Move checking logic into __post_init__()
This commit is contained in:
Josh Meyer
parent
5b4fa27467
commit
4dc565beca
@ -10,7 +10,7 @@ import tensorflow.compat.v1 as tfv1
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from coqui_stt_ctcdecoder import Scorer
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from coqui_stt_training.evaluate import evaluate
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from coqui_stt_training.train import create_model
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from coqui_stt_training.util.config import Config, _SttConfig, initialize_config_globals
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from coqui_stt_training.util.config import Config, initialize_globals_from_cli
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from coqui_stt_training.util.evaluate_tools import wer_cer_batch
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from coqui_stt_training.util.flags import FLAGS, create_flags
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from coqui_stt_training.util.logging import log_error
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@ -52,8 +52,7 @@ def objective(trial):
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def main(_):
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Config = _SttConfig()
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initialize_config_globals(Config)
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initialize_globals_from_cli()
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if not FLAGS.test_files:
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log_error(
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@ -17,9 +17,8 @@ from .util.augmentations import NormalizeSampleRate
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from .util.checkpoints import load_graph_for_evaluation
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from .util.config import (
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Config,
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_SttConfig,
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create_progressbar,
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initialize_config_globals,
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initialize_globals_from_cli,
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log_error,
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log_progress,
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)
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@ -170,8 +169,7 @@ def evaluate(test_csvs, create_model):
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def main():
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Config = _SttConfig()
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initialize_config_globals(Config)
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initialize_globals_from_cli()
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if not Config.test_files:
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log_error(
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@ -44,9 +44,8 @@ from .util.checkpoints import (
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)
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from .util.config import (
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Config,
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_SttConfig,
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create_progressbar,
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initialize_config_globals,
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initialize_globals_from_cli,
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log_debug,
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log_error,
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log_info,
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@ -1250,10 +1249,7 @@ def early_training_checks():
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def main():
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Config = _SttConfig()
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Config.parse_args(arg_prefix="") # parse CLI args
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initialize_config_globals(Config)
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initialize_globals_from_cli()
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early_training_checks()
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if Config.train_files:
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@ -36,6 +36,163 @@ Config = _ConfigSingleton() # pylint: disable=invalid-name
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@dataclass
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class _SttConfig(Coqpit):
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def __post_init__(self):
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# Augmentations
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self.augmentations = parse_augmentations(self.augment)
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if self.augmentations:
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print(f"Parsed augmentations: {self.augmentations}")
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if self.augmentations and self.feature_cache and self.cache_for_epochs == 0:
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print(
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"Due to current feature-cache settings the exact same sample augmentations of the first "
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"epoch will be repeated on all following epochs. This could lead to unintended over-fitting. "
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"You could use --cache_for_epochs <n_epochs> to invalidate the cache after a given number of epochs."
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)
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if self.normalize_sample_rate:
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self.augmentations = [NormalizeSampleRate(self.audio_sample_rate)] + self[
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"augmentations"
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]
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# Caching
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if self.cache_for_epochs == 1:
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print(
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"--cache_for_epochs == 1 is (re-)creating the feature cache on every epoch but will never use it."
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"You can either set --cache_for_epochs > 1, or not use feature caching at all."
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)
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# Read-buffer
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self.read_buffer = parse_file_size(self.read_buffer)
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# Set default dropout rates
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if self.dropout_rate2 < 0:
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self.dropout_rate2 = self.dropout_rate
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if self.dropout_rate3 < 0:
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self.dropout_rate3 = self.dropout_rate
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if self.dropout_rate6 < 0:
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self.dropout_rate6 = self.dropout_rate
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# Set default checkpoint dir
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if not self.checkpoint_dir:
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self.checkpoint_dir = xdg.save_data_path(os.path.join("stt", "checkpoints"))
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if self.load_train not in ["last", "best", "init", "auto"]:
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self.load_train = "auto"
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if self.load_evaluate not in ["last", "best", "auto"]:
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self.load_evaluate = "auto"
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# Set default summary dir
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if not self.summary_dir:
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self.summary_dir = xdg.save_data_path(os.path.join("stt", "summaries"))
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# Standard session configuration that'll be used for all new sessions.
