This program uses trial auth token of Azure Cognitive Services to do speech synthesis for you.

Overview

🗣️ aspeak

GitHub stars GitHub issues GitHub forks GitHub license PyPI version

A simple text-to-speech client using azure TTS API(trial). 😆

TL;DR: This program uses trial auth token of Azure Cognitive Services to do speech synthesis for you.

You can try the Azure TTS API online: https://azure.microsoft.com/en-us/services/cognitive-services/text-to-speech

Installation

$ pip install --upgrade aspeak

Limitations

Since we are using Azure Cognitive Services, there are some limitations:

Quota Free (F0)3
Max number of transactions per certain time period per Speech service resource
Real-time API. Prebuilt neural voices and custom neural voices. 20 transactions per 60 seconds
Adjustable No4
HTTP-specific quotas
Max audio length produced per request 10 min
Max total number of distinct <voice> and <audio> tags in SSML 50
Websocket specific quotas
Max audio length produced per turn 10 min
Max total number of distinct <voice> and <audio> tags in SSML 50
Max SSML message size per turn 64 KB

This table is copied from Azure Cognitive Services documentation

And the limitations may be subject to change. The table above might become outdated in the future. Please refer to the latest Azure Cognitive Services documentation for the latest information.

Attention: If the result audio is longer than 10 minutes, the audio will be truncated to 10 minutes and the program will not report an error.

Using aspeak as a Python library

See DEVELOP.md for more details. You can find examples in src/examples.

Usage

usage: aspeak [-h] [-V | -L | -Q | [-t [TEXT] [-p PITCH] [-r RATE] [-S STYLE] [-R ROLE] [-d STYLE_DEGREE] | -s [SSML]]]
              [-f FILE] [-e ENCODING] [-o OUTPUT_PATH] [-l LOCALE] [-v VOICE]
              [--mp3 [-q QUALITY] | --ogg [-q QUALITY] | --webm [-q QUALITY] | --wav [-q QUALITY] | -F FORMAT] 

This program uses trial auth token of Azure Cognitive Services to do speech synthesis for you

options:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -L, --list-voices     list available voices, you can combine this argument with -v and -l
  -Q, --list-qualities-and-formats
                        list available qualities and formats
  -t [TEXT], --text [TEXT]
                        Text to speak. Left blank when reading from file/stdin
  -s [SSML], --ssml [SSML]
                        SSML to speak. Left blank when reading from file/stdin
  -f FILE, --file FILE  Text/SSML file to speak, default to `-`(stdin)
  -e ENCODING, --encoding ENCODING
                        Text/SSML file encoding, default to "utf-8"(Not for stdin!)
  -o OUTPUT_PATH, --output OUTPUT_PATH
                        Output file path, wav format by default
  --mp3                 Use mp3 format for output. (Only works when outputting to a file)
  --ogg                 Use ogg format for output. (Only works when outputting to a file)
  --webm                Use webm format for output. (Only works when outputting to a file)
  --wav                 Use wav format for output
  -F FORMAT, --format FORMAT
                        Set output audio format (experts only)
  -l LOCALE, --locale LOCALE
                        Locale to use, default to en-US
  -v VOICE, --voice VOICE
                        Voice to use
  -q QUALITY, --quality QUALITY
                        Output quality, default to 0

Options for --text:
  -p PITCH, --pitch PITCH
                        Set pitch, default to 0. Valid values include floats(will be converted to percentages), percentages such as 20% and -10%, absolute values like 300Hz, and
                        relative values like -20Hz, +2st and string values like x-low. See the documentation for more details.
  -r RATE, --rate RATE  Set speech rate, default to 0. Valid values include floats(will be converted to percentages), percentages like -20%, floats with postfix "f" (e.g. 2f means
                        doubling the default speech rate), and string values like x-slow. See the documentation for more details.
  -S STYLE, --style STYLE
                        Set speech style, default to "general"
  -R {Girl,Boy,YoungAdultFemale,YoungAdultMale,OlderAdultFemale,OlderAdultMale,SeniorFemale,SeniorMale}, --role {Girl,Boy,YoungAdultFemale,YoungAdultMale,OlderAdultFemale,OlderAdultMale,SeniorFemale,SeniorMale}
                        Specifies the speaking role-play. This only works for some Chinese voices!
  -d {values in range 0.01-2 (inclusive)}, --style-degree {values in range 0.01-2 (inclusive)}
                        Specifies the intensity of the speaking style.This only works for some Chinese voices!

