├── __init__.py ├── speech ├── __init__.py ├── processing.py └── alphabet.py ├── utils ├── __init__.py ├── fs.py └── list.py ├── prediction ├── __init__.py └── prediction.py ├── training ├── __init__.py ├── errors │ ├── __init__.py │ ├── modelnotfound.py │ └── tolittledata.py ├── callbacks │ ├── __init__.py │ └── trainingcallback.py ├── trainingplan.py ├── trainingstatistics.py ├── trainingconfig.py ├── trainer.py ├── trainingdata.py ├── datagenerator.py └── training.py ├── requirements.txt ├── examples ├── training.config.json └── english.json ├── logger.py ├── statistics.py ├── logging.json ├── continuetraining.py ├── train.py ├── predict.py ├── README.md ├── models.py └── LICENSE /__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /speech/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /prediction/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /training/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /training/errors/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /training/callbacks/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | h5py==2.7.1 2 | Keras==2.1.6 3 | numpy==1.14.3 4 | python-speech-features==0.6 5 | Rx==1.6.1 6 | matplotlib==2.2.2 -------------------------------------------------------------------------------- /training/errors/modelnotfound.py: -------------------------------------------------------------------------------- 1 | class ModelNotFoundError(Exception): 2 | def __init__(self, value): 3 | self.value = value 4 | 5 | def __str__(self): 6 | return repr(self.value) 7 | -------------------------------------------------------------------------------- /training/errors/tolittledata.py: -------------------------------------------------------------------------------- 1 | class ToLittleDataError(Exception): 2 | def __init__(self, value): 3 | self.value = value 4 | 5 | def __str__(self): 6 | return repr(self.value) 7 | -------------------------------------------------------------------------------- /examples/training.config.json: -------------------------------------------------------------------------------- 1 | { 2 | "epochs": 10, 3 | "batchSize": 20, 4 | "trainingDataQuantity": 50000, 5 | "net": "graves", 6 | "trainingData": [ 7 | "speech-to-text/training_data.json" 8 | ], 9 | "alphabetPath": "speech-to-text/examples/english.json" 10 | } -------------------------------------------------------------------------------- /examples/english.json: -------------------------------------------------------------------------------- 1 | [ 2 | "a", 3 | "b", 4 | "c", 5 | "d", 6 | "e", 7 | "f", 8 | "g", 9 | "h", 10 | "i", 11 | "j", 12 | "k", 13 | "l", 14 | "m", 15 | "n", 16 | "o", 17 | "p", 18 | "q", 19 | "r", 20 | "s", 21 | "t", 22 | "u", 23 | "v", 24 | "w", 25 | "x", 26 | "y", 27 | "z" 28 | ] -------------------------------------------------------------------------------- /utils/fs.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | 4 | def safe_open(filename: str, mode: str): 5 | """ 6 | Writes to a file. In addition, it creates the directories, if necessary 7 | :param filename: path to the file 8 | :param mode: writing mode 9 | :return: 10 | """ 11 | try: 12 | os.makedirs(os.path.dirname(filename)) 13 | except OSError as ex: 14 | pass 15 | 16 | return open(filename, mode) 17 | -------------------------------------------------------------------------------- /utils/list.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | 4 | def split(data: List[any], rate=0.5) -> (List[any], List[any]): 5 | """ 6 | Splits a list into two new ones. 7 | :param data: list which shall be divided 8 | :param rate: value between 0 and 1 9 | :return: 10 | """ 11 | if not rate >= 0 and rate <= 1: 12 | raise ValueError('split value is %s but must be between 0 and 1' % split) 13 | 14 | # calculate the list index, where to split the list 15 | split_index = int(round(len(data) * rate)) 16 | 17 | return data[:split_index], data[split_index + 1:] 18 | -------------------------------------------------------------------------------- /logger.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import logging.config 4 | 5 | from utils.fs import safe_open 6 | 7 | def setup_logging(default_path='./logging.json', default_level=logging.INFO): 8 | path = default_path 9 | 10 | if os.path.exists(path): 11 | with safe_open(path, 'rt') as f: 12 | config = json.load(f) 13 | logging.config.dictConfig(config) 14 | else: 15 | logging.basicConfig(level=default_level) 16 | 17 | 18 | def get_logger(name=None, default_path='./logging.json'): 19 | setup_logging(default_path) 20 | return logging.getLogger(name) 21 | -------------------------------------------------------------------------------- /statistics.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import matplotlib.pyplot as plot 3 | 4 | from training.trainingstatistics import load 5 | 6 | from logger import get_logger 7 | 8 | 9 | def main(path: str): 10 | log = get_logger(__name__) 11 | 12 | try: 13 | training = load(path) 14 | 15 | # plot loss and validation loss 16 | plot.plot(training.loss) 17 | plot.plot(training.validation_loss) 18 | plot.title('model loss') 19 | plot.ylabel('loss') 20 | plot.xlabel('epoch') 21 | plot.legend(['train', 'validation'], loc='upper left') 22 | plot.show() 23 | except FileNotFoundError as ex: 24 | log.error('%s' % ex) 25 | 26 | 27 | if __name__ == "__main__": 28 | parser = argparse.ArgumentParser(description="Tool for displaying statistics of trainings") 29 | parser.add_argument("path", help="Path to a training statistics file") 30 | args = parser.parse_args() 31 | 32 | main(args.path) 33 | -------------------------------------------------------------------------------- /speech/processing.py: -------------------------------------------------------------------------------- 1 | from python_speech_features import mfcc 2 | 3 | import scipy.io.wavfile as wavfile 4 | import numpy as np 5 | 6 | 7 | def calculate_mfccs(audio_file_path): 8 | """ 9 | For a given audio clip, calculate the corresponding feature 10 | 11 | :param audio_file_path: 12 | :return: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector. 