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https://github.com/hykilpikonna/SpeechGenderAnalysis.git
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[+] Runtime testing
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Azalea (on HyDEV-Daisy)
parent
3a83aa9a11
commit
33fada11c3
101
experiment/test_runtime.py
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101
experiment/test_runtime.py
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import librosa as librosa
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import numpy as np
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import parselmouth
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import torchaudio
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from inaSpeechSegmenter import Segmenter
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from inaSpeechSegmenter.features import to_wav, _wav2feats
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from inaSpeechSegmenter.sidekit_mfcc import read_wav
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from server.utils import Timer
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np.seterr(invalid='ignore')
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if __name__ == '__main__':
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f = '/workspace/EECS 6414/voice_cnn/VT 150hz baseline example.mp3'
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timer = Timer()
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seg = Segmenter()
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seg('/workspace/EECS 6414/voice_cnn/test.wav')
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timer.log('ML engine loaded (One time expense, not counted in running time).')
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print()
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fp = f
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fp = str(to_wav(f).absolute())
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timer.log('FFMPEG Convert file to WAV 16000Hz')
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fp = str(to_wav(f, sr=None).absolute())
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timer.log('FFMPEG Convert file to WAV (original sr kept)')
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print()
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# Read file
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parselmouth.Sound(fp)
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timer.log('Parselmouth: Read file.')
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sound = parselmouth.Sound(fp)
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timer.log('Parselmouth: Read file.')
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# librosa.load(fp)
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# timer.log('Librosa: Read file')
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# librosa.load(fp)
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# timer.log('Librosa: Read file')
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read_wav(fp)
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timer.log('Read file with read_wav')
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y, sr, _ = read_wav(fp)
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timer.log(f'Read file with read_wav (decoded sr = {sr})')
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torchaudio.load(fp)
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timer.log('Read file with torchaudio')
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torchaudio.load(fp)
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timer.log('Read file with torchaudio')
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print()
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# Calculate features
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pitch = sound.to_pitch(0.01)
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timer.log('Parselmouth: Pitch calculated (0.01)')
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sound.to_pitch(0.01)
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timer.log('Parselmouth: Pitch calculated again (0.01)')
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sound.to_pitch(0.032)
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timer.log('Parselmouth: Pitch calculated again (0.032)')
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print()
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sound.to_formant_burg(0.01)
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timer.log('Parselmouth: Formant calculated (0.01)')
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sound.to_formant_burg(0.01)
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timer.log('Parselmouth: Formant calculated again (0.01)')
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sound.to_formant_burg(0.032)
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timer.log('Parselmouth: Formant calculated again (0.032)')
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print()
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sound.to_spectrogram(window_length=0.128, time_step=0.032)
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timer.log('Parselmouth: Spectrogram calculated (n_fft=0.128, step=0.032)')
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print()
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librosa.core.piptrack(y=y, sr=sr)
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timer.log('Librosa: piptrack')
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spec = np.abs(librosa.stft(y, n_fft=1024, hop_length=512))
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timer.log('Librosa: STFT')
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mel_spect = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=2048, hop_length=512, htk=True)
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timer.log('Librosa: Mel spectrogram')
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print()
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_wav2feats(fp)
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timer.log('ML: Calculate mspect feats')
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mspect, loge, diff_len = _wav2feats(fp)
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timer.log('ML: Calculate mspect feats')
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seg.segment_feats(mspect, loge, diff_len, 0)
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timer.log('ML: Segment feats')
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seg.segment_feats(mspect, loge, diff_len, 0)
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timer.log('ML: Segment feats')
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# Calculate ML
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# seg(f)
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# timer.log('ML Segmented')
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# seg(f)
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# timer.log('ML Segmented again')
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229
experiment/test_runtime_2.py
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229
experiment/test_runtime_2.py
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import base64
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import zlib
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from multiprocessing import cpu_count
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from subprocess import Popen, PIPE
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import librosa
