def read_wav_file(wav_file):
# Try to read file with wavio
try:
return wavio.read(wav_file)
# May have failed due to wav being floating-point encoded
except WavError as e:
pass
except Exception as e:
print("\tERR: Couldn't read data: {}".format(e))
return None
# Use scipy.io.wavfile as fallback
try:
rate, data = wavfile.read(wav_file)
return wavio.Wav(data, rate, dtype_to_sampwidth(data.dtype))
except Exception as e:
print("\tERR: Couldn't read data: {}".format(e))
return None
def load_audio(file: Any) -> np.ndarray:
"""
Args:
file: Audio filename or file object
Returns:
samples: Sample rate and audio samples from 0..1
"""
import wavio
try:
wav = wavio.read(file)
except EOFError:
wav = wavio.Wav(np.array([[]], dtype=np.int16), 16000, 2)
if wav.data.dtype != np.int16:
raise InvalidAudio('Unsupported data type: ' + str(wav.data.dtype))
if wav.rate != pr.sample_rate:
raise InvalidAudio('Unsupported sample rate: ' + str(wav.rate))
data = np.squeeze(wav.data)
return data.astype(np.float32) / float(np.iinfo(data.dtype).max)
import os # This lets us deal with filesystems (to read in wav files)
from scipy.signal import spectrogram
# In[5]:
# Grab all of the files in the "Wav" directory
allFiles = os.listdir("Wav/")
# Now we only take the files which have a the extension ".wav"
wavFiles = ["Wav/" + str(i) for i in allFiles if i[-3:len(i)] == "wav"]
wavFiles.sort()
# Now our array of data, which is blank at first (empty Wav objects)
readData = [wavio.Wav(0, 0, 0) for i in range(len(wavFiles))]
# Read in the data
for i in range(len(wavFiles)):
readData[i] = wavio.read(wavFiles[i])
# Create a dictionary
# This means that we access each individual set of data using the file name instead
# of an index like a regular array
wavData = dict(zip(wavFiles, readData))
# And we also want to read in the background noise
background = wavio.read("background.wav")
avgBackground = np.average(background.data.flatten())
# In[10]: