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https://github.com/mozilla/DeepSpeech.git
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Address review comments and do further filtering and cleanup on the transcription data
This commit is contained in:
Reuben Morais
parent
c7eaf9939b
commit
d8aaffce62
@ -13,7 +13,7 @@ from Queue import Queue
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from threading import Thread
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from util.audio import audiofile_to_input_vector
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from util.gpu import get_available_gpus
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from util.text import texts_to_sparse_tensor
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from util.text import texts_to_sparse_tensor, validate_label
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class DataSets(object):
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def __init__(self, train, dev, test):
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@ -69,25 +69,30 @@ class DataSet(object):
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def _populate_batch_queue(self):
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with self._graph.as_default():
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while True:
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n_steps = 0
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sources = []
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targets = []
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for index, (txt_file, wav_file) in enumerate(self._files_circular_list):
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if index >= self._batch_size:
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break
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next_source = audiofile_to_input_vector(wav_file, self._numcep, self._numcontext)
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if n_steps < next_source.shape[0]:
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n_steps = next_source.shape[0]
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sources.append(next_source)
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with open(txt_file) as open_txt_file:
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targets.append(open_txt_file.read())
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target = texts_to_sparse_tensor(targets)
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for index, next_source in enumerate(sources):
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npad = ((0,(n_steps - next_source.shape[0])), (0,0))
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sources[index] = np.pad(next_source, pad_width=npad, mode="constant")
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source = np.array(sources)
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self._batch_queue.put((source, target))
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n_steps = 0
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sources = []
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targets = []
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batch_index = 0
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for txt_file, wav_file in self._files_circular_list:
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if batch_index == self._batch_size:
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# Put batch on queue
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target = texts_to_sparse_tensor(targets)
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for index, next_source in enumerate(sources):
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npad = ((0,(n_steps - next_source.shape[0])), (0,0))
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sources[index] = np.pad(next_source, pad_width=npad, mode='constant')
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source = np.array(sources)
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self._batch_queue.put((source, target))
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n_steps = 0
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sources = []
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targets = []
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batch_index = 0
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next_source = audiofile_to_input_vector(wav_file, self._numcep, self._numcontext)
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if n_steps < next_source.shape[0]:
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n_steps = next_source.shape[0]
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sources.append(next_source)
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with open(txt_file) as open_txt_file:
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targets.append(open_txt_file.read())
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batch_index = batch_index + 1
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def next_batch(self):
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source, target = self._batch_queue.get()
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@ -106,25 +111,25 @@ def read_data_sets(graph, data_dir, batch_size, numcep, numcontext, thread_count
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_maybe_convert_wav(data_dir, "LDC2005S13", "fisher-2005-wav")
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# Conditionally split Fisher wav data
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_maybe_split_wav(data_dir, os.path.join("LDC2004T19", "fe_03_p1_tran", "data", "trans"), "fisher-2004-wav")
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_maybe_split_wav(data_dir, os.path.join("LDC2005T19", "fe_03_p2_tran", "data", "trans"), "fisher-2005-wav")
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_maybe_split_wav(data_dir, os.path.join("LDC2004T19", "fe_03_p1_tran", "data", "trans"), "fisher-2004-wav", "fisher-2004-split-wav")
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_maybe_split_wav(data_dir, os.path.join("LDC2005T19", "fe_03_p2_tran", "data", "trans"), "fisher-2005-wav", "fisher-2005-split-wav")
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# Conditionally split Fisher transcriptions
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_maybe_split_transcriptions(data_dir, os.path.join("LDC2004T19", "fe_03_p1_tran", "data", "trans"), "fisher-2004-wav")
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_maybe_split_transcriptions(data_dir, os.path.join("LDC2005T19", "fe_03_p2_tran", "data", "trans"), "fisher-2005-wav")
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_maybe_split_transcriptions(data_dir, os.path.join("LDC2004T19", "fe_03_p1_tran", "data", "trans"), "fisher-2004-split-wav")
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_maybe_split_transcriptions(data_dir, os.path.join("LDC2005T19", "fe_03_p2_tran", "data", "trans"), "fisher-2005-split-wav")
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# Conditionally split Fisher data into train/validation/test sets
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_maybe_split_sets(data_dir, "fisher-2004-wav", "fisher-2004-wav-splits")
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_maybe_split_sets(data_dir, "fisher-2005-wav", "fisher-2005-wav-splits")
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_maybe_split_sets(data_dir, "fisher-2004-split-wav", "fisher-2004-split-wav-sets")
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_maybe_split_sets(data_dir, "fisher-2005-split-wav", "fisher-2005-split-wav-sets")
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# Create train DataSet
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train = _read_data_set(graph, data_dir, "fisher-200?-wav/train", thread_count, batch_size, numcep, numcontext)
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train = _read_data_set(graph, data_dir, "fisher-200?-split-wav-sets/train", thread_count, batch_size, numcep, numcontext)
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# Create dev DataSet
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dev = _read_data_set(graph, data_dir, "fisher-200?-wav/dev", thread_count, batch_size, numcep, numcontext)
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dev = _read_data_set(graph, data_dir, "fisher-200?-split-wav-sets/dev", thread_count, batch_size, numcep, numcontext)
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# Create test DataSet
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test = _read_data_set(graph, data_dir, "fisher-200?-wav/test", thread_count, batch_size, numcep, numcontext)
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test = _read_data_set(graph, data_dir, "fisher-200?-split-wav-sets/test", thread_count, batch_size, numcep, numcontext)
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# Return DataSets
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return DataSets(train, dev, test)
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@ -134,18 +139,21 @@ def _maybe_convert_wav(data_dir, original_data, converted_data):
