SDB support

DeepSpeech.py

@@ -33,7 +33,7 @@ from util.config import Config, initialize_globals
 from util.checkpoints import load_or_init_graph
 from util.feeding import create_dataset, samples_to_mfccs, audiofile_to_features
 from util.flags import create_flags, FLAGS
-from util.helpers import check_ctcdecoder_version
+from util.helpers import check_ctcdecoder_version, ExceptionBox
 from util.logging import log_info, log_error, log_debug, log_progress, create_progressbar
 
 check_ctcdecoder_version()
@@ -418,12 +418,17 @@ def train():
             FLAGS.augmentation_sparse_warp):
         do_cache_dataset = False
 
+    exception_box = ExceptionBox()
+
     # Create training and validation datasets
     train_set = create_dataset(FLAGS.train_files.split(','),
                                batch_size=FLAGS.train_batch_size,
                                enable_cache=FLAGS.feature_cache and do_cache_dataset,
                                cache_path=FLAGS.feature_cache,
-                               train_phase=True)
+                               train_phase=True,
+                               exception_box=exception_box,
+                               process_ahead=len(Config.available_devices) * FLAGS.train_batch_size * 2,
+                               buffering=FLAGS.read_buffer)
 
     iterator = tfv1.data.Iterator.from_structure(tfv1.data.get_output_types(train_set),
                                                  tfv1.data.get_output_shapes(train_set),
@@ -433,8 +438,13 @@ def train():
     train_init_op = iterator.make_initializer(train_set)
 
     if FLAGS.dev_files:
-        dev_csvs = FLAGS.dev_files.split(',')
-        dev_sets = [create_dataset([csv], batch_size=FLAGS.dev_batch_size, train_phase=False) for csv in dev_csvs]
+        dev_sources = FLAGS.dev_files.split(',')
+        dev_sets = [create_dataset([source],
+                                   batch_size=FLAGS.dev_batch_size,
+                                   train_phase=False,
+                                   exception_box=exception_box,
+                                   process_ahead=len(Config.available_devices) * FLAGS.dev_batch_size * 2,
+                                   buffering=FLAGS.read_buffer) for source in dev_sources]
         dev_init_ops = [iterator.make_initializer(dev_set) for dev_set in dev_sets]
 
     # Dropout
@@ -540,6 +550,7 @@ def train():
                     _, current_step, batch_loss, problem_files, step_summary = \
                         session.run([train_op, global_step, loss, non_finite_files, step_summaries_op],
                                     feed_dict=feed_dict)
+                    exception_box.raise_if_set()
                 except tf.errors.InvalidArgumentError as err:
                     if FLAGS.augmentation_sparse_warp:
                         log_info("Ignoring sparse warp error: {}".format(err))
@@ -547,6 +558,7 @@ def train():
                     else:
                         raise
                 except tf.errors.OutOfRangeError:
+                    exception_box.raise_if_set()
                     break
 
                 if problem_files.size > 0:
@@ -586,12 +598,12 @@ def train():
                     # Validation
                     dev_loss = 0.0
                     total_steps = 0
-                    for csv, init_op in zip(dev_csvs, dev_init_ops):
-                        log_progress('Validating epoch %d on %s...' % (epoch, csv))
-                        set_loss, steps = run_set('dev', epoch, init_op, dataset=csv)
+                    for source, init_op in zip(dev_sources, dev_init_ops):
+                        log_progress('Validating epoch %d on %s...' % (epoch, source))
+                        set_loss, steps = run_set('dev', epoch, init_op, dataset=source)
                         dev_loss += set_loss * steps
                         total_steps += steps
-                        log_progress('Finished validating epoch %d on %s - loss: %f' % (epoch, csv, set_loss))
+                        log_progress('Finished validating epoch %d on %s - loss: %f' % (epoch, source, set_loss))
 
                     dev_loss = dev_loss / total_steps
                     dev_losses.append(dev_loss)

bin/build_sdb.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+'''
+Tool for building Sample Databases (SDB files) from DeepSpeech CSV files and other SDB files
+Use "python3 build_sdb.py -h" for help
+'''
+from __future__ import absolute_import, division, print_function
+
+# Make sure we can import stuff from util/
+# This script needs to be run from the root of the DeepSpeech repository
+import os
+import sys
+sys.path.insert(1, os.path.join(sys.path[0], '..'))
+
+import argparse
+import progressbar
+
+from util.downloader import SIMPLE_BAR
+from util.audio import change_audio_types, AUDIO_TYPE_WAV, AUDIO_TYPE_OPUS
+from util.sample_collections import samples_from_files, DirectSDBWriter
+
+AUDIO_TYPE_LOOKUP = {
+    'wav': AUDIO_TYPE_WAV,
+    'opus': AUDIO_TYPE_OPUS
+}
+
+
+def build_sdb():
+    audio_type = AUDIO_TYPE_LOOKUP[CLI_ARGS.audio_type]
+    with DirectSDBWriter(CLI_ARGS.target, audio_type=audio_type) as sdb_writer:
+        samples = samples_from_files(CLI_ARGS.sources)
+        bar = progressbar.ProgressBar(max_value=len(samples), widgets=SIMPLE_BAR)
+        for sample in bar(change_audio_types(samples, audio_type=audio_type, processes=CLI_ARGS.workers)):
+            sdb_writer.add(sample)
+
+
+def handle_args():
+    parser = argparse.ArgumentParser(description='Tool for building Sample Databases (SDB files) '
+                                                 'from DeepSpeech CSV files and other SDB files')
+    parser.add_argument('sources', nargs='+', help='Source CSV and/or SDB files - '
+                                                   'Note: For getting a correctly ordered target SDB, source SDBs have '
+                                                   'to have their samples already ordered from shortest to longest.')
+    parser.add_argument('target', help='SDB file to create')
+    parser.add_argument('--audio-type', default='opus', choices=AUDIO_TYPE_LOOKUP.keys(),
+                        help='Audio representation inside target SDB')
+    parser.add_argument('--workers', type=int, default=None, help='Number of encoding SDB workers')
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    CLI_ARGS = handle_args()
+    build_sdb()

bin/play.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python
+"""
+Tool for playing samples from Sample Databases (SDB files) and DeepSpeech CSV files
+Use "python3 build_sdb.py -h" for help
+"""
+from __future__ import absolute_import, division, print_function
+
+# Make sure we can import stuff from util/
+# This script needs to be run from the root of the DeepSpeech repository
+import os
+import sys
+sys.path.insert(1, os.path.join(sys.path[0], '..'))
+
+import random
+import argparse
+
+from util.sample_collections import samples_from_file
+from util.audio import AUDIO_TYPE_PCM
+
+
+def play_sample(samples, index):
+    if index < 0:
+        index = len(samples) + index
+    if CLI_ARGS.random:
+        index = random.randint(0, len(samples))
+    elif index >= len(samples):
+        print('No sample with index {}'.format(CLI_ARGS.start))
+        sys.exit(1)
+    sample = samples[index]
+    print('Sample "{}"'.format(sample.sample_id))
+    print('  "{}"'.format(sample.transcript))
+    sample.change_audio_type(AUDIO_TYPE_PCM)
+    rate, channels, width = sample.audio_format
+    wave_obj = simpleaudio.WaveObject(sample.audio, channels, width, rate)
+    play_obj = wave_obj.play()
+    play_obj.wait_done()
+
+
+def play_collection():
+    samples = samples_from_file(CLI_ARGS.collection, buffering=0)
+    played = 0
+    index = CLI_ARGS.start
+    while True:
+        if 0 <= CLI_ARGS.number <= played:
+            return
+        play_sample(samples, index)
+        played += 1
+        index = (index + 1) % len(samples)
+
+
+def handle_args():
+    parser = argparse.ArgumentParser(description='Tool for playing samples from Sample Databases (SDB files) '
+                                                 'and DeepSpeech CSV files')
+    parser.add_argument('collection', help='Sample DB or CSV file to play samples from')
+    parser.add_argument('--start', type=int, default=0,
+                        help='Sample index to start at (negative numbers are relative to the end of the collection)')
+    parser.add_argument('--number', type=int, default=-1, help='Number of samples to play (-1 for endless)')
+    parser.add_argument('--random', action='store_true', help='If samples should be played in random order')
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    try:
+        import simpleaudio
+    except ModuleNotFoundError:
+        print('play.py requires Python package "simpleaudio"')
+        sys.exit(1)
+    CLI_ARGS = handle_args()
+    try:
+        play_collection()
+    except KeyboardInterrupt:
+        print(' Stopped')
+        sys.exit(0)

