Move checking logic into __post_init__()

lm_optimizer.py

@@ -10,7 +10,7 @@ import tensorflow.compat.v1 as tfv1
 from coqui_stt_ctcdecoder import Scorer
 from coqui_stt_training.evaluate import evaluate
 from coqui_stt_training.train import create_model
-from coqui_stt_training.util.config import Config, _SttConfig, initialize_config_globals
+from coqui_stt_training.util.config import Config, initialize_globals_from_cli
 from coqui_stt_training.util.evaluate_tools import wer_cer_batch
 from coqui_stt_training.util.flags import FLAGS, create_flags
 from coqui_stt_training.util.logging import log_error
@@ -52,8 +52,7 @@ def objective(trial):
 
 
 def main(_):
-    Config = _SttConfig()
+    initialize_globals_from_cli()
-    initialize_config_globals(Config)
 
     if not FLAGS.test_files:
         log_error(

training/coqui_stt_training/evaluate.py

@@ -17,9 +17,8 @@ from .util.augmentations import NormalizeSampleRate
 from .util.checkpoints import load_graph_for_evaluation
 from .util.config import (
     Config,
-    _SttConfig,
     create_progressbar,
-    initialize_config_globals,
+    initialize_globals_from_cli,
     log_error,
     log_progress,
 )
@@ -170,8 +169,7 @@ def evaluate(test_csvs, create_model):
 
 
 def main():
-    Config = _SttConfig()
+    initialize_globals_from_cli()
-    initialize_config_globals(Config)
 
     if not Config.test_files:
         log_error(

training/coqui_stt_training/train.py

@@ -44,9 +44,8 @@ from .util.checkpoints import (
 )
 from .util.config import (
     Config,
-    _SttConfig,
     create_progressbar,
-    initialize_config_globals,
+    initialize_globals_from_cli,
     log_debug,
     log_error,
     log_info,
@@ -1250,10 +1249,7 @@ def early_training_checks():
 
 
 def main():
-    Config = _SttConfig()
+    initialize_globals_from_cli()
-    Config.parse_args(arg_prefix="") # parse CLI args
-    initialize_config_globals(Config)
-
     early_training_checks()
 
     if Config.train_files:

