Merge pull request #1908 from JRMeyer/config-logic

bin/run-ci-graph_augmentations.sh

@@ -14,7 +14,8 @@ fi;
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false --early_stop false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false --early_stop false \
   --train_files ${ldc93s1_csv} --train_batch_size 1 \
   --scorer "" \
   --augment dropout \

bin/run-ci-ldc93s1_checkpoint.sh

@@ -14,7 +14,8 @@ fi;
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false --early_stop false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false --early_stop false \
   --train_files ${ldc93s1_csv} --train_batch_size 1 \
   --dev_files ${ldc93s1_csv} --dev_batch_size 1 \
   --test_files ${ldc93s1_csv} --test_batch_size 1 \

bin/run-ci-ldc93s1_checkpoint_sdb.sh

@@ -20,7 +20,8 @@ fi;
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false --early_stop false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false --early_stop false \
   --train_files ${ldc93s1_sdb} --train_batch_size 1 \
   --dev_files ${ldc93s1_sdb} --dev_batch_size 1 \
   --test_files ${ldc93s1_sdb} --test_batch_size 1 \

bin/run-ci-ldc93s1_new.sh

@@ -17,7 +17,8 @@ fi;
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false --early_stop false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false --early_stop false \
   --train_files ${ldc93s1_csv} --train_batch_size 1 \
   --feature_cache '/tmp/ldc93s1_cache' \
   --dev_files ${ldc93s1_csv} --dev_batch_size 1 \

bin/run-ci-ldc93s1_new_metrics.sh

@@ -17,7 +17,8 @@ fi;
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false --early_stop false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false --early_stop false \
   --train_files ${ldc93s1_csv} --train_batch_size 1 \
   --dev_files ${ldc93s1_csv} --dev_batch_size 1 \
   --test_files ${ldc93s1_csv} --test_batch_size 1 \

bin/run-ci-ldc93s1_new_sdb.sh

@@ -23,7 +23,8 @@ fi;
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false --early_stop false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false --early_stop false \
   --train_files ${ldc93s1_sdb} --train_batch_size 1 \
   --dev_files ${ldc93s1_sdb} --dev_batch_size 1 \
   --test_files ${ldc93s1_sdb} --test_batch_size 1 \

bin/run-ci-ldc93s1_new_sdb_csv.sh

@@ -23,7 +23,8 @@ fi;
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false --early_stop false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false --early_stop false \
   --train_files ${ldc93s1_sdb} ${ldc93s1_csv} --train_batch_size 1 \
   --feature_cache '/tmp/ldc93s1_cache_sdb_csv' \
   --dev_files ${ldc93s1_sdb} ${ldc93s1_csv} --dev_batch_size 1 \

bin/run-ci-ldc93s1_singleshotinference.sh

@@ -14,7 +14,8 @@ fi;
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false --early_stop false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false --early_stop false \
   --train_files ${ldc93s1_csv} --train_batch_size 1 \
   --dev_files ${ldc93s1_csv} --dev_batch_size 1 \
   --test_files ${ldc93s1_csv} --test_batch_size 1 \
@@ -23,7 +24,7 @@ python -u train.py --show_progressbar false --early_stop false \
   --learning_rate 0.001 --dropout_rate 0.05 \
   --scorer_path 'data/smoke_test/pruned_lm.scorer'
 
-python -u train.py \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
   --n_hidden 100 \
   --checkpoint_dir '/tmp/ckpt' \
   --scorer_path 'data/smoke_test/pruned_lm.scorer' \

bin/run-ci-ldc93s1_tflite.sh

@@ -16,7 +16,8 @@ fi;
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false \
   --n_hidden 100 \
   --checkpoint_dir '/tmp/ckpt' \
   --export_dir '/tmp/train_tflite' \
@@ -26,7 +27,8 @@ python -u train.py --show_progressbar false \
 
 mkdir /tmp/train_tflite/en-us
 
-python -u train.py --show_progressbar false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false \
   --n_hidden 100 \
   --checkpoint_dir '/tmp/ckpt' \
   --export_dir '/tmp/train_tflite/en-us' \

bin/run-ldc93s1.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python
+import os
+from import_ldc93s1 import _download_and_preprocess_data as download_ldc
+from coqui_stt_training.util.config import initialize_globals_from_args
+from coqui_stt_training.train import train, test, early_training_checks
+import tensorflow.compat.v1 as tfv1
+
+# only one GPU for only one training sample
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+download_ldc("data/ldc93s1")
+
+initialize_globals_from_args(
+    load_train="init",
+    alphabet_config_path="data/alphabet.txt",
+    train_files=["data/ldc93s1/ldc93s1.csv"],
+    dev_files=["data/ldc93s1/ldc93s1.csv"],
+    test_files=["data/ldc93s1/ldc93s1.csv"],
+    augment=["time_mask"],
+    n_hidden=100,
+    epochs=200,
+)
+
+early_training_checks()
+
+train()
+tfv1.reset_default_graph()
+test()

bin/run-ldc93s1.sh

@@ -20,7 +20,8 @@ fi
 # and when trying to run on multiple devices (like GPUs), this will break
 export CUDA_VISIBLE_DEVICES=0
 
-python -u train.py --show_progressbar false \
+python -u train.py --alphabet_config_path "data/alphabet.txt" \
+  --show_progressbar false \
   --train_files data/ldc93s1/ldc93s1.csv \
   --test_files data/ldc93s1/ldc93s1.csv \
   --train_batch_size 1 \

ci_scripts/train-extra-tests.sh

@@ -66,3 +66,6 @@ time ./bin/run-ci-ldc93s1_checkpoint_sdb.sh
 
 # Bytes output mode, resuming from checkpoint
 time ./bin/run-ci-ldc93s1_checkpoint_bytes.sh
+
+# Training with args set via initialize_globals_from_args()
+time python ./bin/run-ldc93s1.py

