東京タワーライブカメラ / TOKYOTOWER LIVE CAMERA

 

youtu.be/fWxnFcpdiR0

 

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남산서울타워 파노라마 LIVE

 

www.youtube.com/watch?v=xlTKZUPr8Hk

 

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StreamingRecognitionResult

A streaming speech recognition result corresponding to a portion of the audio that is currently being processed.

Fields

 

#alternatives

#channel_tag

 

alternatives[]

SpeechRecognitionAlternative

May contain one or more recognition hypotheses (up to the maximum specified in max_alternatives). These alternatives are ordered in terms of accuracy, with the top (first) alternative being the most probable, as ranked by the recognizer.

is_final

bool

If false, this StreamingRecognitionResult represents an interim result that may change. If true, this is the final time the speech service will return this particular StreamingRecognitionResult, the recognizer will not return any further hypotheses for this portion of the transcript and corresponding audio.

stability

float

An estimate of the likelihood that the recognizer will not change its guess about this interim result. Values range from 0.0 (completely unstable) to 1.0 (completely stable). This field is only provided for interim results (is_final=false). The default of 0.0 is a sentinel value indicating stability was not set.

result_end_time

Duration

Time offset of the end of this result relative to the beginning of the audio.

channel_tag

int32

For multi-channel audio, this is the channel number corresponding to the recognized result for the audio from that channel. For audio_channel_count = N, its output values can range from '1' to 'N'.

https://cloud.google.com/speech-to-text/docs/reference/rpc/google.cloud.speech.v1#streamingrecognitionresult

 

Package google.cloud.speech.v1  |  Cloud Speech-to-Text 문서

phrases[] string A list of strings containing words and phrases "hints" so that the speech recognition is more likely to recognize them. This can be used to improve the accuracy for specific words and phrases, for example, if specific commands are typicall

cloud.google.com

 

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Package google.cloud.speech.v1

RecognitionConfig

Provides information to the recognizer that specifies how to process the request.

Fields

encoding

AudioEncoding

Encoding of audio data sent in all RecognitionAudio messages. This field is optional for FLAC and WAV audio files and required for all other audio formats. For details, see AudioEncoding.

sample_rate_hertz

int32

Sample rate in Hertz of the audio data sent in all RecognitionAudio messages. Valid values are: 8000-48000. 16000 is optimal. For best results, set the sampling rate of the audio source to 16000 Hz. If that's not possible, use the native sample rate of the audio source (instead of re-sampling). This field is optional for FLAC and WAV audio files, but is required for all other audio formats. For details, see AudioEncoding.

audio_channel_count

int32

The number of channels in the input audio data. ONLY set this for MULTI-CHANNEL recognition. Valid values for LINEAR16 and FLAC are 1-8. Valid values for OGG_OPUS are '1'-'254'. Valid value for MULAW, AMR, AMR_WB and SPEEX_WITH_HEADER_BYTE is only 1. If 0 or omitted, defaults to one channel (mono). Note: We only recognize the first channel by default. To perform independent recognition on each channel set enable_separate_recognition_per_channel to 'true'.

enable_separate_recognition_per_channel

bool

This needs to be set to true explicitly and audio_channel_count > 1 to get each channel recognized separately. The recognition result will contain a channel_tag field to state which channel that result belongs to. If this is not true, we will only recognize the first channel. The request is billed cumulatively for all channels recognized: audio_channel_count multiplied by the length of the audio.

language_code

string

Required. The language of the supplied audio as a BCP-47 language tag. Example: "en-US". See Language Support for a list of the currently supported language codes.

max_alternatives

int32

Maximum number of recognition hypotheses to be returned. Specifically, the maximum number of SpeechRecognitionAlternative messages within each SpeechRecognitionResult. The server may return fewer than max_alternatives. Valid values are 0-30. A value of 0 or 1 will return a maximum of one. If omitted, will return a maximum of one.

profanity_filter

bool

If set to true, the server will attempt to filter out profanities, replacing all but the initial character in each filtered word with asterisks, e.g. "f***". If set to false or omitted, profanities won't be filtered out.

speech_contexts[]

SpeechContext

Array of SpeechContext. A means to provide context to assist the speech recognition. For more information, see speech adaptation.

enable_word_time_offsets

bool

If true, the top result includes a list of words and the start and end time offsets (timestamps) for those words. If false, no word-level time offset information is returned. The default is false.

enable_automatic_punctuation

bool

If 'true', adds punctuation to recognition result hypotheses. This feature is only available in select languages. Setting this for requests in other languages has no effect at all. The default 'false' value does not add punctuation to result hypotheses.

