def _process_utterance(out_dir, index, wav_path, text):
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
if hparams.rescaling:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels)
# Trim silences
start, end = audio.start_and_end_indices(out,
hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
out = P.mulaw(wav, hparams.quantize_channels)
constant_values = P.mulaw(0.0, hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size())
# zero pad for quantized signal
out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
timesteps = len(out)
# Write the spectrograms to disk:
audio_filename = 'ljspeech-audio-%05d.npy' % index
mel_filename = 'ljspeech-mel-%05d.npy' % index
# np.save(os.path.join(out_dir, audio_filename),
# out.astype(out_dtype), allow_pickle=False)
# np.save(os.path.join(out_dir, mel_filename),
# mel_spectrogram.astype(np.float32), allow_pickle=False)
# Return a tuple describing this training example:
return (audio_filename, mel_filename, timesteps, text)
def _process_utterance(out_dir, wav_path):
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
if hparams.rescaling:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels)
# Trim silences
start, end = audio.start_and_end_indices(out,
hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
out = P.mulaw(wav, hparams.quantize_channels)
constant_values = P.mulaw(0.0, hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
return mel_spectrogram.astype(np.float32)
def _extract_mel(wav_path):
# Load the audio to a numpy array. Resampled if needed.
wav = audio.load_wav(wav_path)
if hparams.rescaling:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels)
# Trim silences
start, end = audio.start_and_end_indices(out,
hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
out = P.mulaw(wav, hparams.quantize_channels)
constant_values = P.mulaw(0.0, hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjast time resolution between audio and mel-spectrogram
l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size())
# zero pad for quantized signal
out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjastment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
assert len(out) // N == audio.get_hop_size()
timesteps = len(out)
return out, mel_spectrogram, timesteps, out_dtype
def _process_utterance(out_dir, index, wav_path, text, silence_threshold,
fft_size):
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# Mu-law quantize
quantized = P.mulaw_quantize(wav)
# Trim silences
start, end = audio.start_and_end_indices(quantized, silence_threshold)
quantized = quantized[start:end]
wav = wav[start:end]
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjast time resolution between audio and mel-spectrogram
l, r = audio.lws_pad_lr(wav, fft_size, audio.get_hop_size())
# zero pad for quantized signal
quantized = np.pad(quantized, (l, r),
mode="constant",
constant_values=P.mulaw_quantize(0))
N = mel_spectrogram.shape[0]
assert len(quantized) >= N * audio.get_hop_size()
# time resolution adjastment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
quantized = quantized[:N * audio.get_hop_size()]
assert len(quantized) % audio.get_hop_size() == 0
timesteps = len(quantized)
wav_id = wav_path.split('/')[-1].split('.')[0]
# Write the spectrograms to disk:
audio_filename = '{}-audio.npy'.format(wav_id)
mel_filename = '{}-mel.npy'.format(wav_id)
np.save(os.path.join(out_dir, audio_filename),
quantized.astype(np.int16),
allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.astype(np.float32),
allow_pickle=False)
# Return a tuple describing this training example:
return (audio_filename, mel_filename, timesteps, text)
def wavenet_data():
out = P.mulaw_quantize(wav, hparams.quantize_channels)
out8 = P.mulaw_quantize(wav, 256)
# WAVENENT TRANFSORMATIONS
# Mu-law quantize
# Trim silences
start, end = audio.start_and_end_indices(out, hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size())
# zero pad for quantized signal
out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
timesteps = len(out)
import matplotlib.pyplot as plt
plt.subplot(3, 1, 1)
specshow(mel_spectrogram.T, sr=20000, hop_length=hparams.hop_size)
plt.subplot(3, 1, 2)
plt.plot(out)
plt.xlim(0, len(out))
plt.subplot(3, 1, 3)
plt.plot(wav)
plt.xlim(0, len(wav))
plt.show()
out /= out.max()
def _process_utterance(out_dir, index, wav_path, text, trim_silence=False):
