import argparse
import os
from pathlib import Path
import librosa
import numpy as np
import soundfile as sf
import torch
from encoder import inference as encoder
from encoder.params_model import model_embedding_size as speaker_embedding_size
from synthesizer.inference import Synthesizer
from utils.argutils import print_args
from utils.default_models import ensure_default_models
from vocoder import inference as vocoder
if __name__ == '__main__':
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("-e", "--enc_model_fpath", type=Path,
default="saved_models/default/encoder.pt",
help="Path to a saved encoder")
parser.add_argument("-s", "--syn_model_fpath", type=Path,
default="saved_models/default/synthesizer.pt",
help="Path to a saved synthesizer")
parser.add_argument("-v", "--voc_model_fpath", type=Path,
default="saved_models/default/vocoder.pt",
help="Path to a saved vocoder")
parser.add_argument("--cpu", action="store_true", help=\
"If True, processing is done on CPU, even when a GPU is available.")
parser.add_argument("--no_sound", action="store_true", help=\
"If True, audio won't be played.")
parser.add_argument("--seed", type=int, default=None, help=\
"Optional random number seed value to make toolbox deterministic.")
args = parser.parse_args()
arg_dict = vars(args)
print_args(args, parser)
# Hide GPUs from Pytorch to force CPU processing
if arg_dict.pop("cpu"):
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
print("Running a test of your configuration...\n")
if torch.cuda.is_available():
device_id = torch.cuda.current_device()
gpu_properties = torch.cuda.get_device_properties(device_id)
## Print some environment information (for debugging purposes)
print("Found %d GPUs available. Using GPU %d (%s) of compute capability %d.%d with "
"%.1fGb total memory.\n" %
(torch.cuda.device_count(),
device_id,
gpu_properties.name,
gpu_properties.major,
gpu_properties.minor,
gpu_properties.total_memory / 1e9))
else:
print("Using CPU for inference.\n")
## Load the models one by one.
print("Preparing the encoder, the synthesizer and the vocoder...")
ensure_default_models(Path("saved_models"))
encoder.load_model(args.enc_model_fpath)
synthesizer = Synthesizer(args.syn_model_fpath)
vocoder.load_model(args.voc_model_fpath)
## Run a test
print("Testing your configuration with small inputs.")
# Forward an audio waveform of zeroes that lasts 1 second. Notice how we can get the encoder's
# sampling rate, which may differ.
# If you're unfamiliar with digital audio, know that it is encoded as an array of floats
# (or sometimes integers, but mostly floats in this projects) ranging from -1 to 1.
# The sampling rate is the number of values (samples) recorded per second, it is set to
# 16000 for the encoder. Creating an array of length <sampling_rate> will always correspond
# to an audio of 1 second.
print("\tTesting the encoder...")
encoder.embed_utterance(np.zeros(encoder.sampling_rate))
# Create a dummy embedding. You would normally use the embedding that encoder.embed_utterance
# returns, but here we're going to make one ourselves just for the sake of showing that it's
# possible.
embed = np.random.rand(speaker_embedding_size)
# Embeddings are L2-normalized (this isn't important here, but if you want to make your own
# embeddings it will be).
embed /= np.linalg.norm(embed)
# The synthesizer can handle multiple inputs with batching. Let's create another embedding to
# illustrate that
embeds = [embed, np.zeros(speaker_embedding_size)]
texts = ["test 1", "test 2"]
print("\tTesting the synthesizer... (loading the model will output a lot of text)")
mels = synthesizer.synthesize_spectrograms(texts, embeds)
# The vocoder synthesizes one waveform at a time, but it's more efficient for long ones. We
# can concatenate the mel spectrograms to a single one.
mel = np.concatenate(mels, axis=1)
# The vocoder can take a callback function to display the generation. More on that later. For
# now we'll simply hide it like this:
no_action = lambda *args: None
print("\tTesting the vocoder...")
