start merge

.gitignore

@@ -1,8 +1,173 @@
-voices/*.*
-models/*.*
-outputs/*.*
-finetune/*.*
+voices/*
+models/*
+outputs/*
+finetune/*
+alltalk_environment/*
 system/config/at_github_sha.json
 system/tts_engines/tts_engines.json
 confignew.json
 diagnostics.log
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+
+.idea/*

metrics_logger.py

@@ -0,0 +1,161 @@
+import datetime
+import io
+import logging
+
+from matplotlib import pyplot as plt
+from trainer import ConsoleLogger
+from trainer.logging.console_logger import tcolors
+from trainer.utils.distributed import rank_zero_only
+
+logger = logging.getLogger("trainer")
+
+class MetricsLogger(ConsoleLogger):
+    def __init__(self, ):
+        super().__init__()
+        self.cur_epoch_start = None
+        self.cur_epoch_end = None
+        self.cur_epoch = None
+        self.epoch_durations = []
+        self.max_epoch = None
+        self.batch_steps = None
+        self.global_steps = None
+        self.loss = []
+        self.avg_loss = []
+        self.loss_mel_ce = []
+        self.avg_loss_mel_ce = []
+        self.loss_text_ce = []
+        self.avg_loss_text_ce = []
+        self.learning_rates = []
+        self.total_duration = None
+        self.estimated_duration = None
+        self.start = None
+
+    @staticmethod
+    def get_now():
+        return datetime.datetime.now()
+    @staticmethod
+    def format_duration(seconds):
+        if seconds:
+            # Calculate hours, minutes, and seconds
+            hours = seconds // 3600
+            minutes = (seconds % 3600) // 60
+            seconds = seconds % 60
+            return f"{int(hours)}:{int(minutes)}:{int(seconds)}"
+        else:
+            return None
+
+    @rank_zero_only
+    def print_epoch_start(self, epoch, max_epoch, output_path=None):
+        self.cur_epoch = epoch
+        self.cur_epoch_start = self.get_now()
+        self.max_epoch = max_epoch
+        super().print_epoch_start(epoch, max_epoch, output_path)
+
+    @rank_zero_only
+    def print_train_start(self):
+        super().print_train_start()
+
+    @rank_zero_only
+    def print_train_step(self, batch_steps, step, global_step, loss_dict, avg_loss_dict):
+        self.batch_steps = batch_steps
+        self.global_steps = global_step
+
+        for key, value in loss_dict.items():
+            if key == 'loss_mel_ce':
+                self.loss_mel_ce.append((global_step, value))
+            elif key == 'loss':
+                self.loss.append((global_step, value))
+            elif key == 'loss_text_ce':
+                self.loss_text_ce.append((global_step, value))
+            elif key == 'current_lr':
+                self.learning_rates.append((global_step, value))
+        super().print_train_step(batch_steps, step, global_step, loss_dict, avg_loss_dict)
+
+    # pylint: disable=unused-argument
+    @rank_zero_only
+    def print_train_epoch_end(self, global_step, epoch, epoch_time, print_dict):
+        super().print_epoch_end(global_step, epoch, epoch_time, print_dict)
+        if self.estimated_duration:
+            self.log_with_flush(
+                "\n{}{} >> ETA: {} {}".format(tcolors.UNDERLINE, tcolors.BOLD, self.format_duration(self.estimated_duration), tcolors.ENDC),
+            )
+
+    @rank_zero_only
+    def print_eval_start(self):
+        super().print_eval_start()
+
+    @rank_zero_only
+    def print_eval_step(self, step, loss_dict, avg_loss_dict):
+        super().print_eval_step(step, loss_dict, avg_loss_dict)
+
+    @rank_zero_only
+    def print_epoch_end(self, epoch, avg_loss_dict):
+        self.cur_epoch_end = self.get_now()
+        self.epoch_durations.append((self.cur_epoch_end - self.cur_epoch_start).total_seconds())
+        for key, value in avg_loss_dict.items():
+            if key == 'avg_loss_mel_ce':
+                self.avg_loss_mel_ce.append((epoch, value))
+            elif key == 'avg_loss':
+                self.avg_loss.append((epoch, value))
+            elif key == 'avg_loss_text_ce':
+                self.avg_loss_text_ce.append((epoch, value))
+
+        if len(self.epoch_durations) > 1 and self.max_epoch:
+            self.total_duration = sum(self.epoch_durations)
+            additional_data_points_needed = self.max_epoch - len(self.epoch_durations)
+            if additional_data_points_needed > 0:
+                weighted_avg_duration = sum((i + 1) * duration for i, duration in enumerate(self.epoch_durations)) / sum(range(1, len(self.epoch_durations) + 1))
+                self.estimated_duration = (weighted_avg_duration * additional_data_points_needed)
+
+        super().print_epoch_end(epoch, avg_loss_dict)
+
+    def plot_metrics(self, show_loss_mel_ce=False, show_loss_text_ce=False, show_loss=True):
+        fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(24, 18))
+
+        ax2.set_title('Steps')
+        ax2.set_xlabel('Steps')
+        ax2.set_ylabel('Value')
+
+        ax1.set_title('Epochs')
+        ax1.set_xlabel('Epoch')
+        ax1.set_ylabel('Value')
+
+        ax3.set_title('Learning Rates')
+        ax3.set_xlabel('Steps')
+        ax3.set_ylabel('Value')
+
+        if self.loss and self.loss_text_ce and self.loss_mel_ce:
+            if show_loss:
+                ax2.plot(*zip(*self.loss), color='green', linestyle='-', label='Loss')
+                ax2.fill_between(*zip(*self.loss), color='lightgreen', alpha=0.1)
+            if show_loss_text_ce:
+                ax2.plot(*zip(*self.loss_text_ce), color='red', linestyle='-', label='Loss Text')
+                ax2.fill_between(*zip(*self.loss_text_ce), color='lightcoral', alpha=0.1)
+            if show_loss_mel_ce:
+                ax2.plot(*zip(*self.loss_mel_ce), color='blue', linestyle='-', label='Loss MEL')
+                ax2.fill_between(*zip(*self.loss_mel_ce), color='lightblue', alpha=0.1)
+            ax2.legend()
+
+        if self.learning_rates:
+            ax3.plot(*zip(*self.learning_rates), linestyle='-', color='blue', label='Learning Rate')
+            ax3.fill_between(*zip(*self.learning_rates), color='lightblue', alpha=0.1)
+            ax3.legend()
+
+        if self.avg_loss and self.avg_loss_text_ce and self.avg_loss_mel_ce:
+            if show_loss:
+                ax1.plot(*zip(*self.avg_loss), color='green', linestyle='-', label='Avg Loss')
+                ax1.fill_between(*zip(*self.avg_loss), color='lightgreen', alpha=0.1)
+            if show_loss_text_ce:
+                ax1.plot(*zip(*self.avg_loss_text_ce), color='red', linestyle='-', label='Avg Loss Text')
+                ax1.fill_between(*zip(*self.avg_loss_text_ce), color='lightcoral', alpha=0.1)
+            if show_loss_mel_ce:
+                ax1.plot(*zip(*self.avg_loss_mel_ce), color='blue', linestyle='-', label='Avg Loss MEL')
+                ax1.fill_between(*zip(*self.avg_loss_mel_ce), color='lightblue', alpha=0.1)
+            ax1.set_xlim(left=0)
+            ax1.legend()
+
+        plt.style.use('dark_background')
+        plt.tight_layout()
+        plt.savefig("training_metrics.png", format='png', bbox_inches='tight', pad_inches=0.1)
+        plt.close()
+        return "training_metrics.png"