"""
TODO: docstring
"""
import enum
import librosa
import numpy
import os
import random
import tensorflow 
import tflearn
import wave

class ANN:
    """
    TODO: docstring
    """
    def __init__(self):
        """
        TODO: docstring
        """
        batch_size = 64
        classes = 10
        height = 80
        learning_rate = 0.0001
        training_iters = 300000
        width = 20
        batch = SpeechData.mfcc_batch_generator(batch_size)
        X, Y = next(batch)
        trainX, trainY = X, Y
        testX, testY = X, Y
        # architecture
        net = tflearn.input_data([None, width, height])
        net = tflearn.lstm(net, 128, dropout=0.8)
        net = tflearn.fully_connected(net, classes, activation='softmax')
        net = tflearn.regression(
            net, optimizer='adam', learning_rate=learning_rate, loss='categorical_crossentropy')
        # training
        #col = tensorflow.compat.v1.get_collection(tensorflow.compat.v1.GraphKeys.TRAINABLE_VARIABLES)
        #for x in col:
        #    tensorflow.compat.v1.add_to_collection(tensorflow.compat.v1.GraphKeys.VARIABLES, x)
        model = tflearn.DNN(net, tensorboard_verbose=0)
        for _ in range(1):
          model.fit(
              trainX, trainY, n_epoch=10, validation_set=(testX, testY),
              show_metric=True, batch_size=batch_size)
          _y = model.predict(X)
        model.save('data\\output\\tflearn.lstm.model')
        print(_y)

class Target(enum.Enum):
    """
    TODO: docstring
    """
    digits = 1
    first_letter = 8
    speaker = 2

class SpeechData:
    """
    Utilities for downloading and providing data from 
    openslr.org, libriSpeech, Pannous, Gutenberg, WMT, tokenizing, vocabularies.
    """
    pcm_path = 'data\\input\\spoken_numbers_pcm\\'

    def dense_to_one_hot(labels_dense, num_classes=10):
        """
        This function converts class labels from scalars to one-hot vectors.
        """
        return numpy.eye(num_classes)[labels_dense]

    def get_speakers(path=pcm_path):
        """
        TODO: docstring
        """
        files = os.listdir(path)
        def nobad(file):
            return '_' in file and not '.' in file.split('_')[1]
        speakers=list(set(map(speaker,filter(nobad,files))))
        print(len(speakers), 'speakers:', speakers)
        return speakers

    def mfcc_batch_generator(batch_size=10, target=Target.digits):
        """
        TODO: docstring
        """
        if target == Target.speaker: 
            speakers = SpeechData.get_speakers()
        batch_features, labels = [], []
        files = os.listdir(SpeechData.pcm_path)
        while True:
            print('loaded batch of %d files' % len(files))
            random.shuffle(files)
            for wav in files:
                if not wav.endswith('.wav'):
                    continue
                wave, sr = librosa.load(SpeechData.pcm_path + wav, mono=True)
                if target == Target.speaker: 
                    label = SpeechData.one_hot_from_item(speaker(wav), speakers)
                elif target == Target.digits:
                    label = SpeechData.dense_to_one_hot(int(wav[0]), 10)
                elif target == Target.first_letter:
                    label = SpeechData.dense_to_one_hot((ord(wav[0]) - 48) % 32, 32)
                else: 
                    raise Exception('TODO: labels for Target.')
                labels.append(label)
                mfcc = librosa.feature.mfcc(wave, sr)
                mfcc = numpy.pad(
                    mfcc, ((0, 0), (0, 80 - len(mfcc[0]))), mode='constant', constant_values=0)
                batch_features.append(numpy.array(mfcc))
                if len(batch_features) >= batch_size:
                    yield batch_features, labels
                    batch_features = []
                    labels = []

    def one_hot_from_item(item, items):
        """
        TODO: docstring
        """
        x = [0] * len(items)
        i = items.index(item)
        x[i] = 1
        return x

    def speaker(file):
        """
        TODO: docstring
        """
        return file.split('_')[1]

if __name__ == '__main__':
    ANN()