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# Trim begin/end silences
# NOTE: the threshold was chosen for clean signals
# TODO: Remove, get this out of here.
if trim_silence:
wav, _ = librosa.effects.trim(wav,
top_db=60,
frame_length=2048,
hop_length=512)
if hparams.highpass_cutoff > 0.0:
wav = audio.low_cut_filter(wav, hparams.sample_rate,
hparams.highpass_cutoff)
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# Trim silences in mul-aw quantized domain
silence_threshold = 0
if silence_threshold > 0:
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
start, end = audio.start_and_end_indices(out, silence_threshold)
wav = wav[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels - 1)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
constant_values = P.mulaw(0.0, hparams.quantize_channels - 1)
out_dtype = np.float32
else:
# [-1, 1]
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.logmelspectrogram(wav).astype(np.float32).T
if hparams.global_gain_scale > 0:
wav *= hparams.global_gain_scale
# Time domain preprocessing
if hparams.preprocess is not None and hparams.preprocess not in [
"", "none"
]:
f = getattr(audio, hparams.preprocess)
wav = f(wav)
# Clip
if np.abs(wav).max() > 1.0:
print("""Warning: abs max value exceeds 1.0: {}""".format(
np.abs(wav).max()))
# ignore this sample
return ("dummy", "dummy", -1, "dummy")
wav = np.clip(wav, -1.0, 1.0)
# Set waveform target (out)
if is_mulaw_quantize(hparams.input_type):
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
elif is_mulaw(hparams.input_type):
out = P.mulaw(wav, hparams.quantize_channels - 1)
else:
out = wav
# zero pad
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.pad_lr(out, hparams.fft_size, audio.get_hop_size())
if l > 0 or r > 0:
out = np.pad(out, (l, r),
mode="constant",
constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
assert_ready_for_upsampling(out, mel_spectrogram, cin_pad=0, debug=True)
# Write the spectrograms to disk:
name = splitext(basename(wav_path))[0]
audio_filename = "%s-wave.npy" % (name)
mel_filename = "%s-feats.npy" % (name)
np.save(os.path.join(out_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(
os.path.join(out_dir, mel_filename),
mel_spectrogram.astype(np.float32),
allow_pickle=False,
)
# Return a tuple describing this training example:
return (audio_filename, mel_filename, N, text)
def _process_utterance(out_dir, index, speaker_id, wav_path, text):
sr = hparams.sample_rate
# Load the audio to a numpy array. Resampled if needed
wav = audio.load_wav(wav_path)
lab_path = wav_path.replace("wav/", "lab/").replace(".wav", ".lab")
# Trim silence from hts labels if available
# TODO
if exists(lab_path) and False:
labels = hts.load(lab_path)
b = int(start_at(labels) * 1e-7 * sr)
e = int(end_at(labels) * 1e-7 * sr)
wav = wav[b:e]
wav, _ = librosa.effects.trim(wav, top_db=20)
else:
wav, _ = librosa.effects.trim(wav, top_db=20)
if hparams.rescaling:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels)
# Trim silences
start, end = audio.start_and_end_indices(out, hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
out = P.mulaw(wav, hparams.quantize_channels)
constant_values = P.mulaw(0.0, hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size())
# zero pad for quantized signal
out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
timesteps = len(out)
# Write the spectrograms to disk:
audio_filename = 'cmu_arctic-audio-%05d.npy' % index
mel_filename = 'cmu_arctic-mel-%05d.npy' % index
np.save(os.path.join(out_dir, audio_filename),
out.astype(out_dtype), allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.astype(np.float32), allow_pickle=False)
# Return a tuple describing this training example:
return (audio_filename, mel_filename, timesteps, text, speaker_id)
def _process_utterance(out_dir, index, audio_filepath, text):
# Load the audio to a numpy array:
wav_whole = audio.load_wav(audio_filepath)
if hparams.rescaling:
wav_whole = wav_whole / np.abs(wav_whole).max() * hparams.rescaling_max
# This is a librivox source, so the audio files are going to be v. long
# compared to a typical 'utterance' : So split the wav into chunks
tup_results = []
n_samples = int(8.0 * hparams.sample_rate) # All 8 second utterances
