def _process_utterance(out_dir, index, wav_path, text):
'''Preprocesses a single utterance audio/text pair.
This writes the mel and linear scale spectrograms to disk and returns a tuple to write
to the train.txt file.
Args:
out_dir: The directory to write the spectrograms into
index: The numeric index to use in the spectrogram filenames.
wav_path: Path to the audio file containing the speech input
text: The text spoken in the input audio file
Returns:
A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
'''
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# Compute the linear-scale spectrogram from the wav:
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
# Compute a mel-scale spectrogram from the wav:
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
# Write the spectrograms to disk:
spectrogram_filename = 'ljspeech-spec-%05d.npy' % index
mel_filename = 'ljspeech-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename), spectrogram.T, allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.T, allow_pickle=False)
# Return a tuple describing this training example:
return (spectrogram_filename, mel_filename, n_frames, text)
def _process_utterance(out_dir, index, wav_path, labels_path, text):
# Load the wav file and trim silence from the ends:
wav = audio.load_wav(wav_path)
start_offset, end_offset = _parse_labels(labels_path)
start = int(start_offset * hparams.sample_rate)
end = int(end_offset * hparams.sample_rate) if end_offset is not None else -1
wav = wav[start:end]
max_samples = _max_out_length * hparams.frame_shift_ms / 1000 * hparams.sample_rate
if len(wav) > max_samples:
return None
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
spectrogram_filename = 'blizzard-spec-%05d.npy' % index
mel_filename = 'blizzard-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename), spectrogram.T, allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.T, allow_pickle=False)
return (spectrogram_filename, mel_filename, n_frames, text)
def _process_utterance(out_dir, index, fname, wav_path, text, label):
'''Preprocesses a single utterance audio/text pair.
This writes the mel and linear scale spectrograms to disk and returns a tuple to write
to the train.txt file.
Args:
out_dir: The directory to write the spectrograms into
index: The numeric index to use in the spectrogram filenames.
wav_path: Path to the audio file containing the speech input
text: The text spoken in the input audio file
Returns:
A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
'''
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# Compute the linear-scale spectrogram from the wav:
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
# Compute a mel-scale spectrogram from the wav:
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
# Write the spectrograms to disk:
spectrogram_filename = 'emotion-spec-%05d.npy' % index
mel_filename = 'emtion-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename),
spectrogram.T,
allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
# Return a tuple describing this training example:
return (fname, spectrogram_filename, mel_filename, n_frames, text, label)
def preprocess(data_dir, normalized_dir):
# Ingest the text file:
#normalize(data_dir, normalized_dir)
wav_files = sorted(Path(data_dir).glob('**/*.wav'))
train_tuples = []
for wav_file in wav_files:
# Get the transcript
text_filepath = (wav_file.parent
/ ('-'.join(wav_file.stem.split('-')[:-1])
+ '.trans.txt'))
text = ''
with text_filepath.open() as text_file:
for line in text_file:
split_line = line.split()
if split_line[0] == wav_file.name:
text = ' '.join(split_line[1:])
break
wav = audio.load_wav(str(wav_file))
spectrogram = audio.spectrogram(wav).astype(np.float32)
spectrogram_filename = (wav_file.stem + '_spectrogram')
mel_spectrogram_filename = (wav_file.stem + '_mel_spectrogram')
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
np.save(os.path.join(normalized_dir,spectrogram_filename),
spectrogram.T,
allow_pickle=False)
np.save(os.path.join(normalized_dir,mel_spectrogram_filename),
mel_spectrogram.T,
allow_pickle=False)
train_tuples.append((spectrogram_filename, mel_spectrogram_filename,
spectrogram.shape[1],
text))
print('DOne??')
