da55cfae86
This reverts commit 3dcb3743acc14ed9de63110709446791892f8936, reversing changes made to 457198c88d7ad96ee4596cb21deaeca77c277898.
99 lines
3.5 KiB
Python
Executable File
99 lines
3.5 KiB
Python
Executable File
#!/usr/bin/env python
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import glob
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import os
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import tarfile
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import pandas
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from deepspeech_training.util.importers import get_importers_parser
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COLUMN_NAMES = ["wav_filename", "wav_filesize", "transcript"]
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def extract(archive_path, target_dir):
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print("Extracting {} into {}...".format(archive_path, target_dir))
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with tarfile.open(archive_path) as tar:
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tar.extractall(target_dir)
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def preprocess_data(tgz_file, target_dir):
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# First extract main archive and sub-archives
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extract(tgz_file, target_dir)
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main_folder = os.path.join(target_dir, "aidatatang_200zh")
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for targz in glob.glob(os.path.join(main_folder, "corpus", "*", "*.tar.gz")):
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extract(targz, os.path.dirname(targz))
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# Folder structure is now:
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# - aidatatang_200zh/
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# - transcript/aidatatang_200_zh_transcript.txt
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# - corpus/train/*.tar.gz
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# - corpus/train/*/*.{wav,txt,trn,metadata}
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# - corpus/dev/*.tar.gz
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# - corpus/dev/*/*.{wav,txt,trn,metadata}
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# - corpus/test/*.tar.gz
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# - corpus/test/*/*.{wav,txt,trn,metadata}
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# Transcripts file has one line per WAV file, where each line consists of
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# the WAV file name without extension followed by a single space followed
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# by the transcript.
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# Since the transcripts themselves can contain spaces, we split on space but
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# only once, then build a mapping from file name to transcript
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transcripts_path = os.path.join(
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main_folder, "transcript", "aidatatang_200_zh_transcript.txt"
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)
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with open(transcripts_path) as fin:
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transcripts = dict((line.split(" ", maxsplit=1) for line in fin))
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def load_set(glob_path):
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set_files = []
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for wav in glob.glob(glob_path):
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try:
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wav_filename = wav
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wav_filesize = os.path.getsize(wav)
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transcript_key = os.path.splitext(os.path.basename(wav))[0]
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transcript = transcripts[transcript_key].strip("\n")
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set_files.append((wav_filename, wav_filesize, transcript))
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except KeyError:
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print("Warning: Missing transcript for WAV file {}.".format(wav))
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return set_files
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for subset in ("train", "dev", "test"):
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print("Loading {} set samples...".format(subset))
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subset_files = load_set(
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os.path.join(main_folder, "corpus", subset, "*", "*.wav")
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)
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df = pandas.DataFrame(data=subset_files, columns=COLUMN_NAMES)
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# Trim train set to under 10s by removing the last couple hundred samples
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if subset == "train":
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durations = (df["wav_filesize"] - 44) / 16000 / 2
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df = df[durations <= 10.0]
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print("Trimming {} samples > 10 seconds".format((durations > 10.0).sum()))
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dest_csv = os.path.join(target_dir, "aidatatang_{}.csv".format(subset))
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print("Saving {} set into {}...".format(subset, dest_csv))
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df.to_csv(dest_csv, index=False)
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def main():
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# https://www.openslr.org/62/
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parser = get_importers_parser(description="Import aidatatang_200zh corpus")
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parser.add_argument("tgz_file", help="Path to aidatatang_200zh.tgz")
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parser.add_argument(
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"--target_dir",
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default="",
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help="Target folder to extract files into and put the resulting CSVs. Defaults to same folder as the main archive.",
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)
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params = parser.parse_args()
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if not params.target_dir:
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params.target_dir = os.path.dirname(params.tgz_file)
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preprocess_data(params.tgz_file, params.target_dir)
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if __name__ == "__main__":
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main()
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