# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Google Cloud Speech API sample that demonstrates enhanced models
and recognition metadata.

Example usage:
    python beta_snippets.py enhanced-model
    python beta_snippets.py metadata
    python beta_snippets.py punctuation
    python beta_snippets.py diarization
    python beta_snippets.py multi-channel
    python beta_snippets.py multi-language
    python beta_snippets.py word-level-conf
    python beta_snippets.py spoken-punctuation-emojis
"""

import argparse

from google.cloud import speech_v1p1beta1 as speech


def transcribe_file_with_enhanced_model() -> speech.RecognizeResponse:
    """Transcribe the given audio file using an enhanced model."""
    # [START speech_transcribe_enhanced_model_beta]
    from google.cloud import speech_v1p1beta1 as speech

    client = speech.SpeechClient()

    speech_file = "resources/commercial_mono.wav"

    with open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=8000,
        language_code="en-US",
        use_enhanced=True,
        # A model must be specified to use enhanced model.
        model="phone_call",
    )

    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print("-" * 20)
        print(f"First alternative of result {i}")
        print(f"Transcript: {alternative.transcript}")

    return response.results
    # [END speech_transcribe_enhanced_model_beta]


def transcribe_file_with_metadata() -> speech.RecognizeResponse:
    """Send a request that includes recognition metadata."""
    # [START speech_transcribe_recognition_metadata_beta]
    from google.cloud import speech_v1p1beta1 as speech

    client = speech.SpeechClient()

    speech_file = "resources/commercial_mono.wav"

    with open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    # Here we construct a recognition metadata object.
    # Most metadata fields are specified as enums that can be found
    # in speech.enums.RecognitionMetadata
    metadata = speech.RecognitionMetadata()
    metadata.interaction_type = speech.RecognitionMetadata.InteractionType.DISCUSSION
    metadata.microphone_distance = (
        speech.RecognitionMetadata.MicrophoneDistance.NEARFIELD
    )
    metadata.recording_device_type = (
        speech.RecognitionMetadata.RecordingDeviceType.SMARTPHONE
    )

    # Some metadata fields are free form strings
    metadata.recording_device_name = "Pixel 2 XL"
    # And some are integers, for instance the 6 digit NAICS code
    # https://www.naics.com/search/
    metadata.industry_naics_code_of_audio = 519190

    audio = speech.RecognitionAudio(content=content)
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=8000,
        language_code="en-US",
        # Add this in the request to send metadata.
        metadata=metadata,
    )

    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print("-" * 20)
        print(f"First alternative of result {i}")
        print(f"Transcript: {alternative.transcript}")

    return response.results
    # [END speech_transcribe_recognition_metadata_beta]


def transcribe_file_with_auto_punctuation() -> speech.RecognizeResponse:
    """Transcribe the given audio file with auto punctuation enabled."""
    # [START speech_transcribe_auto_punctuation_beta]
    from google.cloud import speech_v1p1beta1 as speech

    client = speech.SpeechClient()

    speech_file = "resources/commercial_mono.wav"

    with open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=8000,
        language_code="en-US",
        # Enable automatic punctuation
        enable_automatic_punctuation=True,
    )

    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print("-" * 20)
        print(f"First alternative of result {i}")
        print(f"Transcript: {alternative.transcript}")

    return response.results
    # [END speech_transcribe_auto_punctuation_beta]


def transcribe_file_with_diarization() -> speech.RecognizeResponse:
    """Transcribe the given audio file synchronously with diarization."""
    # [START speech_transcribe_diarization_beta]
    from google.cloud import speech_v1p1beta1 as speech

    client = speech.SpeechClient()

    speech_file = "resources/commercial_mono.wav"

    with open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)

    diarization_config = speech.SpeakerDiarizationConfig(
        enable_speaker_diarization=True,
        min_speaker_count=2,
        max_speaker_count=10,
    )

    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=8000,
        language_code="en-US",
        diarization_config=diarization_config,
    )

    print("Waiting for operation to complete...")
    response = client.recognize(config=config, audio=audio)

