Merge pull request #1 from eginhard/lint-overhaul

Lint overhaul (pylint to ruff)

.cardboardlint.yml

@@ -1,5 +0,0 @@
-linters:
-- pylint:
-    # pylintrc: pylintrc
-    filefilter: ['- test_*.py', '+ *.py', '- *.npy']
-    # exclude:

.github/workflows/style_check.yml

@@ -23,18 +23,7 @@ jobs:
           architecture: x64
           cache: 'pip'
           cache-dependency-path: 'requirements*'
-      - name: check OS
+      - name: Install/upgrade dev dependencies
-        run: cat /etc/os-release
+        run: python3 -m pip install -r requirements.dev.txt
-      - name: Install dependencies
+      - name: Lint check
-        run: |
+        run: make lint
-          sudo apt-get update
-          sudo apt-get install -y git make gcc
-          make system-deps
-      - name: Install/upgrade Python setup deps
-        run: python3 -m pip install --upgrade pip setuptools wheel
-      - name: Install TTS
-        run: |
-          python3 -m pip install .[all]
-          python3 setup.py egg_info
-      - name: Style check
-        run: make style

.pre-commit-config.yaml

@@ -1,27 +1,19 @@
 repos:
-  - repo: 'https://github.com/pre-commit/pre-commit-hooks'
+  - repo: "https://github.com/pre-commit/pre-commit-hooks"
-    rev: v2.3.0
+    rev: v4.5.0
     hooks:
       - id: check-yaml
-      - id: end-of-file-fixer
+      # TODO: enable these later; there are plenty of violating
-      - id: trailing-whitespace
+      #       files that need to be fixed first
-  - repo: 'https://github.com/psf/black'
+      # - id: end-of-file-fixer
-    rev: 22.3.0
+      # - id: trailing-whitespace
+  - repo: "https://github.com/psf/black"
+    rev: 23.12.0
     hooks:
       - id: black
         language_version: python3
-  - repo: https://github.com/pycqa/isort
+  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 5.8.0
+    rev: v0.3.0
     hooks:
-      - id: isort
+      - id: ruff
-        name: isort (python)
+        args: [--fix, --exit-non-zero-on-fix]
-      - id: isort
-        name: isort (cython)
-        types: [cython]
-      - id: isort
-        name: isort (pyi)
-        types: [pyi]
-  - repo: https://github.com/pycqa/pylint
-    rev: v2.8.2
-    hooks:
-    -   id: pylint