n_chunks = wav_whole.shape[0] // n_samples
for chunk_idx in range(n_chunks):
chunk_start, chunk_end = chunk_idx * \
n_samples, (chunk_idx + 1) * n_samples
if chunk_idx == n_chunks - 1:
# This is the last chunk - allow it
# to extend to the end of the file
chunk_end = None
wav = wav_whole[chunk_start:chunk_end]
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels)
# Trim silences
start, end = audio.start_and_end_indices(out,
hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
out = P.mulaw(wav, hparams.quantize_channels)
constant_values = P.mulaw(0.0, hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjust time resolution
# between audio and mel-spectrogram
l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size())
# zero pad for quantized signal
out = np.pad(out, (l, r),
mode="constant",
constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
timesteps = len(out)
# Write the spectrograms to disk:
audio_filename = 'librivox-audio-%04d-%05d.npy' % (
index,
chunk_idx,
)
mel_filename = 'librivox-mel-%04d-%05d.npy' % (
index,
chunk_idx,
)
text_idx = '%s - %05d' % (
text,
chunk_idx,
)
np.save(os.path.join(out_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.astype(np.float32),
allow_pickle=False)
# Add results tuple describing this training example:
tup_results.append((audio_filename, mel_filename, timesteps, text_idx))
# Return all the audio results tuples (unpack in caller)
return tup_results
def _process_utterance_single(out_dir, text, wav_path, hparams=hparams):
# modified version of LJSpeech _process_utterance
audio.set_hparams(hparams)
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
sr = hparams.sample_rate
# Added from the multispeaker version
lab_path = wav_path.replace("wav48/", "lab/").replace(".wav", ".lab")
if not exists(lab_path):
lab_path = os.path.splitext(wav_path)[0]+'.lab'
# Trim silence from hts labels if available
if exists(lab_path):
labels = hts.load(lab_path)
wav = clean_by_phoneme(labels, wav, sr)
wav, _ = librosa.effects.trim(wav, top_db=25)
else:
if hparams.process_only_htk_aligned:
return None
wav, _ = librosa.effects.trim(wav, top_db=15)
# End added from the multispeaker version
if hparams.rescaling:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
if hparams.max_audio_length != 0 and librosa.core.get_duration(y=wav, sr=sr) > hparams.max_audio_length:
return None
if hparams.min_audio_length != 0 and librosa.core.get_duration(y=wav, sr=sr) < hparams.min_audio_length:
return None
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels)
# Trim silences
start, end = audio.start_and_end_indices(out, hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
out = P.mulaw(wav, hparams.quantize_channels)
constant_values = P.mulaw(0.0, hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size())
# zero pad for quantized signal
out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
timesteps = len(out)
# Write the spectrograms to disk:
# Get filename from wav_path
wav_name = os.path.basename(wav_path)
wav_name = os.path.splitext(wav_name)[0]
out_filename = 'audio-{}.npy'.format(wav_name)
mel_filename = 'mel-{}.npy'.format(wav_name)
np.save(os.path.join(out_dir, out_filename), out.astype(out_dtype), allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.astype(np.float32), allow_pickle=False)
# Return a tuple describing this training example:
return (out_filename, mel_filename, timesteps, text)
def _process_utterance(wav_path, out_dir):
fname = wav_path.split(os.sep)[-1].split(".")[0]
audio_filename = '{}_resolved.npy'.format(fname)
mel_filename = '{}_mel.npy'.format(fname)
apth = os.path.join(out_dir, audio_filename)
mpth = os.path.join(out_dir, mel_filename)
if os.path.exists(apth) and os.path.exists(mpth):
print("File {} already processed".format(wav_path))
return
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
if hparams.rescaling:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels)
# Trim silences
start, end = audio.start_and_end_indices(out, hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
out = P.mulaw(wav, hparams.quantize_channels)
constant_values = P.mulaw(0.0, hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size())