Attention: If the result audio is longer than 10 minutes, the audio will be truncated to 10 minutes and the program will not report an error. Unreasonable high/low values for pitch
and rate will be clipped to reasonable values by Azure Cognitive Services.Please refer to the documentation for other limitations at
https://github.com/kxxt/aspeak/blob/main/README.md#limitations
  • If you don't specify -o, we will use your default speaker.
  • If you don't specify -t or -s, we will assume -t is provided.
  • You must specify voice if you want to use special options for --text.

Special Note for Pitch and Rate

  • rate: The speaking rate of the voice.
    • If you use a float value (say 0.5), the value will be multiplied by 100% and become 50.00%.
    • You can use the following values as well: x-slow, slow, medium, fast, x-fast, default.
    • You can also use percentage values directly: +10%.
    • You can also use a relative float value (with f postfix), 1.2f:
      • According to the Azure documentation,
      • A relative value, expressed as a number that acts as a multiplier of the default.
      • For example, a value of 1f results in no change in the rate. A value of 0.5f results in a halving of the rate. A value of 3f results in a tripling of the rate.
  • pitch: The pitch of the voice.
    • If you use a float value (say -0.5), the value will be multiplied by 100% and become -50.00%.
    • You can also use the following values as well: x-low, low, medium, high, x-high, default.
    • You can also use percentage values directly: +10%.
    • You can also use a relative value, (e.g. -2st or +80Hz):
      • According to the Azure documentation,
      • A relative value, expressed as a number preceded by "+" or "-" and followed by "Hz" or "st" that specifies an amount to change the pitch.
      • The "st" indicates the change unit is semitone, which is half of a tone (a half step) on the standard diatonic scale.
    • You can also use an absolute value: e.g. 600Hz

Note: Unreasonable high/low values will be clipped to reasonable values by Azure Cognitive Services.

About Custom Style Degree and Role

According to the Azure documentation , style degree specifies the intensity of the speaking style. It is a floating point number between 0.01 and 2, inclusive.

At the time of writing, style degree adjustments are supported for Chinese (Mandarin, Simplified) neural voices.

According to the Azure documentation , role specifies the speaking role-play. The voice acts as a different age and gender, but the voice name isn't changed.

At the time of writing, role adjustments are supported for these Chinese (Mandarin, Simplified) neural voices: zh-CN-XiaomoNeural, zh-CN-XiaoxuanNeural, zh-CN-YunxiNeural, and zh-CN-YunyeNeural.

Examples

Speak "Hello, world!" to default speaker.

$ aspeak -t "Hello, world"

List all available voices.

$ aspeak -L

List all available voices for Chinese.

$ aspeak -L -l zh-CN

Get information about a voice.

$ aspeak -L -v en-US-SaraNeural
Output
Microsoft Server Speech Text to Speech Voice (en-US, SaraNeural)
Display Name: Sara
Local Name: Sara @ en-US
Locale: English (United States)
Gender: Female
ID: en-US-SaraNeural
Styles: ['cheerful', 'angry', 'sad']
Voice Type: Neural
Status: GA

Save synthesized speech to a file.

$ aspeak -t "Hello, world" -o output.wav

If you prefer mp3/ogg/webm, you can use --mp3/--ogg/--webm option.