bin/run-tc-ldc93s1_checkpoint_sdb.sh

@@ -0,0 +1,37 @@
+#!/bin/sh
+
+set -xe
+
+ldc93s1_dir="./data/smoke_test"
+ldc93s1_csv="${ldc93s1_dir}/ldc93s1.csv"
+ldc93s1_sdb="${ldc93s1_dir}/ldc93s1.sdb"
+
+if [ ! -f "${ldc93s1_dir}/ldc93s1.csv" ]; then
+    echo "Downloading and preprocessing LDC93S1 example data, saving in ${ldc93s1_dir}."
+    python -u bin/import_ldc93s1.py ${ldc93s1_dir}
+fi;
+
+if [ ! -f "${ldc93s1_dir}/ldc93s1.sdb" ]; then
+    echo "Converting LDC93S1 example data, saving to ${ldc93s1_sdb}."
+    python -u bin/build_sdb.py ${ldc93s1_csv} ${ldc93s1_sdb}
+fi;
+
+# Force only one visible device because we have a single-sample dataset
+# and when trying to run on multiple devices (like GPUs), this will break
+export CUDA_VISIBLE_DEVICES=0
+
+python -u DeepSpeech.py --noshow_progressbar --noearly_stop \
+  --train_files ${ldc93s1_sdb} --train_batch_size 1 \
+  --dev_files ${ldc93s1_sdb} --dev_batch_size 1 \
+  --test_files ${ldc93s1_sdb} --test_batch_size 1 \
+  --n_hidden 100 --epochs 1 \
+  --max_to_keep 1 --checkpoint_dir '/tmp/ckpt_sdb' \
+  --learning_rate 0.001 --dropout_rate 0.05 \
+  --scorer_path 'data/smoke_test/pruned_lm.scorer' | tee /tmp/resume.log
+
+if ! grep "Loading best validating checkpoint from" /tmp/resume.log; then
+  echo "Did not resume training from checkpoint"
+  exit 1
+else
+  exit 0
+fi

bin/run-tc-ldc93s1_new_sdb.sh

@@ -0,0 +1,34 @@
+#!/bin/sh
+
+set -xe
+
+ldc93s1_dir="./data/smoke_test"
+ldc93s1_csv="${ldc93s1_dir}/ldc93s1.csv"
+ldc93s1_sdb="${ldc93s1_dir}/ldc93s1.sdb"
+
+epoch_count=$1
+audio_sample_rate=$2
+
+if [ ! -f "${ldc93s1_dir}/ldc93s1.csv" ]; then
+    echo "Downloading and preprocessing LDC93S1 example data, saving in ${ldc93s1_dir}."
+    python -u bin/import_ldc93s1.py ${ldc93s1_dir}
+fi;
+
+if [ ! -f "${ldc93s1_dir}/ldc93s1.sdb" ]; then
+    echo "Converting LDC93S1 example data, saving to ${ldc93s1_sdb}."
+    python -u bin/build_sdb.py ${ldc93s1_csv} ${ldc93s1_sdb}
+fi;
+
+# Force only one visible device because we have a single-sample dataset
+# and when trying to run on multiple devices (like GPUs), this will break
+export CUDA_VISIBLE_DEVICES=0
+
+python -u DeepSpeech.py --noshow_progressbar --noearly_stop \
+  --train_files ${ldc93s1_sdb} --train_batch_size 1 \
+  --dev_files ${ldc93s1_sdb} --dev_batch_size 1 \
+  --test_files ${ldc93s1_sdb} --test_batch_size 1 \
+  --n_hidden 100 --epochs $epoch_count \
+  --max_to_keep 1 --checkpoint_dir '/tmp/ckpt_sdb' \
+  --learning_rate 0.001 --dropout_rate 0.05  --export_dir '/tmp/train_sdb' \
+  --scorer_path 'data/smoke_test/pruned_lm.scorer' \
+  --audio_sample_rate ${audio_sample_rate}

bin/run-tc-ldc93s1_new_sdb_csv.sh

@@ -0,0 +1,35 @@
+#!/bin/sh
+
+set -xe
+
+ldc93s1_dir="./data/smoke_test"
+ldc93s1_csv="${ldc93s1_dir}/ldc93s1.csv"
+ldc93s1_sdb="${ldc93s1_dir}/ldc93s1.sdb"
+
+epoch_count=$1
+audio_sample_rate=$2
+
+if [ ! -f "${ldc93s1_dir}/ldc93s1.csv" ]; then
+    echo "Downloading and preprocessing LDC93S1 example data, saving in ${ldc93s1_dir}."
+    python -u bin/import_ldc93s1.py ${ldc93s1_dir}
+fi;
+
+if [ ! -f "${ldc93s1_dir}/ldc93s1.sdb" ]; then
+    echo "Converting LDC93S1 example data, saving to ${ldc93s1_sdb}."
+    python -u bin/build_sdb.py ${ldc93s1_csv} ${ldc93s1_sdb}
+fi;
+
+# Force only one visible device because we have a single-sample dataset
+# and when trying to run on multiple devices (like GPUs), this will break
+export CUDA_VISIBLE_DEVICES=0
+
+python -u DeepSpeech.py --noshow_progressbar --noearly_stop \
+  --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 \
+  --test_files ${ldc93s1_sdb},${ldc93s1_csv} --test_batch_size 1 \
+  --n_hidden 100 --epochs $epoch_count \
+  --max_to_keep 1 --checkpoint_dir '/tmp/ckpt_sdb_csv' \
+  --learning_rate 0.001 --dropout_rate 0.05  --export_dir '/tmp/train_sdb_csv' \
+  --scorer_path 'data/smoke_test/pruned_lm.scorer' \
+  --audio_sample_rate ${audio_sample_rate}

requirements.txt

@@ -2,12 +2,12 @@
 tensorflow == 1.15.0
 numpy == 1.18.1
 progressbar2
-pandas
 six
 pyxdg
 attrdict
 absl-py
 semver
+opuslib == 2.0.0
 