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target_dir = os.path.join(data_dir, converted_data)
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# Conditionally convert sph files to wav files
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if not os.path.exists(target_dir):
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# Create target_dir
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os.makedirs(target_dir)
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# Loop over sph files in source_dir and convert each to 16-bit PCM wav
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for root, dirnames, filenames in os.walk(source_dir):
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for filename in fnmatch.filter(filenames, "*.sph"):
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sph_file = os.path.join(root, filename)
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wav_filename = os.path.splitext(os.path.basename(sph_file))[0] + ".wav"
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wav_file = os.path.join(target_dir, wav_filename)
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print("converting {} to {}".format(sph_file, wav_file))
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subprocess.check_call(["sph2pipe", "-p", "-f", "rif", sph_file, wav_file])
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if os.path.exists(target_dir):
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print("skipping maybe_convert_wav")
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return
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# Create target_dir
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os.makedirs(target_dir)
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# Loop over sph files in source_dir and convert each to 16-bit PCM wav
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for root, dirnames, filenames in os.walk(source_dir):
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for filename in fnmatch.filter(filenames, "*.sph"):
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sph_file = os.path.join(root, filename)
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wav_filename = os.path.splitext(os.path.basename(sph_file))[0] + ".wav"
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wav_file = os.path.join(target_dir, wav_filename)
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print("converting {} to {}".format(sph_file, wav_file))
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subprocess.check_call(["sph2pipe", "-p", "-f", "rif", sph_file, wav_file])
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def _parse_transcriptions(trans_file):
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segments = []
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@ -174,23 +182,26 @@ def _parse_transcriptions(trans_file):
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})
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return segments
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def _maybe_split_wav(data_dir, original_data, converted_data):
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def _maybe_split_wav(data_dir, trans_data, original_data, converted_data):
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trans_dir = os.path.join(data_dir, trans_data)
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source_dir = os.path.join(data_dir, original_data)
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target_dir = os.path.join(data_dir, converted_data)
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if os.path.exists(target_dir):
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print("skipping split_wav")
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print("skipping maybe_split_wav")
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return
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os.makedirs(target_dir)
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# Loop over transcription files and split corresponding wav
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for root, dirnames, filenames in os.walk(source_dir):
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for root, dirnames, filenames in os.walk(trans_dir):
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for filename in fnmatch.filter(filenames, "*.txt"):
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trans_file = os.path.join(root, filename)
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segments = _parse_transcriptions(trans_file)
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# Open wav corresponding to transcription file
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wav_filename = os.path.splitext(os.path.basename(trans_file))[0] + ".wav"
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wav_file = os.path.join(target_dir, wav_filename)
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wav_file = os.path.join(source_dir, wav_filename)
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print("splitting {} according to {}".format(wav_file, trans_file))
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@ -212,7 +223,7 @@ def _maybe_split_wav(data_dir, original_data, converted_data):
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origAudio.close()
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# Remove wav_file
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os.remove(wav_file)
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# os.remove(wav_file)
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def _split_wav(origAudio, start_time, stop_time, new_wav_file):
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frameRate = origAudio.getframerate()
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@ -229,8 +240,8 @@ def _maybe_split_transcriptions(data_dir, original_data, converted_data):
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source_dir = os.path.join(data_dir, original_data)
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target_dir = os.path.join(data_dir, converted_data)
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if os.path.exists(target_dir):
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print("skipping split_transcriptions")
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if os.path.exists(os.path.join(source_dir, "split_transcriptions_done")):
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print("skipping maybe_split_transcriptions")
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return
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# Loop over transcription files and split them into individual files for
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@ -247,19 +258,21 @@ def _maybe_split_transcriptions(data_dir, original_data, converted_data):
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txt_filename = os.path.splitext(os.path.basename(trans_file))[0] + "-" + str(start_time) + "-" + str(stop_time) + ".txt"
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txt_file = os.path.join(target_dir, txt_filename)
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# If the txt segment filename does not exist create it
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if not os.path.exists(txt_file):
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transcript = validate_label(segment["transcript"])
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# If the transcript is valid and the txt segment filename does
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# not exist create it
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if transcript != None and not os.path.exists(txt_file):
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with open(txt_file, "w") as fout:
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fout.write(segment["transcript"])
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fout.write(transcript)
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with open(os.path.join(source_dir, "split_transcriptions_done"), "w") as fout:
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fout.write("This file signals to the importer than the transcription of this source dir has already been completed.")
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def _maybe_split_sets(data_dir, original_data, converted_data):
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source_dir = os.path.join(data_dir, original_data)
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target_dir = os.path.join(data_dir, converted_data)
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if os.path.exists(target_dir):
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print("skipping split_sets")
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return
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filelist = sorted(glob(os.path.join(source_dir, "*.txt")))
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# We initially split the entire set into 80% train and 20% test, then
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