Russian Open Speech To Text (STT/ASR) Dataset

Arguably the largest public Russian STT dataset up to date:

• ~16m utterances (1-2m with less perfect annotation, see #7);
• ~20 000 hours;
• 2,3 TB (in .wav format in int16);
• (new!) A new domain - public speech;
• (new!) A huge Radio dataset update with 10 000+ hours;

Prove us wrong! Open issues, collaborate, submit a PR, contribute, share your datasets! Let's make STT in Russian (and more) as open and available as CV models.

Planned releases:

• Refine and publish speaker labels, probably add speakers for old datasets;
• Improve / re-upload some of the existing datasets, refine the STT labels;
• Probably add new languages;
• Add pre-trained models;

Table of contents

• Dataset composition
• Downloads
  • Via torrent
  • Links
  • Download-instructions
  • End-to-end download scripts
• Annotation methodology
• Audio normalization
• Disk db methodology
• Helper functions
• Contacts
• Acknowledgements
• FAQ
• License
• Donations

Dataset composition

Dataset	Utterances	Hours	GB	Av s/chars	Comment	Annotation	Quality/noise
radio_v4	7,603,192	10,430	1,195	4.94s / 68	Radio	Alignment (*)	95% / crisp
public_speech	1,700,060	2,709	301	5,73s / 79	Public speech	Alignment (*)	95% / crisp
audiobook_2	1,149,404	1,511	162	4.7s / 56	Books	Alignment (*)	95% / crisp
radio_2	651,645	1,439	154	7.95s / 110	Radio	Alignment (*)	TBC, should be high
public_youtube1120	1,410,979	1,104	237	2.82s / 34	Youtube videos	Subtitles	95% / ~crisp
public_youtube700	759,483	701	75	3.3s / 43	Youtube videos	Subtitles	95% / ~crisp
tts_russian_addresses	1,741,838	754	81	1.6s / 20	Russian addresses	TTS 4 voices	100% / crisp
asr_public_phone_calls_2	603,797	601	66	3.6s / 37	Phone calls	ASR	70% / noisy
public_youtube1120_hq	369,245	291	31	2.84s / 37	YouTube videos HQ sound	Subtitles	95% / ~crisp
asr_public_phone_calls_1	233,868	211	23	3.3s / 29	Phone calls	ASR	70% / noisy
radio_v4_add	92,679	157	18	6.1s / 80	Radio	Alignment (*)	95% / crisp
asr_public_stories_2	78,186	78	9	3.5s / 43	Books	ASR	80% / crisp
asr_public_stories_1	46,142	38	4	3.0s / 30	Books	ASR	80% / crisp
public_series_1	20,243	17	2	3.1s / 38	Youtube videos	Subtitles	95% / ~crisp
asr_calls_2_val	12,950	7,7	2	2.15s / 34	Phone calls	Manual annotation	99% / crisp
public_lecture_1	6,803	6	1	3.4s / 47	Lectures	Subtitles	95% / crisp
buriy_audiobooks_2_val	7,850	4,9	1	2.25s / 31	Books	Manual annotation	99% / crisp
public_youtube700_val	7,311	4,5	1	2.2 / 35	Youtube videos	Manual annotation	99% / crisp
Total	16,513,202‬	20,108	2,369

(*) Automatic alignment

This alignment was performed using Yuri's alignment tool. Contact him if you need alignment for your own dataset.

Updates

Update 2019-11-04

New train datasets added:

• 10,430 hours radio_v4;
• 2,709 hours public_speech;
• 154 hours radio_v4_add;
• 5% sample of all new datasets with annotation.

Click to expand

Update 2019-06-28

New train datasets added:

- 1,439 hours radio_2;
- 1,104 hours public_youtube1120;
- 291 hours public_youtube1120_hq;

New validation datasets added:

- 8 hours asr_calls_2_val;
- 5 hours buriy_audiobooks_2_val;
- 5 hours public_youtube700_val;

Update 2019-05-19

Also shared a wav version via torrent.

Update 2019-05-13

Added the forgotten txt files to mp3 archives. Updating the torrent.

Update 2019-05-12

Torrent created and uploaded to academictorrents.

Update 2019-05-10

Quickly converted the dataset to MP3 thanks to the community! Waiting for our account for academic torrents to be approved. v0.4 will boast MP3 download links.

Update 2019-05-07 Help needed!

If you want to support the project, you can:

• Help us with hosting (create a mirror) / provide a reliable node for torrent;
• Help us with writing some helper functions;
• Donate (each coffee pays for several full downloads) / use our DO referral link to help;

We are converting the dataset to MP3 now. Please contact us using the below contacts, if you would like to help.

