зеркало из https://github.com/mozilla/DeepSpeech.git
184 строки
8.1 KiB
Python
Executable File
184 строки
8.1 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import absolute_import, division, print_function
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# Make sure we can import stuff from util/
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# This script needs to be run from the root of the DeepSpeech repository
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import sys
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import os
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sys.path.insert(1, os.path.join(sys.path[0], '..'))
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import codecs
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import fnmatch
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import pandas
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import progressbar
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import subprocess
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import tarfile
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import unicodedata
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from sox import Transformer
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from util.downloader import maybe_download
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from tensorflow.python.platform import gfile
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SAMPLE_RATE = 16000
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def _download_and_preprocess_data(data_dir):
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# Conditionally download data to data_dir
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print("Downloading Librivox data set (55GB) into {} if not already present...".format(data_dir))
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with progressbar.ProgressBar(max_value=7, widget=progressbar.AdaptiveETA) as bar:
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TRAIN_CLEAN_100_URL = "http://www.openslr.org/resources/12/train-clean-100.tar.gz"
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TRAIN_CLEAN_360_URL = "http://www.openslr.org/resources/12/train-clean-360.tar.gz"
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TRAIN_OTHER_500_URL = "http://www.openslr.org/resources/12/train-other-500.tar.gz"
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DEV_CLEAN_URL = "http://www.openslr.org/resources/12/dev-clean.tar.gz"
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DEV_OTHER_URL = "http://www.openslr.org/resources/12/dev-other.tar.gz"
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TEST_CLEAN_URL = "http://www.openslr.org/resources/12/test-clean.tar.gz"
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TEST_OTHER_URL = "http://www.openslr.org/resources/12/test-other.tar.gz"
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def filename_of(x): return os.path.split(x)[1]
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train_clean_100 = maybe_download(filename_of(TRAIN_CLEAN_100_URL), data_dir, TRAIN_CLEAN_100_URL)
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bar.update(0)
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train_clean_360 = maybe_download(filename_of(TRAIN_CLEAN_360_URL), data_dir, TRAIN_CLEAN_360_URL)
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bar.update(1)
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train_other_500 = maybe_download(filename_of(TRAIN_OTHER_500_URL), data_dir, TRAIN_OTHER_500_URL)
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bar.update(2)
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dev_clean = maybe_download(filename_of(DEV_CLEAN_URL), data_dir, DEV_CLEAN_URL)
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bar.update(3)
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dev_other = maybe_download(filename_of(DEV_OTHER_URL), data_dir, DEV_OTHER_URL)
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bar.update(4)
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test_clean = maybe_download(filename_of(TEST_CLEAN_URL), data_dir, TEST_CLEAN_URL)
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bar.update(5)
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test_other = maybe_download(filename_of(TEST_OTHER_URL), data_dir, TEST_OTHER_URL)
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bar.update(6)
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# Conditionally extract LibriSpeech data
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# We extract each archive into data_dir, but test for existence in
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# data_dir/LibriSpeech because the archives share that root.
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print("Extracting librivox data if not already extracted...")
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with progressbar.ProgressBar(max_value=7, widget=progressbar.AdaptiveETA) as bar:
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LIBRIVOX_DIR = "LibriSpeech"
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work_dir = os.path.join(data_dir, LIBRIVOX_DIR)
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_maybe_extract(data_dir, os.path.join(LIBRIVOX_DIR, "train-clean-100"), train_clean_100)
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bar.update(0)
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_maybe_extract(data_dir, os.path.join(LIBRIVOX_DIR, "train-clean-360"), train_clean_360)
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bar.update(1)
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_maybe_extract(data_dir, os.path.join(LIBRIVOX_DIR, "train-other-500"), train_other_500)
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bar.update(2)
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_maybe_extract(data_dir, os.path.join(LIBRIVOX_DIR, "dev-clean"), dev_clean)
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bar.update(3)
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_maybe_extract(data_dir, os.path.join(LIBRIVOX_DIR, "dev-other"), dev_other)
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bar.update(4)
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_maybe_extract(data_dir, os.path.join(LIBRIVOX_DIR, "test-clean"), test_clean)
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bar.update(5)
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_maybe_extract(data_dir, os.path.join(LIBRIVOX_DIR, "test-other"), test_other)
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bar.update(6)
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# Convert FLAC data to wav, from:
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# data_dir/LibriSpeech/split/1/2/1-2-3.flac
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# to:
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# data_dir/LibriSpeech/split-wav/1-2-3.wav
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#
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# And split LibriSpeech transcriptions, from:
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# data_dir/LibriSpeech/split/1/2/1-2.trans.txt
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# to:
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# data_dir/LibriSpeech/split-wav/1-2-0.txt
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# data_dir/LibriSpeech/split-wav/1-2-1.txt
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# data_dir/LibriSpeech/split-wav/1-2-2.txt
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# ...