.pylintrc

@@ -1,599 +0,0 @@
-[MASTER]
-
-# A comma-separated list of package or module names from where C extensions may
-# be loaded. Extensions are loading into the active Python interpreter and may
-# run arbitrary code.
-extension-pkg-whitelist=
-
-# Add files or directories to the blacklist. They should be base names, not
-# paths.
-ignore=CVS
-
-# Add files or directories matching the regex patterns to the blacklist. The
-# regex matches against base names, not paths.
-ignore-patterns=
-
-# Python code to execute, usually for sys.path manipulation such as
-# pygtk.require().
-#init-hook=
-
-# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
-# number of processors available to use.
-jobs=1
-
-# Control the amount of potential inferred values when inferring a single
-# object. This can help the performance when dealing with large functions or
-# complex, nested conditions.
-limit-inference-results=100
-
-# List of plugins (as comma separated values of python modules names) to load,
-# usually to register additional checkers.
-load-plugins=
-
-# Pickle collected data for later comparisons.
-persistent=yes
-
-# Specify a configuration file.
-#rcfile=
-
-# When enabled, pylint would attempt to guess common misconfiguration and emit
-# user-friendly hints instead of false-positive error messages.
-suggestion-mode=yes
-
-# Allow loading of arbitrary C extensions. Extensions are imported into the
-# active Python interpreter and may run arbitrary code.
-unsafe-load-any-extension=no
-
-
-[MESSAGES CONTROL]
-
-# Only show warnings with the listed confidence levels. Leave empty to show
-# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
-confidence=
-
-# Disable the message, report, category or checker with the given id(s). You
-# can either give multiple identifiers separated by comma (,) or put this
-# option multiple times (only on the command line, not in the configuration
-# file where it should appear only once). You can also use "--disable=all" to
-# disable everything first and then reenable specific checks. For example, if
-# you want to run only the similarities checker, you can use "--disable=all
-# --enable=similarities". If you want to run only the classes checker, but have
-# no Warning level messages displayed, use "--disable=all --enable=classes
-# --disable=W".
-disable=missing-docstring,
-        too-many-public-methods,
-        too-many-lines,
-        bare-except,
-        ## for avoiding weird p3.6 CI linter error
-        ## TODO: see later if we can remove this
-        assigning-non-slot,
-        unsupported-assignment-operation,
-        ## end
-        line-too-long,
-        fixme,
-        wrong-import-order,
-        ungrouped-imports,
-        wrong-import-position,
-        import-error,
-        invalid-name,
-        too-many-instance-attributes,
-        arguments-differ,
-        arguments-renamed,
-        no-name-in-module,
-        no-member,
-        unsubscriptable-object,
-        print-statement,
-        parameter-unpacking,
-        unpacking-in-except,
-        old-raise-syntax,
-        backtick,
-        long-suffix,
-        old-ne-operator,
-        old-octal-literal,
-        import-star-module-level,
-        non-ascii-bytes-literal,
-        raw-checker-failed,
-        bad-inline-option,
-        locally-disabled,
-        file-ignored,
-        suppressed-message,
-        useless-suppression,
-        deprecated-pragma,
-        use-symbolic-message-instead,
-        useless-object-inheritance,
-        too-few-public-methods,
-        too-many-branches,
-        too-many-arguments,
-        too-many-locals,
-        too-many-statements,
-        apply-builtin,
-        basestring-builtin,
-        buffer-builtin,
-        cmp-builtin,
-        coerce-builtin,
-        execfile-builtin,
-        file-builtin,
-        long-builtin,
-        raw_input-builtin,
-        reduce-builtin,
-        standarderror-builtin,
-        unicode-builtin,
-        xrange-builtin,
-        coerce-method,
-        delslice-method,
-        getslice-method,
-        setslice-method,
-        no-absolute-import,
-        old-division,
-        dict-iter-method,
-        dict-view-method,
-        next-method-called,
-        metaclass-assignment,
-        indexing-exception,
-        raising-string,
-        reload-builtin,
-        oct-method,
-        hex-method,
-        nonzero-method,
-        cmp-method,
-        input-builtin,
-        round-builtin,
-        intern-builtin,
-        unichr-builtin,
-        map-builtin-not-iterating,
-        zip-builtin-not-iterating,
-        range-builtin-not-iterating,
-        filter-builtin-not-iterating,
-        using-cmp-argument,
-        eq-without-hash,
-        div-method,
-        idiv-method,
-        rdiv-method,
-        exception-message-attribute,
-        invalid-str-codec,
-        sys-max-int,
-        bad-python3-import,
-        deprecated-string-function,
-        deprecated-str-translate-call,
-        deprecated-itertools-function,
-        deprecated-types-field,
-        next-method-defined,
-        dict-items-not-iterating,
-        dict-keys-not-iterating,
-        dict-values-not-iterating,
-        deprecated-operator-function,
-        deprecated-urllib-function,
-        xreadlines-attribute,
-        deprecated-sys-function,
-        exception-escape,
-        comprehension-escape,
-        duplicate-code,
-        not-callable,
-        import-outside-toplevel,
-        logging-fstring-interpolation,
-        logging-not-lazy
-
-# Enable the message, report, category or checker with the given id(s). You can
-# either give multiple identifier separated by comma (,) or put this option
-# multiple time (only on the command line, not in the configuration file where
-# it should appear only once). See also the "--disable" option for examples.
-enable=c-extension-no-member
-
-
-[REPORTS]
-
-# Python expression which should return a note less than 10 (10 is the highest
-# note). You have access to the variables errors warning, statement which
-# respectively contain the number of errors / warnings messages and the total
-# number of statements analyzed. This is used by the global evaluation report
-# (RP0004).
-evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
-
-# Template used to display messages. This is a python new-style format string
-# used to format the message information. See doc for all details.
-#msg-template=
-
-# Set the output format. Available formats are text, parseable, colorized, json
-# and msvs (visual studio). You can also give a reporter class, e.g.
-# mypackage.mymodule.MyReporterClass.
-output-format=text
-
-# Tells whether to display a full report or only the messages.
-reports=no
-
-# Activate the evaluation score.
-score=yes
-
-
-[REFACTORING]
-
-# Maximum number of nested blocks for function / method body
-max-nested-blocks=5
-
-# Complete name of functions that never returns. When checking for
-# inconsistent-return-statements if a never returning function is called then
-# it will be considered as an explicit return statement and no message will be
-# printed.
-never-returning-functions=sys.exit
-
-
-[LOGGING]
-
-# Format style used to check logging format string. `old` means using %
-# formatting, while `new` is for `{}` formatting.
-logging-format-style=old
-
-# Logging modules to check that the string format arguments are in logging
-# function parameter format.
-logging-modules=logging
-
-
-[SPELLING]
-
-# Limits count of emitted suggestions for spelling mistakes.
-max-spelling-suggestions=4
-
-# Spelling dictionary name. Available dictionaries: none. To make it working
-# install python-enchant package..
-spelling-dict=
-
-# List of comma separated words that should not be checked.
-spelling-ignore-words=
-
-# A path to a file that contains private dictionary; one word per line.
-spelling-private-dict-file=
-
-# Tells whether to store unknown words to indicated private dictionary in
-# --spelling-private-dict-file option instead of raising a message.
-spelling-store-unknown-words=no
-
-
-[MISCELLANEOUS]
-
-# List of note tags to take in consideration, separated by a comma.
-notes=FIXME,
-      XXX,
-      TODO
-
-
-[TYPECHECK]
-
-# List of decorators that produce context managers, such as
-# contextlib.contextmanager. Add to this list to register other decorators that
-# produce valid context managers.
-contextmanager-decorators=contextlib.contextmanager
-
-# List of members which are set dynamically and missed by pylint inference
-# system, and so shouldn't trigger E1101 when accessed. Python regular
-# expressions are accepted.
-generated-members=numpy.*,torch.*
-
-# Tells whether missing members accessed in mixin class should be ignored. A
-# mixin class is detected if its name ends with "mixin" (case insensitive).
-ignore-mixin-members=yes
-
-# Tells whether to warn about missing members when the owner of the attribute
-# is inferred to be None.
-ignore-none=yes
-
-# This flag controls whether pylint should warn about no-member and similar
-# checks whenever an opaque object is returned when inferring. The inference
-# can return multiple potential results while evaluating a Python object, but
-# some branches might not be evaluated, which results in partial inference. In
-# that case, it might be useful to still emit no-member and other checks for
-# the rest of the inferred objects.
-ignore-on-opaque-inference=yes
-
-# List of class names for which member attributes should not be checked (useful
-# for classes with dynamically set attributes). This supports the use of
-# qualified names.
-ignored-classes=optparse.Values,thread._local,_thread._local
-
-# List of module names for which member attributes should not be checked
-# (useful for modules/projects where namespaces are manipulated during runtime
-# and thus existing member attributes cannot be deduced by static analysis. It
-# supports qualified module names, as well as Unix pattern matching.
-ignored-modules=
-
-# Show a hint with possible names when a member name was not found. The aspect
-# of finding the hint is based on edit distance.
-missing-member-hint=yes
-
-# The minimum edit distance a name should have in order to be considered a
-# similar match for a missing member name.
-missing-member-hint-distance=1
-
-# The total number of similar names that should be taken in consideration when
-# showing a hint for a missing member.
-missing-member-max-choices=1
-
-
-[VARIABLES]
-
-# List of additional names supposed to be defined in builtins. Remember that
-# you should avoid defining new builtins when possible.
-additional-builtins=
-
-# Tells whether unused global variables should be treated as a violation.
-allow-global-unused-variables=yes
-
-# List of strings which can identify a callback function by name. A callback
-# name must start or end with one of those strings.
-callbacks=cb_,
-          _cb
-
-# A regular expression matching the name of dummy variables (i.e. expected to
-# not be used).
-dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
-
-# Argument names that match this expression will be ignored. Default to name
-# with leading underscore.
-ignored-argument-names=_.*|^ignored_|^unused_
-
-# Tells whether we should check for unused import in __init__ files.
-init-import=no
-
-# List of qualified module names which can have objects that can redefine
-# builtins.
-redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
-
-
-[FORMAT]
-
-# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
-expected-line-ending-format=
-
-# Regexp for a line that is allowed to be longer than the limit.
-ignore-long-lines=^\s*(# )?<?https?://\S+>?$
-
-# Number of spaces of indent required inside a hanging or continued line.
-indent-after-paren=4
-
-# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
-# tab).
-indent-string='    '
-
-# Maximum number of characters on a single line.
-max-line-length=120
-
-# Maximum number of lines in a module.
-max-module-lines=1000
-
-# List of optional constructs for which whitespace checking is disabled. `dict-
-# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
-# `trailing-comma` allows a space between comma and closing bracket: (a, ).
-# `empty-line` allows space-only lines.
-no-space-check=trailing-comma,
-               dict-separator
-
-# Allow the body of a class to be on the same line as the declaration if body
-# contains single statement.
-single-line-class-stmt=no
-
-# Allow the body of an if to be on the same line as the test if there is no
-# else.
-single-line-if-stmt=no
-
-
-[SIMILARITIES]
-
-# Ignore comments when computing similarities.
-ignore-comments=yes
-
-# Ignore docstrings when computing similarities.
-ignore-docstrings=yes
-
-# Ignore imports when computing similarities.
-ignore-imports=no
-
-# Minimum lines number of a similarity.
-min-similarity-lines=4
-
-
-[BASIC]
-
-# Naming style matching correct argument names.
-argument-naming-style=snake_case
-
-# Regular expression matching correct argument names. Overrides argument-
-# naming-style.
-argument-rgx=[a-z_][a-z0-9_]{0,30}$
-
-# Naming style matching correct attribute names.
-attr-naming-style=snake_case
-
-# Regular expression matching correct attribute names. Overrides attr-naming-
-# style.
-#attr-rgx=
-
-# Bad variable names which should always be refused, separated by a comma.
-bad-names=
-
-# Naming style matching correct class attribute names.
-class-attribute-naming-style=any
-
-# Regular expression matching correct class attribute names. Overrides class-
-# attribute-naming-style.
-#class-attribute-rgx=
-
-# Naming style matching correct class names.
-class-naming-style=PascalCase
-
-# Regular expression matching correct class names. Overrides class-naming-
-# style.
-#class-rgx=
-
-# Naming style matching correct constant names.
-const-naming-style=UPPER_CASE
-
-# Regular expression matching correct constant names. Overrides const-naming-
-# style.
-#const-rgx=
-
-# Minimum line length for functions/classes that require docstrings, shorter
-# ones are exempt.
-docstring-min-length=-1
-
-# Naming style matching correct function names.
-function-naming-style=snake_case
-
-# Regular expression matching correct function names. Overrides function-
-# naming-style.
-#function-rgx=
-
-# Good variable names which should always be accepted, separated by a comma.
-good-names=i,
-           j,
-           k,
-           x,
-           ex,
-           Run,
-           _
-
-# Include a hint for the correct naming format with invalid-name.
-include-naming-hint=no
-
-# Naming style matching correct inline iteration names.
-inlinevar-naming-style=any
-
-# Regular expression matching correct inline iteration names. Overrides
-# inlinevar-naming-style.
-#inlinevar-rgx=
-
-# Naming style matching correct method names.
-method-naming-style=snake_case
-
-# Regular expression matching correct method names. Overrides method-naming-
-# style.
-#method-rgx=
-
-# Naming style matching correct module names.
-module-naming-style=snake_case
-
-# Regular expression matching correct module names. Overrides module-naming-
-# style.
-#module-rgx=
-
-# Colon-delimited sets of names that determine each other's naming style when
-# the name regexes allow several styles.
-name-group=
-
-# Regular expression which should only match function or class names that do
-# not require a docstring.
-no-docstring-rgx=^_
-
-# List of decorators that produce properties, such as abc.abstractproperty. Add
-# to this list to register other decorators that produce valid properties.
-# These decorators are taken in consideration only for invalid-name.
-property-classes=abc.abstractproperty
-
-# Naming style matching correct variable names.
-variable-naming-style=snake_case
-
-# Regular expression matching correct variable names. Overrides variable-
-# naming-style.
-variable-rgx=[a-z_][a-z0-9_]{0,30}$
-
-
-[STRING]
-
-# This flag controls whether the implicit-str-concat-in-sequence should
-# generate a warning on implicit string concatenation in sequences defined over
-# several lines.
-check-str-concat-over-line-jumps=no
-
-
-[IMPORTS]
-
-# Allow wildcard imports from modules that define __all__.
-allow-wildcard-with-all=no
-
-# Analyse import fallback blocks. This can be used to support both Python 2 and
-# 3 compatible code, which means that the block might have code that exists
-# only in one or another interpreter, leading to false positives when analysed.
-analyse-fallback-blocks=no
-
-# Deprecated modules which should not be used, separated by a comma.
-deprecated-modules=optparse,tkinter.tix
-
-# Create a graph of external dependencies in the given file (report RP0402 must
-# not be disabled).
-ext-import-graph=
-
-# Create a graph of every (i.e. internal and external) dependencies in the
-# given file (report RP0402 must not be disabled).
-import-graph=
-
-# Create a graph of internal dependencies in the given file (report RP0402 must
-# not be disabled).
-int-import-graph=
-
-# Force import order to recognize a module as part of the standard
-# compatibility libraries.
-known-standard-library=
-
-# Force import order to recognize a module as part of a third party library.
-known-third-party=enchant
-
-
-[CLASSES]
-
-# List of method names used to declare (i.e. assign) instance attributes.
-defining-attr-methods=__init__,
-                      __new__,
-                      setUp
-
-# List of member names, which should be excluded from the protected access
-# warning.
-exclude-protected=_asdict,
-                  _fields,
-                  _replace,
-                  _source,
-                  _make
-
-# List of valid names for the first argument in a class method.
-valid-classmethod-first-arg=cls
-
-# List of valid names for the first argument in a metaclass class method.
-valid-metaclass-classmethod-first-arg=cls
-
-
-[DESIGN]
-
-# Maximum number of arguments for function / method.
-max-args=5
-
-# Maximum number of attributes for a class (see R0902).
-max-attributes=7
-
-# Maximum number of boolean expressions in an if statement.
-max-bool-expr=5
-
-# Maximum number of branch for function / method body.
-max-branches=12
-
-# Maximum number of locals for function / method body.
-max-locals=15
-
-# Maximum number of parents for a class (see R0901).
-max-parents=15
-
-# Maximum number of public methods for a class (see R0904).
-max-public-methods=20
-
-# Maximum number of return / yield for function / method body.
-max-returns=6
-
-# Maximum number of statements in function / method body.
-max-statements=50
-
-# Minimum number of public methods for a class (see R0903).
-min-public-methods=2
-
-
-[EXCEPTIONS]
-
-# Exceptions that will emit a warning when being caught. Defaults to
-# "BaseException, Exception".
-overgeneral-exceptions=BaseException,
-                       Exception