# zero pad for quantized signal
out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
timesteps = len(out)
# Write the spectrograms to disk:
np.save(apth,
out.astype(out_dtype), allow_pickle=False)
np.save(mpth,
mel_spectrogram.astype(np.float32), allow_pickle=False)
def _process_utterance(
out_dir,
index,
speaker_id,
wav_path,
text,
silence_threshold,
fft_size,
):
sr = hparams.sample_rate
# Load the audio to a numpy array. Resampled if needed
wav = audio.load_wav(wav_path)
lab_path = wav_path.replace("wav/", "lab/").replace(".wav", ".lab")
# Trim silence from hts labels if available
# TODO
if exists(lab_path) and False:
labels = hts.load(lab_path)
b = int(start_at(labels) * 1e-7 * sr)
e = int(end_at(labels) * 1e-7 * sr)
wav = wav[b:e]
wav, _ = librosa.effects.trim(wav, top_db=20)
else:
wav, _ = librosa.effects.trim(wav, top_db=20)
# Mu-law quantize
quantized = P.mulaw_quantize(wav)
# Trim silences
start, end = audio.start_and_end_indices(quantized, silence_threshold)
quantized = quantized[start:end]
wav = wav[start:end]
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjast time resolution between audio and mel-spectrogram
l, r = audio.lws_pad_lr(wav, fft_size, audio.get_hop_size())
# zero pad for quantized signal
quantized = np.pad(quantized, (l, r),
mode="constant",
constant_values=P.mulaw_quantize(0))
N = mel_spectrogram.shape[0]
assert len(quantized) >= N * audio.get_hop_size()
# time resolution adjastment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
quantized = quantized[:N * audio.get_hop_size()]
assert len(quantized) % audio.get_hop_size() == 0
timesteps = len(quantized)
wav_id = wav_path.split('/')[-1].split('.')[0]
# Write the spectrograms to disk:
audio_filename = '{}-audio.npy'.format(wav_id)
mel_filename = '{}-mel.npy'.format(wav_id)
np.save(os.path.join(out_dir, audio_filename),
quantized.astype(np.int16),
allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.astype(np.float32),
allow_pickle=False)
# Return a tuple describing this training example:
return (audio_filename, mel_filename, timesteps, text, speaker_id)
def _process_utterance(out_dir,wav_path,sp2ind_dir,text):
sp_f = open(sp2ind_dir,'r')
sp2ind = json.load(sp_f)
sp = wav_path.split('/')[-1].split('.')[0].split('_')[0]
if sp in sp2ind:
sp_ind = sp2ind[sp]
else:
sp_ind = -1
wav = audio.load_wav(wav_path)
if not 'test' in wav_path:
wav,_ = librosa.effects.trim(wav,top_db=60,frame_length=2048,hop_length=512)
if hparams.highpass_cutoff > 0.0:
wav = audio.low_cut_filter(wav, hparams.sample_rate, hparams.highpass_cutoff)
if is_mulaw_quantize(hparams.input_type):
# Trim silences in mul-aw quantized domain
silence_threshold = 0
if silence_threshold > 0:
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
start, end = audio.start_and_end_indices(out, silence_threshold)
wav = wav[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels - 1)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
constant_values = P.mulaw(0.0, hparams.quantize_channels - 1)
out_dtype = np.float32
else:
# [-1, 1]
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.logmelspectrogram(wav).astype(np.float32).T
mfcc = audio.mfcc(wav).astype(np.float32).T
if hparams.global_gain_scale > 0:
wav *= hparams.global_gain_scale
# Time domain preprocessing
if hparams.preprocess is not None and hparams.preprocess not in ["", "none"]:
f = getattr(audio, hparams.preprocess)
wav = f(wav)
# Clip
if np.abs(wav).max() > 1.0:
print("""Warning: abs max value exceeds 1.0: {}""".format(np.abs(wav).max()))
# ignore this sample
#return ("dummy", "dummy","dummy", -1,-1, "dummy")
wav = np.clip(wav, -1.0, 1.0)
# Set waveform target (out)
if is_mulaw_quantize(hparams.input_type):
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
elif is_mulaw(hparams.input_type):
out = P.mulaw(wav, hparams.quantize_channels - 1)
else:
out = wav
# zero pad
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.pad_lr(out, hparams.fft_size, audio.get_hop_size())
if l > 0 or r > 0:
out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
# Write the spectrograms to disk:
#name = splitext(basename(wav_path))[0]
#audio_filename = '%s-wave.npy' % (name)
#mel_filename = '%s-feats.npy' % (name)
