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Russian Open Speech To Text

Speech to Text Russian Open STT

A collection of speech samples derived from various audio sources. The dataset contains short audio clips in Russian.

Arguably the largest public Russian STT dataset up to date:

  • ~16m utterances;
  • ~20 000 hours;
  • 2,3 TB (uncompressed in .wav format in int16), 356G in .opus;
  • Now all files were transformed to opus, except for validation datasets;

The main purpose of the dataset is to train speech-to-text models.

Dataset composition

Dataset size is given for .wav files.

Dataset Utterances Hours GB Secs/chars Comment Annotation Quality/noise
radio_v4 (*) 7,603,192 10,430 1,195 5s / 68 Radio Align 95% / crisp
public_speech (*) 1,700,060 2,709 301 6s / 79 Public speech Align 95% / crisp
audiobook_2 1,149,404 1,511 162 5s / 56 Books Align 95% / crisp
radio_2 651,645 1,439 154 8s / 110 Radio Align 95% / crisp
public_youtube1120 1,410,979 1,104 237 3s / 34 Youtube Subtitles 95% / ~crisp
public_youtube700 759,483 701 75 3s / 43 Youtube Subtitles 95% / ~crisp
tts_russian_addresses 1,741,838 754 81 2s / 20 Addresses TTS 4 voices 100% / crisp
asr_public_phone_calls_2 603,797 601 66 4s / 37 Phone calls ASR 70% / noisy
public_youtube1120_hq 369,245 291 31 3s / 37 YouTube HQ Subtitles 95% / ~crisp
asr_public_phone_calls_1 233,868 211 23 3s / 29 Phone calls ASR 70% / noisy
radio_v4_add (*) 92,679 157 18 6s / 80 Radio Align 95% / crisp
asr_public_stories_2 78,186 78 9 4s / 43 Books ASR 80% / crisp
asr_public_stories_1 46,142 38 4 3s / 30 Books ASR 80% / crisp
public_series_1 20,243 17 2 3s / 38 Youtube Subtitles 95% / ~crisp
asr_calls_2_val 12,950 7,7 2 2s / 34 Phone calls Manual annotation 99% / crisp
public_lecture_1 6,803 6 1 3s / 47 Lectures Subtitles 95% / crisp
buriy_audiobooks_2_val 7,850 4,9 1 2s / 31 Books Manual annotation 99% / crisp
public_youtube700_val 7,311 4,5 1 2s / 35 Youtube Manual annotation 99% / crisp

(*) Only a sample of data is provided with txt files.

Annotation methodology

The dataset is compiled using open sources. Long sequences are split into audio chunks using voice activity detection and alignment. Some audio types are annotated automatically and verified statistically / using heuristics.

Data Volumes and Update Frequency

The total size of the whole dataset is 350 GB. The total size of the dataset with publicly shared labels is 130GB.

The dataset itself is not likely to be updated to be backwards compatible. Please follow the original repository for benchmarks and exclude files.

New domains and languages may be added in future.

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;
  • Converted to OPUS;

On disk DB methodology

Each audio file (wav, binary) 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)
Downloads

The dataset is provided in 2 forms:

  • Archives avaiable via Azure blob storage and / or direct links;
  • Original files avaiable via Azure blob storage;

Everything is stored in https://azureopendatastorage.blob.core.windows.net/openstt/

Folder structure:

