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extract_words.py
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extract_words.py
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from deepspeech import Model
import soundfile as sf
import numpy as np
import os
import wave
def read_wav_file(filename):
'''
Reads frame rate, number of frames, and buffer from WAV file.
Must be already resampled (16kHz, mono)
'''
with wave.open(filename, 'rb') as w:
rate = w.getframerate()
frames = w.getnframes()
buffer = w.readframes(frames)
audio = np.frombuffer(buffer, dtype=np.int16)
return audio, buffer, rate
def get_model():
'''
Load DeepSpeech speech-to-text model.
'''
model_file_path = "models/deepspeech-0.9.3-models.pbmm"
lm_file_path = "models/deepspeech-0.9.3-models.scorer"
beam_width = 100
lm_alpha = 0.93
lm_beta = 1.18
model = Model(model_file_path)
model.enableExternalScorer(lm_file_path)
model.setScorerAlphaBeta(lm_alpha, lm_beta)
model.setBeamWidth(beam_width)
return model
def transcribe_batch(audio):
''' Get letters and timestamps '''
model = get_model()
return model.sttWithMetadata(audio).transcripts[0].tokens
def extract_keywords(metadata):
''' Combine letters and timestamps to form words '''
word = ''
transcript = []
start = metadata[0].start_time
for i, token in enumerate(metadata):
letter = token.text
if letter == ' ' or i == len(metadata):
last_letter = metadata[i-1].start_time
transcript.append((word, start, last_letter))
start = token.start_time
word = ''
else:
word += letter
return transcript
def save_keywords(transcript, keyword, audio):
''' Save utterances in individual .wav files '''
# create directory to store keywords if it doesn't exist
if not os.path.exists(keyword):
os.makedirs(keyword)
sample_rate = 16000
num_total = len(os.listdir(keyword))
saved = 0
for entry in transcript:
word = entry[0] # keyword
# save only desired keyword
if word == keyword:
# get start and end times
start = int(entry[1] * sample_rate)
end = int(entry[2] * sample_rate)
# save wav file
save_file = f"{word}_{num_total+saved}.wav"
out_file_path = os.path.join(keyword, save_file)
sf.write(out_file_path, audio[start:end], sample_rate)
saved += 1
return saved
def inspect_keywords(transcript):
''' Returns unique words and their frequencies. '''
words = []
for entry in transcript:
word = entry[0] # keyword
words.append(word)
u_words, counts = np.unique(words, return_counts=True)
word_counts = list(zip(u_words, counts))
sorted_counts = sorted(word_counts, key=lambda x: x[1])
return sorted_counts
def extract(filename, keyword):
'''
Extracts keywords from audio file,
filename: Path to resampled audio file
keyword: Desired keyword to collect
'''
print(f"Extracting '{keyword}' utterances from {filename}.")
# time series, number of samples, sampling rate
audio, buffer, rate = read_wav_file(filename)
# tokens
transcribed_metadata = transcribe_batch(audio=audio)
# list of words with start, end times
transcript = extract_keywords(transcribed_metadata)
num_saved = save_keywords(transcript=transcript, keyword=keyword,
audio=audio)
print(f"Utterances saved: {num_saved}")
return num_saved