gcloud beta ml speech recognize-long-running - get transcripts of longer audio from an audio file
gcloud beta ml speech recognize-long-running AUDIO --language-code=LANGUAGE_CODE [--additional-language-codes=[LANGUAGE_CODE,...]] [--async] [--enable-automatic-punctuation] [--encoding=ENCODING; default="encoding-unspecified"] [--filter-profanity] [--hints=[HINT,...]] [--include-word-confidence] [--include-word-time-offsets] [--max-alternatives=MAX_ALTERNATIVES; default=1] [--model=MODEL] [--output-uri=OUTPUT_URI] [--sample-rate=SAMPLE_RATE] [--audio-channel-count=AUDIO_CHANNEL_COUNT --separate-channel-recognition] [--enable-speaker-diarization : --max-diarization-speaker-count=MAX_DIARIZATION_SPEAKER_COUNT --min-diarization-speaker-count=MIN_DIARIZATION_SPEAKER_COUNT] [GCLOUD_WIDE_FLAG ...]
(BETA) Get a transcript of audio up to 80 minutes in length. If the audio is under 60 seconds, you may also use gcloud beta ml speech recognize to analyze it.
To block the command from completing until analysis is finished, run:
$ gcloud beta ml speech recognize-long-running AUDIO_FILE \ --language-code=LANGUAGE_CODE --sample-rate=SAMPLE_RATE
You can also receive an operation as the result of the command by running:
$ gcloud beta ml speech recognize-long-running AUDIO_FILE \ --language-code=LANGUAGE_CODE --sample-rate=SAMPLE_RATE --async
This will return information about an operation. To get information about the operation, run:
$ gcloud beta ml speech operations describe OPERATION_ID
To poll the operation until it's complete, run:
$ gcloud beta ml speech operations wait OPERATION_ID
- 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).
- --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.
- --additional-language-codes=[LANGUAGE_CODE,...]
The BCP-47 language tags of other languages that the speech may be in. Up to 3 can be provided.
If alternative languages are listed, recognition result will contain recognition in the most likely language detected including the main language-code.
- --async
Return immediately, without waiting for the operation in progress to complete.
- --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-confidence
Include a list of words and the confidence for those words in the top result.
- --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.
- 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.
- --output-uri=OUTPUT_URI
Location to which the results should be written. Must be a Google Cloud Storage URI.
- --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.
- --enable-speaker-diarization
Enable speaker detection for each recognized word in the top alternative of the recognition result using an integer speaker_tag provided in the WordInfo.
- --max-diarization-speaker-count=MAX_DIARIZATION_SPEAKER_COUNT
Maximum estimated number of speakers in the conversation being recognized.
- --min-diarization-speaker-count=MIN_DIARIZATION_SPEAKER_COUNT
Minimum estimated number of speakers in the conversation being recognized.
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.
This command uses the speech/v1p1beta1 API. The full documentation for this API can be found at: https://cloud.google.com/speech-to-text/docs/quickstart-protocol
This command is currently in beta and might change without notice. These variants are also available:
$ gcloud ml speech recognize-long-running
$ gcloud alpha ml speech recognize-long-running