Recognizer gRPC API

with grpc.secure_channel(hostaddr, credentials=channel_credentials) as channel:
    stub = RecognizerStub(channel)
    stream_in = stub.Recognize(client_stream(wf))

def client_stream(wf):
    try:
        # Start the recognition
        init = RecognitionInitMessage(. . .)
        yield RecognitionRequest(recognition_init_message = init)

        # Simulate a typical realtime audio stream
        print(f'stream {wf.name}')
        packet_duration = 0.020
        packet_samples = int(wf.getframerate() * packet_duration)
        for packet in iter(lambda: wf.readframes(packet_samples), b''):
            yield RecognitionRequest(audio=packet)

RecognitionInitMessage(
    parameters = RecognitionParameters(
        language = 'en-US',
        topic = 'GEN',
        audio_format = AudioFormat(
            pcm = PCM(
                sample_rate_hz=wf.getframerate()
            )
        ),
        result_type = 'FINAL',
        utterance_detection_mode = 'MULTIPLE',
        recognition_flags = RecognitionFlags(auto_punctuate=True)
    ),
    resources = [travel_dlm, places_wordset],
    client_data = {'company':'Aardvark','user':'James'},
    user_id = 'james.somebody@aardvark.com'
)

RecognitionInitMessage(
    parameters = RecognitionParameters(
        language = 'en-US',
        audio_format = AudioFormat(pcm=PCM(sample_rate_hz=16000))
    )
)

RecognitionInitMessage(
    parameters = RecognitionParameters(
        language = 'en-US',
        topic = 'GEN',
        audio_format = AudioFormat(
            pcm = PCM(
                sample_rate_hz = wf.getframerate()
            )
        ),
        result_type = 'PARTIAL',
        utterance_detection_mode = 'MULTIPLE',
        recognition_flags = RecognitionFlags(
            auto_punctuate = True,
            filter_wakeup_word = True
        )
    )
)

RecognitionInitMessage(
    parameters = RecognitionParameters(
        language = 'en-US',
        topic = 'GEN',
        audio_format = AudioFormat(
            pcm = PCM(
                sample_rate_hz = wf.getframerate()
            )
        ),
#        audio_format = AudioFormat(pcm = PCM()),
#        audio_format = AudioFormat(pcm = PCM(sample_rate_hz = 16000)),
#        audio_format = AudioFormat(alaw = Alaw()),
#        audio_format = AudioFormat(ulaw = Ulaw()),
#        audio_format = AudioFormat(opus = Opus(source_rate_hz = 16000)),
#        audio_format = AudioFormat(ogg_opus = OggOpus(output_rate_hz = 16000)),
        result_type = 'FINAL',
        utterance_detection_mode = 'MULTIPLE'
    )
)

RecognitionInitMessage(
    parameters = RecognitionParameters(
        language = 'en-US',
        topic = 'GEN',
        audio_format = AudioFormat(pcm=PCM(sample_rate_hz=wf.getframerate())),
        result_type = 'PARTIAL',
        utterance_detection_mode = 'MULTIPLE'
    )
)

RecognitionInitMessage(
    parameters = RecognitionParameters(
        language = 'en-US',
        topic = 'GEN',
        audio_format = AudioFormat(pcm=PCM(sample_rate_hz=wf.getframerate())),
        result_type = 'PARTIAL',
        utterance_detection_mode = 'MULTIPLE'
    )
)

result {
  result_type: PARTIAL
  abs_start_ms: 840
  abs_end_ms: 5600
  ... 
}  

result {
  result_type: NOTIFICATIONS
  notifications {
    code: 1005
    severity: SEVERITY_INFO
    message {
      locale: "en-US"
      message: "Imminent shutdown."
      message_resource_id: "1005"
    }
    data {
      key: "timeout_ms"
      value: "10000"
    }
  }
}

RecognitionInitMessage(
    parameters = RecognitionParameters(
        language = 'en-US',
        topic = 'GEN',
        audio_format = AudioFormat(pcm=PCM(sample_rate_hz=wf.getframerate())),
        result_type = 'PARTIAL',
        utterance_detection_mode = 'MULTIPLE',
        recognition_flags = RecognitionFlags(
            auto_punctuate = True,
            filter_profanity = True,
            suppress_initial_capitalization = True,
            allow_zero_base_lm_weight = True,
            filter_wakeup_word = True
            send_multiple_start_of_speech = True
        )
    )
)

final: it's Monday morning and the sun is shining
final: I'm getting ready to walk to the train and commute into work.
final: I'll catch the 757 train from Cedar Park station
final: it will take me an hour to get into town

final: There is more snow coming to the Montreal area in the next few days. We're 
expecting 10 cm overnight and the winds are blowing hard. Our radar and satellite 
pictures show that we're on the western edge of the storm system as it continues 
to track further to the east.

final: there is more snow coming to the Montreal area in the next few days. We're 
expecting 10 cm overnight and the winds are blowing hard. Our radar and satellite 
pictures show that we're on the western edge of the storm system as it continues 
to track further to the east.

