DeepAsr

DeepAsr is an open-source & Keras (Tensorflow) implementation of end-to-end Automatic Speech Recognition (ASR) engine and it supports multiple Speech Recognition architectures.

Supported Asr Architectures:

• Baidu's Deep Speech 2
• DeepAsrNetwork1

Using DeepAsr you can:

• perform speech-to-text using pre-trained models
• tune pre-trained models to your needs
• create new models on your own

DeepAsr key features:

• Multi GPU support: You can do much more like distribute the training using the Strategy, or experiment with mixed precision policy.
• CuDNN support: Model using CuDNNLSTM implementation by NVIDIA Developers. CPU devices is also supported.
• DataGenerator: The feature extraction during model training for large the data.

Installation

You can use pip:

pip install deepasr

Getting started

The speech recognition is a tough task. You don't need to know all details to use one of the pretrained models. However it's worth to understand conceptional crucial components:

• Input: Audio files (WAV or FLAC) with mono 16-bit 16 kHz (up to 5 seconds)
• FeaturesExtractor: Convert audio files using MFCC Features or Spectrogram
• Model: CTC model defined in Keras (references: [1], [2])
• Decoder: Greedy or BeamSearch algorithms with the language model support decode a sequence of probabilities using Alphabet
• DataGenerator: Stream data to the model via generator
• Callbacks: Set of functions monitoring the training

import numpy as np
import pandas as pd
import tensorflow as tf
import deepasr as asr

# get CTCPipeline
def get_config(feature_type: str = 'spectrogram', multi_gpu: bool = False):
    # audio feature extractor
    features_extractor = asr.features.preprocess(feature_type=feature_type, features_num=161,
                                                 samplerate=16000,
                                                 winlen=0.02,
                                                 winstep=0.025,
                                                 winfunc=np.hanning)

    # input label encoder
    alphabet_en = asr.vocab.Alphabet(lang='en')
    # training model
    model = asr.model.get_deepspeech2(
        input_dim=161,
        output_dim=29,
        is_mixed_precision=True
    )
    # model optimizer
    optimizer = tf.keras.optimizers.Adam(
        lr=1e-4,
        beta_1=0.9,
        beta_2=0.999,
        epsilon=1e-8
    )
    # output label deocder
    decoder = asr.decoder.GreedyDecoder()
    # decoder = asr.decoder.BeamSearchDecoder(beam_width=100, top_paths=1)
    # CTCPipeline
    pipeline = asr.pipeline.ctc_pipeline.CTCPipeline(
        alphabet=alphabet_en, features_extractor=features_extractor, model=model, optimizer=optimizer, decoder=decoder,
        sample_rate=16000, mono=True, multi_gpu=multi_gpu
    )
    return pipeline


train_data = pd.read_csv('train_data.csv')

pipeline = get_config(feature_type = 'fbank', multi_gpu=False)

# train asr model
history = pipeline.fit(train_dataset=train_data, batch_size=128, epochs=500)
# history = pipeline.fit_generator(train_dataset = train_data, batch_size=32, epochs=500)

pipeline.save('./checkpoint')

Loaded pre-trained model has all components. The prediction can be invoked just by calling pipline.predict().

import pandas as pd
import deepasr as asr
import numpy as np
test_data = pd.read_csv('test_data.csv')

# get testing audio and transcript from dataset
index = np.random.randint(test_data.shape[0])
data = test_data.iloc[index]
test_file = data[0]
test_transcript = data[1]
# Test Audio file
print("Audio File:",test_file)
# Test Transcript
print("Audio Transcript:", test_transcript)
print("Transcript length:",len(test_transcript))

pipeline = asr.pipeline.load('./checkpoint')
print("Prediction", pipeline.predict(test_file))

References

The fundamental repositories:

• Baidu - DeepSpeech2 - A PaddlePaddle implementation of DeepSpeech2 architecture for ASR
• NVIDIA - Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP
• TensorFlow - The implementation of DeepSpeech2 model
• Mozilla - DeepSpeech - A TensorFlow implementation of Baidu's DeepSpeech architecture
• Espnet - End-to-End Speech Processing Toolkit
• Automatic Speech Recognition - Distill the Automatic Speech Recognition research
• Python Speech Features - Speech features for ASR including MFCCs and filterbank energies