Undo late-imports
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@ -1,12 +1,5 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# This script is structured in a weird way, with delayed imports. This is due
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# to the use of multiprocessing. TensorFlow cannot handle forking, and even with
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# the spawn strategy set to "spawn" it still leads to weird problems, so we
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# restructure the code so that TensorFlow is only imported inside the child
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# processes.
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import glob
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import itertools
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import json
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@ -18,28 +11,31 @@ from multiprocessing import Pool, Lock, cpu_count
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from pathlib import Path
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from typing import Optional, List, Tuple
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LOG_LEVEL_INDEX = sys.argv.index("--log_level") + 1 if "--log_level" in sys.argv else 0
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DESIRED_LOG_LEVEL = (
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sys.argv[LOG_LEVEL_INDEX] if 0 < LOG_LEVEL_INDEX < len(sys.argv) else "3"
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)
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = DESIRED_LOG_LEVEL
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# Hide GPUs to prevent issues with child processes trying to use the same GPU
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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import tensorflow as tf
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import tensorflow.compat.v1 as tfv1
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from coqui_stt_ctcdecoder import Scorer, ctc_beam_search_decoder_batch
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from coqui_stt_training.train import create_model
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from coqui_stt_training.util.audio import AudioFile
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from coqui_stt_training.util.checkpoints import load_graph_for_evaluation
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from coqui_stt_training.util.config import (
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BaseSttConfig,
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Config,
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initialize_globals_from_instance,
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)
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from coqui_stt_training.util.feeding import split_audio_file
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from coqui_stt_training.util.helpers import check_ctcdecoder_version
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from tqdm import tqdm
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def transcribe_file(audio_path: Path, tlog_path: Path):
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log_level_index = (
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sys.argv.index("--log_level") + 1 if "--log_level" in sys.argv else 0
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)
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desired_log_level = (
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sys.argv[log_level_index] if 0 < log_level_index < len(sys.argv) else "3"
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)
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = desired_log_level
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import tensorflow as tf
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import tensorflow.compat.v1 as tfv1
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from coqui_stt_training.train import create_model
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from coqui_stt_training.util.audio import AudioFile
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from coqui_stt_training.util.checkpoints import load_graph_for_evaluation
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from coqui_stt_training.util.config import Config
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from coqui_stt_training.util.feeding import split_audio_file
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initialize_transcribe_config()
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scorer = None
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@ -113,8 +109,6 @@ def step_function(job):
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def transcribe_many(src_paths, dst_paths):
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from coqui_stt_training.util.config import Config, log_progress
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# Create list of items to be processed: [(i, src_path[i], dst_paths[i])]
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jobs = zip(itertools.count(), src_paths, dst_paths)
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@ -134,11 +128,17 @@ def transcribe_many(src_paths, dst_paths):
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cwd = Path.cwd()
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for result in process_iterable:
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idx, src, dst = result
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# Revert to relative to avoid spamming logs
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if not src.is_absolute():
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# Revert to relative if possible to make logs more concise
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# if path is not relative to cwd, use the absolute path
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# (Path.is_relative_to is only available in Python >=3.9)
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try:
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src = src.relative_to(cwd)
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if not dst.is_absolute():
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except ValueError:
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pass
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try:
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dst = dst.relative_to(cwd)
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except ValueError:
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pass
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tqdm.write(f'[{idx+1}]: "{src}" -> "{dst}"')
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@ -187,14 +187,12 @@ def get_tasks_from_dir(src_dir: Path, recursive: bool) -> Tuple[List[Path], List
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transcription results.
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"""
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glob_method = src_dir.rglob if recursive else src_dir.glob
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src_paths = list(itertools.chain(glob_method("*.wav"), glob_method("*.opus")))
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src_paths = list(glob_method("*.wav"))
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dst_paths = [path.with_suffix(".tlog") for path in src_paths]
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return src_paths, dst_paths
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def transcribe():
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from coqui_stt_training.util.config import Config
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initialize_transcribe_config()
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src_path = Path(Config.src).resolve()
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@ -236,12 +234,6 @@ def transcribe():
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transcribe_many(src_paths, dst_paths)
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def initialize_transcribe_config():
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from coqui_stt_training.util.config import (
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BaseSttConfig,
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initialize_globals_from_instance,
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)
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@dataclass
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class TranscribeConfig(BaseSttConfig):
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src: str = field(
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@ -313,12 +305,14 @@ def initialize_transcribe_config():
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super().__post_init__()
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def initialize_transcribe_config():
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config = TranscribeConfig.init_from_argparse(arg_prefix="")
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initialize_globals_from_instance(config)
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def main():
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from coqui_stt_training.util.helpers import check_ctcdecoder_version
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assert not tf.test.is_gpu_available()
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# Set start method to spawn on all platforms to avoid issues with TensorFlow
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multiprocessing.set_start_method("spawn")
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