└── ru_open_stt_opus <= archived folders │ │ │ ├── archives │ │ ├── asr_calls_2_val.tar.gz <= tar.gz archives with opus and wav files │ │ │ ... <= see the below table for enumeration │ │ └── tts_russian_addresses_rhvoice_4voices.tar.gz │ │ │ └── manifests │ ├── asr_calls_2_val.csv <= csv files with wav_path, text_path, duration (see notebooks) │ │ ... │ └── tts_russian_addresses_rhvoice_4voices.csv └── ru_open_stt_opus_unpacked <= a separate folder for each uploaded domain ├── public_youtube1120 │ ├── 0 <= see "On disk DB methodology" for details │ ├── 1 │ │ ├── 00 │ │ │ ... │ │ └── ff │ │ ├── *.opus <= actual files │ │ └── *.txt │ │ ... │ └── f ├── public_youtube1120_hq ├── public_youtube700_val ├── asr_calls_2_val ├── radio_2 ├── private_buriy_audiobooks_2 ├── asr_public_phone_calls_2 ├── asr_public_stories_2 ├── asr_public_stories_1 ├── public_lecture_1 ├── asr_public_phone_calls_1 ├── public_series_1 └── public_youtube700
Dataset GB, wav GB, archive Archive Source Manifest
Train
Radio and public speech sample - 11.4 opus+txt - manifest
audiobook_2 162 25.8 opus+txt Internet + alignment manifest
radio_2 154 24.6 opus+txt Radio manifest
public_youtube1120 237 19.0 opus+txt YouTube videos manifest
asr_public_phone_calls_2 66 9.4 opus+txt Internet + ASR manifest
public_youtube1120_hq 31 4.9 opus+txt YouTube videos manifest
asr_public_stories_2 9 1.4 opus+txt Internet + alignment manifest
tts_russian_addresses_rhvoice_4voices 80.9 12.9 opus+txt TTS manifest
public_youtube700 75.0 12.2 opus+txt YouTube videos manifest
asr_public_phone_calls_1 22.7 3.2 opus+txt Internet + ASR manifest
asr_public_stories_1 4.1 0.7 opus+txt Public stories manifest
public_series_1 1.9 0.3 opus+txt Public series manifest
public_lecture_1 0.7 0.1 opus+txt Internet + manual manifest
Val
asr_calls_2_val 2 0.8 wav+txt Internet manifest
buriy_audiobooks_2_val 1 0.5 wav+txt Books + manual manifest
public_youtube700_val 2 0.13 wav+txt YouTube videos + manual manifest
Download instructions

Directly

Please refer here - https://github.com/snakers4/open_stt#download-instructions

Via mounting azure blob storage

Please see notebook located in “Data Access” Tab

Contacts

For help or questions about the data please contact the data author(s) at aveysov@gmail.com

License

This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. It includes the following elements:
* BY – Credit must be given to the creator
* NC – Only noncommercial uses of the work are permitted

CC-BY-NC and commercial usage available after agreement with dataset authors.

References / further reading

Original dataset

  • https://github.com/snakers4/open_stt

English articles

  • https://thegradient.pub/towards-an-imagenet-moment-for-speech-to-text/
  • https://thegradient.pub/a-speech-to-text-practitioners-criticisms-of-industry-and-academia/

Chinese articles

  • https://www.infoq.cn/article/4u58WcFCs0RdpoXev1E2

Russian articles

  • https://habr.com/ru/post/494006/
  • https://habr.com/ru/post/474462/

Access

Available inWhen to use
Azure Notebooks

Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine.

Select your preferred service:

Azure Notebooks

Azure Notebooks

Package: Language: Python

Helper functions / dependencies

Building libsndfile

The best efficient way to read opus files in python (the we know of) that does incur any significant overhead is to use pysoundfile (a python CFFI wrapper around libsoundfile).

When this solution was being researched the community had been waiting for a major libsoundfile release for some time.

Opus support has been implemented some time ago upstream, but it has not been properly released. Therefore we opted for a custom build + monkey patching.

At the time when you read / use this - probably there will be decent / proper builds of libsndfile.

Please replace with your faviourite tool if there is one.

Typically, you need to run this in your shell with sudo access:

apt-get update
apt-get install cmake autoconf autogen automake build-essential libasound2-dev \
libflac-dev libogg-dev libtool libvorbis-dev libopus-dev pkg-config -y

cd /usr/local/lib
git clone https://github.com/erikd/libsndfile.git
cd libsndfile
git reset --hard 49b7d61
mkdir -p build && cd build

cmake .. -DBUILD_SHARED_LIBS=ON
make && make install
cmake --build .

Helper functions / dependencies

Install the following libraries (versions do not matter much):

pandas
numpy
scipy
tqdm
soundfile
librosa

Depending on how this notebook is run, this sometimes can be as easy as (if, for example your miniconda is not installed under root):

In [ ]:
!pip install numpy
!pip install tqdm
!pip install scipy
!pip install pandas
!pip install soundfile
!pip install librosa
!pip install azure-storage-blob

Manifests are just csv files with the following columns:

  • Path to audio
  • Path to text file
  • Duration

They proved to be the most simple / helpful format of accessing data.