 # Requirements for building native_client files
 setuptools
@@ -15,6 +15,7 @@ setuptools
 # Requirements for importers
 sox
 bs4
+pandas
 requests
 librosa
 soundfile

taskcluster/.shared.yml

@@ -5,6 +5,9 @@ python:
     apt: 'python3-virtualenv python3-setuptools python3-pip python3-wheel python3-pkg-resources'
   packages_docs_bionic:
     apt: 'python3 python3-pip zip doxygen'
+training:
+  packages_trusty:
+    apt: 'libopus0'
 tensorflow:
   packages_trusty:
     apt: 'make build-essential gfortran git libblas-dev liblapack-dev libsox-dev libmagic-dev libgsm1-dev libltdl-dev libpng-dev python zlib1g-dev'

taskcluster/tc-train-tests.sh

@@ -48,6 +48,11 @@ pushd ${HOME}/DeepSpeech/ds/
     time ./bin/run-tc-ldc93s1_new.sh 249 "${sample_rate}"
     time ./bin/run-tc-ldc93s1_new.sh 1 "${sample_rate}"
     time ./bin/run-tc-ldc93s1_tflite.sh "${sample_rate}"
+    # Testing single SDB source
+    time ./bin/run-tc-ldc93s1_new_sdb.sh 220 "${sample_rate}"
+    # Testing interleaved source (SDB+CSV combination) - run twice to test preprocessed features
+    time ./bin/run-tc-ldc93s1_new_sdb_csv.sh 109 "${sample_rate}"
+    time ./bin/run-tc-ldc93s1_new_sdb_csv.sh 1 "${sample_rate}"
 popd
 
 cp /tmp/train/output_graph.pb ${TASKCLUSTER_ARTIFACTS}
@@ -62,6 +67,7 @@ cp /tmp/train/output_graph.pbmm ${TASKCLUSTER_ARTIFACTS}
 
 pushd ${HOME}/DeepSpeech/ds/
     time ./bin/run-tc-ldc93s1_checkpoint.sh
+    time ./bin/run-tc-ldc93s1_checkpoint_sdb.sh
 popd
 
 virtualenv_deactivate "${pyalias}" "deepspeech"

taskcluster/test-training_16k-linux-amd64-py35m-opt.yml

@@ -2,6 +2,9 @@ build:
   template_file: test-linux-opt-base.tyml
   dependencies:
     - "linux-amd64-ctc-opt"
+  system_setup:
+    >
+      apt-get -qq update && apt-get -qq -y install ${training.packages_trusty.apt}
   args:
     tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-train-tests.sh 3.5.8:m 16k"
   metadata:

taskcluster/test-training_16k-linux-amd64-py36m-opt.yml

@@ -2,6 +2,9 @@ build:
   template_file: test-linux-opt-base.tyml
   dependencies:
     - "linux-amd64-ctc-opt"
+  system_setup:
+    >
+      apt-get -qq update && apt-get -qq -y install ${training.packages_trusty.apt}
   args:
     tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-train-tests.sh 3.6.10:m 16k"
   metadata:

taskcluster/test-training_8k-linux-amd64-py36m-opt.yml

@@ -2,6 +2,9 @@ build:
   template_file: test-linux-opt-base.tyml
   dependencies:
     - "linux-amd64-ctc-opt"
+  system_setup:
+    >
+      apt-get -qq update && apt-get -qq -y install ${training.packages_trusty.apt}
   args:
     tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-train-tests.sh 3.6.10:m 8k"
   metadata:

util/audio.py

@@ -1,34 +1,127 @@
 import os
-import sox
+import io
 import wave
 import tempfile
 import collections
+import numpy as np
+
+from util.helpers import LimitingPool
 
 DEFAULT_RATE = 16000
 DEFAULT_CHANNELS = 1
 DEFAULT_WIDTH = 2
 DEFAULT_FORMAT = (DEFAULT_RATE, DEFAULT_CHANNELS, DEFAULT_WIDTH)
 
+AUDIO_TYPE_NP = 'application/vnd.mozilla.np'
+AUDIO_TYPE_PCM = 'application/vnd.mozilla.pcm'
+AUDIO_TYPE_WAV = 'audio/wav'
+AUDIO_TYPE_OPUS = 'application/vnd.mozilla.opus'
+SERIALIZABLE_AUDIO_TYPES = [AUDIO_TYPE_WAV, AUDIO_TYPE_OPUS]
 
-def get_audio_format(wav_file):
+OPUS_PCM_LEN_SIZE = 4
+OPUS_RATE_SIZE = 4
+OPUS_CHANNELS_SIZE = 1
+OPUS_WIDTH_SIZE = 1
+OPUS_CHUNK_LEN_SIZE = 2
+
+
+class Sample:
+    """Represents in-memory audio data of a certain (convertible) representation.
+    Attributes:
+        audio_type (str): See `__init__`.
+        audio_format (tuple:(int, int, int)): See `__init__`.
+        audio (obj): Audio data represented as indicated by `audio_type`
+        duration (float): Audio duration of the sample in seconds
+    """
+    def __init__(self, audio_type, raw_data, audio_format=None):
+        """
+        Creates a Sample from a raw audio representation.
+        :param audio_type: Audio data representation type
+            Supported types:
+                - AUDIO_TYPE_OPUS: Memory file representation (BytesIO) of Opus encoded audio
+                    wrapped by a custom container format (used in SDBs)
+                - AUDIO_TYPE_WAV: Memory file representation (BytesIO) of a Wave file
+                - AUDIO_TYPE_PCM: Binary representation (bytearray) of PCM encoded audio data (Wave file without header)
+                - AUDIO_TYPE_NP: NumPy representation of audio data (np.float32) - typically used for GPU feeding
+        :param raw_data: Audio data in the form of the provided representation type (see audio_type).
+            For types AUDIO_TYPE_OPUS or AUDIO_TYPE_WAV data can also be passed as a bytearray.
+        :param audio_format: Tuple of sample-rate, number of channels and sample-width.
+            Required in case of audio_type = AUDIO_TYPE_PCM or AUDIO_TYPE_NP,
+            as this information cannot be derived from raw audio data.
+        """
+        self.audio_type = audio_type
+        self.audio_format = audio_format
+        if audio_type in SERIALIZABLE_AUDIO_TYPES:
+            self.audio = raw_data if isinstance(raw_data, io.BytesIO) else io.BytesIO(raw_data)
+            self.duration = read_duration(audio_type, self.audio)
+        else:
+            self.audio = raw_data
+            if self.audio_format is None:
+                raise ValueError('For audio type "{}" parameter "audio_format" is mandatory'.format(self.audio_type))
+            if audio_type == AUDIO_TYPE_PCM:
+                self.duration = get_pcm_duration(len(self.audio), self.audio_format)
+            elif audio_type == AUDIO_TYPE_NP:
+                self.duration = get_np_duration(len(self.audio), self.audio_format)
+            else:
+                raise ValueError('Unsupported audio type: {}'.format(self.audio_type))
+
+    def change_audio_type(self, new_audio_type):
+        """
+        In-place conversion of audio data into a different representation.
+        :param new_audio_type: New audio-type - see `__init__`.
+            Not supported: Converting from AUDIO_TYPE_NP into any other type.
+        """
+        if self.audio_type == new_audio_type:
+            return
+        if new_audio_type == AUDIO_TYPE_PCM and self.audio_type in SERIALIZABLE_AUDIO_TYPES:
+            self.audio_format, audio = read_audio(self.audio_type, self.audio)
+            self.audio.close()
+            self.audio = audio
+        elif new_audio_type == AUDIO_TYPE_NP:
+            self.change_audio_type(AUDIO_TYPE_PCM)
+            self.audio = pcm_to_np(self.audio_format, self.audio)
+        elif new_audio_type in SERIALIZABLE_AUDIO_TYPES:
+            self.change_audio_type(AUDIO_TYPE_PCM)
+            audio_bytes = io.BytesIO()
+            write_audio(new_audio_type, audio_bytes, self.audio_format, self.audio)
+            audio_bytes.seek(0)
+            self.audio = audio_bytes
+        else:
+            raise RuntimeError('Changing audio representation type from "{}" to "{}" not supported'
+                               .format(self.audio_type, new_audio_type))
+        self.audio_type = new_audio_type
+
+
+def change_audio_types(samples, audio_type=AUDIO_TYPE_PCM, processes=None, process_ahead=None):
+    def change_audio_type(sample):
+        sample.change_audio_type(audio_type)
+        return sample
+    with LimitingPool(processes=processes, process_ahead=process_ahead) as pool:
+        yield from pool.imap(change_audio_type, samples)
+
+
+def read_audio_format_from_wav_file(wav_file):
     return wav_file.getframerate(), wav_file.getnchannels(), wav_file.getsampwidth()
 