with open(os.path.join(normalized_dir, 'train.txt'), 'w') as train_file:
for train_tuple in train_tuples:
train_file.write('|'.join([str(x) for x in train_tuple]) + '\n')
def _process_utterance(out_dir, index, wav_path, labels_path, text):
# Load the wav file and trim silence from the ends:
wav = audio.load_wav(wav_path)
start_offset, end_offset = _parse_labels(labels_path)
start = int(start_offset * hparams.sample_rate)
end = int(end_offset *
hparams.sample_rate) if end_offset is not None else -1
wav = wav[start:end]
max_samples = _max_out_length * hparams.frame_shift_ms / 1000 * hparams.sample_rate
if len(wav) > max_samples:
return None
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
spectrogram_filename = 'blizzard-spec-%05d.npy' % index
mel_filename = 'blizzard-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename),
spectrogram.T,
allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
return (spectrogram_filename, mel_filename, n_frames, text)
def _process_utterance(out_dir, index, wav_path, text):
'''Pre-processa um unico par de audio / texto de enunciado.
Isso grava os espectrogramas mel e linear em disco e retorna uma tupla para gravar no arquivo train.txt.
Args:
out_dir: O diretorio para gravar os espectrogramas
index: O indice numerico a ser usado nos nomes de arquivos do espectrograma.
wav_path: Caminho para o arquivo de audio que contem a entrada de fala
text: O texto falado no arquivo de audio de entrada
Returns:
A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
'''
# Carregue o áudio em uma matriz numpy:
wav = audio.load_wav(wav_path)
# Calcule o espectrograma em escala linear a partir do wav:
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
# Calcule um espectrograma em escala de mel a partir do wav:
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
# Escreva os espectrogramas no disco:
spectrogram_filename = 'ptbr-spec-%05d.npy' % index
mel_filename = 'ptbr-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename),
spectrogram.T,
allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
# Retorne uma tupla descrevendo este exemplo de treinamento:
return (spectrogram_filename, mel_filename, n_frames, text)
def _process_utterance(out_dir, index, wav_path, pinyin):
'''Preprocesses a single utterance audio/text pair.
This writes the mel and linear scale spectrograms to disk and returns a tuple to write
to the train.txt file.
Args:
out_dir: The directory to write the spectrograms into
index: The numeric index to use in the spectrogram filenames.
wav_path: Path to the audio file containing the speech input
pinyin: The pinyin of Chinese spoken in the input audio file
Returns:
A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
'''
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# rescale wav for unified measure for all clips
wav = wav / np.abs(wav).max() * 0.999
# trim silence
wav = audio.trim_silence(wav)
# Compute the linear-scale spectrogram from the wav:
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
# Compute a mel-scale spectrogram from the wav:
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
# Write the spectrograms to disk:
spectrogram_filename = 'biaobei-spec-%05d.npy' % index
mel_filename = 'biaobei-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename), spectrogram.T, allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.T, allow_pickle=False)
# Return a tuple describing this training example:
return (spectrogram_filename, mel_filename, n_frames, pinyin)
def _process_utterance(out_dir, index, wav_path, text):
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# Compute the linear-scale spectrogram from the wav:
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
# Compute a mel-scale spectrogram from the wav:
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
# Write the spectrograms to disk:
spectrogram_filename = 'nawar-spec-%05d.npy' % index
mel_filename = 'nawar-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename),
spectrogram.T,
allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
# Return a tuple describing this training example:
return (spectrogram_filename, mel_filename, n_frames, text)
def _process_utterance(out_dir, index, wav_path, pinyin):
# 读取语音
wav = audio.load_wav(wav_path)
wav = wav / np.abs(wav).max() * 0.999
# 消除静音
wav = audio.trim_silence(wav)
# 得到语音的线性频谱和梅尔频谱
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
# 保存两种频谱
spectrogram_filename = 'biaobei-spec-%05d.npy' % index
mel_filename = 'biaobei-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename),
spectrogram.T,
allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
return (spectrogram_filename, mel_filename, n_frames, pinyin)
def _process_utterance(out_dir, index, wav_path, text, pml_cmp, hparams):
'''Preprocesses a single utterance audio/text pair.
This writes the mel and linear scale spectrograms to disk and returns a tuple to write
to the train.txt file.