CONTRIBUTING.md

@@ -82,13 +82,13 @@ The following steps are tested on an Ubuntu system.
     $ make test_all  # run all the tests, report all the errors
     ```
 
-9. Format your code. We use ```black``` for code and ```isort``` for ```import``` formatting.
+9. Format your code. We use ```black``` for code formatting.
 
     ```bash
     $ make style
     ```
 
-10. Run the linter and correct the issues raised. We use ```pylint``` for linting.  It helps to enforce a coding standard, offers simple refactoring suggestions.
+10. Run the linter and correct the issues raised. We use ```ruff``` for linting.  It helps to enforce a coding standard, offers simple refactoring suggestions.
 
     ```bash
     $ make lint

Makefile

@@ -46,12 +46,10 @@ test_failed:  ## only run tests failed the last time.
 
 style:	## update code style.
 	black ${target_dirs}
-	isort ${target_dirs}
 
-lint:	## run pylint linter.
+lint:	## run linters.
-	pylint ${target_dirs}
+	ruff check ${target_dirs}
 	black ${target_dirs} --check
-	isort ${target_dirs} --check-only
 
 system-deps:	## install linux system deps
 	sudo apt-get install -y libsndfile1-dev

TTS/api.py

@@ -1,15 +1,13 @@
 import tempfile
 import warnings
 from pathlib import Path
-from typing import Union
 
-import numpy as np
 from torch import nn
 
+from TTS.config import load_config
 from TTS.utils.audio.numpy_transforms import save_wav
 from TTS.utils.manage import ModelManager
 from TTS.utils.synthesizer import Synthesizer
-from TTS.config import load_config
 
 
 class TTS(nn.Module):
@@ -168,9 +166,7 @@ class TTS(nn.Module):
         self.synthesizer = None
         self.model_name = model_name
 
-        model_path, config_path, vocoder_path, vocoder_config_path, model_dir = self.download_model_by_name(
+        model_path, config_path, vocoder_path, vocoder_config_path, model_dir = self.download_model_by_name(model_name)
-            model_name
-        )
 
         # init synthesizer
         # None values are fetch from the model
@@ -231,7 +227,7 @@ class TTS(nn.Module):
             raise ValueError("Model is not multi-speaker but `speaker` is provided.")
         if not self.is_multi_lingual and language is not None:
             raise ValueError("Model is not multi-lingual but `language` is provided.")
-        if not emotion is None and not speed is None:
+        if emotion is not None and speed is not None:
             raise ValueError("Emotion and speed can only be used with Coqui Studio models. Which is discontinued.")
 
     def tts(

TTS/bin/collect_env_info.py

@@ -1,4 +1,5 @@
 """Get detailed info about the working environment."""
+import json
 import os
 import platform
 import sys
@@ -6,11 +7,10 @@ import sys
 import numpy
 import torch
 
-sys.path += [os.path.abspath(".."), os.path.abspath(".")]
-import json
-
 import TTS
 
+sys.path += [os.path.abspath(".."), os.path.abspath(".")]
+
 
 def system_info():
     return {

TTS/bin/compute_attention_masks.py

@@ -70,7 +70,7 @@ Example run:
 
     # if the vocabulary was passed, replace the default
     if "characters" in C.keys():
-        symbols, phonemes = make_symbols(**C.characters)
+        symbols, phonemes = make_symbols(**C.characters)  # noqa: F811
 
     # load the model
     num_chars = len(phonemes) if C.use_phonemes else len(symbols)

TTS/bin/find_unique_phonemes.py

@@ -13,7 +13,7 @@ from TTS.tts.utils.text.phonemizers import Gruut
 def compute_phonemes(item):
     text = item["text"]
     ph = phonemizer.phonemize(text).replace("|", "")
-    return set(list(ph))
+    return set(ph)
 
 
 def main():

TTS/bin/synthesize.py

@@ -379,10 +379,8 @@ def main():
             if model_item["model_type"] == "tts_models":
                 tts_path = model_path
                 tts_config_path = config_path
-                if "default_vocoder" in model_item:
+                if args.vocoder_name is None and "default_vocoder" in model_item:
-                    args.vocoder_name = (
+                    args.vocoder_name = model_item["default_vocoder"]
-                        model_item["default_vocoder"] if args.vocoder_name is None else args.vocoder_name
-                    )
 
             # voice conversion model
             if model_item["model_type"] == "voice_conversion_models":

TTS/config/__init__.py

@@ -17,9 +17,12 @@ def read_json_with_comments(json_path):
     with fsspec.open(json_path, "r", encoding="utf-8") as f:
         input_str = f.read()
     # handle comments but not urls with //
-    input_str = re.sub(r"(\"(?:[^\"\\]|\\.)*\")|(/\*(?:.|[\\n\\r])*?\*/)|(//.*)", lambda m: m.group(1) or m.group(2) or "", input_str)
+    input_str = re.sub(
+        r"(\"(?:[^\"\\]|\\.)*\")|(/\*(?:.|[\\n\\r])*?\*/)|(//.*)", lambda m: m.group(1) or m.group(2) or "", input_str
+    )
     return json.loads(input_str)
 
+
 def register_config(model_name: str) -> Coqpit:
     """Find the right config for the given model name.
 