$ aspeak -t "Hello, world" -o output.mp3 --mp3
$ aspeak -t "Hello, world" -o output.ogg --ogg
$ aspeak -t "Hello, world" -o output.webm --webm

List available quality levels and formats

$ aspeak -Q
Output
Available qualities:
Qualities for wav:
-2: Riff8Khz16BitMonoPcm
-1: Riff16Khz16BitMonoPcm
 0: Riff24Khz16BitMonoPcm
 1: Riff24Khz16BitMonoPcm
Qualities for mp3:
-3: Audio16Khz32KBitRateMonoMp3
-2: Audio16Khz64KBitRateMonoMp3
-1: Audio16Khz128KBitRateMonoMp3
 0: Audio24Khz48KBitRateMonoMp3
 1: Audio24Khz96KBitRateMonoMp3
 2: Audio24Khz160KBitRateMonoMp3
 3: Audio48Khz96KBitRateMonoMp3
 4: Audio48Khz192KBitRateMonoMp3
Qualities for ogg:
-1: Ogg16Khz16BitMonoOpus
 0: Ogg24Khz16BitMonoOpus
 1: Ogg48Khz16BitMonoOpus
Qualities for webm:
-1: Webm16Khz16BitMonoOpus
 0: Webm24Khz16BitMonoOpus
 1: Webm24Khz16Bit24KbpsMonoOpus

Available formats:
- Riff8Khz16BitMonoPcm
- Riff16Khz16BitMonoPcm
- Audio16Khz128KBitRateMonoMp3
- Raw24Khz16BitMonoPcm
- Raw48Khz16BitMonoPcm
- Raw16Khz16BitMonoPcm
- Audio24Khz160KBitRateMonoMp3
- Ogg24Khz16BitMonoOpus
- Audio16Khz64KBitRateMonoMp3
- Raw8Khz8BitMonoALaw
- Audio24Khz16Bit48KbpsMonoOpus
- Ogg16Khz16BitMonoOpus
- Riff8Khz8BitMonoALaw
- Riff8Khz8BitMonoMULaw
- Audio48Khz192KBitRateMonoMp3
- Raw8Khz16BitMonoPcm
- Audio24Khz48KBitRateMonoMp3
- Raw24Khz16BitMonoTrueSilk
- Audio24Khz16Bit24KbpsMonoOpus
- Audio24Khz96KBitRateMonoMp3
- Webm24Khz16BitMonoOpus
- Ogg48Khz16BitMonoOpus
- Riff48Khz16BitMonoPcm
- Webm24Khz16Bit24KbpsMonoOpus
- Raw8Khz8BitMonoMULaw
- Audio16Khz16Bit32KbpsMonoOpus
- Audio16Khz32KBitRateMonoMp3
- Riff24Khz16BitMonoPcm
- Raw16Khz16BitMonoTrueSilk
- Audio48Khz96KBitRateMonoMp3
- Webm16Khz16BitMonoOpus

Increase/Decrease audio qualities

# Less than default quality.
$ aspeak -t "Hello, world" -o output.mp3 --mp3 -q=-1
# Best quality for mp3
$ aspeak -t "Hello, world" -o output.mp3 --mp3 -q=3

Read text from file and speak it.

$ cat input.txt | aspeak

or

$ aspeak -f input.txt

with custom encoding:

$ aspeak -f input.txt -e gbk

Read from stdin and speak it.

$ aspeak

or (more verbose)

$ aspeak -f -

maybe you prefer:

$ aspeak -l zh-CN << EOF
我能吞下玻璃而不伤身体。
EOF

Speak Chinese.

$ aspeak -t "你好,世界!" -l zh-CN

Use a custom voice.

$ aspeak -t "你好,世界!" -v zh-CN-YunjianNeural

Custom pitch, rate and style

$ aspeak -t "你好,世界!" -v zh-CN-XiaoxiaoNeural -p 1.5 -r 0.5 -S sad
$ aspeak -t "你好,世界!" -v zh-CN-XiaoxiaoNeural -p=-10% -r=+5% -S cheerful
$ aspeak -t "你好,世界!" -v zh-CN-XiaoxiaoNeural -p=+40Hz -r=1.2f -S fearful
$ aspeak -t "你好,世界!" -v zh-CN-XiaoxiaoNeural -p=high -r=x-slow -S calm
$ aspeak -t "你好,世界!" -v zh-CN-XiaoxiaoNeural -p=+1st -r=-7% -S lyrical

Advanced Usage

Use a custom audio format for output

Note: When outputing to default speaker, using a non-wav format may lead to white noises.