 
-def get_num_samples(audio_data, audio_format=DEFAULT_FORMAT):
+def get_num_samples(pcm_buffer_size, audio_format=DEFAULT_FORMAT):
     _, channels, width = audio_format
-    return len(audio_data) // (channels * width)
+    return pcm_buffer_size // (channels * width)
 
 
-def get_duration(audio_data, audio_format=DEFAULT_FORMAT):
-    return get_num_samples(audio_data, audio_format) / audio_format[0]
+def get_pcm_duration(pcm_buffer_size, audio_format=DEFAULT_FORMAT):
+    """Calculates duration in seconds of a binary PCM buffer (typically read from a WAV file)"""
+    return get_num_samples(pcm_buffer_size, audio_format) / audio_format[0]
 
 
-def get_duration_ms(audio_data, audio_format=DEFAULT_FORMAT):
-    return get_duration(audio_data, audio_format) * 1000
+def get_np_duration(np_len, audio_format=DEFAULT_FORMAT):
+    """Calculates duration in seconds of NumPy audio data"""
+    return np_len / audio_format[0]
 
 
 def convert_audio(src_audio_path, dst_audio_path, file_type=None, audio_format=DEFAULT_FORMAT):
     sample_rate, channels, width = audio_format
+    import sox
     transformer = sox.Transformer()
     transformer.set_output_format(file_type=file_type, rate=sample_rate, channels=channels, bits=width*8)
     transformer.build(src_audio_path, dst_audio_path)
@@ -45,7 +138,7 @@ class AudioFile:
     def __enter__(self):
         if self.audio_path.endswith('.wav'):
             self.open_file = wave.open(self.audio_path, 'r')
-            if get_audio_format(self.open_file) == self.audio_format:
+            if read_audio_format_from_wav_file(self.open_file) == self.audio_format:
                 if self.as_path:
                     self.open_file.close()
                     return self.audio_path
@@ -66,12 +159,12 @@ class AudioFile:
 
 
 def read_frames(wav_file, frame_duration_ms=30, yield_remainder=False):
-    audio_format = get_audio_format(wav_file)
+    audio_format = read_audio_format_from_wav_file(wav_file)
     frame_size = int(audio_format[0] * (frame_duration_ms / 1000.0))
     while True:
         try:
             data = wav_file.readframes(frame_size)
-            if not yield_remainder and get_duration_ms(data, audio_format) < frame_duration_ms:
+            if not yield_remainder and get_pcm_duration(len(data), audio_format) * 1000 < frame_duration_ms:
                 break
             yield data
         except EOFError:
@@ -106,7 +199,7 @@ def vad_split(audio_frames,
     frame_duration_ms = 0
     frame_index = 0
     for frame_index, frame in enumerate(audio_frames):
-        frame_duration_ms = get_duration_ms(frame, audio_format)
+        frame_duration_ms = get_pcm_duration(len(frame), audio_format) * 1000
         if int(frame_duration_ms) not in [10, 20, 30]:
             raise ValueError('VAD-splitting only supported for frame durations 10, 20, or 30 ms')
         is_speech = vad.is_speech(frame, sample_rate)
@@ -133,3 +226,123 @@ def vad_split(audio_frames,
         yield b''.join(voiced_frames), \
               frame_duration_ms * (frame_index - len(voiced_frames)), \
               frame_duration_ms * (frame_index + 1)
+
+
+def pack_number(n, num_bytes):
+    return n.to_bytes(num_bytes, 'big', signed=False)
+
+
+def unpack_number(data):
+    return int.from_bytes(data, 'big', signed=False)
+
+
+def get_opus_frame_size(rate):
+    return 60 * rate // 1000
+
+
+def write_opus(opus_file, audio_format, audio_data):
+    rate, channels, width = audio_format
+    frame_size = get_opus_frame_size(rate)
+    import opuslib  # pylint: disable=import-outside-toplevel
+    encoder = opuslib.Encoder(rate, channels, 'audio')
+    chunk_size = frame_size * channels * width
+    opus_file.write(pack_number(len(audio_data), OPUS_PCM_LEN_SIZE))
+    opus_file.write(pack_number(rate, OPUS_RATE_SIZE))
+    opus_file.write(pack_number(channels, OPUS_CHANNELS_SIZE))
+    opus_file.write(pack_number(width, OPUS_WIDTH_SIZE))
+    for i in range(0, len(audio_data), chunk_size):
+        chunk = audio_data[i:i + chunk_size]
+        # Preventing non-deterministic encoding results from uninitialized remainder of the encoder buffer
+        if len(chunk) < chunk_size:
+            chunk = chunk + bytearray(chunk_size - len(chunk))
+        encoded = encoder.encode(chunk, frame_size)
+        opus_file.write(pack_number(len(encoded), OPUS_CHUNK_LEN_SIZE))
+        opus_file.write(encoded)
+
+
+def read_opus_header(opus_file):
+    opus_file.seek(0)
+    pcm_buffer_size = unpack_number(opus_file.read(OPUS_PCM_LEN_SIZE))
+    rate = unpack_number(opus_file.read(OPUS_RATE_SIZE))
+    channels = unpack_number(opus_file.read(OPUS_CHANNELS_SIZE))
+    width = unpack_number(opus_file.read(OPUS_WIDTH_SIZE))
+    return pcm_buffer_size, (rate, channels, width)
+
+
+def read_opus(opus_file):
+    pcm_buffer_size, audio_format = read_opus_header(opus_file)
+    rate, channels, _ = audio_format
+    frame_size = get_opus_frame_size(rate)
+    import opuslib  # pylint: disable=import-outside-toplevel
+    decoder = opuslib.Decoder(rate, channels)
+    audio_data = bytearray()
+    while len(audio_data) < pcm_buffer_size:
+        chunk_len = unpack_number(opus_file.read(OPUS_CHUNK_LEN_SIZE))
+        chunk = opus_file.read(chunk_len)
+        decoded = decoder.decode(chunk, frame_size)
+        audio_data.extend(decoded)
+    audio_data = audio_data[:pcm_buffer_size]
+    return audio_format, audio_data
+
+
+def write_wav(wav_file, audio_format, pcm_data):
+    with wave.open(wav_file, 'wb') as wav_file_writer:
+        rate, channels, width = audio_format
+        wav_file_writer.setframerate(rate)
+        wav_file_writer.setnchannels(channels)
+        wav_file_writer.setsampwidth(width)
+        wav_file_writer.writeframes(pcm_data)
+
+
+def read_wav(wav_file):
+    wav_file.seek(0)
+    with wave.open(wav_file, 'rb') as wav_file_reader:
+        audio_format = read_audio_format_from_wav_file(wav_file_reader)
+        pcm_data = wav_file_reader.readframes(wav_file_reader.getnframes())
+        return audio_format, pcm_data
+
+
+def read_audio(audio_type, audio_file):
+    if audio_type == AUDIO_TYPE_WAV:
+        return read_wav(audio_file)
+    if audio_type == AUDIO_TYPE_OPUS:
+        return read_opus(audio_file)
+    raise ValueError('Unsupported audio type: {}'.format(audio_type))
+
+
+def write_audio(audio_type, audio_file, audio_format, pcm_data):
+    if audio_type == AUDIO_TYPE_WAV:
+        return write_wav(audio_file, audio_format, pcm_data)
+    if audio_type == AUDIO_TYPE_OPUS:
+        return write_opus(audio_file, audio_format, pcm_data)
+    raise ValueError('Unsupported audio type: {}'.format(audio_type))
+
+
+def read_wav_duration(wav_file):
+    wav_file.seek(0)
+    with wave.open(wav_file, 'rb') as wav_file_reader:
+        return wav_file_reader.getnframes() / wav_file_reader.getframerate()
+
+
+def read_opus_duration(opus_file):
+    pcm_buffer_size, audio_format = read_opus_header(opus_file)
+    return get_pcm_duration(pcm_buffer_size, audio_format)
+
+
+def read_duration(audio_type, audio_file):
+    if audio_type == AUDIO_TYPE_WAV:
+        return read_wav_duration(audio_file)
+    if audio_type == AUDIO_TYPE_OPUS:
+        return read_opus_duration(audio_file)
+    raise ValueError('Unsupported audio type: {}'.format(audio_type))
+
+
+def pcm_to_np(audio_format, pcm_data):
+    _, channels, width = audio_format
+    if width not in [1, 2, 4]:
+        raise ValueError('Unsupported sample width: {}'.format(width))
+    dtype = [None, np.int8, np.int16, None, np.int32][width]
+    samples = np.frombuffer(pcm_data, dtype=dtype)
+    assert channels == 1  # only mono supported for now
+    samples = samples.astype(np.float32) / np.iinfo(dtype).max
+    return np.expand_dims(samples, axis=1)

