There is now an official Speech CLI tool from the product team. If you're looking for Microsoft supported and regularly updated CLI experience, use SPX.
Azure Speech Service Manager CLI is an unofficial command-line tool for Microsoft Azure Speech Service management - datasets, models, tests, endpoints etc. Useful especially for automation and CI/CD.
This tool is using Speech Services API v2.0. SDK was generated automatically from the Swagger definition using AutoRest, but a few adjustments had to be made to the generated code.
Until this is refactored, it's not safe to regenerate the SDK with AutoRest.
With .NET Core installed just run:
dotnet tool install -g azurespeechcliAlternatively, you can go to Releases and download a compiled version for your operating system, or build directly from sources.
CLI is created with .NET Core and builds are currently running for Windows, MacOS and Linux.
Before using the tool, you need to set your Speech Service credentials.
speech config set --name Project1 --key ABCD12345 --region northeurope --select
Or shorter version:
speech config set -n Project2 -k ABCD54321 -r westus -s
Both commands store your credentials as configuration set and automatically make these credentials selected (by using the --select parameter). You can have multiple sets and switch between them:
speech config select Project1
This can be useful when you work with multiple subscriptions.
If you're not sure what commands and parameters are available, try adding --help to the command you want to use.
For example:
speech --help
speech dataset --help
speech dataset create --help
Every entity supports basic set of operations:
createlistshowdelete
When working with a specific entity, ID is usually required:
speech dataset show <GUID>
speech model delete <GUID>
Every create command offers optional --wait (-w) flag which makes the CLI block and wait for the create operation to complete (dataset processed, model trained, endpoint provisioned etc.). When new entity is created, it writes corresponding ID to console.
This is useful in automation pipelines when commands are run as individual steps in a complex process.
speech dataset create --name CLI --audio "C:\Test.zip" --transcript "C:\test.txt" --wait
Uploading acoustic dataset...
Processing [..............]
c34d53e4-oooo-48d5-b18f-7492332f287c
After setting your subscription key and endpoint you usually start by preparing data. CLI can help by providing the compile command.
speech compile --audio <source folder> --transcript <txt file> --output <target folder> --test-percentage 10
This command expects a folder with all audio samples as WAV files and TXT file with corresponding transcripts.
It creates the output folder, divides data in two sets ("train" and "test") a compresses them into ZIP files. At the end you will get:
- Train.zip
- train.txt
- Test.zip
- test.txt
There are three types of datasets in the Speech Service: acoustic, language and pronunciation.
To create acoustic dataset, you need to provide a ZIP file with all audio samples and TXT file with corresponding transcriptions.
To create language and pronunciation datasets, you need to provide TXT file with language data.
To create an acoustic dataset use:
dataset create --name CLI --audio "C:\Train.zip" --transcript "C:\train.txt" --wait
To create a language dataset use:
dataset create --name CLI-Lang --language "C:\language.txt" --wait
To create a pronunciation dataset use:
dataset create --name CLI-Pro --pronunciation "C:\pronunciation.txt" --wait
To list available datasets:
dataset list
To show details of dataset:
dataset show 63f20d88-f531-4af0-bc85-58e0e9dAAACCDD
To show available locales:
dataset locales acoustic
dataset locales language
dataset locales pronunciation
Similarly to datasets there are two types of models in the Speech Service: acoustic and language. Both are created from previously uploaded datasets.
To create an acoustic model you first need to get GUID of base model (referred to as scenario):
model list-scenarios --locale en-us
en-us is the default locale, but you can choose a different one.
The list will be order by model date - it's recommended to use the newest. Beware that GUIDs can vary between datacenters!
Scenarios can be filtered by purpose. Possible values are:
OnlineTranscriptionBatchTranscriptionLanguageAdaptationAcousticAdaptationLanguageOnlineInterpolation
Default is AcousticAdaptation, because that's the type you will use when creating custom speech models.
model list-scenarios --purpose AcousticAdaptation
To disable filtering by purpose, use --purpose all.
Output:
Getting scenarios...