audio_filename = f'{out_dir}wave.npy'
mel_filename = f'{out_dir}mel.npy'
mfcc_filename = f'{out_dir}mfcc.npy'
assert mfcc.shape[0] == N
np.save(audio_filename,
out.astype(out_dtype), allow_pickle=False)
np.save(mel_filename,
mel_spectrogram.astype(np.float32), allow_pickle=False)
np.save(mfcc_filename,
mfcc.astype(np.float32), allow_pickle=False)
# Return a tuple describing this training example:
return (out_dir, N, sp_ind,text)
def _process_song(out_dir, index, wav_path, text):
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# Trim begin/end silences
# NOTE: the threshold was chosen for clean signals
wav, _ = librosa.effects.trim(wav,
top_db=60,
frame_length=2048,
hop_length=512)
if hparams.highpass_cutoff > 0.0:
wav = audio.low_cut_filter(wav, hparams.sample_rate,
hparams.highpass_cutoff)
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# Trim silences in mul-aw quantized domain
silence_threshold = 0
if silence_threshold > 0:
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
start, end = audio.start_and_end_indices(out, silence_threshold)
wav = wav[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels - 1)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
constant_values = P.mulaw(0.0, hparams.quantize_channels - 1)
out_dtype = np.float32
else:
# [-1, 1]
constant_values = 0.0
out_dtype = np.float32
#### CLAIRE Work here
wav_name = os.path.splitext(os.path.basename(wav_path))[0]
os.makedirs('./pwavs', exist_ok=True)
pwav_path = './pwavs/{0}.wav'.format(wav_name)
scipy.io.wavfile.write(pwav_path, 16000, wav)
# make the chord directory if it does not exist
chord_dir = "chord_dir"
os.makedirs(chord_dir, exist_ok=True)
# create xml file with notes and timestamps
#subprocess.check_call(['./extract_chord_notes.sh', wav_path, chord_dir], shell=True)
#os.system('./extract_chord_notes.sh {0} {1}'.format(pwav_path, chord_dir))
os.system('./extract_chromagram.sh {0} {1} > /dev/null 2>&1'.format(
pwav_path, chord_dir))
note_filename = '{0}/{1}.csv'.format(chord_dir, wav_name)
#### Instead of computing the Mel Spectrogram, here return a time series of one hot encoded chords.
# vector with 1 in row for each note played
# 1000 samples per second
note_samples = int(len(wav) / 2048)
# 12 notes per octave
chords_time_series = np.zeros((24, note_samples))
#print(np.shape(chords_time_series))
with open(note_filename, newline='\n') as csvfile:
#chordreader = csv.reader(csvfile, delimeter=',')
chordreader = csvfile.readlines()
#print(chordreader)
for idx, row in enumerate(chordreader):
row = row.split(",")
chromogram_samples = np.array(row).astype(np.float)[1:]
chords_time_series[:, idx] = chromogram_samples
chords_time_series = chords_time_series.T
# if hparams.global_gain_scale > 0:
# wav *= hparams.global_gain_scale
# Time domain preprocessing
if hparams.preprocess is not None and hparams.preprocess not in [
"", "none"
]:
f = getattr(audio, hparams.preprocess)
wav = f(wav)
# wav = np.clip(wav, -1.0, 1.0)
# Set waveform target (out)
if is_mulaw_quantize(hparams.input_type):
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
elif is_mulaw(hparams.input_type):
out = P.mulaw(wav, hparams.quantize_channels - 1)
else:
out = wav
# zero pad
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.pad_lr(out, hparams.fft_size, audio.get_hop_size())
if l > 0 or r > 0:
out = np.pad(out, (l, r),
mode="constant",
constant_values=constant_values)
N = chords_time_series.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
# Write the spectrograms to disk:
name = splitext(basename(wav_path))[0]
audio_filename = '%s-wave.npy' % (name)
chords_filename = '%s-feats.npy' % (name)
np.save(os.path.join(out_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(out_dir, chords_filename),
chords_time_series.astype(out_dtype),
allow_pickle=False)
# Return a tuple describing this training example:
return (audio_filename, chords_filename, N, text)
def _process_utterance(out_dir, index, speaker_id, wav_path, text):
sr = hparams.sample_rate
# Load the audio to a numpy array. Resampled if needed
wav = audio.load_wav(wav_path)
if hparams.rescaling:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels)
# Trim silences
start, end = audio.start_and_end_indices(out, hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