RecognitionInitMessage(
    parameters = RecognitionParameters(
        language = 'en-US',
        topic = 'GEN',
        audio_format = AudioFormat(pcm=PCM(sample_rate_hz=wf.getframerate())),
        result_type = 'IMMUTABLE_PARTIAL',
        utterance_detection_mode = 'MULTIPLE',
        formatting = Formatting(
            scheme = 'date',
            options = {
                'abbreviate_titles': True,
                'abbreviate_units': False,
                'censor_profanities': True,
                'censor_full_words': True
            }
        )
    )
)

try:
    # Iterate through messages returned from server
    for message in stream_in:
        if message.HasField('status'):
            if message.status.details:
                 print(f'{message.status.code} {message.status.message} - {message.status.details}')
            else:
                 print(f'{message.status.code} {message.status.message}')
        elif message.HasField('result'):
            restype = 'partial' if message.result.result_type else 'final'
            print(f'{restype}: {message.result.hypotheses[0].formatted_text}')

try:
    # Iterate through messages returned from server, returning all information
    for message in stream_in:
        print(message)

try:
    # Iterate through messages returned from server
    for message in stream_in:
        if message.HasField('status'):
            if message.status.details:
                 print(f'{message.status.code} {message.status.message} - {message.status.details}')
            else:
                 print(f'{message.status.code} {message.status.message}')

stream ../audio/weather16.wav
100 Continue - recognition started on audio/l16;rate=16000 stream
final: There is more snow coming to the Montreal area in the next few days
final: We're expecting 10 cm overnight and the winds are blowing hard
final: Our radar and satellite pictures show that we're on the western edge of the storm system as it continues to track further to the east
stream complete
200 Success

stream ../audio/weather16.wav
status {
  code: 100
  message: "Continue"
  details: "recognition started on audio/l16;rate=16000 stream"
}
*** Results in here ***
stream complete
status {
  code: 200
  message: "Success"
}

init = RecognitionInitMessage(
    parameters = RecognitionParameters(
        language = 'en-us',
        topic = 'GEN',
        audio_format = AudioFormat(pcm=PCM(sample_rate_hz=wf.getframerate())),
        result_type = FINAL, 
        utterance_detection_mode = MULTIPLE,
        recognition_flags = RecognitionFlags(
            filter_wakeup_word = True,
            suppress_call_recording = True,
            send_multiple_start_of_speech = True
        ),

start_of_speech {
  first_audio_to_start_of_speech_ms: 880
}

result {
  abs_start_ms: 880
  abs_end_ms: 5410
  utterance_info {...}
  hypotheses {
    confidence: 0.414000004529953
    average_confidence: 0.8600000143051147
    formatted_text: "There is more snow coming to the Montreal area in the next few days"
    ...
}

start_of_speech {
  first_audio_to_start_of_speech_ms: 5410
}

result {
  abs_start_ms: 5410
  abs_end_ms: 10280
  utterance_info {...}
  hypotheses {
    confidence: 0.1420000046491623
    average_confidence: 0.8790000081062317
    formatted_text: "We\'re expecting 10 cm overnight and the winds are blowing hard"
    ...
}

start_of_speech {
  first_audio_to_start_of_speech_ms: 10280
}

result {
  abs_start_ms: 10280
  abs_end_ms: 18520
  utterance_info {...}
  hypotheses {
    confidence: 0.008999999612569809
    average_confidence: 0.8730000257492065
    formatted_text: "Our radar and satellite pictures show that we\'re on the western edge of the storm system as it continues to track further to the east"
    ...
}

try:
    # Iterate through messages returned from server
    for message in stream_in:
        if message.HasField('status'):
        ...
    elif message.HasField('result'):
        restype = 'partial' if message.result.result_type else 'final'
        print(f'{restype}: {message.result.hypotheses[0].formatted_text}')

try:
    # Iterate through messages returned from server
    for message in stream_in:
        print(message)

try:
    # Iterate through messages returned from server
    for message in stream_in:
        if message.HasField('status'):
            if message.status.details:
                 print(f'{message.status.code} {message.status.message} - {message.status.details}')
            else:
                 print(f'{message.status.code} {message.status.message}')
        elif message.HasField('result'):
            restype = 'partial' if message.result.result_type else 'final'
            print(f'{restype}: {message.result.hypotheses[0].formatted_text}')
            print(f'Speech signal level: {message.result.utterance_info.dsp.level} SNR: {message.result.utterance_info.dsp.snr_estimate_db}')

stream ../audio/weather16.wav
100 Continue - recognition started on audio/l16;rate=16000 stream
final: There is more snow coming to the Montreal area in the next few days
Speech signal level: 20993.0 SNR: 15.0
final: We're expecting 10 cm overnight and the winds are blowing hard
Speech signal level: 18433.0 SNR: 15.0
final: Radar and satellite pictures show that we're on the western edge of the storm system as it continues to track further to the east
Speech signal level: 21505.0 SNR: 14.0
stream complete
200 Success