# For the sake of making this test short, we'll pass a short target length. The target length
# is the length of the wav segments that are processed in parallel. E.g. for audio sampled
# at 16000 Hertz, a target length of 8000 means that the target audio will be cut in chunks of
# 0.5 seconds which will all be generated together. The parameters here are absurdly short, and
# that has a detrimental effect on the quality of the audio. The default parameters are
# recommended in general.
vocoder.infer_waveform(mel, target=200, overlap=50, progress_callback=no_action)
print("All test passed! You can now synthesize speech.\n\n")
## Interactive speech generation
print("This is a GUI-less example of interface to SV2TTS. The purpose of this script is to "
"show how you can interface this project easily with your own. See the source code for "
"an explanation of what is happening.\n")
print("Interactive generation loop")
num_generated = 0
while True:
try:
# Get the reference audio filepath
message = "Reference voice: enter an audio filepath of a voice to be cloned (mp3, " \
"wav, m4a, flac, ...):\n"
in_fpath = Path(input(message).replace("\"", "").replace("\'", ""))
## Computing the embedding
# First, we load the wav using the function that the speaker encoder provides. This is
# important: there is preprocessing that must be applied.
# The following two methods are equivalent:
# - Directly load from the filepath:
preprocessed_wav = encoder.preprocess_wav(in_fpath)
# - If the wav is already loaded:
original_wav, sampling_rate = librosa.load(str(in_fpath))
preprocessed_wav = encoder.preprocess_wav(original_wav, sampling_rate)
print("Loaded file succesfully")
# Then we derive the embedding. There are many functions and parameters that the
# speaker encoder interfaces. These are mostly for in-depth research. You will typically
# only use this function (with its default parameters):
embed = encoder.embed_utterance(preprocessed_wav)
print("Created the embedding")
## Generating the spectrogram
text = input("Write a sentence (+-20 words) to be synthesized:\n")
# If seed is specified, reset torch seed and force synthesizer reload
if args.seed is not None:
torch.manual_seed(args.seed)
synthesizer = Synthesizer(args.syn_model_fpath)
# The synthesizer works in batch, so you need to put your data in a list or numpy array
texts = [text]
embeds = [embed]
# If you know what the attention layer alignments are, you can retrieve them here by
# passing return_alignments=True
specs = synthesizer.synthesize_spectrograms(texts, embeds)
spec = specs[0]
print("Created the mel spectrogram")
## Generating the waveform
print("Synthesizing the waveform:")
# If seed is specified, reset torch seed and reload vocoder
if args.seed is not None:
torch.manual_seed(args.seed)
vocoder.load_model(args.voc_model_fpath)
# Synthesizing the waveform is fairly straightforward. Remember that the longer the
# spectrogram, the more time-efficient the vocoder.
generated_wav = vocoder.infer_waveform(spec)
## Post-generation
# There's a bug with sounddevice that makes the audio cut one second earlier, so we
# pad it.
generated_wav = np.pad(generated_wav, (0, synthesizer.sample_rate), mode="constant")
# Trim excess silences to compensate for gaps in spectrograms (issue #53)
generated_wav = encoder.preprocess_wav(generated_wav)
# Play the audio (non-blocking)
if not args.no_sound:
import sounddevice as sd
try:
sd.stop()
sd.play(generated_wav, synthesizer.sample_rate)
except sd.PortAudioError as e:
print("\nCaught exception: %s" % repr(e))
print("Continuing without audio playback. Suppress this message with the \"--no_sound\" flag.\n")
except:
raise
# Save it on the disk
filename = "demo_output_%02d.wav" % num_generated
print(generated_wav.dtype)
sf.write(filename, generated_wav.astype(np.float32), synthesizer.sample_rate)
num_generated += 1
print("\nSaved output as %s\n\n" % filename)
except Exception as e:
print("Caught exception: %s" % repr(e))
print("Restarting\n")