13 | """ 14 | (rate, data) = wavfile.read(audio_file_path) 15 | return mfcc(data, rate, numcep=26) 16 | 17 | 18 | def normalize_mfcc(feature, window=15, max_freq=8000, eps=1e-14): 19 | """ Center a feature using the mean and std 20 | Params: 21 | feature (numpy.ndarray): Feature to normalize 22 | """ 23 | feat_dim = calculate_feature_dimension(window, max_freq) 24 | feats_mean = np.zeros((feat_dim,)) 25 | feats_std = np.ones((feat_dim,)) 26 | 27 | return (feature - feats_mean) / (feats_std + eps) 28 | 29 | 30 | def calculate_feature_dimension(window=15, max_freq=8000): 31 | return int(0.001 * window * max_freq) + 1 32 | -------------------------------------------------------------------------------- /training/callbacks/trainingcallback.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from keras.callbacks import Callback 4 | 5 | from training.training import Training 6 | 7 | from models import save as save_model, SaveFile 8 | 9 | from training.trainingstatistics import save as save_statistics, TrainingStatistics 10 | from training.trainingconfig import save as save_config, TrainingConfig 11 | 12 | 13 | class TrainingCallback(Callback): 14 | def __init__(self, path: str, statistics: TrainingStatistics, training: Training, config: TrainingConfig): 15 | super().__init__() 16 | self.path = path 17 | self.statistics = statistics 18 | self.training = training 19 | self.config = config 20 | 21 | def on_epoch_end(self, epoch, logs=None): 22 | loss = logs['loss'] 23 | validation_loss = logs['val_loss'] 24 | 25 | self.statistics.loss.append(loss) 26 | self.statistics.validation_loss.append(validation_loss) 27 | 28 | save_model(SaveFile(self.training.alphabet, self.training.model), 29 | self.path + '.json', 30 | self.path + '.weights-%s-%s.h5' % (epoch + 1, validation_loss)) 31 | save_statistics(self.path + '.statistics.json', self.statistics) 32 | save_config(self.path + '.config.json', self.config) 33 | 34 | -------------------------------------------------------------------------------- /logging.json: -------------------------------------------------------------------------------- 1 | { 2 | "version": 1, 3 | "disable_existing_loggers": false, 4 | "formatters": { 5 | "simple": { 6 | "format": "%(asctime)s | %(name)s | %(levelname)s | %(message)s" 7 | } 8 | }, 9 | "handlers": { 10 | "console": { 11 | "class": "logging.StreamHandler", 12 | "level": "DEBUG", 13 | "formatter": "simple", 14 | "stream": "ext://sys.stdout" 15 | }, 16 | "info_file_handler": { 17 | "class": "logging.handlers.RotatingFileHandler", 18 | "level": "INFO", 19 | "formatter": "simple", 20 | "filename": "info.log", 21 | "maxBytes": 10485760, 22 | "backupCount": 20, 23 | "encoding": "utf8" 24 | }, 25 | "error_file_handler": { 26 | "class": "logging.handlers.RotatingFileHandler", 27 | "level": "ERROR", 28 | "formatter": "simple", 29 | "filename": "errors.log", 30 | "maxBytes": 10485760, 31 | "backupCount": 20, 32 | "encoding": "utf8" 33 | } 34 | }, 35 | "loggers": { 36 | "my_module": { 37 | "level": "ERROR", 38 | "handlers": [ 39 | "console" 40 | ], 41 | "propagate": "no" 42 | } 43 | }, 44 | "root": { 45 | "level": "INFO", 46 | "handlers": [ 47 | "console", 48 | "info_file_handler", 49 | "error_file_handler" 50 | ] 51 | } 52 | } -------------------------------------------------------------------------------- /continuetraining.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | 4 | from training.trainer import run_training 5 | from training.training import load 6 | from training.trainingstatistics import load as load_statistics 7 | from training.trainingconfig import load as load_config 8 | 9 | from logger import get_logger 10 | 11 | 12 | def main(path: str): 13 | log = get_logger(name=__name__) 14 | 15 | model_name = os.path.splitext(os.path.basename(path))[0] 16 | model_base = os.path.join(os.path.dirname(path), model_name) 17 | config_path = model_base + '.config.json' 18 | config = load_config(config_path) 19 | statistics_path = model_base + '.statistics.json' 20 | statistics = load_statistics(statistics_path) 21 | weights_path = model_base + '.weights-%s-%s.h5' % (len(statistics.validation_loss), statistics.validation_loss[-1]) 22 | 23 | try: 24 | training = load(path, weights_path, config_path, statistics_path) 25 | run_training(training, model_base, statistics, config) 26 | except Exception as ex: 27 | log.error('%s' % ex) 28 | 29 | 30 | if __name__ == '__main__': 31 | parser = argparse.ArgumentParser(description='Continue an interrupted training') 32 | parser.add_argument('path', help='Path to a directory where the training data is stored') 33 | args = parser.parse_args() 34 | 35 | main(args.path) 36 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | from training.trainer import run_training 4 | from training.trainingconfig import load as load_training_config 5 | from training.training import create as create_training 6 | 7 | from logger import get_logger 8 | from training.trainingstatistics import TrainingStatistics 9 | 10 | 11 | def main(path: str, training_plan_path: str): 12 | log = get_logger(__name__) 13 | 14 | config = load_training_config(training_plan_path) 15 | 16 | try: 17 | training = create_training(training_plan_path) 18 | run_training(training, path, TrainingStatistics([], []), config) 19 | except FileExistsError as ex: 20 | log.error('%s' % ex) 21 | log.info('Please choose another name for the training. ' 22 | 'You can alternatively overwrite the existing data using the -c flag') 23 | log.info('Training canceled') 24 | except Exception as ex: 25 | log.error('%s' % ex) 26 | log.info('Training canceled') 27 | 28 | 29 | if __name__ == '__main__': 30 | logger = get_logger(name=__name__) 31 | 32 | parser = argparse.ArgumentParser(description='Train a model using the given training plan',) 33 | parser.add_argument('path', help='Path to new training file') 34 | parser.add_argument('plan', help='Path to the training configuration') 35 | args = parser.parse_args() 36 | 37 | main(args.path, args.plan) 38 | -------------------------------------------------------------------------------- /speech/alphabet.