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, initialize_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
@@ -52,7 +52,7 @@ def objective(trial):
 
 
 def main(_):
-    initialize_globals()
+    initialize_globals_from_cli()
 
     if not FLAGS.test_files:
         log_error(

training/coqui_stt_training/evaluate.py

@@ -18,7 +18,7 @@ from .util.checkpoints import load_graph_for_evaluation
 from .util.config import (
     Config,
     create_progressbar,
-    initialize_globals,
+    initialize_globals_from_cli,
     log_error,
     log_progress,
 )
@@ -169,7 +169,7 @@ def evaluate(test_csvs, create_model):
 
 
 def main():
-    initialize_globals()
+    initialize_globals_from_cli()
 
     if not Config.test_files:
         log_error(

training/coqui_stt_training/train.py

@@ -45,7 +45,7 @@ from .util.checkpoints import (
 from .util.config import (
     Config,
     create_progressbar,
-    initialize_globals,
+    initialize_globals_from_cli,
     log_debug,
     log_error,
     log_info,
@@ -1249,7 +1249,7 @@ def early_training_checks():
 
 
 def main():
-    initialize_globals()
+    initialize_globals_from_cli()
     early_training_checks()
 
     if Config.train_files:

training/coqui_stt_training/util/config.py

@@ -36,10 +36,201 @@ 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 your feature-cache settings, augmentations of "
+                "the first epoch will be repeated on all following epochs. "
+                "This may lead to unintended over-fitting. "
+                "You can 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 separate save and load flags were not specified, default to load and save
+        # from the same dir.
+
+        # if save_checkpoint_dir hasn't been set, or checkpoint_dir is new
+        if (not self.save_checkpoint_dir) or (
+            self.save_checkpoint_dir is not self.checkpoint_dir
+        ):
+            if not self.checkpoint_dir:
+                self.checkpoint_dir = xdg.save_data_path(
+                    os.path.join("stt", "checkpoints")
+                )
+                self.save_checkpoint_dir = self.checkpoint_dir
+            else:
+                self.save_checkpoint_dir = self.checkpoint_dir
+        # if load_checkpoint_dir hasn't been set, or checkpoint_dir is new
+        if (not self.load_checkpoint_dir) or (
+            self.load_checkpoint_dir is not self.checkpoint_dir
+        ):
+            if not self.checkpoint_dir:
+                self.checkpoint_dir = xdg.load_data_path(
+                    os.path.join("stt", "checkpoints")
+                )
+                self.load_checkpoint_dir = self.checkpoint_dir
+            else:
+                self.load_checkpoint_dir = self.checkpoint_dir
+
+        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 and self.alphabet_config_path:
+            raise RuntimeError(
+                "You cannot set --alphabet_config_path *and* --bytes_output_mode"
+            )
+        elif 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)
+
     train_files: List[str] = field(
         default_factory=list,
         metadata=dict(
-            help="space-separated list of files specifying the dataset used for training. Multiple files will get merged. If empty, training will not be run."
+            help="space-separated list of files specifying the datasets used for training. Multiple files will get merged. If empty, training will not be run."
         ),
     )
     dev_files: List[str] = field(
@@ -472,7 +663,7 @@ class _SttConfig(Coqpit):
         ),
     )
     alphabet_config_path: str = field(
-        default="data/alphabet.txt",
+        default="",
         metadata=dict(
             help="path to the configuration file specifying the alphabet used by the network. See the comment in data/alphabet.txt for a description of the format."
         ),
@@ -540,166 +731,17 @@ class _SttConfig(Coqpit):
         ),
     )
 
-    def check_values(self):
-        c = asdict(self)
-        check_argument("alphabet_config_path", c, is_path=True)
-        check_argument("one_shot_infer", c, is_path=True)
 
-
+def initialize_globals_from_cli():
-def initialize_globals():
     c = _SttConfig()
     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()
-    else:
-        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 sixth layer = number of characters in the target language plus one
-    c.n_hidden_6 = c.alphabet.GetSize() + 1  # +1 for CTC blank label
-
-    # 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
 
 

training/coqui_stt_training/util/helpers.py

@@ -19,14 +19,19 @@ ValueRange = namedtuple("ValueRange", "start end r")
 
 
 def parse_file_size(file_size):
-    file_size = file_size.lower().strip()
+    if type(file_size) is str:
-    if len(file_size) == 0:
+        file_size = file_size.lower().strip()
-        return 0
+        if len(file_size) == 0:
-    n = int(keep_only_digits(file_size))
+            return 0
-    if file_size[-1] == "b":
+        n = int(keep_only_digits(file_size))
-        file_size = file_size[:-1]
+        if file_size[-1] == "b":
-    e = file_size[-1]
+            file_size = file_size[:-1]
-    return SIZE_PREFIX_LOOKUP[e] * n if e in SIZE_PREFIX_LOOKUP else n
+        e = file_size[-1]
+        return SIZE_PREFIX_LOOKUP[e] * n if e in SIZE_PREFIX_LOOKUP else n
+    elif type(file_size) is int:
+        return file_size
+    else:
+        raise ValueError("file_size not of type 'int' or 'str'")
 
 
 def keep_only_digits(txt):

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, initialize_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 (
@@ -42,7 +42,8 @@ def transcribe_file(audio_path, tlog_path):
     )
     from coqui_stt_training.util.checkpoints import load_graph_for_evaluation
 
-    initialize_globals()
+    initialize_globals_from_cli()
+
     scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.scorer_path, Config.alphabet)
     try:
         num_processes = cpu_count()