out = P.mulaw(wav, hparams.quantize_channels)
constant_values = P.mulaw(0.0, hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.0
out_dtype = np.float32
#print("Wavepath is ", wav_path)
filename = wav_path.split('/wav/')[-1].split('.wav')[0]
fname = filename
filename = ccoeffs_feats_path + '/' + filename + '.mcep'
mel_spectrogram = np.loadtxt(filename)
#print("Shape of mel scptrogram is ", mel_spectrogram.shape)
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
#mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjust time resolution between audio and mel-spectrogram
#l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size())
# zero pad for quantized signal
#out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
#out = ensure_divisible(out, N)
#print("Length of out: ", len(out), "N ", N)
#print("Out and N: ", len(out), N)
#if len(out) < N * audio.get_hop_size():
#print("Out and N: ", filename, len(out), N, N * audio.get_hop_size())
# sys.exit()
#assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
#out = out[:N * 80]
#out = ensure_divisible(out, N)
g = open('logfile','a')
g.write("Processing " + fname + '\n')
g.close()
out,mel_spectrogram = ensure_frameperiod(out,mel_spectrogram)
#out = ensure_divisible(out, audio.get_hop_size())
#assert len(out) % audio.get_hop_size() == 0
#assert len(out) % N == 0
timesteps = len(out)
g = open('logfile','a')
g.write(fname + ' ' + str(len(out)) + ' ' + str(N) + ' ' + str(len(out) % N) + '\n')
g.write('\n')
g.close()
# Write the spectrograms to disk:
audio_filename = fname + '-audio-%05d.npy' % index
mel_filename = fname + '-mel-%05d.npy' % index
np.save(os.path.join(out_dir, audio_filename),
out.astype(out_dtype), allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.astype(np.float32), allow_pickle=False)
# Return a tuple describing this training example:
return (audio_filename, mel_filename, timesteps, text, speaker_id)
def _process_utterance(mel_dir,
linear_dir,
wav_dir,
index,
wav_path,
text,
hparams,
step_factor=1):
"""
Preprocesses a single utterance wav/text pair
this writes the mel scale spectogram to disk and return a tuple to write
to the train.txt file
Args:
- mel_dir: the directory to write the mel spectograms into
- linear_dir: the directory to write the linear spectrograms into
- wav_dir: the directory to write the preprocessed wav into
- index: the numeric index to use in the spectogram filename
- wav_path: path to the audio file containing the speech input
- text: text spoken in the input audio file
- hparams: hyper parameters
Returns:
- A tuple: (audio_filename, mel_filename, linear_filename, time_steps, mel_frames, linear_frames, text)
"""
try:
# Load the audio as numpy array
wav = audio.load_wav(wav_path, sr=hparams.sample_rate * step_factor)
if step_factor > 1: wav = wav[::step_factor]
audio_time = len(wav) / hparams.sample_rate
except FileNotFoundError: #catch missing wav exception
print(
'file {} present in csv metadata is not present in wav folder. skipping!'
.format(wav_path))
return None
#Trim lead/trail silences
if hparams.trim_silence:
wav = audio.trim_silence(wav, hparams)
#Pre-emphasize
preem_wav = audio.preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
#rescale wav
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
preem_wav = preem_wav / np.abs(preem_wav).max() * hparams.rescaling_max
#Assert all audio is in [-1, 1]
if (wav > 1.).any() or (wav < -1.).any():
raise RuntimeError('wav has invalid value: {}'.format(wav_path))
if (preem_wav > 1.).any() or (preem_wav < -1.).any():
raise RuntimeError('wav has invalid value: {}'.format(wav_path))
#Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
#[0, quantize_channels)
out = mulaw_quantize(wav, hparams.quantize_channels)
#Trim silences
start, end = audio.start_and_end_indices(out,
hparams.silence_threshold)
wav = wav[start:end]
preem_wav = preem_wav[start:end]
out = out[start:end]
constant_values = mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
#[-1, 1]
out = mulaw(wav, hparams.quantize_channels)
constant_values = mulaw(0., hparams.quantize_channels)
out_dtype = np.float32
else:
#[-1, 1]
out = wav
constant_values = 0.