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | from typing import List 5 | 6 | 7 | class Alphabet: 8 | def __init__(self, characters: List[str]): 9 | self.characters = [" "] 10 | self.characters.extend(characters) 11 | 12 | def __len__(self): 13 | return len(self.characters) 14 | 15 | def __contains__(self, item): 16 | return item in self.characters 17 | 18 | def to_json(self): 19 | return self.characters 20 | 21 | 22 | def load(path: str) -> Alphabet: 23 | if not os.path.exists(path): 24 | raise FileNotFoundError('The path "%s" to the alphabet does not exist' % path) 25 | 26 | with open(path, 'r') as file: 27 | alphabet_json = json.load(file) 28 | 29 | return Alphabet(alphabet_json) 30 | 31 | 32 | def text_to_int_sequence(text: str, alphabet: Alphabet) -> List[int]: 33 | """ 34 | Covnerts a text into a sequence of ints 35 | :param text: text to convert 36 | :param alphabet: alphabet 37 | :return: Int sequence 38 | """ 39 | return [alphabet.characters.index(character) for character in text] 40 | 41 | 42 | def int_to_text_sequence(text: List[int], alphabet: Alphabet) -> List[str]: 43 | """ 44 | Covnerts a sequence of ints into a text 45 | :param text: ints to convert 46 | :param alphabet: alphabet 47 | :return: text 48 | """ 49 | return [alphabet.characters[character] for character in text] -------------------------------------------------------------------------------- /training/trainingplan.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | from typing import List 5 | 6 | 7 | class TrainingPlan: 8 | def __init__(self, 9 | epochs: int, 10 | batch_size: int, 11 | net: str, 12 | training_data_quantity: int, 13 | training_data: List[str]): 14 | self.epochs = epochs 15 | self.batch_size = batch_size 16 | self.net = net 17 | self.training_data_quantity = training_data_quantity 18 | self.training_data = training_data 19 | 20 | def to_json(self): 21 | return { 22 | "epochs": self.epochs, 23 | 'batchSize': self.batch_size, 24 | 'trainingDataQuantity': self.training_data_quantity, 25 | 'net': self.net, 26 | 'trainingData': self.training_data 27 | } 28 | 29 | 30 | def load(training_plan_path: str) -> TrainingPlan: 31 | if not os.path.exists(training_plan_path): 32 | raise FileNotFoundError('The training plan "%s" does not exist' % training_plan_path) 33 | with open(training_plan_path, 'r') as file: 34 | training_plan = json.load(file) 35 | 36 | return TrainingPlan(training_plan["epochs"], 37 | training_plan["batchSize"], 38 | training_plan["net"], 39 | training_plan["trainingDataQuantity"], 40 | training_plan["trainingData"]) 41 | -------------------------------------------------------------------------------- /training/trainingstatistics.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | from typing import List 5 | 6 | from logger import get_logger 7 | 8 | 9 | class TrainingStatistics: 10 | def __init__(self, loss: List[float], validation_loss: List[float]): 11 | self.loss = loss 12 | self.validation_loss = validation_loss 13 | 14 | def to_json(self): 15 | return { 16 | "loss": self.loss, 17 | "validationLoss": self.validation_loss, 18 | } 19 | 20 | @property 21 | def current_epoch(self): 22 | # after each epoch the training loss will be stored. 23 | # therefore, the amount of available losses is equal to the amount of passed epochs. 24 | return len(self.loss) 25 | 26 | 27 | def load(path: str) -> TrainingStatistics: 28 | """ 29 | Gets the history of a training containing the losses 30 | :param path: path to the history of the training 31 | :return: TrainingStatistics 32 | """ 33 | with open(path, 'r') as file: 34 | training_plan = json.load(file) 35 | 36 | return TrainingStatistics(training_plan["loss"], 37 | training_plan["validationLoss"]) 38 | 39 | 40 | def save(path: str, statistics: TrainingStatistics): 41 | log = get_logger(__name__) 42 | 43 | with open(path, 'w') as file: 44 | file.write(json.dumps(statistics.to_json(), indent=4)) 45 | log.info('Saved statistics to %s' % os.path.abspath(path)) 46 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | from training.errors.modelnotfound import ModelNotFoundError 4 | 5 | from prediction.prediction import predict 6 | 7 | from models import load 8 | 9 | from logger import get_logger 10 | 11 | 12 | def main(training_path: str, audio_path: str, weights_path: str): 13 | log = get_logger(name=__name__) 14 | 15 | save_file = load(training_path, weights_path) 16 | 17 | try: 18 | log.info('---------------------------------------------') 19 | log.info('Using net:') 20 | log.info("{}".format(training_path)) 21 | log.info('---------------------------------------------') 22 | log.info('Audio path:') 23 | log.info(audio_path) 24 | log.info('---------------------------------------------') 25 | log.info('Predicted transcription:') 26 | log.info(predict(save_file.model, save_file.alphabet, audio_path)) 27 | except FileNotFoundError as ex: 28 | log.error('%s' % ex) 29 | except ModelNotFoundError as ex: 30 | log.error('%s' % ex) 31 | except Exception as ex: 32 | log.error('%s' % ex) 33 | 34 | 35 | if __name__ == "__main__": 36 | logger = get_logger(name=__name__) 37 | parser = argparse.ArgumentParser(description="Predict an audio file transcription") 38 | parser.add_argument('path', help='Path to a directory where the training data is stored') 39 | parser.add_argument('weights', help='Path to a weights matrix') 40 | parser.add_argument('audio', help='Path to audio file which shall be transcribed') 41 | args = parser.parse_args() 42 | 43 | main(args.path, args.audio, args.weights) 44 | -------------------------------------------------------------------------------- /training/trainingconfig.