training/coqui_stt_training/util/config.py

@@ -36,6 +36,163 @@ Config = _ConfigSingleton()  # pylint: disable=invalid-name
 
 @dataclass
 class _SttConfig(Coqpit):
+    def __post_init__(self):
+        # Augmentations
+        self.augmentations = parse_augmentations(self.augment)
+        if self.augmentations:
+            print(f"Parsed augmentations: {self.augmentations}")
+        if self.augmentations and self.feature_cache and self.cache_for_epochs == 0:
+            print(
+                "Due to current feature-cache settings the exact same sample augmentations of the first "
+                "epoch will be repeated on all following epochs. This could lead to unintended over-fitting. "
+                "You could use --cache_for_epochs <n_epochs> to invalidate the cache after a given number of epochs."
+            )
+
+        if self.normalize_sample_rate:
+            self.augmentations = [NormalizeSampleRate(self.audio_sample_rate)] + self[
+                "augmentations"
+            ]
+
+        # Caching
+        if self.cache_for_epochs == 1:
+            print(
+                "--cache_for_epochs == 1 is (re-)creating the feature cache on every epoch but will never use it."
+                "You can either set --cache_for_epochs > 1, or not use feature caching at all."
+            )
+
+        # Read-buffer
+        self.read_buffer = parse_file_size(self.read_buffer)
+
+        # Set default dropout rates
+        if self.dropout_rate2 < 0:
+            self.dropout_rate2 = self.dropout_rate
+        if self.dropout_rate3 < 0:
+            self.dropout_rate3 = self.dropout_rate
+        if self.dropout_rate6 < 0:
+            self.dropout_rate6 = self.dropout_rate
+
+        # Set default checkpoint dir
+        if not self.checkpoint_dir:
+            self.checkpoint_dir = xdg.save_data_path(os.path.join("stt", "checkpoints"))
+
+        if self.load_train not in ["last", "best", "init", "auto"]:
+            self.load_train = "auto"
+
+        if self.load_evaluate not in ["last", "best", "auto"]:
+            self.load_evaluate = "auto"
+
+        # Set default summary dir
+        if not self.summary_dir:
+            self.summary_dir = xdg.save_data_path(os.path.join("stt", "summaries"))
+
+        # Standard session configuration that'll be used for all new sessions.
+        self.session_config = tfv1.ConfigProto(
+            allow_soft_placement=True,
+            log_device_placement=self.log_placement,
+            inter_op_parallelism_threads=self.inter_op_parallelism_threads,
+            intra_op_parallelism_threads=self.intra_op_parallelism_threads,
+            gpu_options=tfv1.GPUOptions(allow_growth=self.use_allow_growth),
+        )
+
+        # CPU device
+        self.cpu_device = "/cpu:0"
+
+        # Available GPU devices
+        self.available_devices = get_available_gpus(self.session_config)
+
+        # If there is no GPU available, we fall back to CPU based operation
+        if not self.available_devices:
+            self.available_devices = [self.cpu_device]
+
+        if self.bytes_output_mode:
+            self.alphabet = UTF8Alphabet()
+        elif self.alphabet_config_path:
+            self.alphabet = Alphabet(os.path.abspath(self.alphabet_config_path))
+
+        # Geometric Constants
+        # ===================
+
+        # For an explanation of the meaning of the geometric constants
+        # please refer to doc/Geometry.md
+
+        # Number of MFCC features
+        self.n_input = 26 # TODO: Determine this programmatically from the sample rate
+
+        # The number of frames in the context
+        self.n_context = 9 # TODO: Determine the optimal value using a validation data set
+
+        # Number of units in hidden layers
+        self.n_hidden = self.n_hidden
+
+        self.n_hidden_1 = self.n_hidden
+
+        self.n_hidden_2 = self.n_hidden
+
+        self.n_hidden_5 = self.n_hidden
+
+        # LSTM cell state dimension
+        self.n_cell_dim = self.n_hidden
+
+        # The number of units in the third layer, which feeds in to the LSTM
+        self.n_hidden_3 = self.n_cell_dim
+
+        # Dims in last layer = number of characters in alphabet plus one
+        try:
+            # +1 for CTC blank label
+            self.n_hidden_6 = self.alphabet.GetSize() + 1
+        except:
+            AttributeError
+
+        # Size of audio window in samples
+        if (self.feature_win_len * self.audio_sample_rate) % 1000 != 0:
+            log_error(
+                "--feature_win_len value ({}) in milliseconds ({}) multiplied "
+                "by --audio_sample_rate value ({}) must be an integer value. Adjust "
+                "your --feature_win_len value or resample your audio accordingly."
+                "".format(self.feature_win_len, self.feature_win_len / 1000, self.audio_sample_rate)
+            )
+            sys.exit(1)
+
+        self.audio_window_samples = self.audio_sample_rate * (self.feature_win_len / 1000)
+
+        # Stride for feature computations in samples
+        if (self.feature_win_step * self.audio_sample_rate) % 1000 != 0:
+            log_error(
+                "--feature_win_step value ({}) in milliseconds ({}) multiplied "
+                "by --audio_sample_rate value ({}) must be an integer value. Adjust "
+                "your --feature_win_step value or resample your audio accordingly."
+                "".format(
+                    self.feature_win_step, self.feature_win_step / 1000, self.audio_sample_rate
+                )
+            )
+            sys.exit(1)
+
+        self.audio_step_samples = self.audio_sample_rate * (self.feature_win_step / 1000)
+
+        if self.one_shot_infer:
+            if not path_exists_remote(self.one_shot_infer):
+                log_error("Path specified in --one_shot_infer is not a valid file.")
+                sys.exit(1)
+
+        if self.train_cudnn and self.load_cudnn:
+            log_error(
+                "Trying to use --train_cudnn, but --load_cudnn "
+                "was also specified. The --load_cudnn flag is only "
+                "needed when converting a CuDNN RNN checkpoint to "
+                "a CPU-capable graph. If your system is capable of "
+                "using CuDNN RNN, you can just specify the CuDNN RNN "
+                "checkpoint normally with --save_checkpoint_dir."
+            )
+            sys.exit(1)
+
+        # If separate save and load flags were not specified, default to load and save
+        # from the same dir.
+        if not self.save_checkpoint_dir:
+            self.save_checkpoint_dir = self.checkpoint_dir
+
+        if not self.load_checkpoint_dir:
+            self.load_checkpoint_dir = self.checkpoint_dir
+
     train_files: List[str] = field(
         default_factory=list,
         metadata=dict(
@@ -541,165 +698,15 @@ class _SttConfig(Coqpit):
     )
 