For ease of use all the manifests are already rerooted, i.e. all paths in them are relative and you just need to add a root folder.

In [1]:
# manifest utils
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
from urllib.request import urlopen



def reroot_manifest(manifest_df,
                    source_path,
                    target_path):
    if source_path != '':
        manifest_df.wav_path = manifest_df.wav_path.apply(lambda x: x.replace(source_path,
                                                                              target_path))
        manifest_df.text_path = manifest_df.text_path.apply(lambda x: x.replace(source_path,
                                                                                target_path))
    else:
        manifest_df.wav_path = manifest_df.wav_path.apply(lambda x: os.path.join(target_path, x))
        manifest_df.text_path = manifest_df.text_path.apply(lambda x: os.path.join(target_path, x))    
    return manifest_df


def save_manifest(manifest_df,
                  path,
                  domain=False):
    if domain:
        assert list(manifest_df.columns) == ['wav_path', 'text_path', 'duration', 'domain']
    else:
        assert list(manifest_df.columns) == ['wav_path', 'text_path', 'duration']

    manifest_df.reset_index(drop=True).sort_values(by='duration',
                                                   ascending=True).to_csv(path,
                                                                          sep=',',
                                                                          header=False,
                                                                          index=False)
    return True


def read_manifest(manifest_path,
                  domain=False):
    if domain:
        return pd.read_csv(manifest_path,
                        names=['wav_path',
                               'text_path',
                               'duration',
                               'domain'])
    else:
        return pd.read_csv(manifest_path,
                        names=['wav_path',
                               'text_path',
                               'duration'])


def check_files(manifest_df,
                domain=False):
    orig_len = len(manifest_df)
    if domain:
        assert list(manifest_df.columns) == ['wav_path', 'text_path', 'duration']
    else:
        assert list(manifest_df.columns) == ['wav_path', 'text_path', 'duration', 'domain']
    wav_paths = list(manifest_df.wav_path.values)
    text_path = list(manifest_df.text_path.values)

    omitted_wavs = []
    omitted_txts = []

    for wav_path, text_path in zip(wav_paths, text_path):
        if not os.path.exists(wav_path):
            print('Dropping {}'.format(wav_path))
            omitted_wavs.append(wav_path)
        if not os.path.exists(text_path):
            print('Dropping {}'.format(text_path))
            omitted_txts.append(text_path)

    manifest_df = manifest_df[~manifest_df.wav_path.isin(omitted_wavs)]
    manifest_df = manifest_df[~manifest_df.text_path.isin(omitted_txts)]
    final_len = len(manifest_df)

    if final_len != orig_len:
        print('Removed {} lines'.format(orig_len-final_len))
    return manifest_df


def plain_merge_manifests(manifest_paths,
                          MIN_DURATION=0.1,
                          MAX_DURATION=100):

    manifest_df = pd.concat([read_manifest(_)
                             for _ in manifest_paths])
    manifest_df = check_files(manifest_df)

    manifest_df_fit = manifest_df[(manifest_df.duration>=MIN_DURATION) &
                                  (manifest_df.duration<=MAX_DURATION)]

    manifest_df_non_fit = manifest_df[(manifest_df.duration<MIN_DURATION) |
                                      (manifest_df.duration>MAX_DURATION)]

    print(f'Good hours: {manifest_df_fit.duration.sum() / 3600:.2f}')
    print(f'Bad hours: {manifest_df_non_fit.duration.sum() / 3600:.2f}')

    return manifest_df_fit


def save_txt_file(wav_path, text):
    txt_path = wav_path.replace('.wav','.txt')
    with open(txt_path, "w") as text_file:
        print(text, file=text_file)
    return txt_path


def read_txt_file(text_path):
    #with open(text_path, 'r') as file:
    response = urlopen(text_path)
    file = response.readlines()
    for i in range(len(file)):
        file[i] = file[i].decode('utf8')
    return file 

def create_manifest_from_df(df, domain=False):
    if domain:
        columns = ['wav_path', 'text_path', 'duration', 'domain']
    else:
        columns = ['wav_path', 'text_path', 'duration']
    manifest = df[columns]
    return manifest


