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.
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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))
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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’])
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EXAMPLE 3
scss
model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=20, batch_size=64)
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EXAMPLE 4
scss
preprocessed_data = preprocess_audio(new_data)
predicted_probs = model.predict(preprocessed_data)
predicted_word = vocabulary[np.argmax(predicted_probs)]
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EXAMPLE 5
Python 3.x
NumPy
SciPy
PyAudio
SpeechRecognition
TensorFlow
Keras
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EXAMPLE 6
pip install numpy scipy pyaudio SpeechRecognition tensorflow keras
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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
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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)
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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’])
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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])
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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