7 +228,8 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
         self.net_d.train()
 
         # get optims
-        if isinstance(self.trainer.accelerator, IPUAccelerator):
+        self.is_ipu = isinstance(self.trainer.accelerator, IPUAccelerator)
+        if self.is_ipu:
             optim_g = self.optimizers()
         else:
             optim_g, optim_d = self.optimizers()
@@ -246,17 +248,32 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
             norm_lf0,
             lf0,
         ) = self.net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths)
-        y_mel = commons.slice_2d_segments(
-            mel,
-            ids_slice,
-            self.hparams.train.segment_size // self.hparams.data.hop_length,
-        )
+
         y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1), self.hparams)
-        y = commons.slice_2d_segments(
-            y,
-            ids_slice * self.hparams.data.hop_length,
-            self.hparams.train.segment_size,
-        )
+        if self.is_ipu:
+            y_mel = mel[
+                ...,
+                ids_slice : self.hparams.train.segment_size
+                // self.hparams.data.hop_length
+                + ids_slice,
+            ]
+            y = y[
+                ...,
+                ids_slice
+                * self.hparams.data.hop_length : self.hparams.train.segment_size
+                + ids_slice * self.hparams.data.hop_length,
+            ]
+        else:
+            y_mel = commons.slice_2d_segments(
+                mel,
+                ids_slice,
+                self.hparams.train.segment_size // self.hparams.data.hop_length,
+            )
+            y = commons.slice_2d_segments(
+                y,
+                ids_slice * self.hparams.data.hop_length,
+                self.hparams.train.segment_size,
+            )
 
         # generator loss
         LOG.debug("Calculating generator loss")

From ae7d5cfcd12336bc6fd5b65cc83aaf515dee8421 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 16:34:19 +0900
Subject: [PATCH 14/20] fix: fix

---
 src/so_vits_svc_fork/modules/synthesizers.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/so_vits_svc_fork/modules/synthesizers.py b/src/so_vits_svc_fork/modules/synthesizers.py
index fbf24eed..732fbb20 100644
--- a/src/so_vits_svc_fork/modules/synthesizers.py
+++ b/src/so_vits_svc_fork/modules/synthesizers.py
@@ -1,3 +1,4 @@
+import random
 import warnings
 from logging import getLogger
 from typing import Any, Literal, Sequence
@@ -213,9 +214,7 @@ def forward(
             z_slice = commons.slice_2d_segments(z, slice_starts, self.segment_size)
             f0_slice = commons.slice_1d_segments(f0, slice_starts, self.segment_size)
         else:
-            slice_starts = torch.randint(
-                0, spec_lengths - self.segment_size, (1,)
-            ).item()
+            slice_starts = random.randint(0, spec.size(2) - self.segment_size)
             z_slice = z[..., slice_starts : slice_starts + self.segment_size]
             f0_slice = f0[..., slice_starts : slice_starts + self.segment_size]
 

From 26e800c27919147a2ede636efee864c5ca8995e2 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 16:36:51 +0900
Subject: [PATCH 15/20] fix: fuck mel

---
 src/so_vits_svc_fork/modules/mel_processing.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/src/so_vits_svc_fork/modules/mel_processing.py b/src/so_vits_svc_fork/modules/mel_processing.py
index 298acd86..6b325a86 100644
--- a/src/so_vits_svc_fork/modules/mel_processing.py
+++ b/src/so_vits_svc_fork/modules/mel_processing.py
@@ -1,4 +1,4 @@
-"""from logging import getLogger
+from logging import getLogger
 
 import torch
 import torch.utils.data
@@ -42,7 +42,8 @@ def mel_spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor:
         power=1.0,
         window_fn=torch.hann_window,
         normalized=False,
-    ).to(audio.device)(audio)"""
+    ).to(audio.device)(audio)
+
 
 from logging import getLogger
 
@@ -87,7 +88,7 @@ def spectral_de_normalize_torch(magnitudes):
 hann_window = {}
 
 
-def spectrogram_torch(y, hps, center=False):
+def spectrogram_torch_old(y, hps, center=False):
     if torch.min(y) < -1.0:
         LOG.info("min value is ", torch.min(y))
     if torch.max(y) > 1.0:
@@ -127,7 +128,7 @@ def spectrogram_torch(y, hps, center=False):
     return spec
 