diarization_config

SpeakerDiarizationConfig

Config to enable speaker diarization and set additional parameters to make diarization better suited for your application. Note: When this is enabled, we send all the words from the beginning of the audio for the top alternative in every consecutive STREAMING responses. This is done in order to improve our speaker tags as our models learn to identify the speakers in the conversation over time. For non-streaming requests, the diarization results will be provided only in the top alternative of the FINAL SpeechRecognitionResult.

metadata

RecognitionMetadata

Metadata regarding this request.

model

string

Which model to select for the given request. Select the model best suited to your domain to get best results. If a model is not explicitly specified, then we auto-select a model based on the parameters in the RecognitionConfig.

 

use_enhanced

bool

Set to true to use an enhanced model for speech recognition. If use_enhanced is set to true and the model field is not set, then an appropriate enhanced model is chosen if an enhanced model exists for the audio.

If use_enhanced is true and an enhanced version of the specified model does not exist, then the speech is recognized using the standard version of the specified model.

 

 

 

 

https://cloud.google.com/speech-to-text/docs/reference/rpc/google.cloud.speech.v1#recognitionconfig

 

Package google.cloud.speech.v1  |  Cloud Speech-to-Text 문서

phrases[] string A list of strings containing words and phrases "hints" so that the speech recognition is more likely to recognize them. This can be used to improve the accuracy for specific words and phrases, for example, if specific commands are typicall

cloud.google.com

 

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[Python] argparse 사용법 (파이썬 인자값 추가하기)

 

두개의 인자 path, savefilename 을 받음.

(env) C:\__STT>python transcribe_async_gcs.py gs://cloud-samples-tests/speech/vr.flac 12345
Waiting for operation to complete...
Transcript: it's okay so what am I doing here why am I here at GDC talking about VR video it's because I believe my favorite games I love games I believe in games my favorite games are the ones that are all about the stories I love narrative game design I love narrative-based games and I think that when it comes to telling stories in VR bring together capturing the world with narrative based games and narrative based game design is going to unlock some of the killer apps and killer stories of the medium
Confidence: 0.9580045938491821
Transcript: so I'm really here looking for people who are interested in telling us or two stories that are planning projects around telling those types of stories and I would love to talk to you so if it sounds like your project if you're looking at blending VR video and interactivity to tell a story I want to talk to you I want to help you so if this sounds like you please get in touch with you can't find me I'll be here all week I have pink hair I work for Google and I would love to talk with you further about VR video interactivity and storytelling
Confidence: 0.949270486831665
completed
if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
    )
    parser.add_argument("path", help="File or GCS path for audio file to be recognized")  
    parser.add_argument("savefilename", help="Save fileName ")   
    args = parser.parse_args()

    response = transcribe_gcs(args.path)
    
    with open("stt_"+args.savefilename+".txt", "w") as script:
        for result in response.results:
            script.write(u'Transcript: {}'.format(result.alternatives[0].transcript)+"\n")
            script.write(u'Confidence: {}'.format(result.alternatives[0].confidence)+"\n")
            script.write(u'Channel Tag: {}'.format(result.alternatives[0].channel_tag)+"\n")

docs.python.org/ko/3/library/argparse.html

 

argparse — 명령행 옵션, 인자와 부속 명령을 위한 파서 — Python 3.9.0 문서

argparse — 명령행 옵션, 인자와 부속 명령을 위한 파서 소스 코드: Lib/argparse.py argparse 모듈은 사용자 친화적인 명령행 인터페이스를 쉽게 작성하도록 합니다. 프로그램이 필요한 인자를 정의하면

docs.python.org

 

docs.python.org/ko/3/howto/argparse.html

 

Argparse 자습서 — Python 3.9.0 문서

Argparse 자습서 저자 Tshepang Lekhonkhobe 이 자습서는 파이썬 표준 라이브러리에서 권장하는 명령행 파싱 모듈인 argparse 에 대한 소개입니다. 참고 같은 작업을 수행하는 다른 두 모듈이 있습니다, geto

docs.python.org

 

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현재 시간, 일시

 

import datetime
 
now = datetime.datetime.now()
print(now)          # 2015-04-19 12:11:32.669083
 
nowDate = now.strftime('%Y-%m-%d')
print(nowDate)      # 2015-04-19
 
nowTime = now.strftime('%H:%M:%S')
print(nowTime)      # 12:11:32
 
nowDatetime = now.strftime('%Y-%m-%d %H:%M:%S')
print(nowDatetime)  # 2015-04-19 12:11:32
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여러 채널로 오디오 스크립트 작성

transcribe_multichannel.py 

이 페이지에서는 Speech-to-Text를 사용하여 둘 이상의 채널이 포함된 오디오 파일을 텍스트로 변환하는 방법을 설명합니다.