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | from typing import List 5 | 6 | from logger import get_logger 7 | from utils.fs import safe_open 8 | 9 | 10 | class TrainingConfig: 11 | def __init__(self, 12 | epochs: int, 13 | batch_size: int, 14 | net: str, 15 | training_data_quantity: int, 16 | training_data: List[str], 17 | alphabet_path: str): 18 | self.epochs = epochs 19 | self.batch_size = batch_size 20 | self.net = net 21 | self.training_data_quantity = training_data_quantity 22 | self.training_data = training_data 23 | self.alphabet_path = alphabet_path 24 | 25 | def to_json(self): 26 | return { 27 | 'epochs': self.epochs, 28 | 'batchSize': self.batch_size, 29 | 'trainingDataQuantity': self.training_data_quantity, 30 | 'net': self.net, 31 | 'trainingData': self.training_data, 32 | 'alphabetPath': self.alphabet_path 33 | } 34 | 35 | 36 | def load(training_plan_path: str) -> TrainingConfig: 37 | if not os.path.exists(training_plan_path): 38 | raise FileNotFoundError('The training plan "%s" does not exist' % training_plan_path) 39 | with open(training_plan_path, 'r') as file: 40 | training_plan = json.load(file) 41 | 42 | return TrainingConfig(training_plan["epochs"], 43 | training_plan["batchSize"], 44 | training_plan["net"], 45 | training_plan["trainingDataQuantity"], 46 | training_plan["trainingData"], 47 | training_plan["alphabetPath"]) 48 | 49 | 50 | def save(path: str, config: TrainingConfig): 51 | log = get_logger(__name__) 52 | 53 | with safe_open(path, 'w') as file: 54 | file.write(json.dumps(config.to_json(), indent=4)) 55 | log.info('Saved config to %s' % os.path.abspath(path)) 56 | -------------------------------------------------------------------------------- /prediction/prediction.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import keras.backend as K 4 | from keras import Model 5 | 6 | from speech.processing import calculate_mfccs 7 | from speech.alphabet import int_to_text_sequence, Alphabet 8 | 9 | from training.training import Training 10 | 11 | 12 | def predict(model: Model, alphabet: Alphabet, audio_path: str) -> str: 13 | """ 14 | Creates a transcription of an audio file using a trained model 15 | :param model: The model to use 16 | :param alphabet: Alphabet containing the possible characters of the transcription 17 | :param audio_path: path to the audio file to transcribe 18 | :return: transcription 19 | """ 20 | if not os.path.exists(audio_path): 21 | raise FileNotFoundError('The audio file "%s" does not exist' % audio_path) 22 | 23 | mfccs = calculate_mfccs(audio_path) 24 | 25 | # the predict method expects a numpy array of inputs to be predicted. Since there is only one element to predict, 26 | # the input are the mfccs of one audio file. The input matrix is created by expanding the mfccs matrix of shape 27 | # (number of frames, 26) by one axis resulting in (1, number of frames, 26). 28 | prediction = model.predict(np.expand_dims(mfccs, axis=0)) 29 | 30 | # The ctc_decode method expects a list containing the length of each element to be predicted. 31 | # Since there is only element, the list contains the elements length (number of frames). 32 | input_length = [mfccs.shape[0]] 33 | 34 | # ctc_decode returns a Tuple. the first element is a list containing the decoded sequence. 35 | # Reference: https://keras.io/backend/ 36 | decoded_sequence_tensor = K.ctc_decode(prediction, input_length)[0][0] 37 | 38 | # The decoded sequence itself is a tensor and must therefore be evaluated using K.eval first 39 | decoded_sequence = K.eval(decoded_sequence_tensor) 40 | 41 | # The array decoded sequence must be flattened and converted from a numpy.ndarray into an ordinary array. 42 | predicted_ints = decoded_sequence.flatten().tolist() 43 | 44 | return ''.join(int_to_text_sequence(predicted_ints, alphabet)) 45 | -------------------------------------------------------------------------------- /training/trainer.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | from keras.optimizers import Adam 3 | 4 | from training.callbacks.trainingcallback import TrainingCallback 5 | from training.datagenerator import DataGenerator 6 | from training.trainingconfig import TrainingConfig 7 | from training.trainingdata import TrainingData 8 | from training.training import Training 9 | 10 | from models import add_ctc_loss 11 | 12 | from logger import get_logger 13 | from training.trainingstatistics import TrainingStatistics 14 | 15 | from utils.list import split 16 | 17 | 18 | def create_generators(training: Training, rate=0.8) -> (DataGenerator, DataGenerator): 19 | train_data, validation_data = split(training.training_data, rate) 20 | 21 | return (DataGenerator(train_data, training.alphabet, training.batch_size), 22 | DataGenerator(validation_data, training.alphabet, training.batch_size)) 23 | 24 | 25 | def calc_steps_per_epoch(data: List[TrainingData], batch_size): 26 | return len(data) // batch_size 27 | 28 | 29 | def run_training(training: Training, 30 | save_file_path: str, 31 | statistics: TrainingStatistics, 32 | config: TrainingConfig): 33 | log = get_logger(__name__) 34 | 35 | training_data_generator, validation_data_generator = create_generators(training) 36 | 37 | # Take a stochastic gradient descent optimizer 38 | optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8) 39 | 40 | # add ctc layer to the model 41 | model = add_ctc_loss(training.model) 42 | 43 | # the CTC algorithm is implemented in teh softmax layer 44 | # therefore, use a dummy loss function 45 | model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=optimizer) 46 | 47 | steps_per_epoch = calc_steps_per_epoch(training_data_generator.data, training.batch_size) 48 | validation_steps = calc_steps_per_epoch(validation_data_generator.data, training.batch_size) 49 | 50 | if steps_per_epoch == 0 or validation_steps == 0: 51 | raise ValueError("To little data provided. " 52 | "Increase the trainingDataQuantity property in the training plan") 53 | 54 | log.info("starting training ...") 