Separate process per file; less log noise
This commit is contained in:
Tilman Kamp
parent
c24c510fd9
commit
29528ed7b7
8
DeepSpeech.py
Executable file → Normal file
8
DeepSpeech.py
Executable file → Normal file
@ -394,7 +394,7 @@ def log_grads_and_vars(grads_and_vars):
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log_variable(variable, gradient=gradient)
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def try_loading(session, saver, checkpoint_filename, caption, load_step=True):
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def try_loading(session, saver, checkpoint_filename, caption, load_step=True, log_success=True):
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try:
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checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir, checkpoint_filename)
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if not checkpoint:
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@ -403,8 +403,10 @@ def try_loading(session, saver, checkpoint_filename, caption, load_step=True):
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saver.restore(session, checkpoint_path)
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if load_step:
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restored_step = session.run(tfv1.train.get_global_step())
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log_info('Restored variables from %s checkpoint at %s, step %d' % (caption, checkpoint_path, restored_step))
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else:
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if log_success:
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log_info('Restored variables from %s checkpoint at %s, step %d' %
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(caption, checkpoint_path, restored_step))
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elif log_success:
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log_info('Restored variables from %s checkpoint at %s' % (caption, checkpoint_path))
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return True
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except tf.errors.InvalidArgumentError as e:
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127
transcribe.py
127
transcribe.py
@ -3,12 +3,16 @@
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from __future__ import absolute_import, division, print_function
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import os
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import gc
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import sys
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import json
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import tensorflow as tf
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import tensorflow.compat.v1.logging as tflogging
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tflogging.set_verbosity(tflogging.ERROR)
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import logging
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logging.getLogger('sox').setLevel(logging.ERROR)
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from multiprocessing import cpu_count
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from multiprocessing import Process, cpu_count
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from ds_ctcdecoder import ctc_beam_search_decoder_batch, Scorer
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from util.config import Config, initialize_globals
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from util.audio import AudioFile
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@ -17,44 +21,41 @@ from util.flags import create_flags, FLAGS
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from util.logging import log_error, log_info, log_progress, create_progressbar
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def split_audio_file_flags(audio_file):
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return split_audio_file(audio_file,
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batch_size=FLAGS.batch_size,
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aggressiveness=FLAGS.vad_aggressiveness,
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outlier_duration_ms=FLAGS.outlier_duration_ms,
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outlier_batch_size=FLAGS.outlier_batch_size)
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def fail(message, code=1):
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log_error(message)
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sys.exit(code)
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def transcribe(path_pairs, create_model, try_loading):
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def transcribe_file(audio_path, tlog_path):
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from DeepSpeech import create_model, try_loading # pylint: disable=cyclic-import,import-outside-toplevel
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initialize_globals()
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scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.lm_binary_path, FLAGS.lm_trie_path, Config.alphabet)
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audio_path, _ = path_pairs[0]
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data_set = split_audio_file_flags(None)
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iterator = tf.data.Iterator.from_structure(data_set.output_types, data_set.output_shapes,
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output_classes=data_set.output_classes)
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batch_time_start, batch_time_end, batch_x, batch_x_len = iterator.get_next()
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no_dropout = [None] * 6
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logits, _ = create_model(batch_x=batch_x, seq_length=batch_x_len, dropout=no_dropout)
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transposed = tf.nn.softmax(tf.transpose(logits, [1, 0, 2]))
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tf.train.get_or_create_global_step()
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try:
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num_processes = cpu_count()
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except NotImplementedError:
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num_processes = 1
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saver = tf.train.Saver()
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with tf.Session(config=Config.session_config) as session:
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loaded = try_loading(session, saver, 'best_dev_checkpoint', 'best validation')
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if not loaded:
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loaded = try_loading(session, saver, 'checkpoint', 'most recent')
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if not loaded:
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log_error('Checkpoint directory ({}) does not contain a valid checkpoint state.'
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.format(FLAGS.checkpoint_dir))
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sys.exit(1)
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def run_transcription(p_index, p_data_set, p_audio_path, p_tlog_path):
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bar = create_progressbar(prefix='Transcribing file {} "{}" | '.format(p_index, p_audio_path)).start()
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log_progress('Transcribing file {}, "{}"...'.format(p_index, p_audio_path))
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session.run(iterator.make_initializer(p_data_set))
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with AudioFile(audio_path, as_path=True) as wav_path:
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data_set = split_audio_file(wav_path,
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batch_size=FLAGS.batch_size,
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aggressiveness=FLAGS.vad_aggressiveness,
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outlier_duration_ms=FLAGS.outlier_duration_ms,
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outlier_batch_size=FLAGS.outlier_batch_size)
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iterator = tf.data.Iterator.from_structure(data_set.output_types, data_set.output_shapes,
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output_classes=data_set.output_classes)
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batch_time_start, batch_time_end, batch_x, batch_x_len = iterator.get_next()
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no_dropout = [None] * 6
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logits, _ = create_model(batch_x=batch_x, seq_length=batch_x_len, dropout=no_dropout)
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transposed = tf.nn.softmax(tf.transpose(logits, [1, 0, 2]))
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tf.train.get_or_create_global_step()
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saver = tf.train.Saver()
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with tf.Session(config=Config.session_config) as session:
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loaded = try_loading(session, saver, 'best_dev_checkpoint', 'best validation', log_success=False)
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if not loaded:
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loaded = try_loading(session, saver, 'checkpoint', 'most recent', log_success=False)
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if not loaded:
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fail('Checkpoint directory ({}) does not contain a valid checkpoint state.'
