#!/usr/bin/env python
from __future__ import unicode_literals

from google.protobuf.json_format import MessageToJson
from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types
import argparse
import ffmpeg
import logging
import subprocess
import sys
import IPython


logging.basicConfig(level=logging.INFO, format='%(message)s')
logger = logging.getLogger(__file__)
logger.setLevel(logging.INFO)


parser = argparse.ArgumentParser(description='Convert speech audio to text using Google Speech API')
parser.add_argument('in_filename', help='Input filename (`-` for stdin)')
parser.add_argument('--timing', action='store_true', help='Include timing info')


def decode_audio(in_filename, **input_kwargs):
    p = subprocess.Popen(
        (ffmpeg
            .input(in_filename, **input_kwargs)
            .output('-', format='s16le', acodec='pcm_s16le', ac=1, ar='16k')
            .overwrite_output()
            .compile()
        ),
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE
    )
    out = p.communicate()
    if p.returncode != 0:
        sys.stderr.write(out[1])
        sys.exit(1)
    return out[0]


def get_transcripts(audio_data, include_timing_info=False):
    client = speech.SpeechClient()
    audio = types.RecognitionAudio(content=audio_data)
    config = types.RecognitionConfig(
        encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=16000,
        language_code='en-US',
        enable_word_time_offsets=include_timing_info,
    )
    return client.recognize(config, audio)


def transcribe(in_filename, include_timing_info=False):
    audio_data = decode_audio(in_filename)
    response = get_transcripts(audio_data, include_timing_info)
    print(MessageToJson(response, sort_keys=True))


if __name__ == '__main__':
    args = parser.parse_args()
    transcribe(args.in_filename, args.timing)