 
-def initialize_config_globals(c):
+def initialize_globals_from_cli():
-    """
+    c = _SttConfig()
-    input: config class object (i.e. coqpit.Coqpit)
+    c.parse_args(arg_prefix="")
-    """
+    c.__post_init__()
-
+    _ConfigSingleton._config = c  # pylint: disable=protected-access
-    # Augmentations
-    c.augmentations = parse_augmentations(c.augment)
-    print(f"Parsed augmentations from flags: {c.augmentations}")
-    if c.augmentations and c.feature_cache and c.cache_for_epochs == 0:
-        print(
-            "Due to current feature-cache settings the exact same sample augmentations of the first "
-            "epoch will be repeated on all following epochs. This could lead to unintended over-fitting. "
-            "You could use --cache_for_epochs <n_epochs> to invalidate the cache after a given number of epochs."
-        )
-
-    if c.normalize_sample_rate:
-        c.augmentations = [NormalizeSampleRate(c.audio_sample_rate)] + c[
-            "augmentations"
-        ]
-
-    # Caching
-    if c.cache_for_epochs == 1:
-        print(
-            "--cache_for_epochs == 1 is (re-)creating the feature cache on every epoch but will never use it."
-        )
-
-    # Read-buffer
-    c.read_buffer = parse_file_size(c.read_buffer)
-
-    # Set default dropout rates
-    if c.dropout_rate2 < 0:
-        c.dropout_rate2 = c.dropout_rate
-    if c.dropout_rate3 < 0:
-        c.dropout_rate3 = c.dropout_rate
-    if c.dropout_rate6 < 0:
-        c.dropout_rate6 = c.dropout_rate
-
-    # Set default checkpoint dir
-    if not c.checkpoint_dir:
-        c.checkpoint_dir = xdg.save_data_path(os.path.join("stt", "checkpoints"))
-
-    if c.load_train not in ["last", "best", "init", "auto"]:
-        c.load_train = "auto"
-
-    if c.load_evaluate not in ["last", "best", "auto"]:
-        c.load_evaluate = "auto"
-
-    # Set default summary dir
-    if not c.summary_dir:
-        c.summary_dir = xdg.save_data_path(os.path.join("stt", "summaries"))
-
-    # Standard session configuration that'll be used for all new sessions.
-    c.session_config = tfv1.ConfigProto(
-        allow_soft_placement=True,
-        log_device_placement=c.log_placement,
-        inter_op_parallelism_threads=c.inter_op_parallelism_threads,
-        intra_op_parallelism_threads=c.intra_op_parallelism_threads,
-        gpu_options=tfv1.GPUOptions(allow_growth=c.use_allow_growth),
-    )
-
-    # CPU device
-    c.cpu_device = "/cpu:0"
-
-    # Available GPU devices
-    c.available_devices = get_available_gpus(c.session_config)
-
-    # If there is no GPU available, we fall back to CPU based operation
-    if not c.available_devices:
-        c.available_devices = [c.cpu_device]
-
-    if c.bytes_output_mode:
-        c.alphabet = UTF8Alphabet()
-    elif c.alphabet_config_path:
-        c.alphabet = Alphabet(os.path.abspath(c.alphabet_config_path))
-
-    # Geometric Constants
-    # ===================
-
-    # For an explanation of the meaning of the geometric constants, please refer to
-    # doc/Geometry.md
-
-    # Number of MFCC features
-    c.n_input = 26  # TODO: Determine this programmatically from the sample rate
-
-    # The number of frames in the context
-    c.n_context = 9  # TODO: Determine the optimal value using a validation data set
-
-    # Number of units in hidden layers
-    c.n_hidden = c.n_hidden
-
-    c.n_hidden_1 = c.n_hidden
-
-    c.n_hidden_2 = c.n_hidden
-
-    c.n_hidden_5 = c.n_hidden
-
-    # LSTM cell state dimension
-    c.n_cell_dim = c.n_hidden
-