 
-def spec_to_mel_torch(spec, hps):
+def spec_to_mel_torch_old(spec, hps):
     sampling_rate = hps.data.sampling_rate
     n_fft = hps.data.filter_length
     num_mels = hps.data.n_mel_channels
@@ -148,7 +149,7 @@ def spec_to_mel_torch(spec, hps):
     return spec
 
 
-def mel_spectrogram_torch(y, hps, center=False):
+def mel_spectrogram_torch_old(y, hps, center=False):
     sampling_rate = hps.data.sampling_rate
     n_fft = hps.data.filter_length
     num_mels = hps.data.n_mel_channels

From 55b498949dbcfd16ac16fce5dc4d097354a7f9dd Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 16:39:00 +0900
Subject: [PATCH 16/20] Revert "fix: fuck mel"

This reverts commit 26e800c27919147a2ede636efee864c5ca8995e2.
---
 src/so_vits_svc_fork/modules/mel_processing.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/src/so_vits_svc_fork/modules/mel_processing.py b/src/so_vits_svc_fork/modules/mel_processing.py
index 6b325a86..298acd86 100644
--- a/src/so_vits_svc_fork/modules/mel_processing.py
+++ b/src/so_vits_svc_fork/modules/mel_processing.py
@@ -1,4 +1,4 @@
-from logging import getLogger
+"""from logging import getLogger
 
 import torch
 import torch.utils.data
@@ -42,8 +42,7 @@ def mel_spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor:
         power=1.0,
         window_fn=torch.hann_window,
         normalized=False,
-    ).to(audio.device)(audio)
-
+    ).to(audio.device)(audio)"""
 
 from logging import getLogger
 
@@ -88,7 +87,7 @@ def spectral_de_normalize_torch(magnitudes):
 hann_window = {}
 
 
-def spectrogram_torch_old(y, hps, center=False):
+def spectrogram_torch(y, hps, center=False):
     if torch.min(y) < -1.0:
         LOG.info("min value is ", torch.min(y))
     if torch.max(y) > 1.0:
@@ -128,7 +127,7 @@ def spectrogram_torch_old(y, hps, center=False):
     return spec
 
 
-def spec_to_mel_torch_old(spec, hps):
+def spec_to_mel_torch(spec, hps):
     sampling_rate = hps.data.sampling_rate
     n_fft = hps.data.filter_length
     num_mels = hps.data.n_mel_channels
@@ -149,7 +148,7 @@ def spec_to_mel_torch_old(spec, hps):
     return spec
 
 
-def mel_spectrogram_torch_old(y, hps, center=False):
+def mel_spectrogram_torch(y, hps, center=False):
     sampling_rate = hps.data.sampling_rate
     n_fft = hps.data.filter_length
     num_mels = hps.data.n_mel_channels

From 388c02242322f227fda647bddc2b9e12310e3011 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 16:39:58 +0900
Subject: [PATCH 17/20] fix: mel on cpu

---
 src/so_vits_svc_fork/train.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/so_vits_svc_fork/train.py b/src/so_vits_svc_fork/train.py
index 299ad15d..f7c2d6e3 100644
--- a/src/so_vits_svc_fork/train.py
+++ b/src/so_vits_svc_fork/train.py
@@ -249,7 +249,9 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
             lf0,
         ) = self.net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths)
 
-        y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1), self.hparams)
+        y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1).cpu(), self.hparams).to(
+            self.device
+        )
         if self.is_ipu:
             y_mel = mel[
                 ...,

From 58d37332b6ef615e18c61abc7be54dba4fa5e055 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 16:42:26 +0900
Subject: [PATCH 18/20] fix: detach