$ aspeak -t "Hello World" -F Riff48Khz16BitMonoPcm -o high-quality.wav

About This Application

  • I found Azure TTS can synthesize nearly authentic human voice, which is very interesting 😆 .
  • I wrote this program to learn Azure Cognitive Services.
  • And I use this program daily, because espeak and festival outputs terrible 😨 audio.
    • But I respect 🙌 their maintainers' work, both are good open source software and they can be used off-line.
  • I hope you like it ❤️ .

Alternative Applications

Comments
  • Could not extract token from webpage

    Could not extract token from webpage

    Raise this error today

    def _get_auth_token() -> str:
        """
        Get a trial auth token from the trial webpage.
        """
        response = requests.get(TRAIL_URL)
        if response.status_code != 200:
            raise errors.TokenRetrievalError(status_code=response.status_code)
        text = response.text
    
        # We don't need bs4, because a little of regex is enough.
    
        match = re.search(r'\s+var\s+localizedResources\s+=\s+\{((.|\n)*?)\};', text, re.M)
        retrieval_error = errors.TokenRetrievalError(message='Could not extract token from webpage.',
                                                     status_code=response.status_code)
        if match is None:
            raise retrieval_error
        token = re.search(r'\s+token:\s*"([^"]+)"', match.group(1), re.M)
        if token is None:
            raise retrieval_error
        return token.group(1)
    

    https://azure.microsoft.com/en-us/services/cognitive-services/text-to-speech/#overview works normal in browser in same machine.

    opened by ArthurDavidson 16
  • Running in python script

    Running in python script

    How can I run this in my python script, just that without text being typed in, there would be used translated_text instead: def speak_paste(): try: spoken_text = driver1.find_element_by_xpath("/html/body/div/div[2]/div[3]/span").text test_str = (spoken_text) res = " ".join(lookp_dict.get(ele, ele) for ele in test_str.split()) pyperclip.copy(res) translator = deepl.Translator('') result = translator.translate_text((res), target_lang="ru", formality="less", preserve_formatting="1") translated_text = result.text This is basically using subtitle text to translate it with deepl, but I want just to pass this translated text to azure tts to synthesize it.

    opened by Funktionar 11
  • Faster audio output/processing

    Faster audio output/processing

    Possible to use this in real-time communications? Compared with just azure it's slower and I have the deepl API to talk with foreigners. I'd like to get the audio within 200 ms and output it to a sound device, if it's feasible.

    opened by Funktionar 9
  • ImportError from urllib3: cannot import name 'Mapping' from 'collections'

    ImportError from urllib3: cannot import name 'Mapping' from 'collections'

    /Users/meeia ~ aspeak -t "Hello, world"
    Traceback (most recent call last):
      File "/Library/Frameworks/Python.framework/Versions/3.10/bin/aspeak", line 5, in <module>
        from aspeak.__main__ import main
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/aspeak/__main__.py", line 1, in <module>
        from .cli import main
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/aspeak/cli/__init__.py", line 1, in <module>
        from .main import main
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/aspeak/cli/main.py", line 8, in <module>
        from .voices import list_voices
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/aspeak/cli/voices.py", line 1, in <module>
        import requests
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/requests/__init__.py", line 43, in <module>
        import urllib3
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/urllib3/__init__.py", line 8, in <module>
        from .connectionpool import (
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/urllib3/connectionpool.py", line 29, in <module>
        from .connection import (
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/urllib3/connection.py", line 39, in <module>
        from .util.ssl_ import (
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/urllib3/util/__init__.py", line 3, in <module>
        from .connection import is_connection_dropped
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/urllib3/util/connection.py", line 3, in <module>
        from .wait import wait_for_read
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/urllib3/util/wait.py", line 1, in <module>
        from .selectors import (
      File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/urllib3/util/selectors.py", line 14, in <module>
        from collections import namedtuple, Mapping
    ImportError: cannot import name 'Mapping' from 'collections' (/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/collections/__init__.py)
    /Users/meeia ~
    