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self.session_config = tfv1.ConfigProto(
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allow_soft_placement=True,
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log_device_placement=self.log_placement,
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inter_op_parallelism_threads=self.inter_op_parallelism_threads,
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intra_op_parallelism_threads=self.intra_op_parallelism_threads,
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gpu_options=tfv1.GPUOptions(allow_growth=self.use_allow_growth),
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)
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# CPU device
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self.cpu_device = "/cpu:0"
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# Available GPU devices
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self.available_devices = get_available_gpus(self.session_config)
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# If there is no GPU available, we fall back to CPU based operation
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if not self.available_devices:
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self.available_devices = [self.cpu_device]
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if self.bytes_output_mode:
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self.alphabet = UTF8Alphabet()
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elif self.alphabet_config_path:
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self.alphabet = Alphabet(os.path.abspath(self.alphabet_config_path))
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# Geometric Constants
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# ===================
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# For an explanation of the meaning of the geometric constants
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# please refer to doc/Geometry.md
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# Number of MFCC features
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self.n_input = 26 # TODO: Determine this programmatically from the sample rate
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# The number of frames in the context
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self.n_context = 9 # TODO: Determine the optimal value using a validation data set
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# Number of units in hidden layers
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self.n_hidden = self.n_hidden
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self.n_hidden_1 = self.n_hidden
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self.n_hidden_2 = self.n_hidden
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self.n_hidden_5 = self.n_hidden
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# LSTM cell state dimension
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self.n_cell_dim = self.n_hidden
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# The number of units in the third layer, which feeds in to the LSTM
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self.n_hidden_3 = self.n_cell_dim
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# Dims in last layer = number of characters in alphabet plus one
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try:
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# +1 for CTC blank label
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self.n_hidden_6 = self.alphabet.GetSize() + 1
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except:
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AttributeError
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# Size of audio window in samples
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if (self.feature_win_len * self.audio_sample_rate) % 1000 != 0:
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log_error(
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"--feature_win_len value ({}) in milliseconds ({}) multiplied "
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"by --audio_sample_rate value ({}) must be an integer value. Adjust "
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"your --feature_win_len value or resample your audio accordingly."
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"".format(self.feature_win_len, self.feature_win_len / 1000, self.audio_sample_rate)
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)
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sys.exit(1)
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self.audio_window_samples = self.audio_sample_rate * (self.feature_win_len / 1000)
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# Stride for feature computations in samples
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if (self.feature_win_step * self.audio_sample_rate) % 1000 != 0:
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log_error(
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"--feature_win_step value ({}) in milliseconds ({}) multiplied "
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"by --audio_sample_rate value ({}) must be an integer value. Adjust "
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"your --feature_win_step value or resample your audio accordingly."
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"".format(
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self.feature_win_step, self.feature_win_step / 1000, self.audio_sample_rate
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)
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)
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sys.exit(1)
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self.audio_step_samples = self.audio_sample_rate * (self.feature_win_step / 1000)
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if self.one_shot_infer:
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if not path_exists_remote(self.one_shot_infer):
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log_error("Path specified in --one_shot_infer is not a valid file.")
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sys.exit(1)
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if self.train_cudnn and self.load_cudnn:
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log_error(
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"Trying to use --train_cudnn, but --load_cudnn "
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"was also specified. The --load_cudnn flag is only "
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"needed when converting a CuDNN RNN checkpoint to "
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"a CPU-capable graph. If your system is capable of "
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"using CuDNN RNN, you can just specify the CuDNN RNN "
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"checkpoint normally with --save_checkpoint_dir."
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)
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sys.exit(1)
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# If separate save and load flags were not specified, default to load and save
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# from the same dir.
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if not self.save_checkpoint_dir:
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self.save_checkpoint_dir = self.checkpoint_dir
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if not self.load_checkpoint_dir:
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self.load_checkpoint_dir = self.checkpoint_dir
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train_files: List[str] = field(
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default_factory=list,
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metadata=dict(
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@ -541,165 +698,15 @@ class _SttConfig(Coqpit):
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)
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def initialize_config_globals(c):
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"""
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input: config class object (i.e. coqpit.Coqpit)
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"""
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# Augmentations
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c.augmentations = parse_augmentations(c.augment)
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print(f"Parsed augmentations from flags: {c.augmentations}")
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if c.augmentations and c.feature_cache and c.cache_for_epochs == 0:
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print(
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"Due to current feature-cache settings the exact same sample augmentations of the first "
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"epoch will be repeated on all following epochs. This could lead to unintended over-fitting. "
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"You could use --cache_for_epochs <n_epochs> to invalidate the cache after a given number of epochs."
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)
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if c.normalize_sample_rate:
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c.augmentations = [NormalizeSampleRate(c.audio_sample_rate)] + c[
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"augmentations"
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]
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# Caching
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if c.cache_for_epochs == 1:
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print(
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"--cache_for_epochs == 1 is (re-)creating the feature cache on every epoch but will never use it."
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)
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# Read-buffer
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c.read_buffer = parse_file_size(c.read_buffer)
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# Set default dropout rates
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if c.dropout_rate2 < 0:
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c.dropout_rate2 = c.dropout_rate
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if c.dropout_rate3 < 0:
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c.dropout_rate3 = c.dropout_rate
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if c.dropout_rate6 < 0:
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c.dropout_rate6 = c.dropout_rate
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# Set default checkpoint dir
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if not c.checkpoint_dir:
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c.checkpoint_dir = xdg.save_data_path(os.path.join("stt", "checkpoints"))
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if c.load_train not in ["last", "best", "init", "auto"]:
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c.load_train = "auto"
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if c.load_evaluate not in ["last", "best", "auto"]:
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c.load_evaluate = "auto"
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# Set default summary dir
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if not c.summary_dir:
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c.summary_dir = xdg.save_data_path(os.path.join("stt", "summaries"))
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# Standard session configuration that'll be used for all new sessions.
