#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import os
import argparse
import numpy as np
import shlex
import subprocess
import sys
import wave
import json

from deepspeech import Model, version
from timeit import default_timer as timer

try:
    from shhlex import quote
except ImportError:
    from pipes import quote


def convert_samplerate(audio_path, desired_sample_rate):
    sox_cmd = 'sox {} --type raw --bits 16 --channels 1 --rate {} --encoding signed-integer --endian little --compression 0.0 --no-dither - '.format(quote(audio_path), desired_sample_rate)
    try:
        output = subprocess.check_output(shlex.split(sox_cmd), stderr=subprocess.PIPE)
    except subprocess.CalledProcessError as e:
        raise RuntimeError('SoX returned non-zero status: {}'.format(e.stderr))
    except OSError as e:
        raise OSError(e.errno, 'SoX not found, use {}hz files or install it: {}'.format(desired_sample_rate, e.strerror))

    return desired_sample_rate, np.frombuffer(output, np.int16)


def metadata_to_string(metadata):
    return ''.join(token.text for token in metadata.tokens)


def words_from_candidate_transcript(metadata):
    word = ""
    word_list = []
    word_start_time = 0
    # Loop through each character
    for i, token in enumerate(metadata.tokens):
        # Append character to word if it's not a space
        if token.text != " ":
            if len(word) == 0:
                # Log the start time of the new word
                word_start_time = token.start_time

            word = word + token.text
        # Word boundary is either a space or the last character in the array
        if token.text == " " or i == len(metadata.tokens) - 1:
            word_duration = token.start_time - word_start_time

            if word_duration < 0:
                word_duration = 0

            each_word = dict()
            each_word["word"] = word
            each_word["start_time"] = round(word_start_time, 4)
            each_word["duration"] = round(word_duration, 4)

            word_list.append(each_word)
            # Reset
            word = ""
            word_start_time = 0

    return word_list


def metadata_json_output(metadata):
    json_result = dict()
    json_result["transcripts"] = [{
        "confidence": transcript.confidence,
        "words": words_from_candidate_transcript(transcript),
    } for transcript in metadata.transcripts]
    return json.dumps(json_result, indent=2)



class VersionAction(argparse.Action):
    def __init__(self, *args, **kwargs):
        super(VersionAction, self).__init__(nargs=0, *args, **kwargs)

    def __call__(self, *args, **kwargs):
        print('DeepSpeech ', version())
        exit(0)


class wav2text():
    def __init__(self,):

        print('Loading model from file {}'.format(args.model), file=sys.stderr)
        model_load_start = timer()
        # sphinx-doc: python_ref_model_start
        model_path=os.path.dirname(os.path.abspath(__file__))

        ds = Model(os.path.join(model_path,args.model))
        # sphinx-doc: python_ref_model_stop
        model_load_end = timer() - model_load_start
        print('Loaded model in {:.3}s.'.format(model_load_end), file=sys.stderr)

        if args.beam_width:
            ds.setBeamWidth(args.beam_width)

        self.desired_sample_rate = ds.sampleRate()



        if args.scorer:
            print('Loading scorer from files {}'.format(args.scorer), file=sys.stderr)
            scorer_load_start = timer()
            ds.enableExternalScorer(os.path.join(model_path,args.scorer))
            scorer_load_end = timer() - scorer_load_start
            print('Loaded scorer in {:.3}s.'.format(scorer_load_end), file=sys.stderr)

            if args.lm_alpha and args.lm_beta:
                ds.setScorerAlphaBeta(args.lm_alpha, args.lm_beta)

        if args.hot_words:
            print('Adding hot-words', file=sys.stderr)
            for word_boost in args.hot_words.split(','):
                word,boost = word_boost.split(':')
                ds.addHotWord(word,float(boost))
        self.ds=ds
        
    def run(self,audio):
        fin = wave.open(audio, 'rb')
        fs_orig = fin.getframerate()
        if fs_orig != self.desired_sample_rate:
            print('Warning: original sample rate ({}) is different than {}hz. Resampling might produce erratic speech recognition.'.format(fs_orig, desired_sample_rate), file=sys.stderr)
            fs_new, audio = convert_samplerate(args.audio, desired_sample_rate)
        else:
            audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)

        audio_length = fin.getnframes() * (1/fs_orig)
        fin.close()

        inference_start = timer()
        # sphinx-doc: python_ref_inference_start
        text=self.ds.stt(audio)
        #print(text)
        # sphinx-doc: python_ref_inference_stop
        inference_end = timer() - inference_start
        #print('Inference took %0.3fs for %0.3fs audio file.' % (inference_end, audio_length), file=sys.stderr)
        return text



parser = argparse.ArgumentParser(description='Running DeepSpeech inference.')
parser.add_argument('--model', required=False,default='deepspeech-0.9.3-models.pbmm',
                        help='Path to the model (protocol buffer binary file)')
parser.add_argument('--scorer', required=False,default='deepspeech-0.9.3-models.scorer',
                        help='Path to the external scorer file')
parser.add_argument('--audio', required=False,
                        help='Path to the audio file to run (WAV format)')
parser.add_argument('--beam_width', type=int,
                        help='Beam width for the CTC decoder')
parser.add_argument('--lm_alpha', type=float,
                        help='Language model weight (lm_alpha). If not specified, use default from the scorer package.')
parser.add_argument('--lm_beta', type=float,
                        help='Word insertion bonus (lm_beta). If not specified, use default from the scorer package.')
parser.add_argument('--version', action=VersionAction,
                        help='Print version and exits')
parser.add_argument('--extended', required=False, action='store_true',
                        help='Output string from extended metadata')
parser.add_argument('--json', required=False, action='store_true',
                        help='Output json from metadata with timestamp of each word')
parser.add_argument('--candidate_transcripts', type=int, default=3,
                        help='Number of candidate transcripts to include in JSON output')
parser.add_argument('--hot_words', type=str,
                        help='Hot-words and their boosts.')
args = parser.parse_args()


