def run_synthesis(in_dir, out_dir, model_dir, hparams):
# This generates ground truth-aligned mels for vocoder training
synth_dir = Path(out_dir).joinpath("mels_gta")
synth_dir.mkdir(exist_ok=True)
print(hparams_debug_string(hparams))
# Check for GPU
if torch.cuda.is_available():
device = torch.device("cuda")
if hparams.synthesis_batch_size % torch.cuda.device_count() != 0:
raise ValueError("`hparams.synthesis_batch_size` must be evenly divisible by n_gpus!")
else:
device = torch.device("cpu")
print("Synthesizer using device:", device)
# Instantiate Tacotron model
model = Tacotron(embed_dims=hparams.tts_embed_dims,
num_chars=len(symbols),
encoder_dims=hparams.tts_encoder_dims,
decoder_dims=hparams.tts_decoder_dims,
n_mels=hparams.num_mels,
fft_bins=hparams.num_mels,
postnet_dims=hparams.tts_postnet_dims,
encoder_K=hparams.tts_encoder_K,
lstm_dims=hparams.tts_lstm_dims,
postnet_K=hparams.tts_postnet_K,
num_highways=hparams.tts_num_highways,
dropout=0., # Use zero dropout for gta mels
stop_threshold=hparams.tts_stop_threshold,
speaker_embedding_size=hparams.speaker_embedding_size).to(device)
# Load the weights
model_dir = Path(model_dir)
model_fpath = model_dir.joinpath(model_dir.stem).with_suffix(".pt")
print("\nLoading weights at %s" % model_fpath)
model.load(model_fpath)
print("Tacotron weights loaded from step %d" % model.step)
# Synthesize using same reduction factor as the model is currently trained
r = np.int32(model.r)
# Set model to eval mode (disable gradient and zoneout)
model.eval()
# Initialize the dataset
in_dir = Path(in_dir)
metadata_fpath = in_dir.joinpath("train.txt")
mel_dir = in_dir.joinpath("mels")
embed_dir = in_dir.joinpath("embeds")
dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams)
data_loader = DataLoader(dataset,
collate_fn=lambda batch: collate_synthesizer(batch, r,hparams),
batch_size=hparams.synthesis_batch_size,
num_workers=0, #Having an error(Can't pickle local object 'run_synthesis.<locals>.<lambda>') when training in Windows unless you set num_workers=0
shuffle=False,
pin_memory=True)
# Generate GTA mels
meta_out_fpath = Path(out_dir).joinpath("synthesized.txt")
with open(meta_out_fpath, "w") as file:
for i, (texts, mels, embeds, idx) in tqdm(enumerate(data_loader), total=len(data_loader)):
texts = texts.to(device)
mels = mels.to(device)
embeds = embeds.to(device)
# Parallelize model onto GPUS using workaround due to python bug
if device.type == "cuda" and torch.cuda.device_count() > 1:
_, mels_out,_,_ = data_parallel_workaround(model, texts, mels, embeds)
else:
_,mels_out, _,_ = model(texts, mels, embeds)
for j, k in enumerate(idx):
# Note: outputs mel-spectrogram files and target ones have same names, just different folders
mel_filename = Path(synth_dir).joinpath(dataset.metadata[k][1])
mel_out = mels_out[j].detach().cpu().numpy().T
# Use the length of the ground truth mel to remove padding from the generated mels
mel_out = mel_out[:int(dataset.metadata[k][4])]
# Write the spectrogram to disk
np.save(mel_filename, mel_out, allow_pickle=False)
# Write metadata into the synthesized file
file.write("|".join(dataset.metadata[k]))
class Synthesizer:
sample_rate = hparams.sample_rate
hparams = hparams
def __init__(self, model_fpath: Path, verbose=True):
"""
The model isn't instantiated and loaded in memory until needed or until load() is called.
:param model_fpath: path to the trained model file
:param verbose: if False, prints less information when using the model
"""
self.model_fpath = model_fpath
self.verbose = verbose
# Check for GPU
if torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
if self.verbose:
print("Synthesizer using device:", self.device)
# Tacotron model will be instantiated later on first use.
self._model = None
def is_loaded(self):
"""
Whether the model is loaded in memory.
"""
return self._model is not None
def load(self):
"""
Instantiates and loads the model given the weights file that was passed in the constructor.