util/config.py

@@ -12,6 +12,7 @@ from util.flags import FLAGS
 from util.gpu import get_available_gpus
 from util.logging import log_error
 from util.text import Alphabet, UTF8Alphabet
+from util.helpers import parse_file_size
 
 class ConfigSingleton:
     _config = None
@@ -29,6 +30,9 @@ Config = ConfigSingleton() # pylint: disable=invalid-name
 def initialize_globals():
     c = AttrDict()
 
+    # Read-buffer
+    FLAGS.read_buffer = parse_file_size(FLAGS.read_buffer)
+
     # Set default dropout rates
     if FLAGS.dropout_rate2 < 0:
         FLAGS.dropout_rate2 = FLAGS.dropout_rate

util/feeding.py

@@ -1,12 +1,9 @@
 # -*- coding: utf-8 -*-
 from __future__ import absolute_import, division, print_function
 
-import os
-
 from functools import partial
 
 import numpy as np
-import pandas
 import tensorflow as tf
 
 from tensorflow.python.ops import gen_audio_ops as contrib_audio
@@ -15,27 +12,18 @@ from util.config import Config
 from util.text import text_to_char_array
 from util.flags import FLAGS
 from util.spectrogram_augmentations import augment_freq_time_mask, augment_dropout, augment_pitch_and_tempo, augment_speed_up, augment_sparse_warp
-from util.audio import read_frames_from_file, vad_split, DEFAULT_FORMAT
+from util.audio import change_audio_types, read_frames_from_file, vad_split, pcm_to_np, DEFAULT_FORMAT, AUDIO_TYPE_NP
+from util.sample_collections import samples_from_files
+from util.helpers import remember_exception, MEGABYTE
 
 
-def read_csvs(csv_files):
-    sets = []
-    for csv in csv_files:
-        file = pandas.read_csv(csv, encoding='utf-8', na_filter=False)
-        #FIXME: not cross-platform
-        csv_dir = os.path.dirname(os.path.abspath(csv))
-        file['wav_filename'] = file['wav_filename'].str.replace(r'(^[^/])', lambda m: os.path.join(csv_dir, m.group(1))) # pylint: disable=cell-var-from-loop
-        sets.append(file)
-    # Concat all sets, drop any extra columns, re-index the final result as 0..N
-    return pandas.concat(sets, join='inner', ignore_index=True)
-
-
-def samples_to_mfccs(samples, sample_rate, train_phase=False, wav_filename=None):
+def samples_to_mfccs(samples, sample_rate, train_phase=False, sample_id=None):
     if train_phase:
         # We need the lambdas to make TensorFlow happy.
         # pylint: disable=unnecessary-lambda
         tf.cond(tf.math.not_equal(sample_rate, FLAGS.audio_sample_rate),
-                lambda: tf.print('WARNING: sample rate of file', wav_filename, '(', sample_rate, ') does not match FLAGS.audio_sample_rate. This can lead to incorrect results.'),
+                lambda: tf.print('WARNING: sample rate of sample', sample_id, '(', sample_rate, ') '
+                                 'does not match FLAGS.audio_sample_rate. This can lead to incorrect results.'),
                 lambda: tf.no_op(),
                 name='matching_sample_rate')
 
@@ -84,10 +72,8 @@ def samples_to_mfccs(samples, sample_rate, train_phase=False, wav_filename=None)
     return mfccs, tf.shape(input=mfccs)[0]
 
 
-def audiofile_to_features(wav_filename, train_phase=False):
-    samples = tf.io.read_file(wav_filename)
-    decoded = contrib_audio.decode_wav(samples, desired_channels=1)
-    features, features_len = samples_to_mfccs(decoded.audio, decoded.sample_rate, train_phase=train_phase, wav_filename=wav_filename)
+def audio_to_features(audio, sample_rate, train_phase=False, sample_id=None):
+    features, features_len = samples_to_mfccs(audio, sample_rate, train_phase=train_phase, sample_id=sample_id)
 
     if train_phase:
         if FLAGS.data_aug_features_multiplicative > 0:
@@ -99,10 +85,17 @@ def audiofile_to_features(wav_filename, train_phase=False):
     return features, features_len
 
 
-def entry_to_features(wav_filename, transcript, train_phase):
+def audiofile_to_features(wav_filename, train_phase=False):
+    samples = tf.io.read_file(wav_filename)
+    decoded = contrib_audio.decode_wav(samples, desired_channels=1)
+    return audio_to_features(decoded.audio, decoded.sample_rate, train_phase=train_phase, sample_id=wav_filename)
+
+
+def entry_to_features(sample_id, audio, sample_rate, transcript, train_phase=False):
     # https://bugs.python.org/issue32117
-    features, features_len = audiofile_to_features(wav_filename, train_phase=train_phase)
-    return wav_filename, features, features_len, tf.SparseTensor(*transcript)
+    features, features_len = audio_to_features(audio, sample_rate, train_phase=train_phase, sample_id=sample_id)
+    sparse_transcript = tf.SparseTensor(*transcript)
+    return sample_id, features, features_len, sparse_transcript
 
 
 def to_sparse_tuple(sequence):
@@ -114,15 +107,22 @@ def to_sparse_tuple(sequence):
     return indices, sequence, shape
 
 
-def create_dataset(csvs, batch_size, enable_cache=False, cache_path=None, train_phase=False):
-    df = read_csvs(csvs)
-    df.sort_values(by='wav_filesize', inplace=True)
-
-    df['transcript'] = df.apply(text_to_char_array, alphabet=Config.alphabet, result_type='reduce', axis=1)
-
+def create_dataset(sources,
+                   batch_size,
+                   enable_cache=False,
+                   cache_path=None,
+                   train_phase=False,
+                   exception_box=None,
+                   process_ahead=None,
+                   buffering=1 * MEGABYTE):
     def generate_values():
-        for _, row in df.iterrows():
-            yield row.wav_filename, to_sparse_tuple(row.transcript)
+        samples = samples_from_files(sources, buffering=buffering)
+        for sample in change_audio_types(samples,
+                                         AUDIO_TYPE_NP,
+                                         process_ahead=2 * batch_size if process_ahead is None else process_ahead):
+            transcript = text_to_char_array(sample.transcript, Config.alphabet, context=sample.sample_id)
+            transcript = to_sparse_tuple(transcript)
+            yield sample.sample_id, sample.audio, sample.audio_format[0], transcript
 