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import numpy as np
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import pandas as pd
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import parselmouth
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import tensorflow
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import torch
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import torchaudio
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from inaSpeechSegmenter.features import to_wav
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from inaSpeechSegmenter.sidekit_mfcc import read_wav, mfcc
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import tensorflow as tf
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import tensorflow_io as tfio
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from tqdm.contrib.concurrent import process_map
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from server.utils import Timer
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def test_readfile(file: str, iterations: int):
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results = []
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timer = Timer()
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for _ in range(iterations):
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result = []
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results.append(result)
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parselmouth.Sound(file)
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result.append(timer.elapsed())
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librosa.load(file)
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result.append(timer.elapsed())
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read_wav(file)
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result.append(timer.elapsed())
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torchaudio.load(file)
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result.append(timer.elapsed())
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return pd.DataFrame(results, columns=['Parselmouth', 'librosa', 'read_wav', 'torchaudio'])
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def test_resampling(file: str, iterations: int, resample: bool):
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results = []
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timer = Timer()
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sr = 16000 if resample else None
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for _ in range(iterations):
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result = []
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results.append(result)
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# FFMPEG
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to_wav(file, sr=sr)
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result.append(timer.elapsed())
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# SOX
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args = ['sox', file, '-c', '1', '-e', 'floating-point']
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if sr:
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args += ['-r', str(sr)]
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args += ['output-sox.wav']
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p = Popen(args, stdout=PIPE, stderr=PIPE)
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output, error = p.communicate()
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assert p.returncode == 0, error
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result.append(timer.elapsed())
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# MPlayer
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args = ['mplayer', '-ao', 'pcm:fast:waveheader:file=output-mplayer.wav', '-vo', 'null', '-vc', 'null']
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if sr:
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args += ['-af', f'resample={sr},pan=1:0.5:0.5']
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else:
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args += ['-af', 'pan=1:0.5:0.5']
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args += [file]
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p = Popen(args, stdout=PIPE, stderr=PIPE)
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output, error = p.communicate()
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assert p.returncode == 0, error
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result.append(timer.elapsed())
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return pd.DataFrame(results, columns=['ffmpeg', 'sox', 'mplayer'])
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def test_spectrogram(y: np.ndarray, sr: int, iterations: int, n_fft=2048, hop_length=512):
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results = []
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timer = Timer()
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nfft_s = n_fft / sr
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step_s = hop_length / sr
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for _ in range(iterations):
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result = []
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results.append(result)
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sound = parselmouth.Sound(y, float(sr))
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sound.to_spectrogram(window_length=nfft_s, time_step=step_s)
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result.append(timer.elapsed())
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librosa.feature.melspectrogram(y=y, sr=sr, n_fft=n_fft, hop_length=hop_length, htk=True)
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result.append(timer.elapsed())
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mfcc(y.astype(np.float32), get_mspec=True, nwin=nfft_s, shift=step_s, fs=sr)
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result.append(timer.elapsed())
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t = torchaudio.transforms.MelSpectrogram(sample_rate=sr, n_fft=n_fft, hop_length=hop_length)
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tensor = torch.from_numpy(y)
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t(tensor)
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result.append(timer.elapsed())
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t = tfio.audio.spectrogram(y, n_fft, n_fft, hop_length)
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mel_spectrogram = tfio.audio.melscale(t, rate=sr, mels=128, fmin=0, fmax=8000)
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result.append(timer.elapsed())
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return pd.DataFrame(results, columns=['Parselmouth', 'librosa', 'sidekit', 'torchaudio', 'tensorflow-io'])
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def test_pitch(y: np.ndarray, sr: int, iterations: int, n_fft=2048, hop_length=512):
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results = []
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timer = Timer()
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nfft_s = n_fft / sr
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step_s = hop_length / sr
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for _ in range(iterations):
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result = []
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results.append(result)
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sound = parselmouth.Sound(y, float(sr))
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sound.to_pitch(time_step=step_s)
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result.append(timer.elapsed())
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librosa.yin(y=y, sr=sr, frame_length=n_fft, hop_length=hop_length, fmin=75, fmax=600)
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result.append(timer.elapsed())
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librosa.pyin(y=y, sr=sr, frame_length=n_fft, hop_length=hop_length, fmin=75, fmax=600)
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result.append(timer.elapsed())
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# TODO: essentia (yin, pyin), in-formant (yin, mpm, rapt, irapt)
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return pd.DataFrame(results, columns=['Parselmouth (Boersma 1993)', 'librosa.yin (Kawahara 2002)',
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'librosa.pyin (Mauch 2014)'])
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def test_formant(y: np.ndarray, sr: int, iterations: int, n_fft=2048, hop_length=512):
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results = []
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timer = Timer()
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nfft_s = n_fft / sr
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step_s = hop_length / sr
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for _ in range(iterations):
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result = []
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results.append(result)
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sound = parselmouth.Sound(y, float(sr))
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sound.to_formant_burg(time_step=step_s)
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result.append(timer.elapsed())
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# TODO: in-formant (deepformants, filteredlp, simplelp, karma)
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return pd.DataFrame(results, columns=['Parselmouth (Marple 1980)'])
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def _formant(args: tuple[np.ndarray, float]):
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y, sr = args
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sound = parselmouth.Sound(y, sr)
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step = 512 / sr
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formant = sound.to_formant_burg(time_step=512 / sr)
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result = np.ndarray([len(formant), 3], 'float32')
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for i in range(len(formant)):
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for f in range(1, 4):
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result[i][f - 1] = formant.get_value_at_time(f, i * step)
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return result
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if __name__ == '__main__':
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f = '/workspace/EECS 6414/voice_cnn/VT 150hz baseline example.mp3'
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fp = str(to_wav(f, sr=16000).absolute())
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# print(read_wav(f))
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# Test readfile
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# df = test_readfile(fp, 10)
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# print(df)
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# print(df.mean())
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# Test resampling
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# df = test_resampling(f, 10, True)
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# print(df)
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# print(df.mean())
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y, sr, _ = read_wav(fp)
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#
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# # Tensorflow warm-up
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# t = tfio.audio.spectrogram(y, 1, 1, 2048)
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# tfio.audio.melscale(t, rate=sr, mels=128, fmin=0, fmax=8000)
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# print('Warmup done')
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#
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# # Test mel spect
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# df = test_spectrogram(y, sr, 10)
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# print(df)
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# print(df.mean())
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# Test pitch
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# df = test_pitch(y, sr, 10)
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# print(df)
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# print(df.mean())
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# Test formant
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# df = test_formant(y, sr, 10)
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# print(df)
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# print(df.mean())
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# timer = Timer()
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# split = [(y, float(sr)) for y in np.array_split(y, 512 * 30)]
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# print(split)
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# print(len(split))
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# formants = process_map(_formant, split, max_workers=cpu_count(), chunksize=1)
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# timer.log('Done')
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# print(formants)
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# sound = parselmouth.Sound(y, float(sr))
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# formant = sound.to_formant_burg(time_step=512 / sr)
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# sound.to_formant_burg()
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n_fft = 2048
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hop_length = 512
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t = tfio.audio.spectrogram(y, n_fft, n_fft, hop_length)
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mel_spectrogram: tf.Tensor = tfio.audio.melscale(t, rate=sr, mels=128, fmin=0, fmax=8000)
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nd: np.ndarray = mel_spectrogram.numpy()
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print(nd)
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print(nd.shape)
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print(nd.dtype)
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by = nd.tobytes()
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print('Raw Numpy bytes:', type(by), f'{len(by) / 1024 / 1024:.2f}mb')
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zl = zlib.compress(by, 9)
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print('zlib compressed (level 9):', type(zl), f'{len(zl) / 1024 / 1024:.2f}mb')
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b6 = base64.b64encode(by)
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print('base64 encoded utf-8:', type(b6), f'{len(b6) / 1024 / 1024:.2f}mb')
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