---
 src/so_vits_svc_fork/train.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/so_vits_svc_fork/train.py b/src/so_vits_svc_fork/train.py
index f7c2d6e3..b5d30aee 100644
--- a/src/so_vits_svc_fork/train.py
+++ b/src/so_vits_svc_fork/train.py
@@ -249,9 +249,9 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
             lf0,
         ) = self.net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths)
 
-        y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1).cpu(), self.hparams).to(
-            self.device
-        )
+        y_hat_mel = mel_spectrogram_torch(
+            y_hat.squeeze(1).detach().cpu(), self.hparams
+        ).to(self.device)
         if self.is_ipu:
             y_mel = mel[
                 ...,
@@ -344,7 +344,7 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
         optim_g.zero_grad()
         self.untoggle_optimizer(optim_g)
 
-        if isinstance(self.trainer.accelerator, IPUAccelerator):
+        if self.is_ipu:
             return
         # Discriminator
         # train

From 7db599991cf6bcaa7e291039f2a6c0caf361cd9f Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 17:13:37 +0900
Subject: [PATCH 19/20] Revert "Revert "fix: fuck mel""

This reverts commit 55b498949dbcfd16ac16fce5dc4d097354a7f9dd.
---
 src/so_vits_svc_fork/modules/mel_processing.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/src/so_vits_svc_fork/modules/mel_processing.py b/src/so_vits_svc_fork/modules/mel_processing.py
index 298acd86..6b325a86 100644
--- a/src/so_vits_svc_fork/modules/mel_processing.py
+++ b/src/so_vits_svc_fork/modules/mel_processing.py
@@ -1,4 +1,4 @@
-"""from logging import getLogger
+from logging import getLogger
 
 import torch
 import torch.utils.data
@@ -42,7 +42,8 @@ def mel_spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor:
         power=1.0,
         window_fn=torch.hann_window,
         normalized=False,
-    ).to(audio.device)(audio)"""
+    ).to(audio.device)(audio)
+
 
 from logging import getLogger
 
@@ -87,7 +88,7 @@ def spectral_de_normalize_torch(magnitudes):
 hann_window = {}
 
 
-def spectrogram_torch(y, hps, center=False):
+def spectrogram_torch_old(y, hps, center=False):
     if torch.min(y) < -1.0:
         LOG.info("min value is ", torch.min(y))
     if torch.max(y) > 1.0:
@@ -127,7 +128,7 @@ def spectrogram_torch(y, hps, center=False):
     return spec
 
 
-def spec_to_mel_torch(spec, hps):
+def spec_to_mel_torch_old(spec, hps):
     sampling_rate = hps.data.sampling_rate
     n_fft = hps.data.filter_length
     num_mels = hps.data.n_mel_channels
@@ -148,7 +149,7 @@ def spec_to_mel_torch(spec, hps):
     return spec
 
 
-def mel_spectrogram_torch(y, hps, center=False):
+def mel_spectrogram_torch_old(y, hps, center=False):
     sampling_rate = hps.data.sampling_rate
     n_fft = hps.data.filter_length
     num_mels = hps.data.n_mel_channels

From d343cb15a53e2d594ceba297610cf6890edd49e1 Mon Sep 17 00:00:00 2001
From: 34j <55338215+34j@users.noreply.github.com>
Date: Sun, 9 Apr 2023 17:19:16 +0900
Subject: [PATCH 20/20] fix(train): patch melscale