TTS/demos/xtts_ft_demo/utils/formatter.py

@@ -1,23 +1,17 @@
-import os
 import gc
-import torchaudio
+import os
+
 import pandas
-from faster_whisper import WhisperModel
-from glob import glob
-
-from tqdm import tqdm
-
 import torch
 import torchaudio
-# torch.set_num_threads(1)
+from faster_whisper import WhisperModel
+from tqdm import tqdm
 
+# torch.set_num_threads(1)
 from TTS.tts.layers.xtts.tokenizer import multilingual_cleaners
 
 torch.set_num_threads(16)
 
-
-import os
-
 audio_types = (".wav", ".mp3", ".flac")
 
 
@@ -25,9 +19,10 @@ def list_audios(basePath, contains=None):
     # return the set of files that are valid
     return list_files(basePath, validExts=audio_types, contains=contains)
 
+
 def list_files(basePath, validExts=None, contains=None):
     # loop over the directory structure
-    for (rootDir, dirNames, filenames) in os.walk(basePath):
+    for rootDir, dirNames, filenames in os.walk(basePath):
         # loop over the filenames in the current directory
         for filename in filenames:
             # if the contains string is not none and the filename does not contain
@@ -44,7 +39,16 @@ def list_files(basePath, validExts=None, contains=None):
                 audioPath = os.path.join(rootDir, filename)
                 yield audioPath
 
-def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0.2, eval_percentage=0.15, speaker_name="coqui", gradio_progress=None):
+
+def format_audio_list(
+    audio_files,
+    target_language="en",
+    out_path=None,
+    buffer=0.2,
+    eval_percentage=0.15,
+    speaker_name="coqui",
+    gradio_progress=None,
+):
     audio_total_size = 0
     # make sure that ooutput file exists
     os.makedirs(out_path, exist_ok=True)
@@ -69,7 +73,7 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
             wav = torch.mean(wav, dim=0, keepdim=True)
 
         wav = wav.squeeze()
-        audio_total_size += (wav.size(-1) / sr)
+        audio_total_size += wav.size(-1) / sr
 
         segments, _ = asr_model.transcribe(audio_path, word_timestamps=True, language=target_language)
         segments = list(segments)
@@ -127,10 +131,7 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
                 audio = wav[int(sr * sentence_start) : int(sr * word_end)].unsqueeze(0)
                 # if the audio is too short ignore it (i.e < 0.33 seconds)
                 if audio.size(-1) >= sr / 3:
-                    torchaudio.save(absoulte_path,
+                    torchaudio.save(absoulte_path, audio, sr)
-                        audio,
-                        sr
-                    )
                 else:
                     continue
 
@@ -145,12 +146,12 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
     df_eval = df[:num_val_samples]
     df_train = df[num_val_samples:]
 
-    df_train = df_train.sort_values('audio_file')
+    df_train = df_train.sort_values("audio_file")
     train_metadata_path = os.path.join(out_path, "metadata_train.csv")
     df_train.to_csv(train_metadata_path, sep="|", index=False)
 
     eval_metadata_path = os.path.join(out_path, "metadata_eval.csv")
-    df_eval = df_eval.sort_values('audio_file')
+    df_eval = df_eval.sort_values("audio_file")
     df_eval.to_csv(eval_metadata_path, sep="|", index=False)
 
     # deallocate VRAM and RAM

TTS/demos/xtts_ft_demo/utils/gpt_train.py

@@ -1,5 +1,5 @@
-import os
 import gc
+import os
 
 from trainer import Trainer, TrainerArgs
 
@@ -25,7 +25,6 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
     BATCH_SIZE = batch_size  # set here the batch size
     GRAD_ACUMM_STEPS = grad_acumm  # set here the grad accumulation steps
 
-
     # Define here the dataset that you want to use for the fine-tuning on.
     config_dataset = BaseDatasetConfig(
         formatter="coqui",
@@ -43,7 +42,6 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
     CHECKPOINTS_OUT_PATH = os.path.join(OUT_PATH, "XTTS_v2.0_original_model_files/")
     os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True)
 
-
     # DVAE files
     DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/dvae.pth"
     MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/mel_stats.pth"
@@ -55,8 +53,9 @@ def train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv,
     # download DVAE files if needed
     if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(MEL_NORM_FILE):
         print(" > Downloading DVAE files!")
-        ModelManager._download_model_files([MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True)
+        ModelManager._download_model_files(
-
+            [MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True
+        )
 
     # Download XTTS v2.0 checkpoint if needed
     TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/vocab.json"

TTS/demos/xtts_ft_demo/xtts_demo.py

@@ -1,19 +1,16 @@
 import argparse
+import logging
 import os
 import sys
 import tempfile
+import traceback
 
 import gradio as gr
-import librosa.display
-import numpy as np
-
-import os
 import torch
 import torchaudio
-import traceback
+
 from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
 from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
-
 from TTS.tts.configs.xtts_config import XttsConfig
 from TTS.tts.models.xtts import Xtts
 
@@ -23,7 +20,10 @@ def clear_gpu_cache():
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
 
+
 XTTS_MODEL = None
+
+
 def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
     global XTTS_MODEL
     clear_gpu_cache()
@@ -40,11 +40,17 @@ def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
     print("Model Loaded!")
     return "Model Loaded!"
 
+
 def run_tts(lang, tts_text, speaker_audio_file):
     if XTTS_MODEL is None or not speaker_audio_file:
         return "You need to run the previous step to load the model !!", None, None
 
-    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
+    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(
+        audio_path=speaker_audio_file,
+        gpt_cond_len=XTTS_MODEL.config.gpt_cond_len,
+        max_ref_length=XTTS_MODEL.config.max_ref_len,
+        sound_norm_refs=XTTS_MODEL.config.sound_norm_refs,
+    )
     out = XTTS_MODEL.inference(
         text=tts_text,
         language=lang,
@@ -65,8 +71,6 @@ def run_tts(lang, tts_text, speaker_audio_file):
     return "Speech generated !", out_path, speaker_audio_file
 
 
-
-
 # define a logger to redirect
 class Logger:
     def __init__(self, filename="log.out"):
@@ -85,21 +89,19 @@ class Logger:
     def isatty(self):
         return False
 
+
 # redirect stdout and stderr to a file
 sys.stdout = Logger()
 sys.stderr = sys.stdout
 
 
 # logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-import logging
+
 logging.basicConfig(
-    level=logging.INFO,
+    level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", handlers=[logging.StreamHandler(sys.stdout)]
-    format="%(asctime)s [%(levelname)s] %(message)s",
-    handlers=[
-        logging.StreamHandler(sys.stdout)
-    ]
 )
 
+
 def read_logs():
     sys.stdout.flush()
     with open(sys.stdout.log_file, "r") as f:
@@ -107,7 +109,6 @@ def read_logs():
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser(
         description="""XTTS fine-tuning demo\n\n"""
         """
@@ -190,12 +191,10 @@ if __name__ == "__main__":
                     "zh",
                     "hu",
                     "ko",
-                    "ja"
+                    "ja",
                 ],
             )
-            progress_data = gr.Label(
+            progress_data = gr.Label(label="Progress:")
-                label="Progress:"
-            )
             logs = gr.Textbox(
                 label="Logs:",
                 interactive=False,
@@ -209,14 +208,24 @@ if __name__ == "__main__":
                 out_path = os.path.join(out_path, "dataset")
                 os.makedirs(out_path, exist_ok=True)
                 if audio_path is None:
-                    return "You should provide one or multiple audio files! If you provided it, probably the upload of the files is not finished yet!", "", ""
+                    return (
+                        "You should provide one or multiple audio files! If you provided it, probably the upload of the files is not finished yet!",
+                        "",
+                        "",
+                    )
                 else:
                     try:
-                        train_meta, eval_meta, audio_total_size = format_audio_list(audio_path, target_language=language, out_path=out_path, gradio_progress=progress)
+                        train_meta, eval_meta, audio_total_size = format_audio_list(
+                            audio_path, target_language=language, out_path=out_path, gradio_progress=progress
+                        )
                     except:
                         traceback.print_exc()
                         error = traceback.format_exc()
-                        return f"The data processing was interrupted due an error !! Please check the console to verify the full error message! \n Error summary: {error}", "", ""
+                        return (
+                            f"The data processing was interrupted due an error !! Please check the console to verify the full error message! \n Error summary: {error}",
+                            "",
+                            "",
+                        )
 
                 clear_gpu_cache()
 
@@ -264,9 +273,7 @@ if __name__ == "__main__":
                 step=1,
                 value=args.max_audio_length,
             )
-            progress_train = gr.Label(
+            progress_train = gr.Label(label="Progress:")
-                label="Progress:"
-            )
             logs_tts_train = gr.Textbox(
                 label="Logs:",
                 interactive=False,
@@ -274,18 +281,41 @@ if __name__ == "__main__":
             demo.load(read_logs, None, logs_tts_train, every=1)
             train_btn = gr.Button(value="Step 2 - Run the training")
 