55 | 56 | model.fit_generator(generator=training_data_generator.next_batch(), 57 | steps_per_epoch=steps_per_epoch, 58 | epochs=training.epochs, 59 | validation_data=validation_data_generator.next_batch(), 60 | validation_steps=validation_steps, 61 | verbose=1, 62 | callbacks=[TrainingCallback(save_file_path, statistics, training, config)], 63 | initial_epoch=training.passed_epochs) 64 | 65 | log.info('Training finished') 66 | -------------------------------------------------------------------------------- /training/trainingdata.py: -------------------------------------------------------------------------------- 1 | import errno 2 | import json 3 | import os 4 | 5 | from os.path import exists, abspath 6 | 7 | from typing import List 8 | 9 | from speech.alphabet import Alphabet 10 | 11 | from logging import getLogger 12 | 13 | 14 | class TrainingData: 15 | def __init__(self, path: str, text: str): 16 | self.path = path 17 | self.text = text 18 | 19 | def to_json(self): 20 | return { 21 | 'path': self.path, 22 | 'text': self.text 23 | } 24 | 25 | 26 | def validate(training_data: List[TrainingData], alphabet: Alphabet) -> List[TrainingData]: 27 | """ 28 | Checks whether the text is element of the alphabet 29 | and whether the path to the audio file exists 30 | :param training_data: data to validate 31 | :param alphabet: alphabet which will be tested against the text of the training data 32 | :return: List of valid training data 33 | """ 34 | log = getLogger(__name__) 35 | log.info('Validating training data ...') 36 | log.info('Items to validate: %s' % len(training_data)) 37 | 38 | valid_training_data = [] 39 | for data in training_data: 40 | if not is_text_valid(data.text, alphabet): 41 | continue 42 | if exists(data.path): 43 | valid_training_data.append(data) 44 | else: 45 | log.warning('The path to the training data "%s" does not exist' % data.path) 46 | 47 | log.info('Valid Items: %s' % len(valid_training_data)) 48 | log.info('Validated training data') 49 | 50 | return valid_training_data 51 | 52 | 53 | def is_text_valid(text: str, alphabet: Alphabet) -> bool: 54 | """ 55 | Checks whether each character of the text lies inside the alphabet 56 | :param text: text to validate 57 | :param alphabet: alphabet containing the valid characters 58 | :return: Returns whether the character sequence is valid 59 | """ 60 | for character in text: 61 | if character not in alphabet: 62 | return False 63 | 64 | return True 65 | 66 | 67 | def load(*args: str) -> List[TrainingData]: 68 | """ 69 | Loads a file containing training data 70 | :param argv: paths to training data files 71 | :return: List of training data 72 | """ 73 | log = getLogger(__name__) 74 | log.info('Validating training data ...') 75 | 76 | training_data = [] 77 | for path in [item[0] for item in args]: 78 | if not exists(path): 79 | log.error('The training data file at "%s" does not exist' % abspath(path)) 80 | raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), path) 81 | 82 | with open(path, 'r') as file: 83 | training_data = training_data + [TrainingData(data['path'], data['text']) for data in json.load(file)] 84 | 85 | return training_data 86 | 87 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # speech-to-text 2 | This framework provides python scripts to train neural networks for speech recognition. 3 | 4 | ## Requirements 5 | - Python 3.6+ 6 | 7 | ## Prerequisites 8 | ### Installing Dependencies 9 | Install the required python dependencies listed in the `requirements.txt`: 10 | ```shell 11 | pip install -r requirements.txt 12 | ``` 13 | 14 | ### Providing training data 15 | To run a training, training data is required. 16 | A Training accepts a file, which contains metadata about the training data. 17 | The file itself is a JSON file consisting of an array. 18 | Each element has the following properties: 19 | 20 | - `path`: Absolute path to the audio file 21 | - `text`: Transcription of the audio file 22 | 23 | A training data file could look like this: 24 | ```json 25 | [ 26 | { 27 | "path": "path/to/audio/file1.wav", 28 | "text": "hello world" 29 | }, 30 | { 31 | "path": "path/to/audio/file2.wav", 32 | "text": "goodbye world" 33 | } 34 | ] 35 | ``` 36 | You can find a downloader for the voxforge corpus at [https://github.com/KevNetG/speech-to-text-voxforge](https://github.com/KevNetG/speech-to-text-voxforge). 37 | This repo also includes a `generator.py` file, which creates a training data file containing the required metadata for trainings. 38 | 39 | Currently, only WAVE files are supported. 40 | 41 | ## Usage 42 | 43 | ### Configuring a training 44 | The idea is, that you don't write you training configuration into the command line, 45 | but instead into a file, which you can modify and reuse for other trainings. 46 | You can find a sample configuration under `examples/training.config.json` and adjust it to your needs. 