Downloads

Via torrent

Save us a couple of bucks, download via torrent:

• An MP3 version of the dataset (v3) not supported anymore;
• A WAV version of the dataset (v5);

You can download separate files via torrent. Try several torrent clients if some do not work. Looks like that due to large chunk size, most conversional torrent clients just fail silently. No problem (re-calculating the torrent takes much time, and some people have downloaded it already):

apt update
apt install aria2
# list the torrent files
aria2c --show-files ru_open_stt_wav_v10.torrent
# download only one file
aria2c --select-file=4 ru_open_stt_wav_v10.torrent
# for more options visit
# https://aria2.github.io/manual/en/html/aria2c.html#basic-options
# https://aria2.github.io/manual/en/html/aria2c.html#bittorrent-metalink-options
# https://aria2.github.io/manual/en/html/aria2c.html#bittorrent-specific-options

If you are using Windows, you may use Linux subsystem to run these commands.

Links

All WAV files can be downloaded ONLY via torrent

Dataset	GB, wav	GB, mp3	Mp3	Source	Manifest
radio_v4	1059	263	mp3, txt	Radio	manifest file
public_speech	257	38.5	mp3, txt	Sources from the Internet + alignment	manifest file
radio_v4_add	15.7	2.2	mp3, txt	Radio	manifest file
5% of radio_v4 + public_speech	-	15.3	mp3+txt	-	manifest file
audiobook_2	162	21.0	mp3+txt	Sources from the Internet + alignment	manifest file
radio_2	154	25.7	mp3+txt	Radio	manifest file
public_youtube1120	237	32.4	mp3+txt	YouTube videos	manifest file
asr_public_phone_calls_2	66	7.5	mp3+txt	Sources from the Internet + ASR	manifest file
public_youtube1120_hq	31	8.6	mp3+txt	YouTube videos	manifest file
asr_public_stories_2	9	1.1	mp3+txt	Sources from the Internet + alignment	manifest file
tts_russian_addresses_rhvoice_4voices	80.9	9.9	mp3+txt	TTS	manifest file
public_youtube700	75.0	9.6	mp3+txt	YouTube videos	manifest file
asr_public_phone_calls_1	22.7	2.6	mp3+txt	Sources from the Internet + ASR	manifest file
asr_public_stories_1	4.1	0.5	mp3+txt	Public stories	manifest file
public_series_1	1.9	0.2	mp3+txt	Public series	manifest file
asr_calls_2_val	2	0.2	mp3+txt	Sources from the Internet	manifest file
public_lecture_1	0.7	0.1	mp3+txt	Sources from the Internet + manual	manifest file
buriy_audiobooks_2_val	1	0.15	mp3+txt	Books + manual	manifest file
public_youtube700_val	2	0.13	mp3+txt	YouTube videos + manual	manifest file
Total	2,186	391

Download instructions

End to end

download.sh or download.py with this config file. Please check the config first.

Manually

1. Download each dataset separately:

Via wget

wget https://ru-open-stt.ams3.digitaloceanspaces.com/some_file

For multi-threaded downloads use aria2 with -x flag, i.e.

aria2c -c -x5 https://ru-open-stt.ams3.digitaloceanspaces.com/some_file

If necessary, merge chunks like this:

cat ru_open_stt_v01.tar.gz_* > ru_open_stt_v01.tar.gz

2. Download the meta data and manifests for each dataset:
3. Merge files (where applicable), unpack and enjoy!

Annotation methodology

The dataset is compiled using open domain sources. Some audio types are annotated automatically and verified statistically / using heuristics.

Audio normalization

All files are normalized for easier / faster runtime augmentations and processing as follows:

• Converted to mono, if necessary;
• Converted to 16 kHz sampling rate, if necessary;
• Stored as 16-bit integers;

On disk DB methodology

Each audio file is hashed. Its hash is used to create a folder hierarchy for more optimal fs operation.

target_format = 'wav'
wavb = wav.tobytes()

f_hash = hashlib.sha1(wavb).hexdigest()

store_path = Path(root_folder,
                  f_hash[0],
                  f_hash[1:3],
                  f_hash[3:15]+'.'+target_format)

Helper functions

Use helper functions from here for easier work with manifest files.

Read manifests

See example

from utils.open_stt_utils import read_manifest

manifest_df = read_manifest('path/to/manifest.csv')

Merge, check and save manifests

See example

from utils.open_stt_utils import (plain_merge_manifests,
                                  check_files,
                                  save_manifest)
train_manifests = [
 'path/to/manifest1.csv',
 'path/to/manifest2.csv',
]
train_manifest = plain_merge_manifests(train_manifests,
                                        MIN_DURATION=0.1,
                                        MAX_DURATION=100)
check_files(train_manifest)
save_manifest(train_manifest,
             'my_manifest.csv')

Contacts

Please contact us here or just create a GitHub issue!