-            def train_model(language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
+            def train_model(
+                language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length
+            ):
                 clear_gpu_cache()
                 if not train_csv or not eval_csv:
-                    return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
+                    return (
+                        "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !",
+                        "",
+                        "",
+                        "",
+                        "",
+                    )
                 try:
                     # convert seconds to waveform frames
                     max_audio_length = int(max_audio_length * 22050)
-                    config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=output_path, max_audio_length=max_audio_length)
+                    config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(
+                        language,
+                        num_epochs,
+                        batch_size,
+                        grad_acumm,
+                        train_csv,
+                        eval_csv,
+                        output_path=output_path,
+                        max_audio_length=max_audio_length,
+                    )
                 except:
                     traceback.print_exc()
                     error = traceback.format_exc()
-                    return f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}", "", "", "", ""
+                    return (
+                        f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}",
+                        "",
+                        "",
+                        "",
+                        "",
+                    )
 
                 # copy original files to avoid parameters changes issues
                 os.system(f"cp {config_path} {exp_path}")
@@ -312,9 +342,7 @@ if __name__ == "__main__":
                         label="XTTS vocab path:",
                         value="",
                     )
-                    progress_load = gr.Label(
+                    progress_load = gr.Label(label="Progress:")
-                        label="Progress:"
-                    )
                     load_btn = gr.Button(value="Step 3 - Load Fine-tuned XTTS model")
 
                 with gr.Column() as col2:
@@ -342,7 +370,7 @@ if __name__ == "__main__":
                             "hu",
                             "ko",
                             "ja",
-                        ]
+                        ],
                     )
                     tts_text = gr.Textbox(
                         label="Input Text.",
@@ -351,9 +379,7 @@ if __name__ == "__main__":
                     tts_btn = gr.Button(value="Step 4 - Inference")
 
                 with gr.Column() as col3:
-                    progress_gen = gr.Label(
+                    progress_gen = gr.Label(label="Progress:")
-                        label="Progress:"
-                    )
                     tts_output_audio = gr.Audio(label="Generated Audio.")
                     reference_audio = gr.Audio(label="Reference audio used.")
 
@@ -371,7 +397,6 @@ if __name__ == "__main__":
                 ],
             )
 
-
             train_btn.click(
                 fn=train_model,
                 inputs=[
@@ -389,11 +414,7 @@ if __name__ == "__main__":
 
             load_btn.click(
                 fn=load_model,
-                inputs=[
+                inputs=[xtts_checkpoint, xtts_config, xtts_vocab],
-                    xtts_checkpoint,
-                    xtts_config,
-                    xtts_vocab
-                ],
                 outputs=[progress_load],
             )
 
@@ -407,9 +428,4 @@ if __name__ == "__main__":
                 outputs=[progress_gen, tts_output_audio, reference_audio],
             )
 
-    demo.launch(
+    demo.launch(share=True, debug=False, server_port=args.port, server_name="0.0.0.0")
-        share=True,
-        debug=False,
-        server_port=args.port,
-        server_name="0.0.0.0"
-    )

TTS/encoder/configs/emotion_encoder_config.py

@@ -1,4 +1,4 @@
-from dataclasses import asdict, dataclass
+from dataclasses import dataclass
 
 from TTS.encoder.configs.base_encoder_config import BaseEncoderConfig
 

TTS/encoder/configs/speaker_encoder_config.py

@@ -1,4 +1,4 @@
-from dataclasses import asdict, dataclass
+from dataclasses import dataclass
 
 from TTS.encoder.configs.base_encoder_config import BaseEncoderConfig
 

TTS/encoder/utils/generic_utils.py

@@ -34,7 +34,7 @@ class AugmentWAV(object):
                     # ignore not listed directories
                     if noise_dir not in self.additive_noise_types:
                         continue
-                    if not noise_dir in self.noise_list:
+                    if noise_dir not in self.noise_list:
                         self.noise_list[noise_dir] = []
                     self.noise_list[noise_dir].append(wav_file)
 

TTS/tts/layers/bark/hubert/kmeans_hubert.py

@@ -7,8 +7,6 @@ License: MIT
 
 # Modified code from https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch/hubert_kmeans.py
 
-import logging
-from pathlib import Path
 
 import torch
 from einops import pack, unpack

TTS/tts/layers/delightful_tts/acoustic_model.py

@@ -362,7 +362,7 @@ class AcousticModel(torch.nn.Module):
 
         pos_encoding = positional_encoding(
             self.emb_dim,
-            max(token_embeddings.shape[1], max(mel_lens)),
+            max(token_embeddings.shape[1], *mel_lens),
             device=token_embeddings.device,
         )
         encoder_outputs = self.encoder(

TTS/tts/layers/overflow/plotting_utils.py

@@ -71,7 +71,7 @@ def plot_transition_probabilities_to_numpy(states, transition_probabilities, out
     ax.set_title("Transition probability of state")
     ax.set_xlabel("hidden state")
     ax.set_ylabel("probability")
-    ax.set_xticks([i for i in range(len(transition_probabilities))])  # pylint: disable=unnecessary-comprehension
+    ax.set_xticks(list(range(len(transition_probabilities))))
     ax.set_xticklabels([int(x) for x in states], rotation=90)
     plt.tight_layout()
     if not output_fig:

TTS/tts/layers/tortoise/arch_utils.py

@@ -1,6 +1,5 @@
 import functools
 import math
-import os
 
 import fsspec
 import torch

TTS/tts/layers/tortoise/clvp.py

@@ -126,7 +126,7 @@ class CLVP(nn.Module):
         text_latents = self.to_text_latent(text_latents)
         speech_latents = self.to_speech_latent(speech_latents)
 
-        text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents))
+        text_latents, speech_latents = (F.normalize(t, p=2, dim=-1) for t in (text_latents, speech_latents))
 
         temp = self.temperature.exp()
 

TTS/tts/layers/tortoise/diffusion.py

@@ -972,7 +972,7 @@ class GaussianDiffusion:
             assert False  # not currently supported for this type of diffusion.
         elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
             model_outputs = model(x_t, x_start, self._scale_timesteps(t), **model_kwargs)
-            terms.update({k: o for k, o in zip(model_output_keys, model_outputs)})
+            terms.update(dict(zip(model_output_keys, model_outputs)))
             model_output = terms[gd_out_key]
             if self.model_var_type in [
                 ModelVarType.LEARNED,

TTS/tts/layers/tortoise/transformer.py

@@ -37,7 +37,7 @@ def route_args(router, args, depth):
     for key in matched_keys:
         val = args[key]
         for depth, ((f_args, g_args), routes) in enumerate(zip(routed_args, router[key])):
-            new_f_args, new_g_args = map(lambda route: ({key: val} if route else {}), routes)
+            new_f_args, new_g_args = (({key: val} if route else {}) for route in routes)
             routed_args[depth] = ({**f_args, **new_f_args}, {**g_args, **new_g_args})
     return routed_args
 
@@ -152,7 +152,7 @@ class Attention(nn.Module):
         softmax = torch.softmax
 
         qkv = self.to_qkv(x).chunk(3, dim=-1)
-        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), qkv)
+        q, k, v = (rearrange(t, "b n (h d) -> b h n d", h=h) for t in qkv)
 
         q = q * self.scale
 

TTS/tts/layers/tortoise/xtransformers.py

@@ -84,7 +84,7 @@ def init_zero_(layer):
 
 
 def pick_and_pop(keys, d):
-    values = list(map(lambda key: d.pop(key), keys))
+    values = [d.pop(key) for key in keys]
     return dict(zip(keys, values))
 
 
@@ -107,7 +107,7 @@ def group_by_key_prefix(prefix, d):
 
 def groupby_prefix_and_trim(prefix, d):
     kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
-    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix) :], x[1]), tuple(kwargs_with_prefix.items())))
+    kwargs_without_prefix = {x[0][len(prefix) :]: x[1] for x in tuple(kwargs_with_prefix.items())}
     return kwargs_without_prefix, kwargs
 
 
@@ -428,7 +428,7 @@ class ShiftTokens(nn.Module):
         feats_per_shift = x.shape[-1] // segments
         splitted = x.split(feats_per_shift, dim=-1)
         segments_to_shift, rest = splitted[:segments], splitted[segments:]
-        segments_to_shift = list(map(lambda args: shift(*args, mask=mask), zip(segments_to_shift, shifts)))
+        segments_to_shift = [shift(*args, mask=mask) for args in zip(segments_to_shift, shifts)]
         x = torch.cat((*segments_to_shift, *rest), dim=-1)
         return self.fn(x, **kwargs)
 