"""
TODO: docstring
"""
import enum
import librosa
import numpy
import os
import random
import tensorflow 
import tflearn
import wave

class ANN:
    """
    TODO: docstring
    """
    def __init__(self):
        """
        TODO: docstring
        """
        batch_size = 64
        classes = 10
        height = 80
        learning_rate = 0.0001
        training_iters = 300000
        width = 20
        batch = SpeechData.mfcc_batch_generator(batch_size)
        X, Y = next(batch)
        trainX, trainY = X, Y
        testX, testY = X, Y
        # architecture
        net = tflearn.input_data([None, width, height])
        net = tflearn.lstm(net, 128, dropout=0.8)
        net = tflearn.fully_connected(net, classes, activation='softmax')
        net = tflearn.regression(
            net, optimizer='adam', learning_rate=learning_rate, loss='categorical_crossentropy')
        # training
        #col = tensorflow.compat.v1.get_collection(tensorflow.compat.v1.GraphKeys.TRAINABLE_VARIABLES)
        #for x in col:
        #    tensorflow.compat.v1.add_to_collection(tensorflow.compat.v1.GraphKeys.VARIABLES, x)
        model = tflearn.DNN(net, tensorboard_verbose=0)
        for _ in range(1):
          model.fit(
              trainX, trainY, n_epoch=10, validation_set=(testX, testY),
              show_metric=True, batch_size=batch_size)
          _y = model.predict(X)
        model.save('data\\output\\tflearn.lstm.model')
        print(_y)

class Target(enum.Enum):
    """
    TODO: docstring
    """
    digits = 1
    first_letter = 8
    speaker = 2

class SpeechData:
    """
    Utilities for downloading and providing data from 
    openslr.org, libriSpeech, Pannous, Gutenberg, WMT, tokenizing, vocabularies.
    """
    pcm_path = 'data\\input\\spoken_numbers_pcm\\'

    def dense_to_one_hot(labels_dense, num_classes=10):
        """
        This function converts class labels from scalars to one-hot vectors.
        """
        return numpy.eye(num_classes)[labels_dense]

    def get_speakers(path=pcm_path):
        """
        TODO: docstring
        """
        files = os.listdir(path)
        def nobad(file):
            return '_' in file and not '.' in file.split('_')[1]
        speakers=list(set(map(speaker,filter(nobad,files))))
        print(len(speakers), 'speakers:', speakers)
        return speakers

    def mfcc_batch_generator(batch_size=10, target=Target.digits):
        """
        TODO: docstring
        """
        if target == Target.speaker: 
            speakers = SpeechData.get_speakers()
        batch_features, labels = [], []
        files = os.listdir(SpeechData.pcm_path)
        while True:
            print('loaded batch of %d files' % len(files))
            random.shuffle(files)
            for wav in files:
                if not wav.endswith('.wav'):
                    continue
                wave, sr = librosa.load(SpeechData.pcm_path + wav, mono=True)
                if target == Target.speaker: 
                    label = SpeechData.one_hot_from_item(speaker(wav), speakers)
                elif target == Target.digits:
                    label = SpeechData.dense_to_one_hot(int(wav[0]), 10)
                elif target == Target.first_letter:
                    label = SpeechData.dense_to_one_hot((ord(wav[0]) - 48) % 32, 32)
                else: 
                    raise Exception('TODO: labels for Target.')
                labels.append(label)
                mfcc = librosa.feature.mfcc(wave, sr)
                mfcc = numpy.pad(
                    mfcc, ((0, 0), (0, 80 - len(mfcc[0]))), mode='constant', constant_values=0)
                batch_features.append(numpy.array(mfcc))
                if len(batch_features) >= batch_size:
                    yield batch_features, labels
                    batch_features = []
                    labels = []

    def one_hot_from_item(item, items):
        """
        TODO: docstring
        """
        x = [0] * len(items)
        i = items.index(item)
        x[i] = 1
        return x

    def speaker(file):
        """
        TODO: docstring
        """
        return file.split('_')[1]

if __name__ == '__main__':
    ANN()

TODO: docstring TODO: docstring TODO: docstring # architecture # training #col = tensorflow.compat.v1.get_collection(tensorflow.compat.v1.GraphKeys.TRAINABLE_VARIABLES) #for x in col: #    tensorflow.compat.v1.add_to_collection(tensorflow.compat.v1.GraphKeys.VARIABLES, x) TODO: docstring Utilities for downloading and providing data from      openslr.org, libriSpeech, Pannous, Gutenberg, WMT, tokenizing, vocabularies. This function converts class labels from scalars to one-hot vectors. TODO: docstring TODO: docstring TODO: docstring TODO: docstring