import argparse
from ctypes import alignment
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
from pathlib import Path
import spacy
import time
if __name__ == '__main__':
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("--run_id", type=str, default="default", help= \
"Name for this model. By default, training outputs will be stored to saved_models/<run_id>/. If a model state "
"from the same run ID was previously saved, the training will restart from there. Pass -f to overwrite saved "
"states and restart from scratch.")
parser.add_argument("-m", "--models_dir", type=Path, default="saved_models",
help="Directory containing all saved models")
parser.add_argument("--weight", type=float, default=1,
help="weight of input audio for voice filter")
parser.add_argument("--griffin_lim",
action="store_true",
help="if True, use griffin-lim, else use 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")
import numpy as np
import soundfile as sf
import torch
import encoder.inference
import encoder.params_data
from synthesizer.inference import Synthesizer_infer
from synthesizer.utils.cleaners import add_breaks, english_cleaners_predict
from vocoder import inference as vocoder
from vocoder.display import save_attention_multiple, save_spectrogram, save_stop_tokens
from utils.argutils import print_args
from utils.default_models import ensure_default_models
from speed_changer.fixSpeed import *
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.
if not args.griffin_lim:
print("Preparing the encoder, the synthesizer and the vocoder...")
else:
print("Preparing the encoder and the synthesizer...")
ensure_default_models(args.run_id, Path("saved_models"))
encoder.inference.load_model(list(args.models_dir.glob(f"{args.run_id}/encoder.pt"))[0])
synthesizer = Synthesizer_infer(list(args.models_dir.glob(f"{args.run_id}/synthesizer.pt"))[0])
if not args.griffin_lim:
vocoder.load_model(list(args.models_dir.glob(f"{args.run_id}/vocoder.pt"))[0])
# ## 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:
# if not args.griffin_lim:
# 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
nlp = spacy.load('en_core_web_sm')
weight = arg_dict["weight"] # 声音美颜的用户语音权重
amp = 1
while True:
# try:
# Get the reference audio filepath
num_of_input_audio = 1
for i in range(num_of_input_audio):
# 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:
# get duration info from input audio
message2 = "Reference voice: enter an audio folder of a voice to be cloned (mp3, " \
f"wav, m4a, flac, ...):({i+1}/{num_of_input_audio})\n"
in_fpath = Path(input(message2).replace("\"", "").replace("\'", ""))
fpath_without_ext = os.path.splitext(str(in_fpath))[0]
speaker_name = os.path.normpath(fpath_without_ext).split(os.sep)[-1]
is_wav_file, single_wav, wav_path = TransFormat(in_fpath, 'wav')
if not is_wav_file:
os.remove(wav_path) # remove intermediate wav files
# merge
if i == 0:
wav = single_wav
else:
wav = np.append(wav, single_wav)
# write to disk
path_ori, _ = os.path.split(wav_path)
file_ori = 'temp.wav'
fpath = os.path.join(path_ori, file_ori)
sf.write(fpath, wav, samplerate=encoder.params_data.sampling_rate)
# adjust the speed
totDur_ori, nPause_ori, arDur_ori, nSyl_ori, arRate_ori = AudioAnalysis(path_ori, file_ori)
DelFile(path_ori, '.TextGrid')
os.remove(fpath)
preprocessed_wav = encoder.inference.preprocess_wav(wav)
print("Loaded input audio 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):
input_embed = encoder.inference.embed_utterance(preprocessed_wav)
# Choose standard audio
fft_max_freq = vocoder.get_dominant_freq(preprocessed_wav)
print(f"\nthe dominant frequency of input audio is {fft_max_freq}Hz")
if fft_max_freq < encoder.params_data.split_freq:
vocoder.hp.sex = 1