    
    wontfix upstream 
    opened by cnmeeia 3
  • need help

    need help

    need help. C:\Users\公司>aspeak Traceback (most recent call last): File "c:\users\公司\appdata\local\programs\python\python37\lib\runpy.py", line 193, in run_module_as_main "main", mod_spec) File "c:\users\公司\appdata\local\programs\python\python37\lib\runpy.py", line 85, in run_code exec(code, run_globals) File "C:\Users\公司\AppData\Local\Programs\Python\Python37\Scripts\aspeak.exe_main.py", line 5, in File "c:\users\公司\appdata\local\programs\python\python37\lib\site-packages\aspeak_main.py", line 1, in from .cli import main File "c:\users\公司\appdata\local\programs\python\python37\lib\site-packages\aspeak\cli_init_.py", line 1, in from .main import main File "c:\users\公司\appdata\local\programs\python\python37\lib\site-packages\aspeak\cli\main.py", line 11, in from .parser import parser File "c:\users\公司\appdata\local\programs\python\python37\lib\site-packages\aspeak\cli\parser.py", line 5, in from .value_parsers import pitch, rate, format File "c:\users\公司\appdata\local\programs\python\python37\lib\site-packages\aspeak\cli\value_parsers.py", line 26 if (result := try_parse_float(arg)) and result[0]: ^ SyntaxError: invalid syntax

    opened by jkcox2016 3
  • Use self-built TTS instead of `azure.cognitiveservices.speech`

    Use self-built TTS instead of `azure.cognitiveservices.speech`

    I recently finished a rebuild of some essential functions of azure.cognitiveservices.speech, and adapted it in aspeak.

    It has passed all examples, and my own tts has ran properly in my own code.

    opened by flt6 1
  • Error: Speech synthesis canceled: CancellationReason.Error

    Error: Speech synthesis canceled: CancellationReason.Error

    Error: Speech synthesis canceled: CancellationReason.Error
    WebSocket upgrade failed: Unspecified connection error (200). USP state: 2. Received audio size: 0 bytes.
    

    今天突然出现的,不知道什么情况。是我的网络问题吗?

    opened by mephisX 1
  • Speech synthesis canceled: CancellationReason.Error WebSocket operation failed. Internal error: 3. Error details: WS_ERROR_UNDERLYING_IO_ERROR USP state: 4. Received audio size: 1998720 bytes.

    Speech synthesis canceled: CancellationReason.Error WebSocket operation failed. Internal error: 3. Error details: WS_ERROR_UNDERLYING_IO_ERROR USP state: 4. Received audio size: 1998720 bytes.

    Speech synthesis canceled: CancellationReason.Error WebSocket operation failed. Internal error: 3. Error details: WS_ERROR_UNDERLYING_IO_ERROR USP state: 4. Received audio size: 1998720 bytes.

    opened by RouderSky 1
  • RuntimeError in ssml_to_speech_async

    RuntimeError in ssml_to_speech_async

    An RuntimeError occurred while calling ssml_to_speech_async of instance of SpeechToFileService.

    CRITICAL: Traceback (most recent call last):
      ......
      File "E:\***\tts.py", line 17, in tts
        return provider.ssml_to_speech_async(ssml,path=path)  # type: ignore
      File "F:\ProgramData\Miniconda3\lib\site-packages\aspeak\api\api.py", line 110, in wrapper
        self._setup_synthesizer(kwargs['path'])
      File "F:\ProgramData\Miniconda3\lib\site-packages\aspeak\api\api.py", line 139, in _setup_synthesizer
        self._synthesizer = speechsdk.SpeechSynthesizer(self._config, self._output)
      File "F:\ProgramData\Miniconda3\lib\site-packages\azure\cognitiveservices\speech\speech.py", line 1598, in __init__
        self._impl = self._get_impl(impl.SpeechSynthesizer, speech_config, audio_config,
      File "F:\ProgramData\Miniconda3\lib\site-packages\azure\cognitiveservices\speech\speech.py", line 1703, in _get_impl
        _impl = synth_type._from_config(speech_config._impl, None if audio_config is None else audio_config._impl)
    RuntimeError: Exception with an error code: 0x8 (SPXERR_FILE_OPEN_FAILED)
    [CALL STACK BEGIN]
    
        > pal_string_to_wstring
    
        - pal_string_to_wstring
    
        - synthesizer_create_speech_synthesizer_from_config
    
        - synthesizer_create_speech_synthesizer_from_config
    
        - 00007FFE37F772C4 (SymFromAddr() error: 试图访问无效的地址。)
    