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print("Converting FLAC to WAV and splitting transcriptions...")
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with progressbar.ProgressBar(max_value=7, widget=progressbar.AdaptiveETA) as bar:
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train_100 = _convert_audio_and_split_sentences(work_dir, "train-clean-100", "train-clean-100-wav")
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bar.update(0)
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train_360 = _convert_audio_and_split_sentences(work_dir, "train-clean-360", "train-clean-360-wav")
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bar.update(1)
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train_500 = _convert_audio_and_split_sentences(work_dir, "train-other-500", "train-other-500-wav")
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bar.update(2)
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dev_clean = _convert_audio_and_split_sentences(work_dir, "dev-clean", "dev-clean-wav")
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bar.update(3)
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dev_other = _convert_audio_and_split_sentences(work_dir, "dev-other", "dev-other-wav")
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bar.update(4)
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test_clean = _convert_audio_and_split_sentences(work_dir, "test-clean", "test-clean-wav")
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bar.update(5)
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test_other = _convert_audio_and_split_sentences(work_dir, "test-other", "test-other-wav")
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bar.update(6)
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# Write sets to disk as CSV files
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train_100.to_csv(os.path.join(data_dir, "librivox-train-clean-100.csv"), index=False)
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train_360.to_csv(os.path.join(data_dir, "librivox-train-clean-360.csv"), index=False)
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train_500.to_csv(os.path.join(data_dir, "librivox-train-other-500.csv"), index=False)
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dev_clean.to_csv(os.path.join(data_dir, "librivox-dev-clean.csv"), index=False)
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dev_other.to_csv(os.path.join(data_dir, "librivox-dev-other.csv"), index=False)
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test_clean.to_csv(os.path.join(data_dir, "librivox-test-clean.csv"), index=False)
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test_other.to_csv(os.path.join(data_dir, "librivox-test-other.csv"), index=False)
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def _maybe_extract(data_dir, extracted_data, archive):
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# If data_dir/extracted_data does not exist, extract archive in data_dir
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if not gfile.Exists(os.path.join(data_dir, extracted_data)):
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tar = tarfile.open(archive)
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tar.extractall(data_dir)
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tar.close()
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def _convert_audio_and_split_sentences(extracted_dir, data_set, dest_dir):
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source_dir = os.path.join(extracted_dir, data_set)
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target_dir = os.path.join(extracted_dir, dest_dir)
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if not os.path.exists(target_dir):
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os.makedirs(target_dir)
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# Loop over transcription files and split each one
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#
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# The format for each file 1-2.trans.txt is:
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# 1-2-0 transcription of 1-2-0.flac
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# 1-2-1 transcription of 1-2-1.flac
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# ...
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#
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# Each file is then split into several files:
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# 1-2-0.txt (contains transcription of 1-2-0.flac)
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# 1-2-1.txt (contains transcription of 1-2-1.flac)
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# ...
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#
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# We also convert the corresponding FLACs to WAV in the same pass
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files = []
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for root, dirnames, filenames in os.walk(source_dir):
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for filename in fnmatch.filter(filenames, '*.trans.txt'):
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trans_filename = os.path.join(root, filename)
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with codecs.open(trans_filename, "r", "utf-8") as fin:
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for line in fin:
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# Parse each segment line
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first_space = line.find(" ")
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seqid, transcript = line[:first_space], line[first_space+1:]
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# We need to do the encode-decode dance here because encode
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# returns a bytes() object on Python 3, and text_to_char_array
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# expects a string.
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transcript = unicodedata.normalize("NFKD", transcript) \
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.encode("ascii", "ignore") \
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.decode("ascii", "ignore")
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transcript = transcript.lower().strip()
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# Convert corresponding FLAC to a WAV
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flac_file = os.path.join(root, seqid + ".flac")
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wav_file = os.path.join(target_dir, seqid + ".wav")
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if not os.path.exists(wav_file):
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tfm = Transformer()
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tfm.set_output_format(rate=SAMPLE_RATE)
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tfm.build(flac_file, wav_file)
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wav_filesize = os.path.getsize(wav_file)
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files.append((os.path.abspath(wav_file), wav_filesize, transcript))
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return pandas.DataFrame(data=files, columns=["wav_filename", "wav_filesize", "transcript"])
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if __name__ == "__main__":
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_download_and_preprocess_data(sys.argv[1])
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