out_dtype = np.float32
# Compute the mel scale spectrogram from the wav
mel_spectrogram = audio.melspectrogram(preem_wav,
hparams).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
return None
#Compute the linear scale spectrogram from the wav
linear_spectrogram = audio.linearspectrogram(preem_wav,
hparams).astype(np.float32)
linear_frames = linear_spectrogram.shape[1]
#sanity check
assert linear_frames == mel_frames
if hparams.use_lws:
#Ensure time resolution adjustement between audio and mel-spectrogram
fft_size = hparams.n_fft if hparams.win_size is None else hparams.win_size
l, r = audio.pad_lr(wav, fft_size, audio.get_hop_size(hparams))
#Zero pad audio signal
out = np.pad(out, (l, r),
mode='constant',
constant_values=constant_values)
else:
#Ensure time resolution adjustement between audio and mel-spectrogram
l_pad, r_pad = audio.librosa_pad_lr(wav, hparams.n_fft,
audio.get_hop_size(hparams),
hparams.wavenet_pad_sides)
#Reflect pad audio signal on the right (Just like it's done in Librosa to avoid frame inconsistency)
out = np.pad(out, (l_pad, r_pad),
mode='constant',
constant_values=constant_values)
assert len(out) >= mel_frames * audio.get_hop_size(hparams)
#time resolution adjustement
#ensure length of raw audio is multiple of hop size so that we can use
#transposed convolution to upsample
out = out[:mel_frames * audio.get_hop_size(hparams)]
assert len(out) % audio.get_hop_size(hparams) == 0
time_steps = len(out)
# Write the spectrogram and audio to disk
audio_filename = 'audio-{}.npy'.format(index)
mel_filename = 'mel-{}.npy'.format(index)
linear_filename = 'linear-{}.npy'.format(index)
np.save(os.path.join(wav_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(mel_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
np.save(os.path.join(linear_dir, linear_filename),
linear_spectrogram.T,
allow_pickle=False)
# Return a tuple describing this training example
return (wav_path, audio_filename, mel_filename, linear_filename,
time_steps, mel_frames, audio_time, text, len(text))
def _process_utterance(out_dir, index, wav_path, text, sample_rate, fft_size,
hop_size, n_mels, redis_connection):
# Load the audio to a numpy array:
wav = load_wav(wav_path)
if hparams.rescaling:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Mu-law quantize
# this really gets called if input_type in hparams
# is changed from raw to mulaw
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels)
# Trim silences
start, end = start_and_end_indices(out, hparams.silence_threshold)
wav = wav[start:end]
out = out[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
out = P.mulaw(wav, hparams.quantize_channels)
constant_values = P.mulaw(0.0, hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
# mel_spectrogram =
# audio.melspectrogram(wav, 22050, 1024, 40).astype(np.float32).T
# change this line to adjust hyperparams
mel_spectrogram = melspectrogram(wav, sample_rate, fft_size, hop_size,
n_mels).astype(np.float32).T
# lws pads zeros internally before performing stft
# this is needed to adjust time resolution
# between audio and mel-spectrogram
l, r = lws_pad_lr(wav, fft_size, hop_size)
# zero pad for quantized signal
out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
# assert len(out) >= N * audio.get_hop_size()
assert len(out) >= N * hop_size
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
# out = out[:N * audio.get_hop_size()]
# assert len(out) % audio.get_hop_size() == 0
out = out[:N * hop_size]
assert len(out) % hop_size == 0
timesteps = len(out)
# compute example reconstruction
# change this line to adjust hparams
# signal = audio.inv_mel_spectrogram(mel_spectrogram,
# sample_rate, fft_size, n_mels)
# mel_spectrogram = mel_spectrogram.T
# Write the spectrograms to disk:
audio_filename = 'ljspeech-audio-%05d.npy' % index
mel_filename = 'ljspeech-mel-%05d.npy' % index
# recon_audio_filename = 'ljspeech-audio-%05d.wav' % index
data = out.tobytes()
target = np.asarray(text).tobytes()
redis_connection.set(index, data + target)
# np.save(os.path.join(out_dir, audio_filename),
# out.astype(out_dtype), allow_pickle=False)
# np.save(os.path.join(out_dir, mel_filename),
# mel_spectrogram.astype(np.float32), allow_pickle=False)
# audio.save_wav(signal, os.path.join(out_dir, recon_audio_filename))
# Return a tuple describing this training example:
return (audio_filename, mel_filename, timesteps, text)
