Example use of streaming recognition with API v2

The example shows how you can recognize speech in LPCM format in real time using the SpeechKit API v2.

The example uses the following parameters:

• Language: Russian.
• Format of the audio stream: LPCM with a sampling rate of 8000 Hz.
• Profanity filter: True.
• Intermediate result filter: True.
• Other parameters are left at their defaults.

To use the API, you need the grpcio-tools package for Python and grpc for Node.js.

The Yandex account or federated account are authenticated using an IAM token. If you are using your service account, you do not need to include the folder ID in the request header. Learn more about authentication in the SpeechKit API.

To try the examples in this section:

1. Clone the Yandex Cloud API repository:

   git clone https://github.com/yandex-cloud/cloudapi
   

2. Download a sample audio file for recognition.

3. Create a client application:

   Python 3

   Node.js

   1. Use the pip package manager to install the grpcio-tools package:

      pip install grpcio-tools
      

   2. Go to the folder with the Yandex Cloud API repository, create a folder named output and generate the client interface code in it:

      cd cloudapi
      mkdir output
      python3 -m grpc_tools.protoc -I . -I third_party/googleapis \
        --python_out=output \
        --grpc_python_out=output \
          google/api/http.proto \
          google/api/annotations.proto \
          yandex/cloud/api/operation.proto \
          google/rpc/status.proto \
          yandex/cloud/operation/operation.proto \
          yandex/cloud/ai/stt/v2/stt_service.proto
      

      This will create the stt_service_pb2.py and stt_service_pb2_grpc.py client interface files and dependency files in the output folder.

   3. Create a file (e.g., test.py) in the output folder root and add the following code to it:

      #coding=utf8
      import argparse
      
      import grpc
      
      import yandex.cloud.ai.stt.v2.stt_service_pb2 as stt_service_pb2
      import yandex.cloud.ai.stt.v2.stt_service_pb2_grpc as stt_service_pb2_grpc
      
      CHUNK_SIZE = 4000
      
      def gen(folder_id, audio_file_name):
          # Specify recognition settings.
          specification = stt_service_pb2.RecognitionSpec(
              language_code='ru-RU',
              profanity_filter=True,
              model='general',
              partial_results=True,
              audio_encoding='LINEAR16_PCM',
              sample_rate_hertz=8000
          )
          streaming_config = stt_service_pb2.RecognitionConfig(specification=specification, folder_id=folder_id)
      
          # Send a message with recognition settings.
          yield stt_service_pb2.StreamingRecognitionRequest(config=streaming_config)
      
          # Read the audio file and send its contents in chunks.
          with open(audio_file_name, 'rb') as f:
              data = f.read(CHUNK_SIZE)
              while data != b'':
                  yield stt_service_pb2.StreamingRecognitionRequest(audio_content=data)
                  data = f.read(CHUNK_SIZE)
      
      def run(folder_id, iam_token, audio_file_name):
          # Establish a connection with the server.
          cred = grpc.ssl_channel_credentials()
          channel = grpc.secure_channel('stt.api.cloud.yandex.net:443', cred)
          stub = stt_service_pb2_grpc.SttServiceStub(channel)
      
          # Send data for recognition.
          it = stub.StreamingRecognize(gen(folder_id, audio_file_name), metadata=(
              ('authorization', 'Bearer %s' % iam_token),
          ))
      
          # Process the server responses and output the result to the console.
          try:
              for r in it:
                  try:
                      print('Start chunk: ')
                      for alternative in r.chunks[0].alternatives:
                          print('alternative: ', alternative.text)
                      print('Is final: ', r.chunks[0].final)
                      print('')
                  except LookupError:
                      print('Not available chunks')
          except grpc._channel._Rendezvous as err:
              print('Error code %s, message: %s' % (err._state.code, err._state.details))
      
      if __name__ == '__main__':
          parser = argparse.ArgumentParser()
          parser.add_argument('--token', required=True, help='IAM token')
          parser.add_argument('--folder_id', required=True, help='folder ID')
          parser.add_argument('--path', required=True, help='audio file path')
          args = parser.parse_args()
      
          run(args.folder_id, args.token, args.path)
      

      Where:

      • language_code: Recognition language.
      • profanity_filter: Profanity filter.
      • model: Language model.
      • partial_results: Filter of intermediate recognition results.
      • audio_encoding: Audio stream format.
      • sample_rate_hertz: Audio stream sampling rate.