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.format(FLAGS.checkpoint_dir))
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session.run(iterator.make_initializer(data_set))
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transcripts = []
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while True:
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try:
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@ -67,21 +68,28 @@ def transcribe(path_pairs, create_model, try_loading):
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scorer=scorer)
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decoded = list(d[0][1] for d in decoded)
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transcripts.extend(zip(starts, ends, decoded))
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bar.update(len(transcripts))
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bar.finish()
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transcripts.sort(key=lambda t: t[0])
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transcripts = [{'start': int(start),
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'end': int(end),
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'transcript': transcript} for start, end, transcript in transcripts]
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log_info('Writing transcript log to "{}"...'.format(p_tlog_path))
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with open(p_tlog_path, 'w') as tlog_file:
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with open(tlog_path, 'w') as tlog_file:
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json.dump(transcripts, tlog_file, default=float)
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for index, (audio_path, tlog_path) in enumerate(path_pairs):
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with AudioFile(audio_path, as_path=True) as wav_path:
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data_set = split_audio_file_flags(wav_path)
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run_transcription(index, data_set, audio_path, tlog_path)
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gc.collect()
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def transcribe_many(path_pairs):
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pbar = create_progressbar(prefix='Transcribing files | ', max_value=len(path_pairs)).start()
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for i, (src_path, dst_path) in enumerate(path_pairs):
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p = Process(target=transcribe_file, args=(src_path, dst_path))
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p.start()
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p.join()
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log_progress('Transcribed file {} of {} from "{}" to "{}"'.format(i + 1, len(path_pairs), src_path, dst_path))
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pbar.update(i)
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pbar.finish()
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def transcribe_one(src_path, dst_path):
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transcribe_file(src_path, dst_path)
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log_info('Transcribed file "{}" to "{}"'.format(src_path, dst_path))
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def resolve(base_path, spec_path):
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@ -93,49 +101,36 @@ def resolve(base_path, spec_path):
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def main(_):
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initialize_globals()
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if not FLAGS.src:
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log_error('You have to specify which file or catalog to transcribe via the --src flag.')
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sys.exit(1)
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from DeepSpeech import create_model, try_loading # pylint: disable=cyclic-import,import-outside-toplevel
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fail('You have to specify which file or catalog to transcribe via the --src flag.')
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src_path = os.path.abspath(FLAGS.src)
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if not os.path.isfile(src_path):
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log_error('Path in --src not existing')
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sys.exit(1)
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fail('Path in --src not existing')
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if src_path.endswith('.catalog'):
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if FLAGS.dst:
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log_error('Parameter --dst not supported if --src points to a catalog')
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sys.exit(1)
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fail('Parameter --dst not supported if --src points to a catalog')
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catalog_dir = os.path.dirname(src_path)
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with open(src_path, 'r') as catalog_file:
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catalog_entries = json.load(catalog_file)
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catalog_entries = [(resolve(catalog_dir, e['audio']), resolve(catalog_dir, e['tlog'])) for e in catalog_entries]
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if any(map(lambda e: not os.path.isfile(e[0]), catalog_entries)):
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log_error('Missing source file(s) in catalog')
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sys.exit(1)
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fail('Missing source file(s) in catalog')
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if not FLAGS.force and any(map(lambda e: os.path.isfile(e[1]), catalog_entries)):
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log_error('Destination file(s) from catalog already existing, use --force for overwriting')
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sys.exit(1)
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fail('Destination file(s) from catalog already existing, use --force for overwriting')
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if any(map(lambda e: not os.path.isdir(os.path.dirname(e[1])), catalog_entries)):
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log_error('Missing destination directory for at least one catalog entry')
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sys.exit(1)
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transcribe(catalog_entries, create_model, try_loading)
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fail('Missing destination directory for at least one catalog entry')
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transcribe_many(catalog_entries)
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else:
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dst_path = os.path.abspath(FLAGS.dst) if FLAGS.dst else os.path.splitext(src_path)[0] + '.tlog'
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if os.path.isfile(dst_path):
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if FLAGS.force:
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transcribe([(src_path, dst_path)], create_model, try_loading)
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transcribe_one(src_path, dst_path)
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else:
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log_error('Destination file "{}" already existing - requires --force for overwriting'.format(dst_path))
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sys.exit(0)
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fail('Destination file "{}" already existing - use --force for overwriting'.format(dst_path), code=0)
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elif os.path.isdir(os.path.dirname(dst_path)):
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transcribe([(src_path, dst_path)], create_model, try_loading)
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transcribe_one(src_path, dst_path)
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else:
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log_error('Missing destination directory')
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sys.exit(1)
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fail('Missing destination directory')
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if __name__ == '__main__':
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@ -1,6 +1,5 @@
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import os
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import sox
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import time
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import wave
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import tempfile
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import collections
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@ -55,13 +54,6 @@ class AudioFile:
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self.open_file.close()
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_, self.tmp_file_path = tempfile.mkstemp(suffix='.wav')
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convert_audio(self.audio_path, self.tmp_file_path, file_type='wav', audio_format=self.audio_format)
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retry_count = 10
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while retry_count > 0 and not (os.path.exists(self.tmp_file_path) and os.path.getsize(self.tmp_file_path) > 0):
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retry_count -= 1
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if retry_count == 0:
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raise RuntimeError('Unable to read temporary .wav file')
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time.sleep(1)
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print('Trying to read temporary .wav file...')
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if self.as_path:
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return self.tmp_file_path
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self.open_file = wave.open(self.tmp_file_path, 'r')
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