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c.session_config = tfv1.ConfigProto(
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allow_soft_placement=True,
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log_device_placement=c.log_placement,
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inter_op_parallelism_threads=c.inter_op_parallelism_threads,
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intra_op_parallelism_threads=c.intra_op_parallelism_threads,
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gpu_options=tfv1.GPUOptions(allow_growth=c.use_allow_growth),
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)
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# CPU device
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c.cpu_device = "/cpu:0"
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# Available GPU devices
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c.available_devices = get_available_gpus(c.session_config)
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# If there is no GPU available, we fall back to CPU based operation
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if not c.available_devices:
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c.available_devices = [c.cpu_device]
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if c.bytes_output_mode:
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c.alphabet = UTF8Alphabet()
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elif c.alphabet_config_path:
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c.alphabet = Alphabet(os.path.abspath(c.alphabet_config_path))
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# Geometric Constants
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# ===================
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# For an explanation of the meaning of the geometric constants, please refer to
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# doc/Geometry.md
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# Number of MFCC features
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c.n_input = 26 # TODO: Determine this programmatically from the sample rate
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# The number of frames in the context
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c.n_context = 9 # TODO: Determine the optimal value using a validation data set
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# Number of units in hidden layers
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c.n_hidden = c.n_hidden
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c.n_hidden_1 = c.n_hidden
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c.n_hidden_2 = c.n_hidden
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c.n_hidden_5 = c.n_hidden
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# LSTM cell state dimension
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c.n_cell_dim = c.n_hidden
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# The number of units in the third layer, which feeds in to the LSTM
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c.n_hidden_3 = c.n_cell_dim
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# Units in the last layer = number of characters in the alphabet plus one
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try:
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# +1 for CTC blank label
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c.n_hidden_6 = c.alphabet.GetSize() + 1
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except:
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AttributeError
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# Size of audio window in samples
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if (c.feature_win_len * c.audio_sample_rate) % 1000 != 0:
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log_error(
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"--feature_win_len value ({}) in milliseconds ({}) multiplied "
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"by --audio_sample_rate value ({}) must be an integer value. Adjust "
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"your --feature_win_len value or resample your audio accordingly."
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"".format(c.feature_win_len, c.feature_win_len / 1000, c.audio_sample_rate)
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)
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sys.exit(1)
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c.audio_window_samples = c.audio_sample_rate * (c.feature_win_len / 1000)
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# Stride for feature computations in samples
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if (c.feature_win_step * c.audio_sample_rate) % 1000 != 0:
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log_error(
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"--feature_win_step value ({}) in milliseconds ({}) multiplied "
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"by --audio_sample_rate value ({}) must be an integer value. Adjust "
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"your --feature_win_step value or resample your audio accordingly."
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"".format(
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c.feature_win_step, c.feature_win_step / 1000, c.audio_sample_rate
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)
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)
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sys.exit(1)
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c.audio_step_samples = c.audio_sample_rate * (c.feature_win_step / 1000)
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if c.one_shot_infer:
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if not path_exists_remote(c.one_shot_infer):
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log_error("Path specified in --one_shot_infer is not a valid file.")
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sys.exit(1)
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if c.train_cudnn and c.load_cudnn:
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log_error(
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"Trying to use --train_cudnn, but --load_cudnn "
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"was also specified. The --load_cudnn flag is only "
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"needed when converting a CuDNN RNN checkpoint to "
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"a CPU-capable graph. If your system is capable of "
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"using CuDNN RNN, you can just specify the CuDNN RNN "
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"checkpoint normally with --save_checkpoint_dir."
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)
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sys.exit(1)
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# If separate save and load flags were not specified, default to load and save
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# from the same dir.
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if not c.save_checkpoint_dir:
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c.save_checkpoint_dir = c.checkpoint_dir
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if not c.load_checkpoint_dir:
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c.load_checkpoint_dir = c.checkpoint_dir
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def initialize_globals_from_cli():
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c = _SttConfig()
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c.parse_args(arg_prefix="")
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c.__post_init__()
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_ConfigSingleton._config = c # pylint: disable=protected-access
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def initialize_globals_from_args(**override_args):
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# Update Config with new args
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c = _SttConfig(**override_args)
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_ConfigSingleton._config = c # pylint: disable=protected-access
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@ -20,7 +20,7 @@ from multiprocessing import Process, cpu_count
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from coqui_stt_ctcdecoder import Scorer, ctc_beam_search_decoder_batch
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from coqui_stt_training.util.audio import AudioFile
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from coqui_stt_training.util.config import Config, _SttConfig, initialize_config_globals
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from coqui_stt_training.util.config import Config, initialize_globals_from_cli
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from coqui_stt_training.util.feeding import split_audio_file
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from coqui_stt_training.util.flags import FLAGS, create_flags
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from coqui_stt_training.util.logging import (
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@ -42,8 +42,8 @@ def transcribe_file(audio_path, tlog_path):
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)
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from coqui_stt_training.util.checkpoints import load_graph_for_evaluation
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Config = _SttConfig()
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initialize_config_globals(Config)
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initialize_globals_from_cli()
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scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.scorer_path, Config.alphabet)
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try:
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num_processes = cpu_count()
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