   4. Set the folder ID:

      export FOLDER_ID=<folder_ID>
      

   5. Set the IAM token:

      export IAM_TOKEN=<IAM_token>
      

   6. Run the created file:

      python3 test.py --token ${IAM_TOKEN} --folder_id ${FOLDER_ID} --path speech.pcm
      

      Where --path is the path to the audio file for recognition.

      Result:

      Start chunk:
      alternative: Hello
      Is final: False
      
      Start chunk:
      alternative: Hello world
      Is final: True
      

   1. Go to the folder with the Yandex Cloud API repository, create a folder named src, and generate a dependency file named package.json in it:

      cd cloudapi
      mkdir src
      cd src
      npm init
      

   2. Install the necessary packages using npm:

      npm install grpc @grpc/proto-loader google-proto-files --save
      

   3. Download a gRPC public certificate from the official repository and save it in the root of the src folder.

   4. Create a file (e.g., index.js) in the src folder root and add the following code to it:

      const fs = require('fs');
      const grpc = require('grpc');
      const protoLoader = require('@grpc/proto-loader');
      const CHUNK_SIZE = 4000;
      
      // Get the folder ID and IAM token from the environment variables.
      const folderId = process.env.FOLDER_ID;
      const iamToken = process.env.IAM_TOKEN;
      
      // Read the file specified in the arguments.
      const audio = fs.readFileSync(process.argv[2]);
      
      // Set the recognition settings.
      const request = {
          config: {
              specification: {
                  languageCode: 'ru-RU',
                  profanityFilter: true,
                  model: 'general',
                  partialResults: true,
                  audioEncoding: 'LINEAR16_PCM',
                  sampleRateHertz: '8000'
              },
              folderId: folderId
          }
      };
      
      // // Set audio send frequency in milliseconds.
      // For LPCM, you can calculate the frequency using this formula: CHUNK_SIZE * 1000 / ( 2 * sampleRateHertz);
      const FREQUENCY = 250;
      
      const serviceMetadata = new grpc.Metadata();
      serviceMetadata.add('authorization', `Bearer ${iamToken}`);
      
      const packageDefinition = protoLoader.loadSync('../yandex/cloud/ai/stt/v2/stt_service.proto', {
          includeDirs: ['node_modules/google-proto-files', '..']
      });
      const packageObject = grpc.loadPackageDefinition(packageDefinition);
      
      // Establish a connection with the server.
      const serviceConstructor = packageObject.yandex.cloud.ai.stt.v2.SttService;
      const grpcCredentials = grpc.credentials.createSsl(fs.readFileSync('./roots.pem'));
      const service = new serviceConstructor('stt.api.cloud.yandex.net:443', grpcCredentials);
      const call = service['StreamingRecognize'](serviceMetadata);
      
      // Send a message with recognition settings.
      call.write(request);
      
      // Read the audio file and send its contents in chunks.
      let i = 1;
      const interval = setInterval(() => {
          if (i * CHUNK_SIZE <= audio.length) {
              const chunk = new Uint16Array(audio.slice((i - 1) * CHUNK_SIZE, i * CHUNK_SIZE));
              const chunkBuffer = Buffer.from(chunk);
              call.write({audioContent: chunkBuffer});
              i++;
          } else {
              call.end();
              clearInterval(interval);
          }
      }, FREQUENCY);
      
      // Process the server responses and output the result to the console.
      call.on('data', (response) => {
          console.log('Start chunk: ');
          response.chunks[0].alternatives.forEach((alternative) => {
              console.log('alternative: ', alternative.text)
          });
          console.log('Is final: ', Boolean(response.chunks[0].final));
          console.log('');
      });
      
      call.on('error', (response) => {
          // Output errors to the console.
          console.log(response);
      });
      

      Where:

      • languageCode: Recognition language.
      • pprofanityFilter: Profanity filter.
      • model: Language model.
      • partialResults: Filter of intermediate recognition results.
      • audioEncoding: Audio stream format.
      • sampleRateHertz: Audio stream sampling rate.

   5. Set the folder ID:

      export FOLDER_ID=<folder_ID>
      

   6. Set the IAM token:

      export IAM_TOKEN=<IAM_token>
      

   7. Run the created file:

      node index.js speech.pcm
      

      Where speech.pcm is the name of the audio file for recognition.

      Result:

      Start chunk:
      alternative: Hello world
      Is final: true
      

See alsoSee also

• API v2 for streaming recognition
• Authentication with the SpeechKit API