standard_fpath = "standard_audios/male_1.wav"
else:
vocoder.hp.sex = 0
standard_fpath = "standard_audios/female_1.wav"
if os.path.exists(standard_fpath):
standard_wav = Synthesizer_infer.load_preprocess_wav(standard_fpath)
preprocessed_standard_wav = encoder.inference.preprocess_wav(standard_wav)
print("Loaded standard audio file successfully")
standard_embed = encoder.inference.embed_utterance(preprocessed_standard_wav)
embed1=np.copy(input_embed).dot(weight)
embed2=np.copy(standard_embed).dot(1 - weight)
embed=embed1+embed2
else:
embed = np.copy(input_embed)
embed[embed < encoder.params_data.set_zero_thres]=0 # 噪声值置零
embed = embed * amp
start_syn = time.time()
# Generating the spectrogram
text = input("Write a sentence 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_infer(args.syn_model_fpath)
# The synthesizer works in batch, so you need to put your data in a list or numpy array
def preprocess_text(text):
text = add_breaks(text)
text = english_cleaners_predict(text)
texts = [i.text.strip() for i in nlp(text).sents] # split paragraph to sentences
return texts
texts = preprocess_text(text)
print(f"the list of inputs texts:\n{texts}")
# embeds = [embed] * len(texts)
specs = []
alignments = []
stop_tokens = []
for text in texts:
spec, align, stop_token = synthesizer.synthesize_spectrograms([text], [embed], require_visualization=True)
specs.append(spec[0])
alignments.append(align[0])
stop_tokens.append(stop_token[0])
breaks = [spec.shape[1] for spec in specs]
spec = np.concatenate(specs, axis=1)
## Save synthesizer visualization results
if not os.path.exists("syn_results"):
os.mkdir("syn_results")
save_attention_multiple(alignments, "syn_results/attention")
save_stop_tokens(stop_tokens, "syn_results/stop_tokens")
save_spectrogram(spec, "syn_results/mel")
print("Created the mel spectrogram")
end_syn = time.time()
print(f"Prediction time of synthesizer is {end_syn - start_syn}s")
start_voc = time.time()
## 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.
if not args.griffin_lim:
wav = vocoder.infer_waveform(spec, target=vocoder.hp.voc_target, overlap=vocoder.hp.voc_overlap, crossfade=vocoder.hp.is_crossfade)
else:
wav = Synthesizer_infer.griffin_lim(spec)
end_voc = time.time()
print(f"Prediction time of vocoder is {end_voc - start_voc}s")
print(f"Prediction time of TTS is {end_voc - start_syn}s")
# Add breaks
b_ends = np.cumsum(np.array(breaks) * Synthesizer_infer.hparams.hop_size)
b_starts = np.concatenate(([0], b_ends[:-1]))
wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
breaks = [np.zeros(int(0.15 * Synthesizer_infer.sample_rate))] * len(breaks)
wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
# Trim excess silences to compensate for gaps in spectrograms (issue #53)
# generated_wav = encoder.inference.preprocess_wav(wav)
wav = wav / np.abs(wav).max() * 4
# Save it on the disk
# filename = "demo_output_%02d.wav" % num_generated
if not os.path.exists("out_audios"):
os.mkdir("out_audios")
dir_path = os.path.dirname(os.path.realpath(__file__)) # current dir
filename = os.path.join(dir_path, f"out_audios/{speaker_name}_syn.wav")
# print(wav.dtype)
sf.write(filename, wav.astype(np.float32), synthesizer.sample_rate)
num_generated += 1
print("\nSaved output (havent't change speed) as %s\n\n" % filename)
# Fix Speed(generate new audio)
fix_file = work(totDur_ori,
nPause_ori,
arDur_ori,
nSyl_ori,
arRate_ori,
filename)
print(f"\nSaved output (fixed speed) as {fix_file}\n\n")
# # Play the audio (non-blocking)
# if not args.no_sound:
# import sounddevice as sd
# try:
# sd.stop()
# sd.play(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
# except Exception as e:
# print("Caught exception: %s" % repr(e))
# print("Restarting\n")