Args:
out_dir: The directory to write the spectrograms into
index: The numeric index to use in the spectrogram filenames
wav_path: Path to the audio file containing the speech input
text: The text spoken in the input audio file
pml_cmp: One dimensional array containing vocoder features read from .cmp file
Returns:
A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
'''
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# Write the PML features to disk
pml_filename = 'nick-pml-%05d.npy' % index
pml_dimension = hparams.pml_dimension
pml_features = pml_cmp.reshape((-1, pml_dimension))
pml_frames = pml_features.shape[0]
np.save(os.path.join(out_dir, pml_filename),
pml_features,
allow_pickle=False)
# Remove silence from the wav
# silence_removed = audio.remove_silence(wav)
# Compute the linear-scale spectrogram from the wav:
spectrogram = audio.spectrogram(wav).astype(np.float32)
n_frames = spectrogram.shape[1]
# Compute a mel-scale spectrogram from the wav:
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
# Ensure lengths of spectrograms and PML features are the same
if n_frames > pml_frames:
spectrogram = spectrogram[:, :pml_frames]
# Check the shape of the mel target
if mel_frames > pml_frames:
mel_spectrogram = mel_spectrogram[:, :pml_frames]
# Write the spectrograms to disk:
spectrogram_filename = 'nick-spec-%05d.npy' % index
mel_filename = 'nick-mel-%05d.npy' % index
np.save(os.path.join(out_dir, spectrogram_filename),
spectrogram.T,
allow_pickle=False)
np.save(os.path.join(out_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
# Return a tuple describing this training example:
return (spectrogram_filename, mel_filename, n_frames, pml_filename,
pml_frames, text)
from util import audio
point_of_interest = '001'
# read from Nick
CMP = np.fromfile(
f'/home/josh/tacotron/Nick/pml/herald_{point_of_interest}_1.cmp',
dtype=np.float32)
# pml_features = nick_parameters.reshape()
f = open(f'/home/josh/tacotron/Nick/txt/herald_{point_of_interest}_1.txt', 'r')
# Load the audio to a numpy array:
wav = audio.load_wav(
f'/home/josh/tacotron/Nick/wav/herald_{point_of_interest}_1.wav')
# Compute the linear-scale spectrogram from the wav:
spectrogram = audio.spectrogram(wav).astype(np.float32)
# try to reshape the Nick PML features into an 86 x something matrix
# pml_dimension = 86
pml_dimension = 163
pml_features = CMP.reshape((-1, pml_dimension))
# Get the saved spectrogram
linear_target = np.load(f'/home/josh/tacotron/training/nick-spec-00605.npy')
print(f.read())
print('PML Feature Info', CMP.shape, CMP.size / pml_dimension,
pml_features.shape)
print('Audio Info', spectrogram.shape, linear_target.shape, wav.shape)
def _process_utterance(mel_dir, linear_dir, wav_dir, index, wav_path, text,
hparams):
"""
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)
except FileNotFoundError: #catch missing wav exception
print(
'file {} present in csv metadata is not present in wav folder. skipping!'
.format(wav_path))
return None
# M-AILABS extra silence specific
wav = audio.trim_silence(wav)
#Pre-emphasize
wav = audio.preemphasis(wav)
#rescale wav
#wav = wav / np.abs(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))
print('file {} has invalid value. skipping!'.format(wav_path))
return None
#[-1, 1]
out = wav
constant_values = 0.
out_dtype = np.float32
# Compute the mel scale spectrogram from the wav
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
if mel_frames > hparams.max_frame_num:
return None
#Compute the linear scale spectrogram from the wav
linear_spectrogram = audio.spectrogram(wav).astype(np.float32)
linear_frames = linear_spectrogram.shape[1]
#sanity check
assert linear_frames == mel_frames
#Ensure time resolution adjustement between audio and mel-spectrogram
l_pad, r_pad, hop_size = audio.librosa_pad_lr(wav)
#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 * hop_size
#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 * hop_size]
assert len(out) % hop_size == 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 (audio_filename, mel_filename, linear_filename, time_steps,
mel_frames, text)
def _process_utterance(out_dir, fid, wav_path, label_path, phn_seq,
phn2idx_map, frame_phn_idx, trim_silence, audio_config,
f0_config):
'''Preprocesses a single utterance audio/text pair.