def create_txt_files(manifest_df):
    assert 'text' in manifest_df.columns
    assert 'wav_path' in manifest_df.columns
    wav_paths, texts = list(manifest_df['wav_path'].values), list(manifest_df['text'].values)
    # not using multiprocessing for simplicity
    txt_paths = [save_txt_file(*_) for _ in tqdm(zip(wav_paths, texts), total=len(wav_paths))]
    manifest_df['text_path'] = txt_paths
    return manifest_df


def replace_encoded(text):
    text = text.lower()
    if '2' in text:
        text = list(text)
        _text = []
        for i,char in enumerate(text):
            if char=='2':
                try:
                    _text.extend([_text[-1]])
                except:
                    print(''.join(text))
            else:
                _text.extend([char])
        text = ''.join(_text)
    return text
In [2]:
# reading opus files
import os
import soundfile as sf



# Fx for soundfile read/write functions
def fx_seek(self, frames, whence=os.SEEK_SET):
    self._check_if_closed()
    position = sf._snd.sf_seek(self._file, frames, whence)
    return position


def fx_get_format_from_filename(file, mode):
    format = ''
    file = getattr(file, 'name', file)
    try:
        format = os.path.splitext(file)[-1][1:]
        format = format.decode('utf-8', 'replace')
    except Exception:
        pass
    if format == 'opus':
        return 'OGG'
    if format.upper() not in sf._formats and 'r' not in mode:
        raise TypeError("No format specified and unable to get format from "
                        "file extension: {0!r}".format(file))
    return format


#sf._snd = sf._ffi.dlopen('/usr/local/lib/libsndfile/build/libsndfile.so.1.0.29')
sf._subtypes['OPUS'] = 0x0064
sf.SoundFile.seek = fx_seek
sf._get_format_from_filename = fx_get_format_from_filename


def read(file, **kwargs):
    return sf.read(file, **kwargs)


def write(file, data, samplerate, **kwargs):
    return sf.write(file, data, samplerate, **kwargs)
In [3]:
# display utils
import gc
from IPython.display import HTML, Audio, display_html
pd.set_option('display.max_colwidth', 3000)
#Prepend_path is set to read directly from Azure. To read from local replace below string with path to the downloaded dataset files
prepend_path = 'https://azureopendatastorage.blob.core.windows.net/openstt/ru_open_stt_opus_unpacked/'


def audio_player(audio_path):
    return '<audio preload="none" controls="controls"><source src="{}" type="audio/wav"></audio>'.format(audio_path)

def display_manifest(manifest_df):
    display_df = manifest_df
    display_df['wav'] = [audio_player(prepend_path+path) for path in display_df.wav_path]
    display_df['txt'] = [read_txt_file(prepend_path+path) for path in tqdm(display_df.text_path)]
    audio_style = '<style>audio {height:44px;border:0;padding:0 20px 0px;margin:-10px -20px -20px;}</style>'
    display_df = display_df[['wav','txt', 'duration']]
    display(HTML(audio_style + display_df.to_html(escape=False)))
    del display_df
    gc.collect()

Play with a dataset

Play a sample of files

On most platforms browsers usually support native audio playback.

So we can leverage HTML5 audio players to view our data.

In [4]:
manifest_df = read_manifest(prepend_path +'/manifests/public_series_1.csv')
#manifest_df = reroot_manifest(manifest_df,
                              #source_path='',
                              #target_path='../../../../../nvme/stt/data/ru_open_stt/')
In [5]:
sample = manifest_df.sample(n=20)
display_manifest(sample)
100%|██████████| 20/20 [00:07<00:00,  2.66it/s]
wav txt duration
5963 [пожалуйста прости всё в порядке\n] 2.48
19972 [хотелось бы хотя бы разок глазком на неё посмотреть раз такое дело\n] 5.68
15555 [они с егерем на след напали до инспектора не дозвониться\n] 3.84
430 [что то случилось\n] 1.36
4090 [так давай опаздываем\n] 2.16
18590 [да саид слушаю тебя троих нашли а в полётном листе\n] 4.60
17734 [надо сначала самому серьёзным человеком стать понимаешь\n] 4.32
978 [вот что случилось\n] 1.56
13269 [да паш юль пожалуйста не делай глупостей\n] 3.48
4957 [полусладкое или сухое\n] 2.32
1913 [ищи другую машину\n] 1.80
10522 [гражданин финн не зная что я полицейский\n] 3.08
9214 [ты чего трубку не берёшь я же переживаю\n] 2.88
10014 [я не окажу сопротивления я без оружия\n] 3.00
8351 [звони партнёру пусть он напишет\n] 2.80
3818 [ну что пойдём обсудим\n] 2.12
11097 [вы простите понимаете все об этом знают\n] 3.16
2989 [какие уж разводки\n] 2.00
12229 [я получается какой то диспетчер а не напарник\n] 3.28
5348 [я же тебе сказала никакой карелии\n] 2.40