@@ -635,7 +635,7 @@ class Attention(nn.Module):
         v = self.to_v(v_input)
 
         if not collab_heads:
-            q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
+            q, k, v = (rearrange(t, "b n (h d) -> b h n d", h=h) for t in (q, k, v))
         else:
             q = einsum("b i d, h d -> b h i d", q, self.collab_mixing)
             k = rearrange(k, "b n d -> b () n d")
@@ -650,9 +650,9 @@ class Attention(nn.Module):
 
         if exists(rotary_pos_emb) and not has_context:
             l = rotary_pos_emb.shape[-1]
-            (ql, qr), (kl, kr), (vl, vr) = map(lambda t: (t[..., :l], t[..., l:]), (q, k, v))
+            (ql, qr), (kl, kr), (vl, vr) = ((t[..., :l], t[..., l:]) for t in (q, k, v))
-            ql, kl, vl = map(lambda t: apply_rotary_pos_emb(t, rotary_pos_emb), (ql, kl, vl))
+            ql, kl, vl = (apply_rotary_pos_emb(t, rotary_pos_emb) for t in (ql, kl, vl))
-            q, k, v = map(lambda t: torch.cat(t, dim=-1), ((ql, qr), (kl, kr), (vl, vr)))
+            q, k, v = (torch.cat(t, dim=-1) for t in ((ql, qr), (kl, kr), (vl, vr)))
 
         input_mask = None
         if any(map(exists, (mask, context_mask))):
@@ -664,7 +664,7 @@ class Attention(nn.Module):
             input_mask = q_mask * k_mask
 
         if self.num_mem_kv > 0:
-            mem_k, mem_v = map(lambda t: repeat(t, "h n d -> b h n d", b=b), (self.mem_k, self.mem_v))
+            mem_k, mem_v = (repeat(t, "h n d -> b h n d", b=b) for t in (self.mem_k, self.mem_v))
             k = torch.cat((mem_k, k), dim=-2)
             v = torch.cat((mem_v, v), dim=-2)
             if exists(input_mask):
@@ -964,9 +964,7 @@ class AttentionLayers(nn.Module):
             seq_len = x.shape[1]
             if past_key_values is not None:
                 seq_len += past_key_values[0][0].shape[-2]
-            max_rotary_emb_length = max(
+            max_rotary_emb_length = max([(m.shape[1] if exists(m) else 0) + seq_len for m in mems] + [expected_seq_len])
-                list(map(lambda m: (m.shape[1] if exists(m) else 0) + seq_len, mems)) + [expected_seq_len]
-            )
             rotary_pos_emb = self.rotary_pos_emb(max_rotary_emb_length, x.device)
 
         present_key_values = []
@@ -1200,7 +1198,7 @@ class TransformerWrapper(nn.Module):
 
         res = [out]
         if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            attn_maps = [t.post_softmax_attn for t in intermediates.attn_intermediates]
             res.append(attn_maps)
         if use_cache:
             res.append(intermediates.past_key_values)
@@ -1249,7 +1247,7 @@ class ContinuousTransformerWrapper(nn.Module):
 
         res = [out]
         if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            attn_maps = [t.post_softmax_attn for t in intermediates.attn_intermediates]
             res.append(attn_maps)
         if use_cache:
             res.append(intermediates.past_key_values)

TTS/tts/layers/vits/discriminator.py

@@ -2,7 +2,7 @@ import torch
 from torch import nn
 from torch.nn.modules.conv import Conv1d
 
-from TTS.vocoder.models.hifigan_discriminator import DiscriminatorP, MultiPeriodDiscriminator
+from TTS.vocoder.models.hifigan_discriminator import DiscriminatorP
 
 
 class DiscriminatorS(torch.nn.Module):

TTS/tts/layers/xtts/dvae.py

@@ -260,7 +260,7 @@ class DiscreteVAE(nn.Module):
             dec_init_chan = codebook_dim if not has_resblocks else dec_chans[0]
             dec_chans = [dec_init_chan, *dec_chans]
 
-            enc_chans_io, dec_chans_io = map(lambda t: list(zip(t[:-1], t[1:])), (enc_chans, dec_chans))
+            enc_chans_io, dec_chans_io = (list(zip(t[:-1], t[1:])) for t in (enc_chans, dec_chans))
 
             pad = (kernel_size - 1) // 2
             for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io):
@@ -306,9 +306,9 @@ class DiscreteVAE(nn.Module):
         if not self.normalization is not None:
             return images
 
-        means, stds = map(lambda t: torch.as_tensor(t).to(images), self.normalization)
+        means, stds = (torch.as_tensor(t).to(images) for t in self.normalization)
         arrange = "c -> () c () ()" if self.positional_dims == 2 else "c -> () c ()"
-        means, stds = map(lambda t: rearrange(t, arrange), (means, stds))
+        means, stds = (rearrange(t, arrange) for t in (means, stds))
         images = images.clone()
         images.sub_(means).div_(stds)
         return images

TTS/tts/layers/xtts/gpt.py

@@ -1,7 +1,6 @@
 # ported from: https://github.com/neonbjb/tortoise-tts
 
 import functools
-import math
 import random
 
 import torch

TTS/tts/layers/xtts/gpt_inference.py

@@ -1,5 +1,3 @@
-import math
-
 import torch
 from torch import nn
 from transformers import GPT2PreTrainedModel

TTS/tts/layers/xtts/perceiver_encoder.py

@@ -155,10 +155,6 @@ def Sequential(*mods):
     return nn.Sequential(*filter(exists, mods))
 
 
-def exists(x):
-    return x is not None
-
-
 def default(val, d):
     if exists(val):
         return val

TTS/tts/layers/xtts/stream_generator.py

@@ -43,7 +43,7 @@ class StreamGenerationConfig(GenerationConfig):
 
 class NewGenerationMixin(GenerationMixin):
     @torch.no_grad()
-    def generate(
+    def generate(  # noqa: PLR0911
         self,
         inputs: Optional[torch.Tensor] = None,
         generation_config: Optional[StreamGenerationConfig] = None,
@@ -885,10 +885,10 @@ def init_stream_support():
 
 
 if __name__ == "__main__":
-    from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    init_stream_support()
 
-    PreTrainedModel.generate = NewGenerationMixin.generate
-    PreTrainedModel.sample_stream = NewGenerationMixin.sample_stream
     model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m", torch_dtype=torch.float16)
 
     tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")

TTS/tts/layers/xtts/trainer/dataset.py

@@ -1,4 +1,3 @@
-import os
 import random
 import sys
 

TTS/tts/layers/xtts/trainer/gpt_trainer.py

@@ -5,7 +5,6 @@ import torch
 import torch.nn as nn
 import torchaudio
 from coqpit import Coqpit
-from torch.nn import functional as F
 from torch.utils.data import DataLoader
 from trainer.torch import DistributedSampler
 from trainer.trainer_utils import get_optimizer, get_scheduler

TTS/tts/layers/xtts/xtts_manager.py

@@ -1,6 +1,7 @@
 import torch
 
-class SpeakerManager():
+
+class SpeakerManager:
     def __init__(self, speaker_file_path=None):
         self.speakers = torch.load(speaker_file_path)
 
@@ -17,7 +18,7 @@ class SpeakerManager():
         return list(self.name_to_id.keys())
 
 
-class LanguageManager():
+class LanguageManager:
     def __init__(self, config):
         self.langs = config["languages"]
 

TTS/tts/layers/xtts/zh_num2words.py

@@ -4,13 +4,11 @@
 
 import argparse
 import csv
-import os
 import re
 import string
 import sys
 
 # fmt: off
-
 # ================================================================================ #
 #                                    basic constant
 # ================================================================================ #
@@ -491,8 +489,6 @@ class NumberSystem(object):
     中文数字系统
     """
 
-    pass
-
 
 class MathSymbol(object):
     """

TTS/tts/models/align_tts.py

@@ -415,7 +415,7 @@ class AlignTTS(BaseTTS):
         """Decide AlignTTS training phase"""
         if isinstance(config.phase_start_steps, list):
             vals = [i < global_step for i in config.phase_start_steps]
-            if not True in vals:
+            if True not in vals:
                 phase = 0
             else:
                 phase = (

