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config update, audio.py update and modularize synthesize.py

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This commit is contained in:
Eren Golge
2019-06-14 16:18:49 +02:00
parent e061ed091a
commit 037ec13453
5 changed files with 184 additions and 143 deletions
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utils/audio.py
+4 -13
View File
@@ -216,32 +216,23 @@ class AudioProcessor(object):
return librosa.effects.trim(
wav, top_db=40, frame_length=1024, hop_length=256)[0]
def mulaw_encode(self, wav, qc):
@staticmethod
def mulaw_encode(wav, qc):
mu = 2 ** qc - 1
# wav_abs = np.minimum(np.abs(wav), 1.0)
signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1. + mu)
# Quantize signal to the specified number of levels.
signal = (signal + 1) / 2 * mu + 0.5
return np.floor(signal,)
return np.floor(signal)
@staticmethod
def mulaw_decode(wav, qc):
"""Recovers waveform from quantized values."""
# from IPython.core.debugger import set_trace
# set_trace()
mu = 2 ** qc - 1
x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
return x
# mu = 2 ** qc - 1.
# # Map values back to [-1, 1].
# # casted = wav.astype(np.float32)
# # signal = 2 * casted / mu - 1
# # Perform inverse of mu-law transformation.
# magnitude = (1 / mu) * ((1 + mu) ** abs(wav) - 1)
# return np.sign(wav) * magnitude
def load_wav(self, filename, encode=False):
x, sr = sf.read(filename)
# x, sr = librosa.load(filename, sr=self.sample_rate)
if self.do_trim_silence:
x = self.trim_silence(x)
# sr, x = io.wavfile.read(filename)
utils/synthesis.py
+56 -39
View File
@@ -10,36 +10,73 @@ from matplotlib import pylab as plt
def text_to_seqvec(text, CONFIG, use_cuda):
text_cleaner = [CONFIG.text_cleaner]
# text ot phonemes to sequence vector
if CONFIG.use_phonemes:
seq = np.asarray(
phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language, enable_eos_bos_chars),
phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language,
CONFIG.enable_eos_bos_chars),
dtype=np.int32)
else:
seq = np.asarray(text_to_sequence(text, text_cleaner), dtype=np.int32)
# torch tensor
chars_var = torch.from_numpy(seq).unsqueeze(0)
if use_cuda:
chars_var = chars_var.cuda()
return chars_var.long()
def compute_style_mel(style_wav, ap):
style_mel = torch.FloatTensor(ap.melspectrogram(ap.load_wav(style_wav))).unsqueeze(0)
return style_mel
def compute_style_mel(style_wav, ap, use_cuda):
print(style_wav)
style_mel = torch.FloatTensor(ap.melspectrogram(
ap.load_wav(style_wav))).unsqueeze(0)
if use_cuda:
return style_mel.cuda()
else:
return style_mel
def run_model():
pass
def run_model(model, inputs, CONFIG, truncated, style_mel=None):
if CONFIG.model == "TacotronGST" and style_mel is not None:
decoder_output, postnet_output, alignments, stop_tokens = model.inference(
inputs, style_mel)
else:
if truncated:
decoder_output, postnet_output, alignments, stop_tokens = model.inference_truncated(
inputs)
else:
decoder_output, postnet_output, alignments, stop_tokens = model.inference(
inputs)
return decoder_output, postnet_output, alignments, stop_tokens
def parse_outputs():
pass
def parse_outputs(postnet_output, decoder_output, alignments):
postnet_output = postnet_output[0].data.cpu().numpy()
decoder_output = decoder_output[0].data.cpu().numpy()
alignment = alignments[0].cpu().data.numpy()
return postnet_output, decoder_output, alignment
def trim_silence():
pass
def trim_silence(wav):
return wav[:ap.find_endpoint(wav)]
def synthesis(model, text, CONFIG, use_cuda, ap, style_wav=None, truncated=False, enable_eos_bos_chars=False, trim_silence=False):
def inv_spectrogram(postnet_output, ap, CONFIG):
if CONFIG.model in ["Tacotron", "TacotronGST"]:
wav = ap.inv_spectrogram(postnet_output.T)
else:
wav = ap.inv_mel_spectrogram(postnet_output.T)
return wav
def synthesis(model,
text,
CONFIG,
use_cuda,
ap,
style_wav=None,
truncated=False,
enable_eos_bos_chars=False,
trim_silence=False):
"""Synthesize voice for the given text.
Args:
@@ -57,38 +94,18 @@ def synthesis(model, text, CONFIG, use_cuda, ap, style_wav=None, truncated=False
"""
# GST processing
if CONFIG.model == "TacotronGST" and style_wav is not None:
style_mel = compute_style_mel(style_wav, ap)
style_mel = compute_style_mel(style_wav, ap, use_cuda)
# preprocess the given text
text_cleaner = [CONFIG.text_cleaner]
if CONFIG.use_phonemes:
seq = np.asarray(
phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language, enable_eos_bos_chars),
dtype=np.int32)
else:
seq = np.asarray(text_to_sequence(text, text_cleaner), dtype=np.int32)
chars_var = torch.from_numpy(seq).unsqueeze(0)
inputs = text_to_seqvec(text, CONFIG, use_cuda)
# synthesize voice
if CONFIG.model == "TacotronGST" and style_wav is not None:
decoder_output, postnet_output, alignments, stop_tokens = model.inference(
chars_var.long(), style_mel)
else:
if truncated:
decoder_output, postnet_output, alignments, stop_tokens = model.inference_truncated(
chars_var.long())
else:
decoder_output, postnet_output, alignments, stop_tokens = model.inference(
chars_var.long())
decoder_output, postnet_output, alignments, stop_tokens = run_model(
model, inputs, CONFIG, truncated, style_mel)
# convert outputs to numpy
postnet_output = postnet_output[0].data.cpu().numpy()
decoder_output = decoder_output[0].data.cpu().numpy()
alignment = alignments[0].cpu().data.numpy()
postnet_output, decoder_output, alignment = parse_outputs(
postnet_output, decoder_output, alignments)
# plot results
if CONFIG.model in ["Tacotron", "TacotronGST"]:
wav = ap.inv_spectrogram(postnet_output.T)
else:
wav = ap.inv_mel_spectrogram(postnet_output.T)
wav = inv_spectrogram(postnet_output, ap, CONFIG)
# trim silence
if trim_silence:
wav = wav[:ap.find_endpoint(wav)]
wav = trim_silence(wav)
return wav, alignment, decoder_output, postnet_output, stop_tokens