Authors (in alphabetic order):

• Anna Slizhikova;
• Alexander Veysov;
• Diliara Nurtdinova;
• Dmitry Voronin;
• Yuri Baburov;

Acknowledgements

This repo would not be possible without these people:

• Many thanks for helping to encode the initial bulk of the data into mp3 to akreal;
• 18 hours of ground truth annotation datasets for validation are a courtesy of activebc;

Kudos!

FAQ

0. Why not MP3? MP3 encoding / decoding

Encoding

Mostly we used pydub (via ffmpeg) or sox (much much faster way) to convert to MP3. We omitted blank files (YouTube mostly). We used the following parameters:

• 16kHz;
• 32 kbps;
• Mono;

Usually 128-192 kbps is enough for music with sr of 44 kHz, 64-96 is enough for speech. But here we have mono, 16 kHz and usually only one speaker. So 32 kbps was a good choice. We did not use other formats like .ogg, because .mp3 is much more popular.

See example `pydub`

from pydub import AudioSegment

sound = AudioSegment.from_file(temp_path,
                               format="wav")

file_handle = sound.export(store_mp3_path,
                           format="mp3",
                           parameters =["-ar", "{}".format(str(16000)),"-ac", "1"],
                           bitrate="{}k".format(str(32)))

See example `sox`

import subprocess
cmd = 'sox "{}" -C 32.01 -c 1 "{}"'.format(
            wav_path,
            store_mp3_path)
    
res = subprocess.call([cmd], shell=True)

if res != 0:
    print('Problems with {}'.format(wav_path))

Decoding

It is up to you, but to save space and spare CPU during training, I would suggest the following pipeline to extract the files:

See example

# you can also use pydub, torchaudio, sox or whatever
# we ended up using scipy for speed
# this example also includes hashing step which is not necessary
import librosa
import hashlib
import numpy as np
from pathlib import Path
from scipy.io import wavfile

def save_wav_diskdb(wav,
                    root_folder='../data/ru_open_stt/',
                    target_sr=16000):
    assert type(wav) == np.ndarray
    assert wav.dtype == np.dtype('int16')
    assert len(wav.shape)==1

    target_format = 'wav'
    wavb = wav.tobytes()

    # f_path = Path(audio_path)
    f_hash = hashlib.sha1(wavb).hexdigest()

    store_path = Path(root_folder,
                      f_hash[0],
                      f_hash[1:3],
                      f_hash[3:15]+'.'+target_format)

    store_path.parent.mkdir(parents=True,
                            exist_ok=True)

    wavfile.write(filename=str(store_path),
                  rate=target_sr,
                  data=wav)

    return str(store_path)

root_folder = '../data/'
# save to int16, mono, 16 kHz to save space
target_dtype = np.dtype('int16')
target_sr = 16000
# librosa reads mp3
wav, sr = librosa.load(source_mp3_path,
                       mono=True,
                       sr=target_sr)

# librosa converts to float32 by default
wav = (wav * 32767).astype(target_dtype) # cast to int

wav_path = save_wav_diskdb(wav,
                           root_folder=root_folder,
                           target_sr=target_sr)

Why not OGG/ Opus

Even though OGG / Opus is considered to be better for speech with higher compression, we opted for a more conventional well known format.

Also LPC net codec boasts ultra-low bitrate speech compression as well. But we decided to opt for a more familiar format to avoid worry about actually losing signal in compression.

1. Issues with reading files

Maybe try this approach:

See example

from scipy.io import wavfile

sample_rate, sound = wavfile.read(path)

abs_max = np.abs(sound).max()
sound = sound.astype('float32')
if abs_max>0:
    sound *= 1/abs_max

2. Why share such dataset?

We are not altruists, life just is not a zero sum game.

Consider the progress in computer vision, that was made possible by:

• Public datasets;
• Public pre-trained models;
• Open source frameworks;
• Open research;

TTS does not enjoy the same attention by ML community because it is data hungry and public datasets are lacking, especially for languages other than English. Ultimately it leads to worse-off situation for the general community.

3. Known issues with the dataset to be fixed

• Speaker labels coming soon;
• Validation sets for new domains: Radio/Public Speech will be added in next releases.

License

Сc-by-nc and commercial usage available after agreement with dataset authors.

Donations

Donate (each coffee pays for several full downloads) or via open_collective or just use our DO referral link to help.