47 | A configuration file has the following properties: 48 | 49 | - `epochs`: Number of epochs to train 50 | - `batchSize`: Batch Size 51 | - `trainingDataQuantity`: Amount of training data that is taken from the provided sources 52 | - `net`: Name of the model. Models are specified in the `models.py` file 53 | - `trainingData`: Absolute Path to a training data file. You can specify multiple sources to simply scale your amount of available training data 54 | - `alphabetPath`: Path to an alphabet file 55 | 56 | ```json 57 | { 58 | "epochs": 10, 59 | "batchSize": 20, 60 | "trainingDataQuantity": 50000, 61 | "net": "graves", 62 | "trainingData": [ 63 | "speech-to-text/training_data.json" 64 | ], 65 | "alphabetPath": "speech-to-text/examples/english.json" 66 | } 67 | ``` 68 | 69 | You can use the english alphabet available under `examples/english.json` or create one yourself for any other language. 70 | The alphabet file is a simple JSON file consisting of an array containing the characters from the alphabet: 71 | 72 | ### Running a training 73 | To run a training execute the `train.py` and provide two arguments: 74 | 75 | - `path`: Where to store the training. You don't have to specify a file extension 76 | - `plan`: The path to a training configuration 77 | 78 | like this: 79 | 80 | ```shell 81 | python train.py "trainings/graves" "training_data.json" 82 | ``` 83 | 84 | Trainings are saved after each epoch. 85 | 86 | ### Continuing an interrupted training 87 | If you had to stop a training prematurely, you can continue it from the last checkpoint. 88 | Simply execute the `continuetraining.py` and pass the path to the training save file. 89 | You don't have to specify the file extension: 90 | 91 | ``` 92 | python continuetraining.py "trainings/graves.json" 93 | ``` 94 | 95 | ### Making a prediction 96 | In order to create a transcription of an audio file, use the `predict.py` script. 97 | Pass the following arguments: 98 | 99 | - `path`: Path to a training save file 100 | - `weights`, Path to a weights matrix 101 | - `audio`: Path to the audio file which shall be transcribed 102 | 103 | For example: 104 | ```shell 105 | python predict.py "trainings/graves.json" "trainings/graves.weights-20-65.68075.h5" "media/audio.wav" 106 | ``` 107 | 108 | ### Displaying training statistics 109 | If you want to display the training loss and the validation loss of a training, execute the `statistics.py` script: 110 | ```shell 111 | python statistics.py "trainings/graves.statistics.json" 112 | ``` 113 | -------------------------------------------------------------------------------- /training/datagenerator.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | from speech.processing import calculate_mfccs 3 | from rx import Observable 4 | 5 | import numpy as np 6 | 7 | from speech.alphabet import Alphabet, text_to_int_sequence 8 | 9 | from training.trainingdata import TrainingData 10 | 11 | 12 | class Batch: 13 | def __init__(self, data: List[TrainingData], alphabet: Alphabet): 14 | self.data = data 15 | self.alphabet = alphabet 16 | self.max_length = 0 17 | self.max_string_length = 0 18 | self.features = [] 19 | 20 | # Calculate mfccs 21 | Observable.from_(self.data) \ 22 | .map(lambda item: item.path) \ 23 | .map(lambda path: calculate_mfccs(path)) \ 24 | .subscribe(lambda features: self.add_features(features)) 25 | 26 | # Determine longest mfcc 27 | Observable.from_(self.features) \ 28 | .map(lambda mfcc: mfcc.shape[0]) \ 29 | .max() \ 30 | .subscribe(lambda max_length: self.set_max_length(max_length)) 31 | 32 | # Determine longest text 33 | Observable.from_(self.data) \ 34 | .map(lambda item: item.text) \ 35 | .map(lambda text: len(text)) \ 36 | .max() \ 37 | .subscribe(lambda max_string_length: self.set_max_string(max_string_length)) 38 | 39 | def add_features(self, features): 40 | self.features.append(features) 41 | 42 | def set_max_length(self, length): 43 | self.max_length = length 44 | 45 | def set_max_string(self, length): 46 | self.max_string_length = length 47 | 48 | def get_data(self): 49 | return self.data 50 | 51 | def get(self): 52 | # Create Matrices which are going to hold the input and the output of the batch items 53 | input = np.zeros([len(self.data), self.max_length, 26]) 54 | labels = np.ones([len(self.data), self.max_string_length]) * len(self.alphabet) 55 | 56 | # Needed for CTC loss algorithm 57 | input_length = np.zeros([len(self.data), 1]) 58 | label_length = np.zeros([len(self.data), 1]) 59 | 60 | for i in range(0, len(self.data)): 61 | # Insert the features of each batch item into the created input matrix 62 | feat = self.features[i] 63 | input_length[i] = feat.shape[0] 64 | input[i, :feat.shape[0], :] = feat 65 | 66 | # the labels will be represented as numbers. They will be added into the created label matrix 67 | label = np.array(text_to_int_sequence(self.data[i].text, self.alphabet)) 68 | label_length[i] = len(label) 69 | labels[i, :len(label)] = label 70 | 71 | # The output will be the Lambda layer called 'ctc'. 