TTS/tts/models/base_tts.py

@@ -14,7 +14,7 @@ from TTS.model import BaseTrainerModel
 from TTS.tts.datasets.dataset import TTSDataset
 from TTS.tts.utils.data import get_length_balancer_weights
 from TTS.tts.utils.languages import LanguageManager, get_language_balancer_weights
-from TTS.tts.utils.speakers import SpeakerManager, get_speaker_balancer_weights, get_speaker_manager
+from TTS.tts.utils.speakers import SpeakerManager, get_speaker_balancer_weights
 from TTS.tts.utils.synthesis import synthesis
 from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
 

TTS/tts/models/vits.py

@@ -1880,7 +1880,7 @@ class Vits(BaseTTS):
         self.forward = _forward
         if training:
             self.train()
-        if not disc is None:
+        if disc is not None:
             self.disc = disc
 
     def load_onnx(self, model_path: str, cuda=False):
@@ -1914,9 +1914,9 @@ class Vits(BaseTTS):
             dtype=np.float32,
         )
         input_params = {"input": x, "input_lengths": x_lengths, "scales": scales}
-        if not speaker_id is None:
+        if speaker_id is not None:
             input_params["sid"] = torch.tensor([speaker_id]).cpu().numpy()
-        if not language_id is None:
+        if language_id is not None:
             input_params["langid"] = torch.tensor([language_id]).cpu().numpy()
 
         audio = self.onnx_sess.run(
@@ -1948,8 +1948,7 @@ class VitsCharacters(BaseCharacters):
     def _create_vocab(self):
         self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank]
         self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)}
-        # pylint: disable=unnecessary-comprehension
+        self._id_to_char = dict(enumerate(self.vocab))
-        self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)}
 
     @staticmethod
     def init_from_config(config: Coqpit):
@@ -1996,4 +1995,4 @@ class FairseqVocab(BaseVocabulary):
         self.blank = self._vocab[0]
         self.pad = " "
         self._char_to_id = {s: i for i, s in enumerate(self._vocab)}  # pylint: disable=unnecessary-comprehension
-        self._id_to_char = {i: s for i, s in enumerate(self._vocab)}  # pylint: disable=unnecessary-comprehension
+        self._id_to_char = dict(enumerate(self._vocab))

TTS/tts/models/xtts.py

@@ -11,7 +11,7 @@ from TTS.tts.layers.xtts.gpt import GPT
 from TTS.tts.layers.xtts.hifigan_decoder import HifiDecoder
 from TTS.tts.layers.xtts.stream_generator import init_stream_support
 from TTS.tts.layers.xtts.tokenizer import VoiceBpeTokenizer, split_sentence
-from TTS.tts.layers.xtts.xtts_manager import SpeakerManager, LanguageManager
+from TTS.tts.layers.xtts.xtts_manager import LanguageManager, SpeakerManager
 from TTS.tts.models.base_tts import BaseTTS
 from TTS.utils.io import load_fsspec
 
@@ -410,12 +410,14 @@ class Xtts(BaseTTS):
         if speaker_id is not None:
             gpt_cond_latent, speaker_embedding = self.speaker_manager.speakers[speaker_id].values()
             return self.inference(text, language, gpt_cond_latent, speaker_embedding, **settings)
-        settings.update({
+        settings.update(
+            {
                 "gpt_cond_len": config.gpt_cond_len,
                 "gpt_cond_chunk_len": config.gpt_cond_chunk_len,
                 "max_ref_len": config.max_ref_len,
                 "sound_norm_refs": config.sound_norm_refs,
-        })
+            }
+        )
         return self.full_inference(text, speaker_wav, language, **settings)
 
     @torch.inference_mode()

TTS/tts/utils/languages.py

@@ -59,7 +59,7 @@ class LanguageManager(BaseIDManager):
                 languages.add(dataset["language"])
             else:
                 raise ValueError(f"Dataset {dataset['name']} has no language specified.")
-        return {name: i for i, name in enumerate(sorted(list(languages)))}
+        return {name: i for i, name in enumerate(sorted(languages))}
 
     def set_language_ids_from_config(self, c: Coqpit) -> None:
         """Set language IDs from config samples.

TTS/tts/utils/managers.py

@@ -193,7 +193,7 @@ class EmbeddingManager(BaseIDManager):
         embeddings = load_file(file_path)
         speakers = sorted({x["name"] for x in embeddings.values()})
         name_to_id = {name: i for i, name in enumerate(speakers)}
-        clip_ids = list(set(sorted(clip_name for clip_name in embeddings.keys())))
+        clip_ids = list(set(clip_name for clip_name in embeddings.keys()))
         # cache embeddings_by_names for fast inference using a bigger speakers.json
         embeddings_by_names = {}
         for x in embeddings.values():

TTS/tts/utils/text/characters.py

@@ -87,9 +87,7 @@ class BaseVocabulary:
         if vocab is not None:
             self._vocab = vocab
             self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)}
-            self._id_to_char = {
+            self._id_to_char = dict(enumerate(self._vocab))
-                idx: char for idx, char in enumerate(self._vocab)  # pylint: disable=unnecessary-comprehension
-            }
 
     @staticmethod
     def init_from_config(config, **kwargs):
@@ -269,9 +267,7 @@ class BaseCharacters:
     def vocab(self, vocab):
         self._vocab = vocab
         self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)}
-        self._id_to_char = {
+        self._id_to_char = dict(enumerate(self.vocab))
-            idx: char for idx, char in enumerate(self.vocab)  # pylint: disable=unnecessary-comprehension
-        }
 
     @property
     def num_chars(self):

TTS/tts/utils/text/japanese/phonemizer.py

@@ -350,8 +350,8 @@ def hira2kata(text: str) -> str:
     return text.replace("う゛", "ヴ")
 
 
-_SYMBOL_TOKENS = set(list("・、。？！"))
+_SYMBOL_TOKENS = set("・、。？！")
-_NO_YOMI_TOKENS = set(list("「」『』―（）［］[]　…"))
+_NO_YOMI_TOKENS = set("「」『』―（）［］[]　…")
 _TAGGER = MeCab.Tagger()
 
 

TTS/tts/utils/text/phonemizers/__init__.py

@@ -10,7 +10,6 @@ try:
     from TTS.tts.utils.text.phonemizers.ja_jp_phonemizer import JA_JP_Phonemizer
 except ImportError:
     JA_JP_Phonemizer = None
-    pass
 
 PHONEMIZERS = {b.name(): b for b in (ESpeak, Gruut, KO_KR_Phonemizer, BN_Phonemizer)}
 

TTS/utils/manage.py

@@ -5,7 +5,7 @@ import tarfile
 import zipfile
 from pathlib import Path
 from shutil import copyfile, rmtree
-from typing import Dict, List, Tuple
+from typing import Dict, Tuple
 
 import fsspec
 import requests
@@ -516,7 +516,7 @@ class ModelManager(object):
                     sub_conf[field_names[-1]] = new_path
             else:
                 # field name points to a top-level field
-                if not field_name in config:
+                if field_name not in config:
                     return
                 if isinstance(config[field_name], list):
                     config[field_name] = [new_path]

TTS/vc/configs/shared_configs.py

@@ -1,7 +1,5 @@
-from dataclasses import asdict, dataclass, field
+from dataclasses import dataclass, field
-from typing import Dict, List
+from typing import List
-
-from coqpit import Coqpit, check_argument
 
 from TTS.config import BaseAudioConfig, BaseDatasetConfig, BaseTrainingConfig
 

TTS/vc/models/freevc.py

@@ -164,7 +164,7 @@ class DiscriminatorP(torch.nn.Module):
         super(DiscriminatorP, self).__init__()
         self.period = period
         self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
         self.convs = nn.ModuleList(
             [
                 norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
@@ -201,7 +201,7 @@ class DiscriminatorP(torch.nn.Module):
 class DiscriminatorS(torch.nn.Module):
     def __init__(self, use_spectral_norm=False):
         super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
         self.convs = nn.ModuleList(
             [
                 norm_f(Conv1d(1, 16, 15, 1, padding=7)),
@@ -468,7 +468,7 @@ class FreeVC(BaseVC):
         Returns:
             torch.Tensor: Output tensor.
         """
-        if c_lengths == None:
+        if c_lengths is None:
             c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
         if not self.use_spk:
             g = self.enc_spk.embed_utterance(mel)

TTS/vc/modules/freevc/commons.py

@@ -1,8 +1,6 @@
 import math
 
-import numpy as np
 import torch
-from torch import nn
 from torch.nn import functional as F
 
