How to make money by creating artificial intelligence speech recognition software. Practical examples

The episode of Tech Talk discusses the basics of speech recognition software, its challenges, and the steps involved in creating it using Python and AI libraries. Speech recognition software can recognize human speech and convert it into text using speech signal processing and language processing. Python is a powerful language with libraries such as PyAudio and SpeechRecognition for working with AI and machine learning. The podcast explains how to set up a speech recognition engine using the Recognizer class in the SpeechRecognition library. The challenges involved include dealing with different accents and dialects. The podcast also shows how to use Python and the Keras library to implement a recurrent neural network with long short-term memory units to train a speech recognition model. The podcast provides examples of code to define the architecture of the model, train the model, and transcribe new audio data. ############ EXAMPLE 1 python import speech_recognition as sr # create an instance of the Recognizer class r = sr.Recognizer() # use the default microphone as the audio source with sr.Microphone() as source: print("Say something!") audio = r.listen(source) # recognize speech using Google Speech Recognition try: print("Google Speech Recognition thinks you said: " + r.recognize_google(audio)) except sr.UnknownValueError: print("Google Speech Recognition could not understand audio") except sr.RequestError as e: print("Could not request results from Google Speech Recognition service; {0}".format(e)) ############ EXAMPLE 2 python from keras.models import Sequential from keras.layers import LSTM, Dense model = Sequential() model.add(LSTM(128, return_sequences=True, input_shape=(None, num_mfcc))) model.add(LSTM(128)) model.add(Dense(num_classes, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) ############ EXAMPLE 3 scss model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=20, batch_size=64) ############ EXAMPLE 4 scss preprocessed_data = preprocess_audio(new_data) predicted_probs = model.predict(preprocessed_data) predicted_word = vocabulary[np.argmax(predicted_probs)] ############ EXAMPLE 5 Python 3.x NumPy SciPy PyAudio SpeechRecognition TensorFlow Keras ############ EXAMPLE 6 pip install numpy scipy pyaudio SpeechRecognition tensorflow keras ############ EXAMPLE 7 python import pyaudio # Set up audio stream p = pyaudio.PyAudio() stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=1024) # Capture audio input while True: data = stream.read(1024) # Process audio data here ############ EXAMPLE 8 python import speech_recognition as sr # Set up recognizer r = sr.Recognizer() # Transcribe speech with sr.Microphone() as source: audio = r.listen(source) text = r.recognize_google(audio) print(text) ############ EXAMPLE 9 python import tensorflow as tf from tensorflow.keras.layers import Input, Dense, Dropout, LSTM, TimeDistributed from tensorflow.keras.models import Model # Define model architecture inputs = Input(shape=(None, 13)) x = LSTM(128, return_sequences=True)(inputs) x = Dropout(0.2)(x) x = LSTM(128, return_sequences=True)(x) x = Dropout(0.2)(x) x = TimeDistributed(Dense(29, activation='softmax'))(x) model = Model(inputs=inputs, outputs=x) # Compile model model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) ############ EXAMPLE 10 python # Load data X_train, y_train = load_data() # Train model model.fit(X_train, y_train train-clean-100: Contains the cleanest 100 hours of the training set dev-clean: Contains the development set test-clean: Contains the test set Once we have extracted the dataset, we can use the following code to process the audio files and their transcriptions: python import os import shutil import librosa import pandas as pd def extract_features(file_name): X, sample_rate = librosa.load(file_name) stft = np.abs(librosa.stft(X)) mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0) chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0) mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0) contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0) tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X),sr=sample_rate).T,axis=0) return mfccs,chroma,mel,contrast,tonnetz def preprocess_data(dataset_dir): audio_files_dir = os.path.join(dataset_dir, "audio_files") transcripts_dir = os.path.join(dataset_dir, "transcripts") output_dir = os.path.join(dataset_dir, "processed_data") if os.path.exists(output_dir): shutil.rmtree(output_dir) os.makedirs(output_dir) transcripts_df = pd.read_csv(os.path.join(transcripts_dir, "transcripts.csv"), header=None, names=["file_name", "transcription"], delimiter=" ") for index, row in transcripts_df.iterrows(): file_name = row["file_name"] transcription = row["transcription"] audio_file_path = os.path.join(audio_files_dir, file_name + ".flac") mfccs, chroma, mel, contrast, tonnetz = extract_features(audio_file_path) output_file_path = os.path.join(output_dir, file_name + ".npy") np.save(output_file_path, [mfccs, chroma, mel, contrast, tonnetz, transcription]) ############ EXAMPLE 12 python from kaldi import kaldi_io from kaldi.feat.mfcc import Mfcc, MfccOptions from kaldi.feat.functions import compute_cmvn_stats, apply_cmvn from kaldi.matrix import Vector, SubVector, Matrix from kaldi.hmm import DecodableInterface, GaussDiag, TransitionModel, AmDiagGmm, GmmFlags from kaldi.decoder import Decoder, LatticeFasterDecoderOptions from kaldi.util.table import SequentialMatrixReader, SequentialIntVectorReader, RandomAccessInt32VectorReader from kaldi.util.io import xopen # Set up feature extraction options mfcc_opts = MfccOptions() mfcc_opts.frame_opts.samp_freq = 16000 mfcc_opts.use_energy = False mfcc_opts.num_ceps = 13 # Load training data and transcriptions feats_reader = SequentialMatrixReader('train/feats.scp') labels_reader = SequentialIntVectorReader('train/text') # Extract

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