Reuben Morais
GitHub
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#!/usr/bin/env python
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# VCTK used in wavenet paper https://arxiv.org/pdf/1609.03499.pdf
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# Licenced under Open Data Commons Attribution License (ODC-By) v1.0.
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# as per https://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html
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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 os
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import random
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import sys
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sys.path.insert(1, os.path.join(sys.path[0], ".."))
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import re
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import librosa
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import progressbar
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from os import path
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from multiprocessing.dummy import Pool
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from multiprocessing import cpu_count
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from util.downloader import maybe_download, SIMPLE_BAR
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from zipfile import ZipFile
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SAMPLE_RATE = 16000
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MAX_SECS = 10
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MIN_SECS = 1
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ARCHIVE_DIR_NAME = "VCTK-Corpus"
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ARCHIVE_NAME = "VCTK-Corpus.zip?sequence=2&isAllowed=y"
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ARCHIVE_URL = (
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"https://datashare.is.ed.ac.uk/bitstream/handle/10283/2651/" + ARCHIVE_NAME
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)
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def _download_and_preprocess_data(target_dir):
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# Making path absolute
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target_dir = path.abspath(target_dir)
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# Conditionally download data
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archive_path = maybe_download(ARCHIVE_NAME, target_dir, ARCHIVE_URL)
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# Conditionally extract common voice data
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_maybe_extract(target_dir, ARCHIVE_DIR_NAME, archive_path)
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# Conditionally convert common voice CSV files and mp3 data to DeepSpeech CSVs and wav
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_maybe_convert_sets(target_dir, ARCHIVE_DIR_NAME)
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def _maybe_extract(target_dir, extracted_data, archive_path):
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# If target_dir/extracted_data does not exist, extract archive in target_dir
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extracted_path = path.join(target_dir, extracted_data)
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if not path.exists(extracted_path):
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print(f"No directory {extracted_path} - extracting archive...")
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with ZipFile(archive_path, "r") as zipobj:
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# Extract all the contents of zip file in current directory
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zipobj.extractall(target_dir)
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else:
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print(f"Found directory {extracted_path} - not extracting it from archive.")
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def _maybe_convert_sets(target_dir, extracted_data):
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extracted_dir = path.join(target_dir, extracted_data, "wav48")
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txt_dir = path.join(target_dir, extracted_data, "txt")
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cnt = 1
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directory = os.path.expanduser(extracted_dir)
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srtd = len(sorted(os.listdir(directory)))
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for target in sorted(os.listdir(directory)):
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print(f"\nSpeaker {cnt} of {srtd}")
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_maybe_convert_set(path.join(extracted_dir, os.path.split(target)[-1]))
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cnt += 1
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_write_csv(extracted_dir, txt_dir, target_dir)
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def _maybe_convert_set(target_csv):
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def one_sample(sample):
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if is_audio_file(sample):
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sample = os.path.join(target_csv, sample)
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y, sr = librosa.load(sample, sr=16000)
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# Trim the beginning and ending silence
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yt, index = librosa.effects.trim(y) # pylint: disable=unused-variable
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duration = librosa.get_duration(yt, sr)
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if duration > MAX_SECS or duration < MIN_SECS:
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os.remove(sample)
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else:
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librosa.output.write_wav(sample, yt, sr)
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samples = sorted(os.listdir(target_csv))
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num_samples = len(samples)
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print(f"Converting wav files to {SAMPLE_RATE}hz...")
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pool = Pool(cpu_count())
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bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
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for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
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bar.update(i)
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bar.update(num_samples)
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pool.close()
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pool.join()
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def _write_csv(extracted_dir, txt_dir, target_dir):
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print(f"Writing CSV file")
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dset_abs_path = extracted_dir
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dset_txt_abs_path = txt_dir
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audios = make_manifest(dset_abs_path)
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utterences = load_txts(dset_txt_abs_path)
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csv = []
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for file in audios:
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st = os.stat(file)
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file_size = st.st_size
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# Seems to be one wav directory missing from txts - skip it
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file_parts = file.split(os.sep)
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file_subdir = file_parts[-2]
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if file_subdir == "p315":
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continue
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file_name = file_parts[-1]
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file_name_no_ext = file_name.split(".")[0]
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utterence = utterences[file_name_no_ext]
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utterence_clean = re.sub(r"[^a-zA-Z' ]+", "", utterence).lower().strip()
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csv_line = f"{file},{file_size},{utterence_clean}\n"
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csv.append(csv_line)
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random.seed(1454)
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random.shuffle(csv)
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train_data = csv[:37000]
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dev_data = csv[37000:40200]
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test_data = csv[40200:]
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with open(os.path.join(target_dir, "vctk_full.csv"), "w") as fd:
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fd.write("wav_filename,wav_filesize,transcript\n")
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for i in csv:
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fd.write(i)
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with open(os.path.join(target_dir, "vctk_train.csv"), "w") as fd:
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fd.write("wav_filename,wav_filesize,transcript\n")
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for i in train_data:
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fd.write(i)
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with open(os.path.join(target_dir, "vctk_dev.csv"), "w") as fd:
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fd.write("wav_filename,wav_filesize,transcript\n")
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for i in dev_data:
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fd.write(i)
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with open(os.path.join(target_dir, "vctk_test.csv"), "w") as fd:
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fd.write("wav_filename,wav_filesize,transcript\n")
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for i in test_data:
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fd.write(i)
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print(f"Wrote {len(csv)} entries")
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def make_manifest(directory):
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audios = []
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directory = os.path.expanduser(directory)
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for target in sorted(os.listdir(directory)):
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d = os.path.join(directory, target)
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if not os.path.isdir(d):
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continue
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for root, _, fnames in sorted(os.walk(d)):
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for fname in fnames:
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new_path = os.path.join(root, fname)
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item = new_path
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audios.append(item)
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return audios
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def load_txts(directory):
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utterences = dict()
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directory = os.path.expanduser(directory)
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for target in sorted(os.listdir(directory)):
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d = os.path.join(directory, target)
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if not os.path.isdir(d):
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continue
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for root, _, fnames in sorted(os.walk(d)):
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for fname in fnames:
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if fname.endswith(".txt"):
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with open(os.path.join(root, fname), "r") as f:
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fname_no_ext = os.path.basename(fname).rsplit(".", 1)[0]
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utterences[fname_no_ext] = f.readline()
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return utterences
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AUDIO_EXTENSIONS = [".wav", "WAV"]
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def is_audio_file(filename):
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return any(filename.endswith(extension) for extension in AUDIO_EXTENSIONS)
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if __name__ == "__main__":
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_download_and_preprocess_data(sys.argv[1])
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