신체는 지속적으로 자기 재생을 한다. 신체는 일생 동안 매일 300억 개의 새로운 세포(동화 작용)를 만드는데, 항상성을 유지하기 위해 같은 양의 오래된 세포도 파괴한다. 죽은 지 오래된 세포는 분해되면서 엄청난 양의 세포 잔해를 남기고, 이 파편들은 림프계에 즉시 흡수되어 제거된다. 이 쓰레기는 체외로 운반하는 물이 충분해야 제거할 수 있다.
- 안드레아스 모리츠의《건강과 치유의 비밀》중에서 -
* 작은 세포 하나가 모든 생명체의 원천입니다. 수많은 세포가 매일 매 순간 만들어지고, 또 그만큼의 세포가 파괴되고 죽어 잔해로 쌓입니다. 그 잔해와 파편들을 제때제때 배출하고 씻어내야 세포의 재생이 원활해집니다. 물과 피, 림프계의 역할이 중요한 이유입니다. 잘 흘려보내고 비워내면 세포의 파괴는 두려울 게 없습니다. 재생의 시작입니다.
상유심생(相由心生). 외모는 마음에서 생겨난다는 뜻이다. 사람은 각자의 얼굴에 세월의 흔적을 새기며 산다. 우리가 지나온 세월, 생각과 가치관, 심리 상태의 모든 변화 하나하나가 얼굴에 흔적을 남긴다. 여기에는 어느 정도 과학적 근거가 있다. 심리 변화는 신경전달 물질의 농도 차이를 발생시키고 근육을 만들어 표정에 변화를 만든다. 오랫동안 일정한 정서를 유지한 사람은 표정에 크게 변화가 없지만 항상 초조하고 우울한 사람에게는 '불안한 얼굴'이 생긴다.
- 레몬심리의《기분이 태도가 되지 않게》중에서 -
* 나이 마흔이면 자기 얼굴에 책임을 져야 한다고 하지요. 그 책임을 다하기 위해서라도 종종 거울을 봐야 합니다. 내 얼굴 속에 평온함이 있는지, 불안함이 있는지... 내 낯빛에 깃든 초조함이나 우울함을 유쾌함과 생동감으로 바꾸는 일이 자기 얼굴을 바꾸는 길입니다.
May contain one or more recognition hypotheses (up to the maximum specified inmax_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
Iffalse, thisStreamingRecognitionResultrepresents an interim result that may change. Iftrue, this is the final time the speech service will return this particularStreamingRecognitionResult, 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 indicatingstabilitywas not set.
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'.
Encoding of audio data sent in allRecognitionAudiomessages. This field is optional forFLACandWAVaudio files and required for all other audio formats. For details, seeAudioEncoding.
sample_rate_hertz
int32
Sample rate in Hertz of the audio data sent in allRecognitionAudiomessages. 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, seeAudioEncoding.
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 are1-8. Valid values for OGG_OPUS are '1'-'254'. Valid value for MULAW, AMR, AMR_WB and SPEEX_WITH_HEADER_BYTE is only1. If0or omitted, defaults to one channel (mono). Note: We only recognize the first channel by default. To perform independent recognition on each channel setenable_separate_recognition_per_channelto 'true'.
enable_separate_recognition_per_channel
bool
This needs to be set totrueexplicitly andaudio_channel_count> 1 to get each channel recognized separately. The recognition result will contain achannel_tagfield 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_countmultiplied by the length of the audio.
language_code
string
Required. The language of the supplied audio as aBCP-47language tag. Example: "en-US". SeeLanguage Supportfor a list of the currently supported language codes.
max_alternatives
int32
Maximum number of recognition hypotheses to be returned. Specifically, the maximum number ofSpeechRecognitionAlternativemessages within eachSpeechRecognitionResult. The server may return fewer thanmax_alternatives. Valid values are0-30. A value of0or1will return a maximum of one. If omitted, will return a maximum of one.
profanity_filter
bool
If set totrue, 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 tofalseor omitted, profanities won't be filtered out.
Array ofSpeechContext. A means to provide context to assist the speech recognition. For more information, seespeech adaptation.
enable_word_time_offsets
bool
Iftrue, the top result includes a list of words and the start and end time offsets (timestamps) for those words. Iffalse, no word-level time offset information is returned. The default isfalse.
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.
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.
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. Ifuse_enhancedis set to true and themodelfield is not set, then an appropriate enhanced model is chosen if an enhanced model exists for the audio.
Ifuse_enhancedis 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.
(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")