     # Batching a dataset of 2D SparseTensors creates 3D batches, which fail
     # when passed to tf.nn.ctc_loss, so we reshape them to remove the extra
@@ -131,27 +131,23 @@ def create_dataset(csvs, batch_size, enable_cache=False, cache_path=None, train_
         shape = sparse.dense_shape
         return tf.sparse.reshape(sparse, [shape[0], shape[2]])
 
-    def batch_fn(wav_filenames, features, features_len, transcripts):
+    def batch_fn(sample_ids, features, features_len, transcripts):
         features = tf.data.Dataset.zip((features, features_len))
-        features = features.padded_batch(batch_size,
-                                         padded_shapes=([None, Config.n_input], []))
+        features = features.padded_batch(batch_size, padded_shapes=([None, Config.n_input], []))
         transcripts = transcripts.batch(batch_size).map(sparse_reshape)
-        wav_filenames = wav_filenames.batch(batch_size)
-        return tf.data.Dataset.zip((wav_filenames, features, transcripts))
+        sample_ids = sample_ids.batch(batch_size)
+        return tf.data.Dataset.zip((sample_ids, features, transcripts))
 
-    num_gpus = len(Config.available_devices)
     process_fn = partial(entry_to_features, train_phase=train_phase)
 
-    dataset = (tf.data.Dataset.from_generator(generate_values,
-                                              output_types=(tf.string, (tf.int64, tf.int32, tf.int64)))
+    dataset = (tf.data.Dataset.from_generator(remember_exception(generate_values, exception_box),
+                                              output_types=(tf.string, tf.float32, tf.int32,
+                                                            (tf.int64, tf.int32, tf.int64)))
                               .map(process_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE))
-
     if enable_cache:
         dataset = dataset.cache(cache_path)
-
     dataset = (dataset.window(batch_size, drop_remainder=True).flat_map(batch_fn)
-                      .prefetch(num_gpus))
-
+                      .prefetch(len(Config.available_devices)))
     return dataset
 
 
@@ -160,27 +156,24 @@ def split_audio_file(audio_path,
                      batch_size=1,
                      aggressiveness=3,
                      outlier_duration_ms=10000,
-                     outlier_batch_size=1):
-    sample_rate, _, sample_width = audio_format
-    multiplier = 1.0 / (1 << (8 * sample_width - 1))
-
+                     outlier_batch_size=1,
+                     exception_box=None):
     def generate_values():
         frames = read_frames_from_file(audio_path)
         segments = vad_split(frames, aggressiveness=aggressiveness)
         for segment in segments:
             segment_buffer, time_start, time_end = segment
-            samples = np.frombuffer(segment_buffer, dtype=np.int16)
-            samples = samples * multiplier
-            samples = np.expand_dims(samples, axis=1)
+            samples = pcm_to_np(audio_format, segment_buffer)
             yield time_start, time_end, samples
 
     def to_mfccs(time_start, time_end, samples):
-        features, features_len = samples_to_mfccs(samples, sample_rate)
+        features, features_len = samples_to_mfccs(samples, audio_format[0])
         return time_start, time_end, features, features_len
 
     def create_batch_set(bs, criteria):
         return (tf.data.Dataset
-                .from_generator(generate_values, output_types=(tf.int32, tf.int32, tf.float32))
+                .from_generator(remember_exception(generate_values, exception_box),
+                                output_types=(tf.int32, tf.int32, tf.float32))
                 .map(to_mfccs, num_parallel_calls=tf.data.experimental.AUTOTUNE)
                 .filter(criteria)
                 .padded_batch(bs, padded_shapes=([], [], [None, Config.n_input], [])))
@@ -192,9 +185,3 @@ def split_audio_file(audio_path,
     dataset = nds.concatenate(ods)
     dataset = dataset.prefetch(len(Config.available_devices))
     return dataset
-
-
-def secs_to_hours(secs):
-    hours, remainder = divmod(secs, 3600)
-    minutes, seconds = divmod(remainder, 60)
-    return '%d:%02d:%02d' % (hours, minutes, seconds)

util/flags.py

@@ -15,6 +15,7 @@ def create_flags():
     f.DEFINE_string('dev_files', '', 'comma separated list of files specifying the dataset used for validation. Multiple files will get merged. If empty, validation will not be run.')
     f.DEFINE_string('test_files', '', 'comma separated list of files specifying the dataset used for testing. Multiple files will get merged. If empty, the model will not be tested.')
 
+    f.DEFINE_string('read_buffer', '1MB', 'buffer-size for reading samples from datasets (supports file-size suffixes KB, MB, GB, TB)')
     f.DEFINE_string('feature_cache', '', 'cache MFCC features to disk to speed up future training runs ont he same data. This flag specifies the path where cached features extracted from --train_files will be saved. If empty, or if online augmentation flags are enabled, caching will be disabled.')
 
     f.DEFINE_integer('feature_win_len', 32, 'feature extraction audio window length in milliseconds')

util/helpers.py

@@ -1,10 +1,32 @@
 import os
-import semver
 import sys
+import time
+import heapq
+import semver
+
+from multiprocessing.dummy import Pool as ThreadPool
+
+KILO = 1024
+KILOBYTE = 1 * KILO
+MEGABYTE = KILO * KILOBYTE
+GIGABYTE = KILO * MEGABYTE
+TERABYTE = KILO * GIGABYTE
+SIZE_PREFIX_LOOKUP = {'k': KILOBYTE, 'm': MEGABYTE, 'g': GIGABYTE, 't': TERABYTE}
+
+
+def parse_file_size(file_size):
+    file_size = file_size.lower().strip()
+    if len(file_size) == 0:
+        return 0
+    n = int(keep_only_digits(file_size))
+    if file_size[-1] == 'b':
+        file_size = file_size[:-1]
+    e = file_size[-1]
+    return SIZE_PREFIX_LOOKUP[e] * n if e in SIZE_PREFIX_LOOKUP else n
 
 
 def keep_only_digits(txt):
-    return ''.join(filter(lambda c: c.isdigit(), txt))
+    return ''.join(filter(str.isdigit, txt))
 
 
 def secs_to_hours(secs):
@@ -21,7 +43,8 @@ def check_ctcdecoder_version():
         from ds_ctcdecoder import __version__ as decoder_version
     except ImportError as e:
         if e.msg.find('__version__') > 0:
-            print("DeepSpeech version ({ds_version}) requires CTC decoder to expose __version__. Please upgrade the ds_ctcdecoder package to version {ds_version}".format(ds_version=ds_version_s))
+            print("DeepSpeech version ({ds_version}) requires CTC decoder to expose __version__. "
+                  "Please upgrade the ds_ctcdecoder package to version {ds_version}".format(ds_version=ds_version_s))
             sys.exit(1)
         raise e
 