오디오 데이터에는 녹음된 화자에 대한 각각의 채널이 포함되어 있는 경우가 많습니다. 예를 들어 두 사람의 전화 통화를 녹음한 오디오라면 각 회선이 별도로 녹음된 채널 두 개가 포함될 수 있습니다.

여러 채널이 포함된 오디오 데이터를 텍스트로 변환하려면 Speech-to-Text API에 대한 요청에 채널 수를 제공해야 합니다. 요청의 audioChannelCount 필드를 오디오에 있는 채널 수로 설정합니다.

여러 채널이 포함된 요청을 보내면 Speech-to-Text가 오디오에 있는 서로 다른 채널을 식별하는 결과를 반환하며 channelTag 필드를 사용하여 각 결과를 대신하는 항목에 라벨을 지정합니다.

 

오디오 채널 설명 : https://cloud.google.com/speech-to-text/docs/multi-channel

 

여러 채널로 오디오 스크립트 작성  |  Cloud Speech-to-Text 문서  |  Google Cloud

이 페이지에서는 Speech-to-Text를 사용하여 둘 이상의 채널이 포함된 오디오 파일을 텍스트로 변환하는 방법을 설명합니다. 오디오 데이터에는 녹음된 화자에 대한 각각의 채널이 포함되어 있는

cloud.google.com

 

from google.cloud import speech

client = speech.SpeechClient()

with open(speech_file, "rb") as audio_file:
    content = audio_file.read()

audio = speech.RecognitionAudio(content=content)

config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=44100,
    language_code="en-US",
    audio_channel_count=2,
    enable_separate_recognition_per_channel=True,
)

response = client.recognize(config=config, audio=audio)

for i, result in enumerate(response.results):
    alternative = result.alternatives[0]
    print("-" * 20)
    print("First alternative of result {}".format(i))
    print(u"Transcript: {}".format(alternative.transcript))
    print(u"Channel Tag: {}".format(result.channel_tag))

 

github.com/googleapis/python-speech/blob/master/samples/snippets/transcribe_multichannel.py

 

googleapis/python-speech

Contribute to googleapis/python-speech development by creating an account on GitHub.

github.com

 

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google.api_core.exceptions.InvalidArgument: 400 Request payload size exceeds the limit: 10485760 bytes.

 

 

일련의 오디오 파일을 텍스트로 변환하는 프로젝트에 처음으로 GCS Speech API를 사용하고 있습니다. 각 파일은 약 60 분이 소요되며 전체 시간 동안 지속적으로 말하는 사람입니다. GC SDK를 설치했으며 다음과 같이 요청을 수행하는 데 사용하고 있습니다.gcloud ml speech recognize-long-running \ "/path/to/file/audio.flac" \ --language-code="pt-PT" --async

내 기록 중 하나에서 이것을 실행할 때마다 다음 오류 메시지가 표시됩니다.

ERROR: (gcloud.ml.speech.recognize-long-running) INVALID_ARGUMENT: Request payload size exceeds the limit: 10485760 bytes.

API가 최대 180 분까지 파일을 처리 할 수있는 경우 최대 10,000 자의 음성을 출력 할 방법이 없기 때문에 매우 어려운 제한 인 것 같습니다 .
오디오 파일을 더 작은 조각으로 나누려고했고 최대 4 개의 15 분 샘플에 도달했지만 동일한 오류가 발생했습니다. 게다가, 그것이 효과가 있더라도 여기에서 내가 만드는 모든 새로운 녹음을 앞으로 나누는 것은 매우 지루하고 비실용적 일 것입니다.

 

(env) C:\__STT>
(env) C:\__STT>
(env) C:\__STT>python transcribe_async.py test_Linda_audio_converter.flac
Traceback (most recent call last):
  File "C:\__STT\env\lib\site-packages\google\api_core\grpc_helpers.py", line 57, in error_remapped_callable
    return callable_(*args, **kwargs)
  File "C:\__STT\env\lib\site-packages\grpc\_channel.py", line 923, in __call__
    return _end_unary_response_blocking(state, call, False, None)
  File "C:\__STT\env\lib\site-packages\grpc\_channel.py", line 826, in _end_unary_response_blocking
    raise _InactiveRpcError(state)
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
        status = StatusCode.INVALID_ARGUMENT
        details = "Request payload size exceeds the limit: 10485760 bytes."
        debug_error_string = "{"created":"@1605235850.871000000","description":"Error received from peer ipv4:216.58.220.138:443","file":"src/core/lib/surface/call.cc","file_line":1062,"grpc_message":"Request payload size exceeds the limit: 10485760 bytes.","grpc_status":3}"
>