    # The transcript within each result is separate and sequential per result.
    # However, the words list within an alternative includes all the words
    # from all the results thus far. Thus, to get all the words with speaker
    # tags, you only have to take the words list from the last result:
    result = response.results[-1]

    words_info = result.alternatives[0].words

    # Printing out the output:
    for word_info in words_info:
        print(f"word: '{word_info.word}', speaker_tag: {word_info.speaker_tag}")

    return result
    # [END speech_transcribe_diarization_beta]


def transcribe_file_with_multichannel() -> speech.RecognizeResponse:
    """Transcribe the given audio file synchronously with
    multi channel."""
    # [START speech_transcribe_multichannel_beta]
    from google.cloud import speech_v1p1beta1 as speech

    client = speech.SpeechClient()

    speech_file = "resources/Google_Gnome.wav"

    with open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)

    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=16000,
        language_code="en-US",
        audio_channel_count=1,
        enable_separate_recognition_per_channel=True,
    )

    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print("-" * 20)
        print(f"First alternative of result {i}")
        print(f"Transcript: {alternative.transcript}")
        print(f"Channel Tag: {result.channel_tag}")

    return response.results
    # [END speech_transcribe_multichannel_beta]


def transcribe_file_with_multilanguage() -> speech.RecognizeResponse:
    """Transcribe the given audio file synchronously with
    multi language."""
    # [START speech_transcribe_multilanguage_beta]
    from google.cloud import speech_v1p1beta1 as speech

    client = speech.SpeechClient()

    speech_file = "resources/multi.wav"
    first_lang = "en-US"
    second_lang = "es"

    with open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)

    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=44100,
        audio_channel_count=2,
        language_code=first_lang,
        alternative_language_codes=[second_lang],
    )

    print("Waiting for operation to complete...")
    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print("-" * 20)
        print(f"First alternative of result {i}: {alternative}")
        print(f"Transcript: {alternative.transcript}")

    return response.results
    # [END speech_transcribe_multilanguage_beta]


def transcribe_file_with_word_level_confidence() -> speech.RecognizeResponse:
    """Transcribe the given audio file synchronously with
    word level confidence."""
    # [START speech_transcribe_word_level_confidence_beta]
    from google.cloud import speech_v1p1beta1 as speech

    client = speech.SpeechClient()

    speech_file = "resources/Google_Gnome.wav"

    with open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)

    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=16000,
        language_code="en-US",
        enable_word_confidence=True,
    )

    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print("-" * 20)
        print(f"First alternative of result {i}")
        print(f"Transcript: {alternative.transcript}")
        print(
            "First Word and Confidence: ({}, {})".format(
                alternative.words[0].word, alternative.words[0].confidence
            )
        )

    return response.results
    # [END speech_transcribe_word_level_confidence_beta]


def transcribe_file_with_spoken_punctuation_end_emojis() -> speech.RecognizeResponse:
    """Transcribe the given audio file with spoken punctuation and emojis enabled."""
    # [START speech_transcribe_spoken_punctuation_emojis_beta]
    from google.cloud import speech_v1p1beta1 as speech
    from google.protobuf import wrappers_pb2

    client = speech.SpeechClient()

    speech_file = "resources/commercial_mono.wav"

    with open(speech_file, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=8000,
        language_code="en-US",
        # Enable spoken punctuation
        enable_spoken_punctuation=wrappers_pb2.BoolValue(value=True),
        # Enable spoken emojis
        enable_spoken_emojis=wrappers_pb2.BoolValue(value=True),
    )

    response = client.recognize(config=config, audio=audio)

    for i, result in enumerate(response.results):
        alternative = result.alternatives[0]
        print("-" * 20)
        print(f"First alternative of result {i}")
        print(f"Transcript: {alternative.transcript}")

    return response.results
    # [END speech_transcribe_spoken_punctuation_emojis_beta]


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
    )
    parser.add_argument("command")

    args = parser.parse_args()

    if args.command == "enhanced-model":
        transcribe_file_with_enhanced_model()
    elif args.command == "metadata":
        transcribe_file_with_metadata()
    elif args.command == "punctuation":
        transcribe_file_with_auto_punctuation()
    elif args.command == "diarization":
        transcribe_file_with_diarization()
    elif args.command == "multi-channel":
        transcribe_file_with_multichannel()
    elif args.command == "multi-language":
        transcribe_file_with_multilanguage()
    elif args.command == "word-level-conf":
        transcribe_file_with_word_level_confidence()
    elif args.command == "spoken-punctuation-emojis":
        transcribe_file_with_spoken_punctuation_end_emojis()