 

TTS/vc/modules/freevc/speaker_encoder/audio.py

@@ -1,13 +1,17 @@
-import struct
 from pathlib import Path
 from typing import Optional, Union
 
 # import webrtcvad
 import librosa
 import numpy as np
-from scipy.ndimage.morphology import binary_dilation
 
-from TTS.vc.modules.freevc.speaker_encoder.hparams import *
+from TTS.vc.modules.freevc.speaker_encoder.hparams import (
+    audio_norm_target_dBFS,
+    mel_n_channels,
+    mel_window_length,
+    mel_window_step,
+    sampling_rate,
+)
 
 int16_max = (2**15) - 1
 

TTS/vc/modules/freevc/speaker_encoder/speaker_encoder.py

@@ -1,4 +1,3 @@
-from pathlib import Path
 from time import perf_counter as timer
 from typing import List, Union
 
@@ -8,7 +7,15 @@ from torch import nn
 
 from TTS.utils.io import load_fsspec
 from TTS.vc.modules.freevc.speaker_encoder import audio
-from TTS.vc.modules.freevc.speaker_encoder.hparams import *
+from TTS.vc.modules.freevc.speaker_encoder.hparams import (
+    mel_n_channels,
+    mel_window_step,
+    model_embedding_size,
+    model_hidden_size,
+    model_num_layers,
+    partials_n_frames,
+    sampling_rate,
+)
 
 
 class SpeakerEncoder(nn.Module):

TTS/vc/modules/freevc/wavlm/wavlm.py

@@ -387,7 +387,7 @@ class ConvFeatureExtractionModel(nn.Module):
                 nn.init.kaiming_normal_(conv.weight)
                 return conv
 
-            assert (is_layer_norm and is_group_norm) == False, "layer norm and group norm are exclusive"
+            assert (is_layer_norm and is_group_norm) is False, "layer norm and group norm are exclusive"
 
             if is_layer_norm:
                 return nn.Sequential(

TTS/vocoder/layers/losses.py

@@ -298,7 +298,7 @@ class GeneratorLoss(nn.Module):
             adv_loss = adv_loss + self.hinge_gan_loss_weight * hinge_fake_loss
 
         # Feature Matching Loss
-        if self.use_feat_match_loss and not feats_fake is None:
+        if self.use_feat_match_loss and feats_fake is not None:
             feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
             return_dict["G_feat_match_loss"] = feat_match_loss
             adv_loss = adv_loss + self.feat_match_loss_weight * feat_match_loss

TTS/vocoder/utils/generic_utils.py

@@ -40,7 +40,7 @@ def plot_results(y_hat: torch.tensor, y: torch.tensor, ap: AudioProcessor, name_
 
     Returns:
         Dict: output figures keyed by the name of the figures.
-    """ """Plot vocoder model results"""
+    """
     if name_prefix is None:
         name_prefix = ""
 

pyproject.toml

@@ -7,14 +7,60 @@ requires = [
     "packaging",
 ]
 
-[flake8]
+[tool.ruff]
-max-line-length=120
+line-length = 120
+lint.extend-select = [
+    "B033", # duplicate-value
+    "C416", # unnecessary-comprehension
+    "D419", # empty-docstring
+    "E999", # syntax-error
+    "F401", # unused-import
+    "F704", # yield-outside-function
+    "F706", # return-outside-function
+    "F841", # unused-variable
+    "I", # import sorting
+    "PIE790", # unnecessary-pass
+    "PLC",
+    "PLE",
+    "PLR0124", # comparison-with-itself
+    "PLR0206", # property-with-parameters
+    "PLR0911", # too-many-return-statements
+    "PLR1711", # useless-return
+    "PLW",
+    "W291", # trailing-whitespace
+]
+
+lint.ignore = [
+    "E501", # line too long
+    "E722", # bare except (TODO: fix these)
+    "E731", # don't use lambdas
+    "E741", # ambiguous variable name
+    "PLR0912", # too-many-branches
+    "PLR0913", # too-many-arguments
+    "PLR0915", # too-many-statements
+    "UP004", # useless-object-inheritance
+    "F821", # TODO: enable
+    "F841", # TODO: enable
+    "PLW0602", # TODO: enable
+    "PLW2901", # TODO: enable
+    "PLW0127", # TODO: enable
+    "PLW0603", # TODO: enable
+]
+
+[tool.ruff.lint.pylint]
+max-args = 5
+max-public-methods = 20
+max-returns = 7
+
+[tool.ruff.lint.per-file-ignores]
+"**/__init__.py" = [
+    "F401", # init files may have "unused" imports for now
+    "F403", # init files may have star imports for now
+]
+"hubconf.py" = [
+    "E402", # module level import not at top of file
+]
 
 [tool.black]
 line-length = 120
 target-version = ['py39']
-
-[tool.isort]
-line_length = 120
-profile = "black"
-multi_line_output = 3

recipes/bel-alex73/train_hifigan.py

@@ -1,11 +1,8 @@
-import os
-
-from coqpit import Coqpit
 from trainer import Trainer, TrainerArgs
 
 from TTS.tts.configs.shared_configs import BaseAudioConfig
 from TTS.utils.audio import AudioProcessor
-from TTS.vocoder.configs.hifigan_config import *
+from TTS.vocoder.configs.hifigan_config import HifiganConfig
 from TTS.vocoder.datasets.preprocess import load_wav_data
 from TTS.vocoder.models.gan import GAN
 

recipes/multilingual/cml_yourtts/train_yourtts.py

@@ -4,7 +4,6 @@ import torch
 from trainer import Trainer, TrainerArgs
 
 from TTS.bin.compute_embeddings import compute_embeddings
-from TTS.bin.resample import resample_files
 from TTS.config.shared_configs import BaseDatasetConfig
 from TTS.tts.configs.vits_config import VitsConfig
 from TTS.tts.datasets import load_tts_samples

requirements.dev.txt

@@ -1,5 +1,4 @@
 black
 coverage
-isort
 nose2
-pylint==2.10.2
+ruff==0.3.0

setup.py

@@ -23,12 +23,12 @@
 import os
 import subprocess
 import sys
-from packaging.version import Version
 
 import numpy
 import setuptools.command.build_py
 import setuptools.command.develop
 from Cython.Build import cythonize
+from packaging.version import Version
 from setuptools import Extension, find_packages, setup
 
 python_version = sys.version.split()[0]

tests/data_tests/test_loader.py

@@ -8,7 +8,8 @@ from torch.utils.data import DataLoader
 
 from tests import get_tests_data_path, get_tests_output_path
 from TTS.tts.configs.shared_configs import BaseDatasetConfig, BaseTTSConfig
-from TTS.tts.datasets import TTSDataset, load_tts_samples
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.datasets.dataset import TTSDataset
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 

tests/tts_tests/test_tacotron2_model.py

@@ -278,7 +278,7 @@ class TacotronCapacitronTrainTest(unittest.TestCase):
             },
         )
 
-        batch = dict({})
+        batch = {}
         batch["text_input"] = torch.randint(0, 24, (8, 128)).long().to(device)
         batch["text_lengths"] = torch.randint(100, 129, (8,)).long().to(device)
         batch["text_lengths"] = torch.sort(batch["text_lengths"], descending=True)[0]

tests/tts_tests/test_tacotron_model.py

@@ -266,7 +266,7 @@ class TacotronCapacitronTrainTest(unittest.TestCase):
             },
         )
 
-        batch = dict({})
+        batch = {}
         batch["text_input"] = torch.randint(0, 24, (8, 128)).long().to(device)
         batch["text_lengths"] = torch.randint(100, 129, (8,)).long().to(device)
         batch["text_lengths"] = torch.sort(batch["text_lengths"], descending=True)[0]

tests/tts_tests/test_vits.py

@@ -64,7 +64,6 @@ class TestVits(unittest.TestCase):
 
     def test_dataset(self):
         """TODO:"""
-        ...
 
     def test_init_multispeaker(self):
         num_speakers = 10

tests/vc_tests/test_freevc.py

@@ -4,8 +4,7 @@ import unittest
 import torch
 
 from tests import get_tests_input_path
-from TTS.vc.configs.freevc_config import FreeVCConfig
+from TTS.vc.models.freevc import FreeVC, FreeVCConfig
-from TTS.vc.models.freevc import FreeVC
 
 # pylint: disable=unused-variable
 # pylint: disable=no-self-use