Read a file

In [ ]:
!ls ru_open_stt_opus/manifests/*.csv

A couple of simplistic examples showing how to best read wav and opus files.

Scipy is the fastest for wav, pysoundfile is the best overall for opus.

In [6]:
%matplotlib inline

import librosa
from scipy.io import wavfile
from librosa import display as ldisplay
from matplotlib import pyplot as plt

Read a wav

In [7]:
manifest_df = read_manifest(prepend_path +'manifests/asr_calls_2_val.csv')
#manifest_df = reroot_manifest(manifest_df,
                              #source_path='',
                              #target_path='../../../../../nvme/stt/data/ru_open_stt/')
In [8]:
sample = manifest_df.sample(n=5)
display_manifest(sample)
100%|██████████| 5/5 [00:01<00:00,  2.61it/s]
wav txt duration
7802 [это же позитивные новости не негативные\n] 2.01
3590 [белый цветочек\n] 1.17
10594 [какое отношение имеет ваша пенсия к моему отделению\n] 3.14
4630 [есть есть видео\n] 1.35
468 [что ещё раз\n] 0.62
In [9]:
from io import BytesIO

wav_path = sample.iloc[0].wav_path
response = urlopen(prepend_path+wav_path)
data = response.read()
sr, wav = wavfile.read(BytesIO(data))
wav.astype('float32')
absmax = np.max(np.abs(wav))
wav =  wav / absmax
In [10]:
# shortest way to plot a spectrogram
D = librosa.amplitude_to_db(np.abs(librosa.stft(wav)), ref=np.max)
plt.figure(figsize=(12, 6))
ldisplay.specshow(D, y_axis='log')
plt.colorbar(format='%+2.0f dB')
plt.title('Log-frequency power spectrogram')
# shortest way to plot an envelope
plt.figure(figsize=(12, 6))
ldisplay.waveplot(wav, sr=sr, max_points=50000.0, x_axis='time', offset=0.0, max_sr=1000, ax=None)
Out[10]:
<matplotlib.collections.PolyCollection at 0x7fdf62f7e8d0>

Read opus

In [11]:
manifest_df = read_manifest(prepend_path +'manifests/asr_public_phone_calls_2.csv')
#manifest_df = reroot_manifest(manifest_df,
                              #source_path='',
                              #target_path='../../../../../nvme/stt/data/ru_open_stt/')
In [12]:
sample = manifest_df.sample(n=5)
display_manifest(sample)
100%|██████████| 5/5 [00:02<00:00,  2.24it/s]
wav txt duration
5018 [а вы кто\n] 0.96
143473 [пьеса дружбы нету\n] 1.86
272155 [не знаю где находится\n] 2.64
334225 [ты куда звонишь то куда ты звонишь ты знаешь\n] 3.12
143789 [помощник дежурного\n] 1.86
In [13]:
opus_path = sample.iloc[0].wav_path
response = urlopen(prepend_path+opus_path)
data = response.read()
wav, sr = sf.read(BytesIO(data))
wav.astype('float32')
absmax = np.max(np.abs(wav))
wav =  wav / absmax
In [14]:
# shortest way to plot a spectrogram
D = librosa.amplitude_to_db(np.abs(librosa.stft(wav)), ref=np.max)
plt.figure(figsize=(12, 6))
ldisplay.specshow(D, y_axis='log')
plt.colorbar(format='%+2.0f dB')
plt.title('Log-frequency power spectrogram')
# shortest way to plot an envelope
plt.figure(figsize=(12, 6))
ldisplay.waveplot(wav, sr=sr, max_points=50000.0, x_axis='time', offset=0.0, max_sr=1000, ax=None)
Out[14]:
<matplotlib.collections.PolyCollection at 0x7fdf62f8ee10>
In [ ]: