Run Watson Speech to Text for Embed on your local computer with Docker

This is an example about, how to use Watson Speech to Text based on the official example documentation:IBM Watson Libraries for Embed.

Note:Watson Speech to Text API documentation

Step 1: Clone the example project to your local computer

git clone https://github.com/thomassuedbroecker/watson-stt-example-local-docker
cd watson-stt-example-local-docker/code

Step 2: Set your IBM_ENTITLEMENT_KEY in the .env file

cat .env_template > .env

Edit the .env file.

# used as 'environment' variables
IBMCLOUD_ENTITLEMENT_KEY="YOUR_KEY"

Step 3: Execute the run-watson-stt-with-docker.sh bash script

sh run-watson-stt-with-docker.sh

• Example output:

# ******
# Connect to IBM Cloud Container Image Registry: cp.icr.io/cp/ai
# ******

IBM_ENTITLEMENT_KEY: XXXX

...

# ******
# Connect to IBM Cloud Container Image Registry: cp.icr.io/cp/ai
# ******

[+] Building 181.3s (14/21)                                                                                      
 => [runtime 1/1] FROM cp.icr.io/cp/ai/watson-tts-runtime:1.1.0@sha256:44c3e80bfbc4c539bb17556e9e764a073  178.1s
 ...
 [+] Building 543.6s (14/21)                                                                              ...      
 => => sha256:2daf64cad6c98a23301bc1880448faea6f94b29ee06b32e8ba21f3d8ef997ffb 551.55MB / 777.81MB        540.4s
 [+] Building 1042.2s (17/23)                                                                              ...     
 => => sha256:6694f134430355d7c78923b5c001f71cae8f67c9c03147137f2882d605211cec 7.34MB / 8.85MB           1039.0s
 ... 
  => [model_cache 4/4] RUN prepare_models.sh                                                                58.7s
 => => # Waiting for server to initialize models                                                                
 => => # Waiting for server to initialize models                                                                
 => => # Waiting for server to initialize models

...

# ******
# Run STT
# ******

# Run a custom container based on runtime and imported models
# custom-watson-stt-image:1.0.0

"INFO: Will download the following models defined in group default in catalog b'var/catalog.json' : ['en-US_Multimedia', 'es-LA_Telephony']"
"INFO: All 6 models files ext
 
...

Step 4: List models

curl "http://localhost:1080/speech-to-text/api/v1/models"

• Example output:

{
   "models": [
      {
         "name": "en-US_Multimedia",
         "rate": 16000,
         "language": "en-US",
         "description": "US English multimedia model for broadband audio (16kHz or more)",
         "supported_features": {
            "custom_acoustic_model": false,
            "custom_language_model": true,
            "low_latency": true,
            "speaker_labels": true
         },
         "url": "http://localhost:1080/speech-to-text/api/v1/models/en-US_Multimedia"
      },
      {
         "name": "es-LA_Telephony",
         "rate": 8000,
         "language": "es-LA",
         "description": "Latin American Spanish telephony model for narrowband audio (8kHz)",
         "supported_features": {
            "custom_acoustic_model": false,
            "custom_language_model": true,
            "low_latency": true,
            "speaker_labels": true
         },
         "url": "http://localhost:1080/speech-to-text/api/v1/models/es-LA_Telephony"
      }
   ]
}

Step 5: Download an example audio in flac format

curl "https://github.com/watson-developer-cloud/doc-tutorial-downloads/raw/master/speech-to-text/0001.flac" \
  -sLo example.flac

Step 6: Verify the audio

curl "http://localhost:1080/speech-to-text/api/v1/recognize" \
  --header "Content-Type: audio/flac" \
  --data-binary @example.flac

• Example output:

{
   "result_index": 0,
   "results": [
      {
         "final": true,
         "alternatives": [
            {
               "transcript": "several tornadoes touched down as a line of severe thunderstorms swept through colorado on sunday ",
               "confidence": 0.99
            }
         ]
      }
   ]
}

Used API commands:

• We list the models v1/speech-to-text/api/v1/models REST API methode and the syntax_izumo_lang_en_stock model.

• We verify a audio recording speech-to-text/api/v1/recognize