---
 src/so_vits_svc_fork/train.py | 90 +++++++++++++++++++++++++++++++++--
 1 file changed, 87 insertions(+), 3 deletions(-)

diff --git a/src/so_vits_svc_fork/train.py b/src/so_vits_svc_fork/train.py
index b5d30aee..195dbff8 100644
--- a/src/so_vits_svc_fork/train.py
+++ b/src/so_vits_svc_fork/train.py
@@ -120,6 +120,92 @@ def stft(
 
             torch.stft = stft
 
+        if isinstance(self.trainer.accelerator, IPUAccelerator):
+            # patch mel_scale_fbanks
+            LOG.warning(
+                "Using IPU. Patching torchaudio.functional.mel_scale_fbanks not to use max()"
+            )
+
+            import torchaudio.functional
+            from torchaudio.functional.functional import (
+                _create_triangular_filterbank,
+                _hz_to_mel,
+                _mel_to_hz,
+            )
+
+            def melscale_fbanks(
+                n_freqs: int,
+                f_min: float,
+                f_max: float,
+                n_mels: int,
+                sample_rate: int,
+                norm: str | None = None,
+                mel_scale: str = "htk",
+            ) -> torch.Tensor:
+                r"""Create a frequency bin conversion matrix.
+
+                .. devices:: CPU
+
+                .. properties:: TorchScript
+
+                Note:
+                    For the sake of the numerical compatibility with librosa, not all the coefficients
+                    in the resulting filter bank has magnitude of 1.
+
+                    .. image:: https://download.pytorch.org/torchaudio/doc-assets/mel_fbanks.png
+                    :alt: Visualization of generated filter bank
+
+                Args:
+                    n_freqs (int): Number of frequencies to highlight/apply
+                    f_min (float): Minimum frequency (Hz)
+                    f_max (float): Maximum frequency (Hz)
+                    n_mels (int): Number of mel filterbanks
+                    sample_rate (int): Sample rate of the audio waveform
+                    norm (str or None, optional): If "slaney", divide the triangular mel weights by the width of the mel band
+                        (area normalization). (Default: ``None``)
+                    mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
+
+                Returns:
+                    Tensor: Triangular filter banks (fb matrix) of size (``n_freqs``, ``n_mels``)
+                    meaning number of frequencies to highlight/apply to x the number of filterbanks.
+                    Each column is a filterbank so that assuming there is a matrix A of
+                    size (..., ``n_freqs``), the applied result would be
+                    ``A * melscale_fbanks(A.size(-1), ...)``.
+
+                """
+
+                if norm is not None and norm != "slaney":
+                    raise ValueError('norm must be one of None or "slaney"')
+
+                # freq bins
+                all_freqs = torch.linspace(0, sample_rate // 2, n_freqs)
+
+                # calculate mel freq bins
+                m_min = _hz_to_mel(f_min, mel_scale=mel_scale)
+                m_max = _hz_to_mel(f_max, mel_scale=mel_scale)
+
+                m_pts = torch.linspace(m_min, m_max, n_mels + 2)
+                f_pts = _mel_to_hz(m_pts, mel_scale=mel_scale)
+
+                # create filterbank
+                fb = _create_triangular_filterbank(all_freqs, f_pts)
+
+                if norm is not None and norm == "slaney":
+                    # Slaney-style mel is scaled to be approx constant energy per channel
+                    enorm = 2.0 / (f_pts[2 : n_mels + 2] - f_pts[:n_mels])
+                    fb *= enorm.unsqueeze(0)
+
+                # if (fb.max(dim=0).values == 0.0).any():
+                #    warnings.warn(
+                #        "At least one mel filterbank has all zero values. "
+                #        f"The value for `n_mels` ({n_mels}) may be set too high. "
+                #        f"Or, the value for `n_freqs` ({n_freqs}) may be set too low."
+                #    )
+
+                return fb
+
+            torchaudio.functional.melscale_fbanks = melscale_fbanks
+
     def set_current_epoch(self, epoch: int):
         LOG.info(f"Setting current epoch to {epoch}")
         self.trainer.fit_loop.epoch_progress.current.completed = epoch
@@ -249,9 +335,7 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None:
             lf0,
         ) = self.net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths)
 
-        y_hat_mel = mel_spectrogram_torch(
-            y_hat.squeeze(1).detach().cpu(), self.hparams
-        ).to(self.device)
+        y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1), self.hparams)
         if self.is_ipu:
             y_mel = mel[
                 ...,