from encoder.params_model import model_embedding_size as speaker_embedding_size

from utils.argutils import print_args

from synthesizer.inference import Synthesizer

from encoder import inference as encoder

from vocoder import inference as vocoder

from pathlib import Path

import numpy as np

import librosa

import argparse

import torch

import sys

from g2p.train import g2p

import soundfile as sf

if __name__ == '__main__':

## Info & args

parser = argparse.ArgumentParser(

formatter_class=argparse.ArgumentDefaultsHelpFormatter

)

parser.add_argument("-e", "--enc_model_fpath", type=Path,

default="encoder/saved_models/pretrained.pt",

help="Path to a saved encoder")

parser.add_argument("-s", "--syn_model_dir", type=Path,

default="synthesizer/saved_models/logs-pretrained/",

help="Directory containing the synthesizer model")

parser.add_argument("-v", "--voc_model_fpath", type=Path,

default="vocoder/saved_models/pretrained/pretrained.pt",

help="Path to a saved vocoder")

parser.add_argument("--low_mem", action="store_true", help=\

"If True, the memory used by the synthesizer will be freed after each use. Adds large "

"overhead but allows to save some GPU memory for lower-end GPUs.")

parser.add_argument("--no_sound", action="store_true", help=\

"If True, audio won't be played.")

parser.add_argument("-t", "--text",

default="Hello my friends. Я многоязычный синтез построенный на tacotron. Шла саша по шоссе и сосала сушку",

help="Text")

parser.add_argument("-p", "--path_wav", type=Path,

default="ex.wav",

help="wav file")

args = parser.parse_args()

print_args(args, parser)

if not args.no_sound:

import sounddevice as sd

## Print some environment information (for debugging purposes)

print("Running a test of your configuration...\n")

if not torch.cuda.is_available():

print("Your PyTorch installation is not configured to use CUDA. If you have a GPU ready "

"for deep learning, ensure that the drivers are properly installed, and that your "

"CUDA version matches your PyTorch installation. CPU-only inference is currently "

"not supported.", file=sys.stderr)

quit(-1)

device_id = torch.cuda.current_device()

gpu_properties = torch.cuda.get_device_properties(device_id)

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))

## Load the models one by one.

print("Preparing the encoder, the synthesizer and the vocoder...")

encoder.load_model(args.enc_model_fpath)

synthesizer = Synthesizer(args.syn_model_dir.joinpath("taco_pretrained"), low_mem=args.low_mem)

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

# Get the reference audio filepath

#message = "Reference voice: enter an audio filepath of a voice to be cloned(Введите путь до клонируемого файла, например ex.wav) (mp3, " \

# "wav, m4a, flac, ...):\n"

#in_fpath = Path(input(message).replace("\"", "").replace("\'", ""))

in_fpath = args.path_wav

## 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(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:(Введите предложение для синтеза)\n")

# The synthesizer works in batch, so you need to put your data in a list or numpy array

texts = [args.text]

texts = g2p(texts)

print(texts)

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:")

# 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")

# Play the audio (non-blocking)

if not args.no_sound:

sd.stop()

sd.play(generated_wav, synthesizer.sample_rate)

# Save it on the disk

fpath = "demo_output_%02d.wav" % num_generated

print(generated_wav.dtype)

sf.write(fpath, generated_wav.astype(np.float32),

synthesizer.sample_rate)

num_generated += 1

print("\nSaved output as %s\n\n" % fpath)