72 | # A matrix will hold the predicted labels 73 | outputs = {'ctc': np.zeros([len(self.data)])} 74 | 75 | # The final input lists all parameters needed for the CTC algorithm 76 | inputs = { 77 | 'input': input, 78 | 'labels': labels, 79 | 'input_length': input_length, 80 | 'label_length': label_length, 81 | } 82 | 83 | return inputs, outputs 84 | 85 | 86 | class DataGenerator: 87 | def __init__(self, 88 | data: List[TrainingData], 89 | alphabet: Alphabet, 90 | batch_size=20): 91 | 92 | self.data = data 93 | self.alphabet = alphabet 94 | self.batch_size = batch_size 95 | self.current_batch_index = 0 96 | 97 | def get_batch(self): 98 | return Batch(self.data[self.current_batch_index:self.current_batch_index + self.batch_size], self.alphabet)\ 99 | .get() 100 | 101 | def next_batch(self) -> Batch: 102 | while True: 103 | ret = self.get_batch() 104 | self.current_batch_index += self.batch_size 105 | 106 | if self.current_batch_index >= len(self.data) - self.batch_size: 107 | self.current_batch_index = 0 108 | self.shuffle_data() 109 | 110 | yield ret 111 | 112 | def shuffle_data(self): 113 | # One could use the validation_split and shuffle option when training, but the validation 114 | # data won't be shuffled. See issue https://github.com/keras-team/keras/issues/597 or 115 | # http://forums.fast.ai/t/for-keras-fit-method-does-shuffle-true-shuffle-both-the-training-and-validation-samples-or-just-the-training-dataset/2992 116 | p = np.random.permutation(len(self.data)) 117 | self.data = [self.data[i] for i in p] 118 | -------------------------------------------------------------------------------- /training/training.py: -------------------------------------------------------------------------------- 1 | import errno 2 | import os 3 | 4 | from typing import List 5 | 6 | from keras.models import Model 7 | 8 | from speech.alphabet import Alphabet 9 | from speech.alphabet import load as load_alphabet 10 | 11 | from training.trainingdata import TrainingData 12 | from training.trainingconfig import load as load_config 13 | from training.trainingdata import load as load_training_data, validate as validate_training_data 14 | 15 | from models import load as load_model_save_file, get as get_model 16 | 17 | from logger import get_logger 18 | from training.trainingstatistics import TrainingStatistics, load as load_statistics 19 | 20 | 21 | class Training: 22 | def __init__(self, 23 | model: Model, 24 | alphabet: Alphabet, 25 | batch_size: int, 26 | epochs: int, 27 | training_statistics: TrainingStatistics, 28 | training_data: List[TrainingData]): 29 | self.model = model 30 | self.alphabet = alphabet 31 | self.batch_size = batch_size 32 | self.epochs = epochs 33 | self.training_statistics = training_statistics 34 | self.training_data = training_data 35 | 36 | @property 37 | def passed_epochs(self): 38 | return len(self.training_statistics.loss) 39 | 40 | 41 | def load(model_save_file_path: str, 42 | weights_path: str, 43 | training_configuration_path: str, 44 | training_statistics_path: str) -> Training: 45 | """ 46 | Loads a training from an existing directory 47 | :param path: Path to the directory where the training data is stored 48 | :return: A training 49 | """ 50 | log = get_logger(__name__) 51 | log.info('Loading training ...') 52 | 53 | # Check paths 54 | if not os.path.exists(model_save_file_path): 55 | raise FileNotFoundError('The model file "%s" does not exist' % model_save_file_path) 56 | 57 | if not os.path.exists(weights_path): 58 | raise FileNotFoundError('The weights file "%s" for this training is missing' % weights_path) 59 | 60 | if not os.path.exists(training_configuration_path): 61 | raise FileNotFoundError('The configuration file "%s" for this training is missing' % training_configuration_path) 62 | 63 | if not os.path.exists(training_statistics_path): 64 | raise FileNotFoundError('The configuration file "%s" for this training is missing' % training_statistics_path) 65 | 66 | config = load_config(training_configuration_path) 67 | 68 | # load all training data like specified in the config 69 | try: 70 | training_data = load_training_data(config.training_data) 71 | except FileNotFoundError as ex: 72 | log.error("Please check your configuration file at %s" % os.path.abspath(training_configuration_path)) 73 | raise ex 74 | 75 | # load model from the save file 76 | model_save = load_model_save_file(model_save_file_path, weights_path) 77 | 78 | # only use valid training data and set to limitation defined in config 79 | training_data = validate_training_data(training_data, model_save.alphabet) 80 | training_data = training_data[:config.training_data_quantity] 81 | 82 | batch_size = config.batch_size 83 | epochs = config.epochs 84 | statistics = load_statistics(training_statistics_path) 85 | 86 | log.info('Training loaded') 87 | 88 | return Training(model_save.model, model_save.alphabet, batch_size, epochs, statistics, training_data) 89 | 90 | 91 | def create(training_configuration_path: str) -> Training: 92 | """ 93 | Creates a training object using the given configuration 94 | :param training_configuration_path: Training plan 95 | :return: Training 96 | """ 97 | log = get_logger(__name__) 98 | log.info('Preparing training ...') 99 | 100 | #check paths 101 | if not os.path.exists(training_configuration_path): 102 | log.error('The configuration file at "%s" does not exist' % os.path.abspath(training_configuration_path)) 103 | raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), training_configuration_path) 104 | 105 | config = load_config(training_configuration_path) 106 | 107 | # create data for training object 108 | model = get_model(config.net) 109 | batch_size = config.batch_size 110 | epochs = config.epochs 111 | 112 | # load all training data like specified in the config 113 | try: 114 | training_data = load_training_data(config.training_data) 115 | except FileNotFoundError as ex: 116 | log.error("Please check your configuration file at %s" % os.path.abspath(training_configuration_path)) 117 | raise ex 118 | 119 | alphabet = load_alphabet(config.alphabet_path) 120 | 121 | # only use valid training data and set to limitation defined in config 122 | training_data = validate_training_data(training_data, alphabet) 123 | training_data = training_data[:config.training_data_quantity] 124 | 125 | log.info("Created training") 126 | 127 | return Training(model, alphabet, batch_size, epochs, TrainingStatistics([], []), training_data) 128 | 129 | -------------------------------------------------------------------------------- /models.