#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function

import argparse
import sys
import scipy.io.wavfile as wav

from deepspeech.model import Model

# These constants control the beam search decoder

# Beam width used in the CTC decoder when building candidate transcriptions
BEAM_WIDTH = 500

# The alpha hyperparameter of the CTC decoder. Language Model weight
LM_WEIGHT = 1.75

# The beta hyperparameter of the CTC decoder. Word insertion weight (penalty)
WORD_COUNT_WEIGHT = 1.00

# Valid word insertion weight. This is used to lessen the word insertion penalty
# when the inserted word is part of the vocabulary
VALID_WORD_COUNT_WEIGHT = 1.00


# These constants are tied to the shape of the graph used (changing them changes
# the geometry of the first layer), so make sure you use the same constants that
# were used during training

# Number of MFCC features to use
N_FEATURES = 26

# Size of the context window used for producing timesteps in the input vector
N_CONTEXT = 9

def main():
    parser = argparse.ArgumentParser(description='Benchmarking tooling for DeepSpeech native_client.')
    parser.add_argument('model', type=str,
                        help='Path to the model (protocol buffer binary file)')
    parser.add_argument('audio', type=str,
                        help='Path to the audio file to run (WAV format)')
    parser.add_argument('alphabet', type=str,
                        help='Path to the configuration file specifying the alphabet used by the network')
    parser.add_argument('lm', type=str, nargs='?',
                        help='Path to the language model binary file')
    parser.add_argument('trie', type=str, nargs='?',
                        help='Path to the language model trie file created with native_client/generate_trie')
    args = parser.parse_args()

    ds = Model(args.model, N_FEATURES, N_CONTEXT, args.alphabet, BEAM_WIDTH)

    if args.lm and args.trie:
        ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_WEIGHT,
                               WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)

    fs, audio = wav.read(args.audio)
    print(ds.stt(audio, fs))

if __name__ == '__main__':
    main()