042de52f-d7b7-489d-921c-1b0a59d89dd1 v4.5 Unified 11. 1. 2019 13:38:33
7a1d51ce-a26d-4ee3-aee8-0fa020a65086 v3.3 Unified 26. 11. 2018 19:59:20
5d3b7fb0-f493-4e97-9616-e20130327304 v3.2 Unified 26. 11. 2018 18:59:20
d36f6c4b-8f75-41d1-b126-c38e46a059af Unified V3 EMBR - ULM 2. 8. 2018 15:12:17
c7a69da3-27de-4a4b-ab75-b6716f6321e5 V2.5 Conversational (AM/LM adapt) 16. 4. 2018 11:55:00
a1f8db59-40ff-4f0e-b011-37629c3a1a53 V2.0 Conversational (AM/LM adapt) - Deprecated 17. 8. 2017 12:00:00
cc7826ac-5355-471d-9bc6-a54673d06e45 V1.0 Conversational (AM/LM adapt) - Deprecated 4. 11. 2016 12:01:02
a3d8aab9-6f36-44cd-9904-b37389ce2bfa V1.0 Interactive (AM/LM adapt) - Deprecated 4. 11. 2016 8:23:42
To use in scripts, you may want to get just a list of IDs:
model list-scenarios --simple
Output:
042de52f-d7b7-489d-921c-1b0a59d89dd1
7a1d51ce-a26d-4ee3-aee8-0fa020a65086
5d3b7fb0-f493-4e97-9616-e20130327304
d36f6c4b-8f75-41d1-b126-c38e46a059af
c7a69da3-27de-4a4b-ab75-b6716f6321e5
a1f8db59-40ff-4f0e-b011-37629c3a1a53
cc7826ac-5355-471d-9bc6-a54673d06e45
a3d8aab9-6f36-44cd-9904-b37389ce2bfa
Then you can use GUID of selected scenario (see Base models) in the create command:
model create --name CLI --locale en-us --audio-dataset <GUID> --scenario c7a69da3-27de-4a4b-ab75-b6716f6321e5 --wait
To create a language model you need the same scenario GUID and then call:
model create --name CLI-Lang --locale en-us --language-dataset <GUID> --scenario c7a69da3-27de-4a4b-ab75-b6716f6321e5 --wait
Pronunciation models work the same, just provide ID of the pronunciation dataset:
model create --name CLI-Pro --locale en-us --pronunciation-dataset <GUID> --scenario c7a69da3-27de-4a4b-ab75-b6716f6321e5 --wait
To show available locales:
model locales acoustic
model locales language
To create an accuracy test you need three GUIDs: testing audio dataset ID, ID of the acoustic model you are testing and ID of a language model:
speech test create --name CLI --audio-dataset <GUID> --model <GUID> --language-model <GUID> --wait
To see the detail of particular test, call:
speech test list
...
speech test show <GUID>
And finally, to be able to use the model, you need to create an endpoint.
To create an endpoint use:
speech endpoint create --name CLI --locale en-us --model <GUID> --language-model <GUID> --concurrent-recognitions 1 --wait
A bonus command, which doesn't revolve around entities. Batch transcription generates a transcript of long audio file with timestamps, using your custom model.
speech transcript create --name CLI --locale en-us --recording <URL> --model <GUID> --language <GUID> --wait
To include word-level timestamps, use the --word-level-timestamps (-wt) parameter.
To activate diariazation (speaker separation), use the --diarization (-di) parameter. This will also force word-level timestamps.
To include sentiment score, use the --sentiment (-s) parameter.
Once the batch is done, you can call:
speech transcript show <GUID>
And get result URLs from response JSON.
Or you can call download to get it as file:
speech transcript download <GUID> --out-dir <PATH> --format <format> --file-name <filename>
Supported output formats:
- JSON (default)
- VTT
- TXT
If you specify --file-name this value will be used for the output file (with extension given by format).
If you don't specify --out-dir current working directory will be used.
To update name or description of batch transcription, use the update command:
speech transcript update <GUID> --name <name> --description <description>
If you want to perform transcript of a single, short WAV file, you can use single transcription command like this:
speech transcript single --input "C:\test.wav" --endpoint <GUID> --output-format detailed
- Work with names too, not just GUIDs
- Check if uploaded files are in the correct format (UTF-8 BOM text files)
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