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "transcribe_async.py", line 117, in <module>
    transcribe_file(args.path)
  File "transcribe_async.py", line 54, in transcribe_file
    request={"config": config, "audio": audio}
  File "C:\__STT\env\lib\site-packages\google\cloud\speech_v1\services\speech\client.py", line 425, in long_running_recognize
    response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
  File "C:\__STT\env\lib\site-packages\google\api_core\gapic_v1\method.py", line 145, in __call__
    return wrapped_func(*args, **kwargs)
  File "C:\__STT\env\lib\site-packages\google\api_core\grpc_helpers.py", line 59, in error_remapped_callable
    six.raise_from(exceptions.from_grpc_error(exc), exc)
  File "<string>", line 3, in raise_from
google.api_core.exceptions.InvalidArgument: 400 Request payload size exceeds the limit: 10485760 bytes.

(env) C:\__STT>

(env) C:\__STT>

Google Cloud 지원팀과 이야기를 나눈 후 무료 평가판 구독 제한과 파일 크기 (~ 60 분) 때문이라는 결론에 도달했습니다.

유료 구독으로 업그레이드하고 내 파일을 Google Cloud Storage에 업로드 한 후 트랜스 크립 션에서 페이로드를받을 수있었습니다.

 

stackoverflow.com/questions/51601697/invalid-argument-request-payload-size-exceeds-the-limit-10485760-bytes

 

INVALID_ARGUMENT: Request payload size exceeds the limit: 10485760 bytes

I'm using for the first time the GCS Speech API for a project to convert a series of audio files to text. Each file has around 60 minutes and is a person talking continuously during the whole time....

stackoverflow.com

 

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google.api_core.exceptions.OutOfRange: 400 Exceeded maximum allowed stream duration of 305 seconds.

"최대 허용 스트림 길이 인 65 초를 초과했습니다."
더 많은 시간 동안 "DEADLINE_SECS"를 수정하려고했지만 작동하지 않았습니다.

 

5 분마다 예외를 포착하는 것은 최적이 아니지만 작동합니다.

 

o think about and already seen in your mom about this is more like a summarizing and reflecting you know back and and the ones were doing studi study the first part was a mobile diary and now a lot of the things we're going to talk about during this interview you have already started to think about and already seen in your mom about this is more like a summarizing and reflecting you know back and and the ones were doing studiTraceback (most recent call last): like how was it for the sweetest person consume that you know and
  File "C:\__STT\env\lib\site-packages\google\api_core\grpc_helpers.py", line 97, in next
    return six.next(self._wrapped)
  File "C:\__STT\env\lib\site-packages\grpc\_channel.py", line 416, in __next__
    return self._next()
  File "C:\__STT\env\lib\site-packages\grpc\_channel.py", line 803, in _next
    raise self
grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
        status = StatusCode.OUT_OF_RANGE
        details = "Exceeded maximum allowed stream duration of 305 seconds."
        debug_error_string = "{"created":"@1605234860.496000000","description":"Error received from peer ipv4:216.58.197.234:443","file":"src/core/lib/surface/call.cc","file_line":1062,"grpc_message":"Exceeded maximum allowed stream duration of 305 seconds.","grpc_status":11}"
>

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "test.py", line 189, in <module>
    main()
  File "test.py", line 185, in main
    listen_print_loop(responses)
  File "test.py", line 124, in listen_print_loop
    for response in responses:
  File "C:\__STT\env\lib\site-packages\google\api_core\grpc_helpers.py", line 100, in next
    six.raise_from(exceptions.from_grpc_error(exc), exc)
  File "<string>", line 3, in raise_from
google.api_core.exceptions.OutOfRange: 400 Exceeded maximum allowed stream duration of 305 seconds.

(env) C:\__STT>
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코웨이는 넷플릭스의 추격을 따돌릴까

 

 

www.bloter.net/archives/474628

 

[넘버스]코웨이는 넷플릭스의 추격을 따돌릴까

숫자들(Numbers)로 기업과 경제, 기술을 해석해 보겠습니다. 숫자는 정보의 원천입니다. 정보는 누구에게나 공개되어 있고 숫자도 누구나 볼 수 있지만, 그 뒤에 숨어 있는 진실을 보는 눈은 누구

www.bloter.net

 

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