        - 00007FFE37FC76A8 (SymFromAddr() error: 试图访问无效的地址。)
    
        - 00007FFE37FC87A8 (SymFromAddr() error: 试图访问无效的地址。)
    
        - PyArg_CheckPositional
    
        - Py_NewReference
    
        - PyEval_EvalFrameDefault
    
        - Py_NewReference
    
        - PyEval_EvalFrameDefault
    
        - PyFunction_Vectorcall
    
        - PyFunction_Vectorcall
    
        - PyMem_RawStrdup
    
        - Py_NewReference
    
    
    
    [CALL STACK END]
    

    tts.py

    from aspeak import SpeechToFileService,AudioFormat,FileFormat
    
    provider=None
    fmt=AudioFormat(FileFormat.MP3,-1)
    
    def init():
        global provider
        provider=SpeechToFileService(locale="zh-CN",audio_format=fmt)
    
    def tts(ssml:str,path:str):
        if provider is None:
            init()
        return provider.ssml_to_speech_async(ssml,path=path)  # type: ignore
    

    The thing is, this error seemed to occurred randomly, and only when I created(Finished) over 20 ssml_to_speech_async instance does it occurs. This error seems can't be catch through try

    bug wontfix upstream 
    opened by flt6 1
  • Use correct function at ssml_to_speech_async

    Use correct function at ssml_to_speech_async

    I updated to the latest version, and found both example for ssml_to_speech_async and my own code couldn't run correctly. I found in api/api.py file function ssml_to_speech_async uses speak_ssml not speak_ssml_async, and I think this is the reason.

    opened by flt6 1
  • Can you add an option for output file name same to text?

    Can you add an option for output file name same to text?

    for example, if the text is a simple text, the output name would be same.

    aspeak -t "Hello" -ot --mp3 then the output would be "Hello.mp3"

    enhancement wontfix 
    opened by bk111 1
  • How can I use the variable for input parameter?

    How can I use the variable for input parameter?

    print(type(names1["hot50_cn_topic_" + str(i) ][0:280])) #结果为 str

    input1 = names1["hot50_cn_topic_" + str(i) ][0:280] input2 = "近日在全国多地,许多新冠感染者们已陆续转阴,回归到正常的生活和工作中。"

    #os.system('aspeak -t names1["hot50_cn_topic_" + str(i) ][0:280] -o "./1/{}{}{}_".format(year, month, day)+str(i)+".mp3" -l zh-CN') # 结果: -t names1["hot50_cn_topic_" + str(i) ][0:280] 格式不正常 #os.system('aspeak -t input1 -v zh-CN-YunjianNeural -R YoungAdultMale -o "{}".mp3'.format(out1)) # 结果: -t input1 格式不正常 #os.system('aspeak -t input2 -v zh-CN-YunjianNeural -R YoungAdultMale -o "{}".mp3'.format(out1)) # 结果: -t input2 格式不正常

    os.system('aspeak -t """近日在全国多地,许多新冠感染者们已陆续转阴,回归到正常的生活和工作中。""" -v zh-CN-YunjianNeural -R YoungAdultMale -o "{}".mp3'.format(out1)) #这个是正常的

    -t 之后的输入参数 怎么才能换成变量呢?

    opened by bk111 1
  • certifi-2022.9.24-py3-none-any.whl: 1 vulnerabilities (highest severity is: 6.8)

    certifi-2022.9.24-py3-none-any.whl: 1 vulnerabilities (highest severity is: 6.8)

    Vulnerable Library - certifi-2022.9.24-py3-none-any.whl

    Python package for providing Mozilla's CA Bundle.