try:
    # Iterate through messages returned from server
    for message in stream_in:
        if message.HasField('status'):
            if message.status.details:
                 print(f'{message.status.code} {message.status.message} - {message.status.details}')
            else:
                 print(f'{message.status.code} {message.status.message}')
        elif message.HasField('result'):
            restype = 'partial' if message.result.result_type else 'final'
            print(f'{restype}: {message.result.hypotheses[0].formatted_text}')
            print(f'Average confidence: {message.result.hypotheses[0].average_confidence} Rejected? {message.result.hypotheses[0].rejected}')

stream ../audio/weather16.wav
100 Continue - recognition started on audio/l16;rate=16000 stream
final: There is more snow coming to the Montreal area in the next few days
Average confidence: 0.4129999876022339 Rejected? False
final: We're expecting 10 cm overnight and the winds are blowing hard
Average confidence: 0.7960000038146973 Rejected? False
final: Radar and satellite pictures show that we're on the western edge of the storm system as it continues to track further to the east
Average confidence: 0.6150000095367432 Rejected? False
stream complete
200 Success

  hypotheses {
    confidence: 0.8019999861717224
    average_confidence: 0.9599999785423279
    formatted_text: "Pay my Visa card from my checking account tomorrow for $10"
    minimally_formatted_text: "Pay my Visa card from my checking account tomorrow for ten dollars"
    words {
      text: "Pay"
      confidence: 0.8579999804496765
      start_ms: 2180
      end_ms: 2300
    }
    words {
      text: "my"
      confidence: 0.9610000252723694
      start_ms: 2300
      end_ms: 2600
    }
    words {
      text: "Visa"
      confidence: 0.9490000009536743
      start_ms: 2600
      end_ms: 3040
    }
    words {
      text: "card"
      confidence: 0.9679999947547913
      start_ms: 3040
      end_ms: 3440
    }
    words {
      text: "from"
      confidence: 0.9649999737739563
      start_ms: 3440
      end_ms: 3720
    }
    words {
      text: "my"
      confidence: 0.9869999885559082
      start_ms: 3720
      end_ms: 3840
    }
    words {
      text: "checking"
      confidence: 0.9900000095367432
      start_ms: 3840
      end_ms: 4320
    }
    words {
      text: "account"
      confidence: 0.9739999771118164
      start_ms: 4320
      end_ms: 4780
      silence_after_word_ms: 120
    }
    words {
      text: "tomorrow"
      confidence: 0.9509999752044678
      start_ms: 4900
      end_ms: 5320
    }
    words {
      text: "for"
      confidence: 0.8980000019073486
      start_ms: 5320
      end_ms: 5600
    }
    words {
      text: "$10"
      confidence: 0.9700000286102295
      start_ms: 5600
      end_ms: 6360
    }
  }

try:
    # Iterate through messages returned from server
    dp_displayed = False
    for message in stream_in:
        if message.HasField('status'):
            if message.status.details:
                 print(f'{message.status.code} {message.status.message} - {message.status.details}')
            else:
                 print(f'{message.status.code} {message.status.message}')
        elif message.HasField('result'):
            restype = 'partial' if message.result.result_type else 'final'
            if restype == 'final' and not dp_displayed:
                print(f'Data pack: {message.result.data_pack.language} {message.result.data_pack.version}')
                dp_displayed = True
            print(f'{restype}: {message.result.hypotheses[0].formatted_text}')

stream ../audio/monday_morning_16.wav
100 Continue - recognition started on audio/l16;rate=16000 stream
Data pack: eng-USA 4.2.0
final: It's Monday morning and the sun is shining
final: I'm getting ready to walk to the train and and commute into work
final: I'll catch the 757 train from Cedar Park station
final: It will take me an hour to get into town
stream complete
200 Success

result {
  abs_start_ms: 1020
  abs_end_ms: 4330
  utterance_info {
    duration_ms: 3310
    dsp {
      snr_estimate_db: 39
      level: 9217
      num_channels: 1
      initial_silence_ms: 220
      initial_energy: -51.201
      final_energy: -76.437
      mean_energy: 146.309
    }
  }
  hypotheses {
    confidence: 0.788
    average_confidence: 0.967
    formatted_text: "It\'s Monday morning and the sun is shining."
    minimally_formatted_text: "It\'s Monday morning and the sun is shining."
    words {
    *** Individual words in the hypothesis ***
    }
  }

  data_pack {
    language: "eng-USA"
    topic: "GEN"
    version: "4.12.1"
    id: "GMT20231026205000"
  }
}

result: {
  result_type: PARTIAL
  abs_start_ms: 160
  abs_end_ms :3510
  hypotheses: [ 
  *** Hypotheses here *** 
  ],
  data_pack: {
    language: "eng-USA"
    topic: "GEN"
    version: "4.11.1"
    id: "GMT20230830154712"
  }
  notifications: [
    {
      code: 1002
      severity: SEVERITY_WARNING
      message: {
        locale: "en-US"
        message: "Wordset-pkg should be recompiled."
        message_resource_id: 1002
      }
      data: {
        application/x-nuance-wordset-pkg: "urn:nuance-mix:tag:wordset:lang/names-places/places-compiled-ws/eng-USA/mix.asr"
        application/x-nuance-domainlm: "urn:nuance-mix:tag:model/names-places/mix.asr?=language=eng-usa"
      }
    }
  ]
}