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | from typing import List 4 | 5 | from keras import backend as K 6 | from keras.models import Model, model_from_json 7 | from keras.layers import (BatchNormalization, 8 | Dense, 9 | Input, 10 | TimeDistributed, 11 | Activation, 12 | Bidirectional, 13 | LSTM, 14 | Lambda, 15 | GaussianNoise, Conv1D, ZeroPadding1D) 16 | 17 | from logger import get_logger 18 | from speech.alphabet import Alphabet 19 | from training.errors.modelnotfound import ModelNotFoundError 20 | from utils.fs import safe_open 21 | 22 | 23 | class SaveFile: 24 | def __init__(self, alphabet: Alphabet, model: Model): 25 | self.alphabet = alphabet 26 | self.model = model 27 | 28 | def to_json(self): 29 | return { 30 | "alphabet": self.alphabet.to_json(), 31 | "model": self.model.to_json() 32 | } 33 | 34 | 35 | def ctc_batch_cost(args): 36 | """ 37 | Custom implementation of the Keras.backend.ctc_batch_cost function. 38 | It is needed, because the arguments are mixed up. 39 | 40 | Reference: https://keras.io/backend/ 41 | 42 | y_true: tensor (samples, max_string_length) containing the truth labels. 43 | y_pred: tensor (samples, time_steps, num_categories) containing the prediction, or output of the softmax. 44 | input_length: tensor (samples, 1) containing the sequence length for each batch item in y_pred. 45 | label_length: tensor (samples, 1) containing the sequence length for each batch item in y_true. 46 | 47 | :param args: y_pred, labels, input_length, label_length 48 | :return: Tensor with shape (samples,1) containing the CTC loss of each element. 49 | """ 50 | 51 | y_pred, y_true, input_length, label_length = args 52 | return K.ctc_batch_cost(y_true, y_pred, input_length, label_length) 53 | 54 | 55 | def add_ctc_loss(model: Model): 56 | labels = Input(name='labels', shape=(None,), dtype='float32') 57 | input_length = Input(name='input_length', shape=(1,), dtype='int64') 58 | label_length = Input(name='label_length', shape=(1,), dtype='int64') 59 | output_lengths = Lambda(model.output_length)(input_length) 60 | 61 | # The lambda layer calls the ctc_batch_cost, which calculates the CTC loss for each element of the batch 62 | # output_shape=(1,0): corresponds to the CTC loss of the element 63 | loss_out = Lambda(ctc_batch_cost, output_shape=(1,), name='ctc')( 64 | [model.output, labels, output_lengths, label_length]) 65 | 66 | # Return a new model containing the old one and the new inputs. 67 | # The output of the model will be the loss calculated by the CTC algorithm. 68 | return Model(inputs=[model.input, labels, input_length, label_length], 69 | outputs=loss_out) 70 | 71 | 72 | def graves(input_dim=26, rnn_size=512, output_dim=29, std=0.6) -> Model: 73 | """ 74 | Implementation of the graves model 75 | Reference: ftp://ftp.idsia.ch/pub/juergen/icml2006.pdf 76 | """ 77 | K.set_learning_phase(1) 78 | input_layer = Input(name='input', shape=(None, input_dim)) 79 | 80 | x = BatchNormalization(axis=-1)(input_layer) 81 | x = GaussianNoise(std)(x) 82 | x = Bidirectional(LSTM(rnn_size, return_sequences=True))(x) 83 | x = TimeDistributed(Dense(output_dim))(x) 84 | prediction_layer = Activation('softmax', name='softmax')(x) 85 | 86 | model = Model(inputs=input_layer, outputs=prediction_layer) 87 | model.output_length = lambda x: x 88 | 89 | return model 90 | 91 | 92 | def cnn_lstm(input_dim=26, filters=1024, rnn_size=512, output_dim=29, convolutional_layers=3, lstm_layers=5): 93 | """ 94 | A model using a combination of CNNs and LSTMs inspired by DeepSpeech 2 95 | Reference: https://arxiv.org/abs/1512.02595 96 | """ 97 | K.set_learning_phase(1) 98 | input_layer = Input(name='input', shape=(None, input_dim)) 99 | 100 | x = BatchNormalization(axis=-1)(input_layer) 101 | x = ZeroPadding1D(padding=(0, 512))(x) 102 | 103 | # Add convolutional layers 104 | for l in range(convolutional_layers): 105 | x = Conv1D(filters=filters, 106 | name='convolution_{}'.format(l + 1), 107 | kernel_size=11, 108 | padding='valid', 109 | activation='relu', 110 | strides=2)(x) 111 | 112 | x = BatchNormalization(axis=-1)(x) 113 | 114 | # add lstm layers 115 | for l in range(lstm_layers): 116 | x = Bidirectional(LSTM(rnn_size, return_sequences=True))(x) 117 | x = TimeDistributed(Dense(output_dim))(x) 118 | 119 | x = BatchNormalization(axis=-1)(x) 120 | 121 | # add Dense layer 122 | x = TimeDistributed(Dense(filters, activation='relu'))(x) 123 | prediction_layer = TimeDistributed(Dense(output_dim, name="y_pred", activation="softmax"))(x) 124 | 125 | model = Model(inputs=input_layer, outputs=prediction_layer) 126 | model.output_length = lambda x: x 127 | 128 | return model 129 | 130 | 131 | def get(name: str) -> Model: 132 | if name == 'graves': 133 | return graves() 134 | if name == 'ds2': 135 | return cnn_lstm() 136 | 137 | raise ModelNotFoundError(name) 138 | 139 | 140 | def load(save_file_path: str, weights_path: str) -> SaveFile: 141 | with open(save_file_path, 'r') as file: 142 | save_file = json.load(file) 143 | 144 | model = model_from_json(save_file['model']) 145 | model.load_weights(weights_path) 146 | model.output_length = lambda x: x 147 | alphabet = Alphabet(save_file['alphabet']) 148 | 149 | return SaveFile(alphabet, model) 150 | 151 | 152 | def save(save_file: SaveFile, model_path: str, weights_path: str): 153 | log = get_logger(__name__) 154 | 155 | # store date to save file 156 | with safe_open(model_path, 'w') as file: 157 | file.write(json.dumps(save_file.to_json(), indent=4)) 158 | log.info('Saved model to %s' % os.path.abspath(model_path)) 159 | 160 | # save weights 161 | save_file.model.save_weights(weights_path) 162 | log.info('Saved weights to %s' % os.path.abspath(weights_path)) 163 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------