    Library home page: https://files.pythonhosted.org/packages/1d/38/fa96a426e0c0e68aabc68e896584b83ad1eec779265a028e156ce509630e/certifi-2022.9.24-py3-none-any.whl

    Path to dependency file: /tmp/ws-scm/aspeak

    Path to vulnerable library: /tmp/ws-scm/aspeak,/requirements.txt

    Vulnerabilities

    | CVE | Severity | CVSS | Dependency | Type | Fixed in (certifi version) | Remediation Available | | ------------- | ------------- | ----- | ----- | ----- | ------------- | --- | | CVE-2022-23491 | Medium | 6.8 | certifi-2022.9.24-py3-none-any.whl | Direct | certifi - 2022.12.07 | ❌ |

    Details

    CVE-2022-23491

    Vulnerable Library - certifi-2022.9.24-py3-none-any.whl

    Python package for providing Mozilla's CA Bundle.

    Library home page: https://files.pythonhosted.org/packages/1d/38/fa96a426e0c0e68aabc68e896584b83ad1eec779265a028e156ce509630e/certifi-2022.9.24-py3-none-any.whl

    Path to dependency file: /tmp/ws-scm/aspeak

    Path to vulnerable library: /tmp/ws-scm/aspeak,/requirements.txt

    Dependency Hierarchy:

    • :x: certifi-2022.9.24-py3-none-any.whl (Vulnerable Library)

    Found in base branch: main

    Vulnerability Details

    Certifi is a curated collection of Root Certificates for validating the trustworthiness of SSL certificates while verifying the identity of TLS hosts. Certifi 2022.12.07 removes root certificates from "TrustCor" from the root store. These are in the process of being removed from Mozilla's trust store. TrustCor's root certificates are being removed pursuant to an investigation prompted by media reporting that TrustCor's ownership also operated a business that produced spyware. Conclusions of Mozilla's investigation can be found in the linked google group discussion.

    Publish Date: 2022-12-07

    URL: CVE-2022-23491

    CVSS 3 Score Details (6.8)

    Base Score Metrics:

    • Exploitability Metrics:
      • Attack Vector: Network
      • Attack Complexity: Low
      • Privileges Required: High
      • User Interaction: None
      • Scope: Changed
    • Impact Metrics:
      • Confidentiality Impact: None
      • Integrity Impact: High
      • Availability Impact: None

    For more information on CVSS3 Scores, click here.

    Suggested Fix

    Type: Upgrade version

    Origin: https://www.cve.org/CVERecord?id=CVE-2022-23491

    Release Date: 2022-12-07

    Fix Resolution: certifi - 2022.12.07

    Step up your Open Source Security Game with Mend here

    security vulnerability 
    opened by mend-bolt-for-github[bot] 0
  • After upgrading to 3.1.0, the error still occurs WebSocket upgrade failed.

    After upgrading to 3.1.0, the error still occurs WebSocket upgrade failed.

    [91mError: Speech synthesis canceled: CancellationReason.Error WebSocket upgrade failed: Unspecified connection error (200). USP state: 2. Received audio size: 0 bytes.

    opened by carllx 2
  • ERROR: Cannot install aspeak because these package versions have conflicting dependencies.

    ERROR: Cannot install aspeak because these package versions have conflicting dependencies.

    To fix this you could try to:

    1. loosen the range of package versions you've specified
    2. remove package versions to allow pip attempt to solve the dependency conflict

    ERROR: Cannot install aspeak==0.1, aspeak==0.1.1, aspeak==0.2.0, aspeak==0.2.1, aspeak==0.3.0, aspeak==0.3.1, aspeak==0.3.2, aspeak==1.0.0, aspeak==1.1.0, aspeak==1.1.1, aspeak==1.1.2, aspeak==1.1.3, aspeak==1.1.4, aspeak==1.2.0, aspeak==1.3.0, aspeak==1.3.1, aspeak==1.4.0, aspeak==1.4.1, aspeak==1.4.2, aspeak==2.0.0, aspeak==2.0.1, aspeak==2.1.0, aspeak==3.0.0, aspeak==3.0.1 and aspeak==3.0.2 because these package versions have conflicting dependencies.

    opened by qirenzhidao 3
Releases(v3.1.0)
Owner
Levi Zim
Developer / Student / AI / Data Science :octocat: Telegram channel: t.me/kxxtchannel :octocat: PGP: 0x57670CCFA42CCF0A :octocat: Reading: t.me/kxxt_read
Levi Zim
QuakeLabeler is a Python package to create and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing.