@@ -29,7 +52,79 @@ def check_ctcdecoder_version():
 
     rv = semver.compare(ds_version_s, decoder_version_s)
     if rv != 0:
-        print("DeepSpeech version ({}) and CTC decoder version ({}) do not match. Please ensure matching versions are in use.".format(ds_version_s, decoder_version_s))
+        print("DeepSpeech version ({}) and CTC decoder version ({}) do not match. "
+              "Please ensure matching versions are in use.".format(ds_version_s, decoder_version_s))
         sys.exit(1)
 
     return rv
+
+
+class Interleaved:
+    """Collection that lazily combines sorted collections in an interleaving fashion.
+    During iteration the next smallest element from all the sorted collections is always picked.
+    The collections must support iter() and len()."""
+    def __init__(self, *iterables, key=lambda obj: obj):
+        self.iterables = iterables
+        self.key = key
+        self.len = sum(map(len, iterables))
+
+    def __iter__(self):
+        return heapq.merge(*self.iterables, key=self.key)
+
+    def __len__(self):
+        return self.len
+
+
+class LimitingPool:
+    """Limits unbound ahead-processing of multiprocessing.Pool's imap method
+    before items get consumed by the iteration caller.
+    This prevents OOM issues in situations where items represent larger memory allocations."""
+    def __init__(self, processes=None, process_ahead=None, sleeping_for=0.1):
+        self.process_ahead = os.cpu_count() if process_ahead is None else process_ahead
+        self.sleeping_for = sleeping_for
+        self.processed = 0
+        self.pool = ThreadPool(processes=processes)
+
+    def __enter__(self):
+        return self
+
+    def _limit(self, it):
+        for obj in it:
+            while self.processed >= self.process_ahead:
+                time.sleep(self.sleeping_for)
+            self.processed += 1
+            yield obj
+
+    def imap(self, fun, it):
+        for obj in self.pool.imap(fun, self._limit(it)):
+            self.processed -= 1
+            yield obj
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.pool.close()
+
+
+class ExceptionBox:
+    """Helper class for passing-back and re-raising an exception from inside a TensorFlow dataset generator.
+    Used in conjunction with `remember_exception`."""
+    def __init__(self):
+        self.exception = None
+
+    def raise_if_set(self):
+        if self.exception is not None:
+            exception = self.exception
+            self.exception = None
+            raise exception  # pylint: disable = raising-bad-type
+
+
+def remember_exception(iterable, exception_box=None):
+    """Wraps a TensorFlow dataset generator for catching its actual exceptions
+    that would otherwise just interrupt iteration w/o bubbling up."""
+    def do_iterate():
+        try:
+            yield from iterable()
+        except StopIteration:
+            return
+        except Exception as ex:  # pylint: disable = broad-except
+            exception_box.exception = ex
+    return iterable if exception_box is None else do_iterate

