gcloud-help/gcloud/ml/speech/recognize

NAME
    gcloud ml speech recognize - get transcripts of short
        (less than 60 seconds) audio from an audio file

SYNOPSIS
    gcloud ml speech recognize AUDIO --language-code=LANGUAGE_CODE
        [--enable-automatic-punctuation]
        [--encoding=ENCODING; default="encoding-unspecified"]
        [--filter-profanity] [--hints=[HINT,...]] [--include-word-time-offsets]
        [--max-alternatives=MAX_ALTERNATIVES; default=1] [--model=MODEL]
        [--sample-rate=SAMPLE_RATE]
        [--audio-channel-count=AUDIO_CHANNEL_COUNT
          --separate-channel-recognition] [GCLOUD_WIDE_FLAG ...]

DESCRIPTION
    Get a transcript of an audio file that is less than 60 seconds. You can use
    an audio file that is on your local drive or a Google Cloud Storage URL.

    If the audio is longer than 60 seconds, you will get an error. Please use
    gcloud ml speech recognize-long-running instead.

EXAMPLES
    To get a transcript of an audio file 'my-recording.wav':

        $ gcloud ml speech recognize 'my-recording.wav' --language-code=en-US

    To get a transcript of an audio file in bucket 'gs://bucket/myaudio' with a
    custom sampling rate and encoding that uses hints and filters profanity:

        $ gcloud ml speech recognize 'gs://bucket/myaudio' \
          --language-code=es-ES --sample-rate=2200 --hints=Bueno \
          --encoding=OGG_OPUS --filter-profanity

POSITIONAL ARGUMENTS
     AUDIO
        The location of the audio file to transcribe. Must be a local path or a
        Google Cloud Storage URL (in the format gs://bucket/object).

REQUIRED FLAGS
     --language-code=LANGUAGE_CODE
        The language of the supplied audio as a BCP-47
        (https://www.rfc-editor.org/rfc/bcp/bcp47.txt) language tag. Example:
        "en-US". See https://cloud.google.com/speech/docs/languages for a list
        of the currently supported language codes.

OPTIONAL FLAGS
     --enable-automatic-punctuation
        Adds punctuation to recognition result hypotheses.

     --encoding=ENCODING; default="encoding-unspecified"
        The type of encoding of the file. Required if the file format is not
        WAV or FLAC. ENCODING must be one of: amr, amr-wb,
        encoding-unspecified, flac, linear16, mp3, mulaw, ogg-opus,
        speex-with-header-byte, webm-opus.

     --filter-profanity
        If True, the server will attempt to filter out profanities, replacing
        all but the initial character in each filtered word with asterisks,
        e.g. f***.

     --hints=[HINT,...]
        A list of strings containing word and phrase "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 typically spoken by the user. This can also be
        used to add additional words to the vocabulary of the recognizer. See
        https://cloud.google.com/speech/limits#content.

     --include-word-time-offsets
        If True, the top result includes a list of words with the start and end
        time offsets (timestamps) for those words. If False, no word-level time
        offset information is returned.

     --max-alternatives=MAX_ALTERNATIVES; default=1
        Maximum number of recognition hypotheses to be returned. 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.

     --model=MODEL
        Select the model best suited to your domain to get best results. If you
        do not explicitly specify a model, Speech-to-Text will auto-select a
        model based on your other specified parameters. Some models are premium
        and cost more than standard models (although you can reduce the price
        by opting into
        https://cloud.google.com/speech-to-text/docs/data-logging). MODEL must
        be one of:

         command_and_search
            short queries such as voice commands or voice search.
         default
            audio that is not one of the specific audio models. For example,
            long-form audio. Ideally the audio is high-fidelity, recorded at a
            16khz or greater sampling rate.
         latest_long
            Use this model for any kind of long form content such as media or
            spontaneous speech and conversations. Consider using this model in
            place of the video model, especially if the video model is not
            available in your target language. You can also use this in place
            of the default model.
         latest_short
            Use this model for short utterances that are a few seconds in
            length. It is useful for trying to capture commands or other single
            shot directed speech use cases. Consider using this model instead
            of the command and search model.
         medical_conversation
            Best for audio that originated from a conversation between a
            medical provider and patient.
         medical_dictation
            Best for audio that originated from dictation notes by a medical
            provider.
         phone_call
            audio that originated from a phone call (typically recorded at an
            8khz sampling rate).
         phone_call_enhanced
            audio that originated from a phone call (typically recorded at an
            8khz sampling rate). This is a premium model and can produce better
            results but costs more than the standard rate.
         telephony
            Improved version of the "phone_call" model, best for audio that
            originated from a phone call, typically recorded at an 8kHz
            sampling rate.
         telephony_short
            Dedicated version of the modern "telephony" model for short or even
            single-word utterances for audio that originated from a phone call,
            typically recorded at an 8kHz sampling rate.
         video_enhanced
            audio that originated from video or includes multiple speakers.
            Ideally the audio is recorded at a 16khz or greater sampling rate.
            This is a premium model that costs more than the standard rate.

     --sample-rate=SAMPLE_RATE
        The sample rate in Hertz. 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).

     Audio channel settings.

       --audio-channel-count=AUDIO_CHANNEL_COUNT
          The number of channels in the input audio data. Set this for
          separate-channel-recognition. Valid values are: 1)LINEAR16 and FLAC
          are 1-8 2)OGG_OPUS are 1-254 3) MULAW, AMR, AMR_WB and
          SPEEX_WITH_HEADER_BYTE is only 1.

          This flag argument must be specified if any of the other arguments in
          this group are specified.

       --separate-channel-recognition
          Recognition result will contain a channel_tag field to state which
          channel that result belongs to. If this is not true, only the first
          channel will be recognized.

          This flag argument must be specified if any of the other arguments in
          this group are specified.

GCLOUD WIDE FLAGS
    These flags are available to all commands: --access-token-file, --account,
    --billing-project, --configuration, --flags-file, --flatten, --format,
    --help, --impersonate-service-account, --log-http, --project, --quiet,
    --trace-token, --user-output-enabled, --verbosity.

    Run $ gcloud help for details.

API REFERENCE
    This command uses the speech/v1 API. The full documentation for this API
    can be found at:
    https://cloud.google.com/speech-to-text/docs/quickstart-protocol

NOTES
    These variants are also available:

        $ gcloud alpha ml speech recognize

        $ gcloud beta ml speech recognize