QuakeLabeler Quake Labeler was born from the need for seismologists and developers who are not AI specialists to easily, quickly, and independently bu

Hao Mai 15 Nov 04, 2022
An original implementation of "Noisy Channel Language Model Prompting for Few-Shot Text Classification"

Channel LM Prompting (and beyond) This includes an original implementation of Sewon Min, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer. "Noisy Cha

Sewon Min 92 Jan 07, 2023
Self-supervised Product Quantization for Deep Unsupervised Image Retrieval - ICCV2021

Self-supervised Product Quantization for Deep Unsupervised Image Retrieval Pytorch implementation of SPQ Accepted to ICCV 2021 - paper Young Kyun Jang

Young Kyun Jang 71 Dec 27, 2022
Neural network chess engine trained on Gary Kasparov's games.

Neural Chess It's not the best chess engine, but it is a chess engine. Proof of concept neural network chess engine (feed-forward multi-layer perceptr

3 Jun 22, 2022
Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

This video in better quality. einops Flexible and powerful tensor operations for readable and reliable code. Supports numpy, pytorch, tensorflow, and

Alex Rogozhnikov 6.2k Jan 01, 2023
CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).

CCNet: Criss-Cross Attention for Semantic Segmentation Paper Links: Our most recent TPAMI version with improvements and extensions (Earlier ICCV versi

Zilong Huang 1.3k Dec 27, 2022
A Protein-RNA Interface Predictor Based on Semantics of Sequences

PRIP PRIP:A Protein-RNA Interface Predictor Based on Semantics of Sequences installation gensim==3.8.3 matplotlib==3.1.3 xgboost==1.3.3 prettytable==2

李优 0 Mar 25, 2022
docTR by Mindee (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.

docTR by Mindee (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.

Mindee 1.5k Jan 01, 2023
Mall-Customers-Segmentation - Customer Segmentation Using K-Means Clustering

Overview Customer Segmentation is one the most important applications of unsupervised learning. Using clustering techniques, companies can identify th

NelakurthiSudheer 2 Jan 03, 2022
Code for ICE-BeeM paper - NeurIPS 2020

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA This repository contains code to run and reproduce the experiments

Ilyes Khemakhem 65 Dec 22, 2022
PHOTONAI is a high level python API for designing and optimizing machine learning pipelines.

PHOTONAI is a high level python API for designing and optimizing machine learning pipelines. We've created a system in which you can easily select and

Medical Machine Learning Lab - University of Münster 57 Nov 12, 2022
MAU: A Motion-Aware Unit for Video Prediction and Beyond, NeurIPS2021

MAU (NeurIPS2021) Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xinguang Xiang, Wen GAo. Official PyTorch Code for "MAU: A Motion-Aware

ZhengChang 20 Nov 25, 2022
Constructing Neural Network-Based Models for Simulating Dynamical Systems

Constructing Neural Network-Based Models for Simulating Dynamical Systems Note this repo is work in progress prior to reviewing This is a companion re

Christian Møldrup Legaard 21 Nov 25, 2022
Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

Nils Thuerey 1.3k Jan 08, 2023
All supplementary material used by me while TA-ing CS3244: Machine Learning

CS3244-Tutorial-Material All supplementary material used by me while TA-ing CS3244: Machine Learning at NUS School of Computing. What is this? I teach

Rishabh Anand 18 Sep 23, 2022
Framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample resolution

Sample-specific Bayesian Networks A framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample or per-patient re

Caleb Ellington 1 Sep 23, 2022
The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble

Wordle RL The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble I know there are more deterministic

Aditya Arora 3 Feb 22, 2022
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Energy-based Conditional Generative Adversarial Network (ECGAN) This is the code for the NeurIPS 2021 paper "A Unified View of cGANs with and without

sianchen 22 May 28, 2022
Differentiable simulation for system identification and visuomotor control

gradsim gradSim: Differentiable simulation for system identification and visuomotor control gradSim is a unified differentiable rendering and multiphy

105 Dec 18, 2022
GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]

GANimation: Anatomically-aware Facial Animation from a Single Image [Project] [Paper] Official implementation of GANimation. In this work we introduce

Albert Pumarola 1.8k Dec 28, 2022