util/sample_collections.py

@@ -0,0 +1,262 @@
+# -*- coding: utf-8 -*-
+import os
+import csv
+import json
+
+from pathlib import Path
+from functools import partial
+from util.helpers import MEGABYTE, GIGABYTE, Interleaved
+from util.audio import Sample, DEFAULT_FORMAT, AUDIO_TYPE_WAV, AUDIO_TYPE_OPUS, SERIALIZABLE_AUDIO_TYPES
+
+BIG_ENDIAN = 'big'
+INT_SIZE = 4
+BIGINT_SIZE = 2 * INT_SIZE
+MAGIC = b'SAMPLEDB'
+
+BUFFER_SIZE = 1 * MEGABYTE
+CACHE_SIZE = 1 * GIGABYTE
+
+SCHEMA_KEY = 'schema'
+CONTENT_KEY = 'content'
+MIME_TYPE_KEY = 'mime-type'
+MIME_TYPE_TEXT = 'text/plain'
+CONTENT_TYPE_SPEECH = 'speech'
+CONTENT_TYPE_TRANSCRIPT = 'transcript'
+
+
+class LabeledSample(Sample):
+    """In-memory labeled audio sample representing an utterance.
+    Derived from util.audio.Sample and used by sample collection readers and writers."""
+    def __init__(self, audio_type, raw_data, transcript, audio_format=DEFAULT_FORMAT, sample_id=None):
+        """
+        Creates an in-memory speech sample together with a transcript of the utterance (label).
+        :param audio_type: See util.audio.Sample.__init__ .
+        :param raw_data: See util.audio.Sample.__init__ .
+        :param transcript: Transcript of the sample's utterance
+        :param audio_format: See util.audio.Sample.__init__ .
+        :param sample_id: Tracking ID - typically assigned by collection readers
+        """
+        super().__init__(audio_type, raw_data, audio_format=audio_format)
+        self.sample_id = sample_id
+        self.transcript = transcript
+
+
+class DirectSDBWriter:
+    """Sample collection writer for creating a Sample DB (SDB) file"""
+    def __init__(self, sdb_filename, buffering=BUFFER_SIZE, audio_type=AUDIO_TYPE_OPUS, id_prefix=None):
+        self.sdb_filename = sdb_filename
+        self.id_prefix = sdb_filename if id_prefix is None else id_prefix
+        if audio_type not in SERIALIZABLE_AUDIO_TYPES:
+            raise ValueError('Audio type "{}" not supported'.format(audio_type))
+        self.audio_type = audio_type
+        self.sdb_file = open(sdb_filename, 'wb', buffering=buffering)
+        self.offsets = []
+        self.num_samples = 0
+
+        self.sdb_file.write(MAGIC)
+
+        meta_data = {
+            SCHEMA_KEY: [
+                {CONTENT_KEY: CONTENT_TYPE_SPEECH, MIME_TYPE_KEY: audio_type},
+                {CONTENT_KEY: CONTENT_TYPE_TRANSCRIPT, MIME_TYPE_KEY: MIME_TYPE_TEXT}
+            ]
+        }
+        meta_data = json.dumps(meta_data).encode()
+        self.write_big_int(len(meta_data))
+        self.sdb_file.write(meta_data)
+
+        self.offset_samples = self.sdb_file.tell()
+        self.sdb_file.seek(2 * BIGINT_SIZE, 1)
+
+    def write_int(self, n):
+        return self.sdb_file.write(n.to_bytes(INT_SIZE, BIG_ENDIAN))
+
+    def write_big_int(self, n):
+        return self.sdb_file.write(n.to_bytes(BIGINT_SIZE, BIG_ENDIAN))
+
+    def __enter__(self):
+        return self
+
+    def add(self, sample):
+        def to_bytes(n):
+            return n.to_bytes(INT_SIZE, BIG_ENDIAN)
+        sample.change_audio_type(self.audio_type)
+        opus = sample.audio.getbuffer()
+        opus_len = to_bytes(len(opus))
+        transcript = sample.transcript.encode()
+        transcript_len = to_bytes(len(transcript))
+        entry_len = to_bytes(len(opus_len) + len(opus) + len(transcript_len) + len(transcript))
+        buffer = b''.join([entry_len, opus_len, opus, transcript_len, transcript])
+        self.offsets.append(self.sdb_file.tell())
+        self.sdb_file.write(buffer)
+        sample.sample_id = '{}:{}'.format(self.id_prefix, self.num_samples)
+        self.num_samples += 1
+        return sample.sample_id
+
+    def close(self):
+        if self.sdb_file is None:
+            return
+        offset_index = self.sdb_file.tell()
+        self.sdb_file.seek(self.offset_samples)
+        self.write_big_int(offset_index - self.offset_samples - BIGINT_SIZE)
+        self.write_big_int(self.num_samples)
+
+        self.sdb_file.seek(offset_index + BIGINT_SIZE)
+        self.write_big_int(self.num_samples)
+        for offset in self.offsets:
+            self.write_big_int(offset)
+        offset_end = self.sdb_file.tell()
+        self.sdb_file.seek(offset_index)
+        self.write_big_int(offset_end - offset_index - BIGINT_SIZE)
+        self.sdb_file.close()
+        self.sdb_file = None
+
+    def __len__(self):
+        return len(self.offsets)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
+
+
+class SDB:  # pylint: disable=too-many-instance-attributes
+    """Sample collection reader for reading a Sample DB (SDB) file"""
+    def __init__(self, sdb_filename, buffering=BUFFER_SIZE, id_prefix=None):
+        self.sdb_filename = sdb_filename
+        self.id_prefix = sdb_filename if id_prefix is None else id_prefix
+        self.sdb_file = open(sdb_filename, 'rb', buffering=buffering)
+        self.offsets = []
+        if self.sdb_file.read(len(MAGIC)) != MAGIC:
+            raise RuntimeError('No Sample Database')
+        meta_chunk_len = self.read_big_int()
+        self.meta = json.loads(self.sdb_file.read(meta_chunk_len).decode())
+        if SCHEMA_KEY not in self.meta:
+            raise RuntimeError('Missing schema')
+        self.schema = self.meta[SCHEMA_KEY]
+
+        speech_columns = self.find_columns(content=CONTENT_TYPE_SPEECH, mime_type=SERIALIZABLE_AUDIO_TYPES)
+        if not speech_columns:
+            raise RuntimeError('No speech data (missing in schema)')
+        self.speech_index = speech_columns[0]
+        self.audio_type = self.schema[self.speech_index][MIME_TYPE_KEY]
+
+        transcript_columns = self.find_columns(content=CONTENT_TYPE_TRANSCRIPT, mime_type=MIME_TYPE_TEXT)
+        if not transcript_columns:
+            raise RuntimeError('No transcript data (missing in schema)')
+        self.transcript_index = transcript_columns[0]
+
+        sample_chunk_len = self.read_big_int()
+        self.sdb_file.seek(sample_chunk_len + BIGINT_SIZE, 1)
+        num_samples = self.read_big_int()
+        for _ in range(num_samples):
+            self.offsets.append(self.read_big_int())
+
+    def read_int(self):
+        return int.from_bytes(self.sdb_file.read(INT_SIZE), BIG_ENDIAN)
+
+    def read_big_int(self):
+        return int.from_bytes(self.sdb_file.read(BIGINT_SIZE), BIG_ENDIAN)
+
+    def find_columns(self, content=None, mime_type=None):
+        criteria = []
+        if content is not None:
+            criteria.append((CONTENT_KEY, content))
+        if mime_type is not None:
+            criteria.append((MIME_TYPE_KEY, mime_type))
+        if len(criteria) == 0:
+            raise ValueError('At least one of "content" or "mime-type" has to be provided')
+        matches = []
+        for index, column in enumerate(self.schema):
+            matched = 0
+            for field, value in criteria:
+                if column[field] == value or (isinstance(value, list) and column[field] in value):
+                    matched += 1
+            if matched == len(criteria):
+                matches.append(index)
+        return matches
+
+    def read_row(self, row_index, *columns):
+        columns = list(columns)
+        column_data = [None] * len(columns)
+        found = 0
+        if not 0 <= row_index < len(self.offsets):
+            raise ValueError('Wrong sample index: {} - has to be between 0 and {}'
+                             .format(row_index, len(self.offsets) - 1))
+        self.sdb_file.seek(self.offsets[row_index] + INT_SIZE)
+        for index in range(len(self.schema)):
+            chunk_len = self.read_int()
+            if index in columns:
+                column_data[columns.index(index)] = self.sdb_file.read(chunk_len)
+                found += 1
+                if found == len(columns):
+                    return tuple(column_data)
+            else:
+                self.sdb_file.seek(chunk_len, 1)
+        return tuple(column_data)
+
+    def __getitem__(self, i):
+        audio_data, transcript = self.read_row(i, self.speech_index, self.transcript_index)
+        transcript = transcript.decode()
+        sample_id = '{}:{}'.format(self.id_prefix, i)
+        return LabeledSample(self.audio_type, audio_data, transcript, sample_id=sample_id)
+
+    def __iter__(self):
+        for i in range(len(self.offsets)):
+            yield self[i]
+
+    def __len__(self):
+        return len(self.offsets)
+
+    def close(self):
+        if self.sdb_file is not None:
+            self.sdb_file.close()
+
+    def __del__(self):
+        self.close()
+
+
+class CSV:
+    """Sample collection reader for reading a DeepSpeech CSV file"""
+    def __init__(self, csv_filename):
+        self.csv_filename = csv_filename
+        self.rows = []
+        csv_dir = Path(csv_filename).parent
+        with open(csv_filename, 'r', encoding='utf8') as csv_file:
+            reader = csv.DictReader(csv_file)
+            for row in reader:
+                wav_filename = Path(row['wav_filename'])
+                if not wav_filename.is_absolute():
+                    wav_filename = csv_dir / wav_filename
+                self.rows.append((str(wav_filename), int(row['wav_filesize']), row['transcript']))
+        self.rows.sort(key=lambda r: r[1])
+
+    def __getitem__(self, i):
+        wav_filename, _, transcript = self.rows[i]
+        with open(wav_filename, 'rb') as wav_file:
+            return LabeledSample(AUDIO_TYPE_WAV, wav_file.read(), transcript, sample_id=wav_filename)
+
+    def __iter__(self):
+        for i in range(len(self.rows)):
+            yield self[i]
+
+    def __len__(self):
+        return len(self.rows)
+
+
+def samples_from_file(filename, buffering=BUFFER_SIZE):
+    """Returns an iterable of LabeledSample objects loaded from a file."""
+    ext = os.path.splitext(filename)[1].lower()
+    if ext == '.sdb':
+        return SDB(filename, buffering=buffering)
+    if ext == '.csv':
+        return CSV(filename)
+    raise ValueError('Unknown file type: "{}"'.format(ext))
+
+
+def samples_from_files(filenames, buffering=BUFFER_SIZE):
+    """Returns an iterable of LabeledSample objects from a list of files."""
+    if len(filenames) == 0:
+        raise ValueError('No files')
+    if len(filenames) == 1:
+        return samples_from_file(filenames[0], buffering=buffering)
+    cols = list(map(partial(samples_from_file, buffering=buffering), filenames))
+    return Interleaved(*cols, key=lambda s: s.duration)

util/text.py

@@ -4,7 +4,6 @@ import numpy as np
 import re
 import struct
 
-from util.flags import FLAGS
 from six.moves import range
 
 class Alphabet(object):
@@ -120,19 +119,22 @@ class UTF8Alphabet(object):
         return ''
 
 
-def text_to_char_array(series, alphabet):
+def text_to_char_array(transcript, alphabet, context=''):
     r"""
-    Given a Pandas Series containing transcript string, map characters to
+    Given a transcript string, map characters to
     integers and return a numpy array representing the processed string.
+    Use a string in `context` for adding text to raised exceptions.
     """
     try:
-        transcript = np.asarray(alphabet.encode(series['transcript']))
+        transcript = alphabet.encode(transcript)
         if len(transcript) == 0:
-            raise ValueError('While processing: {}\nFound an empty transcript! You must include a transcript for all training data.'.format(series['wav_filename']))
+            raise ValueError('While processing {}: Found an empty transcript! '
+                             'You must include a transcript for all training data.'
+                             .format(context))
         return transcript
     except KeyError as e:
         # Provide the row context (especially wav_filename) for alphabet errors
-        raise ValueError('While processing: {}\n{}'.format(series['wav_filename'], e))
+        raise ValueError('While processing: {}\n{}'.format(context, e))
 
 
 # The following code is from: http://hetland.org/coding/python/levenshtein.py