This writes the mel and linear scale spectrograms to disk and returns a tuple to write
to the train.txt file.
Args:
out_dir: The directory to write the spectrograms into
fid: utterance id.
wav_path: Path to the audio file containing the speech input
label_path: Path to full label file.
phn_seq: text-phn-seq from ppp file.
phn2idx_map: map phn (str) to index (int).
frame_phn_idx: frame-level text-phn positions.
uttid: utterance id.
trim_silence:
audio_config:
f0_config:
Returns:
tuple (expanded phn-seq length, fid)
'''
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path, audio_config.sample_rate)
# Trim Slience
if trim_silence:
start_offset, end_offset = _parse_labels(label_path)
start_offset_frames = int(start_offset /
(audio_config.frame_shift_ms / 1000.0))
end_offset_frames = int(end_offset /
(audio_config.frame_shift_ms / 1000.0))
else:
start_offset_frames = 0
end_offset_frames = 1000000
# Compute a mel-scale spectrogram for wavenet, wavernn
mel_spectrogram = melspectrogram(
wave=wav,
sample_rate=audio_config.sample_rate,
num_freq=audio_config.num_freq,
num_mels=audio_config.num_mels,
frame_length_ms=audio_config.frame_length_ms,
frame_shift_ms=audio_config.frame_shift_ms,
pre_emphasis=audio_config.pre_emphasis,
fmin=audio_config.fmin,
min_level_db=audio_config.min_level_db,
ref_level_db=audio_config.ref_level_db).astype(np.float32)
mel_spectrogram = mel_spectrogram.T # -> [T, num_mels]
stft = spectrogram(wave=wav,
sample_rate=audio_config.sample_rate,
num_freq=audio_config.num_freq,
frame_length_ms=audio_config.frame_length_ms,
frame_shift_ms=audio_config.frame_shift_ms,
pre_emphasis=audio_config.pre_emphasis,
min_level_db=audio_config.min_level_db,
ref_level_db=audio_config.ref_level_db).astype(
np.float32)
stft = stft.T # -> [T, num_freq]
cont_f0 = compute_f0_from_wav(speaker_name='song_guiniang',
wave_path=wav_path,
f0_config=f0_config)
if len(cont_f0) > len(mel_spectrogram):
cont_f0 = cont_f0[:len(mel_spectrogram)]
elif len(cont_f0) < len(mel_spectrogram):
pad_len = len(mel_spectrogram) - len(cont_f0)
cont_f0 = np.pad(cont_f0, (0, pad_len),
mode="constant",
constant_values=cont_f0[-1])
else:
pass
# Process phn sequence
phn_seq_idx = np.asarray([phn2idx_map[i] for i in phn_seq])
dur = np.asarray([
len(list(group)) for (key, group) in itertools.groupby(frame_phn_idx)
])
pos_list = [key for (key, group) in itertools.groupby(frame_phn_idx)]
txt_phnseq = np.array(phn_seq)[pos_list]
# Cut off tailing silence
cut_len = mel_spectrogram.shape[0] - sum(dur)
if cut_len > 0:
mel_spectrogram = mel_spectrogram[:-cut_len]
stft = stft[:-cut_len]
cont_f0 = cont_f0[:-cut_len]
else:
assert dur[-1] > -cut_len, "Not enough tailing silence."
dur[-1] = dur[-1] + cut_len
# Save features
with open(f"{out_dir}/{fid}.phntone.txt", 'w') as f:
f.write(' '.join(txt_phnseq) + '\n')
np.save(f'{out_dir}/{fid}.mel.npy', mel_spectrogram, allow_pickle=False)
np.save(f'{out_dir}/{fid}.stft.npy', stft, allow_pickle=False)
np.save(f'{out_dir}/{fid}.dur.npy', dur, allow_pickle=False)
np.save(f'{out_dir}/{fid}.f0.npy', cont_f0, allow_pickle=False)
return len(frame_phn_idx), fid