"""
self._model = Tacotron(
embed_dims=hparams.tts_embed_dims,
num_chars=len(symbols),
encoder_dims=hparams.tts_encoder_dims,
decoder_dims=hparams.tts_decoder_dims,
n_mels=hparams.num_mels,
fft_bins=hparams.num_mels,
postnet_dims=hparams.tts_postnet_dims,
encoder_K=hparams.tts_encoder_K,
lstm_dims=hparams.tts_lstm_dims,
postnet_K=hparams.tts_postnet_K,
num_highways=hparams.tts_num_highways,
dropout=hparams.tts_dropout,
stop_threshold=hparams.tts_stop_threshold,
speaker_embedding_size=hparams.speaker_embedding_size).to(
self.device)
self._model.load(self.model_fpath)
self._model.eval()
if self.verbose:
print("Loaded synthesizer \"%s\" trained to step %d" %
(self.model_fpath.name, self._model.state_dict()["step"]))
def synthesize_spectrograms(self,
texts: List[str],
embeddings: Union[np.ndarray,
List[np.ndarray]],
return_alignments=False):
"""
Synthesizes mel spectrograms from texts and speaker embeddings.
:param texts: a list of N text prompts to be synthesized
:param embeddings: a numpy array or list of speaker embeddings of shape (N, 256)
:param return_alignments: if True, a matrix representing the alignments between the
characters
and each decoder output step will be returned for each spectrogram
:return: a list of N melspectrograms as numpy arrays of shape (80, Mi), where Mi is the
sequence length of spectrogram i, and possibly the alignments.
"""
# Load the model on the first request.
if not self.is_loaded():
self.load()
# Print some info about the model when it is loaded
tts_k = self._model.get_step() // 1000
simple_table([("Tacotron", str(tts_k) + "k"),
("r", self._model.r)])
# Preprocess text inputs
inputs = [
text_to_sequence(text.strip(), hparams.tts_cleaner_names)
for text in texts
]
if not isinstance(embeddings, list):
embeddings = [embeddings]
# Batch inputs
batched_inputs = [
inputs[i:i + hparams.synthesis_batch_size]
for i in range(0, len(inputs), hparams.synthesis_batch_size)
]
batched_embeds = [
embeddings[i:i + hparams.synthesis_batch_size]
for i in range(0, len(embeddings), hparams.synthesis_batch_size)
]
specs = []
for i, batch in enumerate(batched_inputs, 1):
if self.verbose:
print(f"\n| Generating {i}/{len(batched_inputs)}")
# Pad texts so they are all the same length
text_lens = [len(text) for text in batch]
max_text_len = max(text_lens)
chars = [pad1d(text, max_text_len) for text in batch]
chars = np.stack(chars)
# Stack speaker embeddings into 2D array for batch processing
speaker_embeds = np.stack(batched_embeds[i - 1])
# Convert to tensor
chars = torch.tensor(chars).long().to(self.device)
speaker_embeddings = torch.tensor(speaker_embeds).float().to(
self.device)
# Inference
_, mels, alignments = self._model.generate(chars,
speaker_embeddings)
mels = mels.detach().cpu().numpy()
for m in mels:
# Trim silence from end of each spectrogram
while np.max(m[:, -1]) < hparams.tts_stop_threshold:
m = m[:, :-1]
specs.append(m)
if self.verbose:
print("\n\nDone.\n")
return (specs, alignments) if return_alignments else specs
@staticmethod
def load_preprocess_wav(fpath):
"""
Loads and preprocesses an audio file under the same conditions the audio files were used to
train the synthesizer.
"""
wav = librosa.load(str(fpath), hparams.sample_rate)[0]
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
return wav
@staticmethod
def make_spectrogram(fpath_or_wav: Union[str, Path, np.ndarray]):
"""
Creates a mel spectrogram from an audio file in the same manner as the mel spectrograms that
were fed to the synthesizer when training.
"""
if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
wav = Synthesizer.load_preprocess_wav(fpath_or_wav)
else:
wav = fpath_or_wav
mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
return mel_spectrogram
@staticmethod
def griffin_lim(mel):
"""
Inverts a mel spectrogram using Griffin-Lim. The mel spectrogram is expected to have been built
with the same parameters present in hparams.py.
"""
return audio.inv_mel_spectrogram(mel, hparams)