-    # The number of units in the third layer, which feeds in to the LSTM
-    c.n_hidden_3 = c.n_cell_dim
-
-    # Units in the last layer = number of characters in the alphabet plus one
-    try:
-        # +1 for CTC blank label
-        c.n_hidden_6 = c.alphabet.GetSize() + 1
-    except:
-        AttributeError
-
-    # Size of audio window in samples
-    if (c.feature_win_len * c.audio_sample_rate) % 1000 != 0:
-        log_error(
-            "--feature_win_len value ({}) in milliseconds ({}) multiplied "
-            "by --audio_sample_rate value ({}) must be an integer value. Adjust "
-            "your --feature_win_len value or resample your audio accordingly."
-            "".format(c.feature_win_len, c.feature_win_len / 1000, c.audio_sample_rate)
-        )
-        sys.exit(1)
-
-    c.audio_window_samples = c.audio_sample_rate * (c.feature_win_len / 1000)
-
-    # Stride for feature computations in samples
-    if (c.feature_win_step * c.audio_sample_rate) % 1000 != 0:
-        log_error(
-            "--feature_win_step value ({}) in milliseconds ({}) multiplied "
-            "by --audio_sample_rate value ({}) must be an integer value. Adjust "
-            "your --feature_win_step value or resample your audio accordingly."
-            "".format(
-                c.feature_win_step, c.feature_win_step / 1000, c.audio_sample_rate
-            )
-        )
-        sys.exit(1)
-
-    c.audio_step_samples = c.audio_sample_rate * (c.feature_win_step / 1000)
-
-    if c.one_shot_infer:
-        if not path_exists_remote(c.one_shot_infer):
-            log_error("Path specified in --one_shot_infer is not a valid file.")
-            sys.exit(1)
-
-    if c.train_cudnn and c.load_cudnn:
-        log_error(
-            "Trying to use --train_cudnn, but --load_cudnn "
-            "was also specified. The --load_cudnn flag is only "
-            "needed when converting a CuDNN RNN checkpoint to "
-            "a CPU-capable graph. If your system is capable of "
-            "using CuDNN RNN, you can just specify the CuDNN RNN "
-            "checkpoint normally with --save_checkpoint_dir."
-        )
-        sys.exit(1)
-
-    # If separate save and load flags were not specified, default to load and save
-    # from the same dir.
-    if not c.save_checkpoint_dir:
-        c.save_checkpoint_dir = c.checkpoint_dir
-
-    if not c.load_checkpoint_dir:
-        c.load_checkpoint_dir = c.checkpoint_dir
 
+def initialize_globals_from_args(**override_args):
+    # Update Config with new args
+    c = _SttConfig(**override_args)
     _ConfigSingleton._config = c  # pylint: disable=protected-access
 
 

transcribe.py

@@ -20,7 +20,7 @@ from multiprocessing import Process, cpu_count
 
 from coqui_stt_ctcdecoder import Scorer, ctc_beam_search_decoder_batch
 from coqui_stt_training.util.audio import AudioFile
-from coqui_stt_training.util.config import Config, _SttConfig, initialize_config_globals
+from coqui_stt_training.util.config import Config, initialize_globals_from_cli
 from coqui_stt_training.util.feeding import split_audio_file
 from coqui_stt_training.util.flags import FLAGS, create_flags
 from coqui_stt_training.util.logging import (
@@ -42,8 +42,8 @@ def transcribe_file(audio_path, tlog_path):
     )
     from coqui_stt_training.util.checkpoints import load_graph_for_evaluation
 
-    Config = _SttConfig()
+    initialize_globals_from_cli()
-    initialize_config_globals(Config)
+
     scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.scorer_path, Config.alphabet)
     try:
         num_processes = cpu_count()