def audiosegment_google_speech(audio, filename, sample_rate, lang='ms'):
if os.path.exists('output-wav/' + filename):
return False
sf.write(filename, audio.array, sample_rate)
try:
with sr.AudioFile(filename) as source:
a = r.record(source)
text = r.recognize_google(a, language=lang)
except:
text = ''
if len(text):
text_filename = f'output-text/{filename}.txt'
with open(text_filename, 'w') as fopen:
fopen.write(text)
a = malaya_speech.resample(
malaya_speech.astype.int_to_float(audio.array), sample_rate, 16000)
sf.write('output-wav/' + filename, a, 16000)
os.remove(filename)
return True
def split(file, max_duration=10.0):
print(file)
audio = AudioSegment.from_mp3(file).set_channels(1)
y = np.array(audio.get_array_of_samples())
y = malaya_speech.astype.int_to_float(y)
y = p_noise(y)['concatenate']
y_int = malaya_speech.astype.float_to_int(y)
y_ = malaya_speech.resample(y_int, audio.frame_rate, 16000).astype(int)
frames = generator.frames(y, 30, audio.frame_rate)
frames_ = generator.frames(y_, 30, 16000, append_ending_trail=False)
frames_webrtc = [(frames[no], vad(frame))
for no, frame in enumerate(frames_)]
splitted = split_vad_duration(
frames_webrtc,
max_duration=max_duration,
negative_threshold=0.1,
)
results = [s.array for s in splitted]
return results, audio, audio.frame_rate
def parallel(f):
y = read_wav(f)[0]
y = random_sampling(y, length = 1000)
y_ = malaya_speech.resample(y, sr, sr // reduction_factor)
return y_, y
def downsample(y, sr, down_sr):
y_ = malaya_speech.resample(y, sr, down_sr)
return malaya_speech.resample(y_, down_sr, sr)
frame_duration_ms=30).batching(20).foreach_map(
model_v2.predict).flatten())
from glob import glob
mp3s = glob('*.mp3')
mp3s
for file in mp3s:
print(file)
try:
audio = AudioSegment.from_mp3(file)
sample_rate = audio.frame_rate
samples = np.array(audio.get_array_of_samples())
samples = malaya_speech.astype.int_to_float(samples)
samples_16k = malaya_speech.resample(samples, sample_rate, 16000)
frames_16k = list(
malaya_speech.utils.generator.frames(samples_16k, 30, 16000))
frames = list(
malaya_speech.utils.generator.frames(samples, 30, sample_rate))
result = p.emit(samples_16k)
frames_deep_v2_batch = [(frame, result['flatten'][no])
for no, frame in enumerate(frames)]
results = malaya_speech.split.split_vad(frames_deep_v2_batch,
n=5,
negative_threshold=0.1)
for no in tqdm(range(len(results))):
result = results[no]
sf.write('test.wav', result.array, sample_rate)
