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real-time-gui.py
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import os
import sys
from dotenv import load_dotenv
import shutil
load_dotenv()
os.environ["OMP_NUM_THREADS"] = "4"
if sys.platform == "darwin":
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
now_dir = os.getcwd()
sys.path.append(now_dir)
import multiprocessing
import warnings
import yaml
warnings.simplefilter("ignore")
from tqdm import tqdm
from modules.commons import *
import librosa
import torchaudio
import torchaudio.compliance.kaldi as kaldi
from hf_utils import load_custom_model_from_hf
import os
import sys
import torch
from modules.commons import str2bool
# Load model and configuration
device = None
flag_vc = False
prompt_condition, mel2, style2 = None, None, None
reference_wav_name = ""
prompt_len = 3 # in seconds
ce_dit_difference = 2.0 # 2 seconds
fp16 = False
@torch.no_grad()
def custom_infer(model_set,
reference_wav,
new_reference_wav_name,
input_wav_res,
block_frame_16k,
skip_head,
skip_tail,
return_length,
diffusion_steps,
inference_cfg_rate,
max_prompt_length,
cd_difference=2.0,
):
global prompt_condition, mel2, style2
global reference_wav_name
global prompt_len
global ce_dit_difference
(
model,
semantic_fn,
vocoder_fn,
campplus_model,
to_mel,
mel_fn_args,
) = model_set
sr = mel_fn_args["sampling_rate"]
hop_length = mel_fn_args["hop_size"]
if ce_dit_difference != cd_difference:
ce_dit_difference = cd_difference
print(f"Setting ce_dit_difference to {cd_difference} seconds.")
if prompt_condition is None or reference_wav_name != new_reference_wav_name or prompt_len != max_prompt_length:
prompt_len = max_prompt_length
print(f"Setting max prompt length to {max_prompt_length} seconds.")
reference_wav = reference_wav[:int(sr * prompt_len)]
reference_wav_tensor = torch.from_numpy(reference_wav).to(device)
ori_waves_16k = torchaudio.functional.resample(reference_wav_tensor, sr, 16000)
S_ori = semantic_fn(ori_waves_16k.unsqueeze(0))
feat2 = torchaudio.compliance.kaldi.fbank(
ori_waves_16k.unsqueeze(0), num_mel_bins=80, dither=0, sample_frequency=16000
)
feat2 = feat2 - feat2.mean(dim=0, keepdim=True)
style2 = campplus_model(feat2.unsqueeze(0))
mel2 = to_mel(reference_wav_tensor.unsqueeze(0))
target2_lengths = torch.LongTensor([mel2.size(2)]).to(mel2.device)
prompt_condition = model.length_regulator(
S_ori, ylens=target2_lengths, n_quantizers=3, f0=None
)[0]
reference_wav_name = new_reference_wav_name
converted_waves_16k = input_wav_res
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
torch.cuda.synchronize()
start_event.record()
S_alt = semantic_fn(converted_waves_16k.unsqueeze(0))
end_event.record()
torch.cuda.synchronize() # Wait for the events to be recorded!
elapsed_time_ms = start_event.elapsed_time(end_event)
print(f"Time taken for semantic_fn: {elapsed_time_ms}ms")
ce_dit_frame_difference = int(ce_dit_difference * 50)
S_alt = S_alt[:, ce_dit_frame_difference:]
target_lengths = torch.LongTensor([(skip_head + return_length + skip_tail - ce_dit_frame_difference) / 50 * sr // hop_length]).to(S_alt.device)
print(f"target_lengths: {target_lengths}")
cond = model.length_regulator(
S_alt, ylens=target_lengths , n_quantizers=3, f0=None
)[0]
cat_condition = torch.cat([prompt_condition, cond], dim=1)
with torch.autocast(device_type=device.type, dtype=torch.float16 if fp16 else torch.float32):
vc_target = model.cfm.inference(
cat_condition,
torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
mel2,
style2,
None,
n_timesteps=diffusion_steps,
inference_cfg_rate=inference_cfg_rate,
)
vc_target = vc_target[:, :, mel2.size(-1) :]
print(f"vc_target.shape: {vc_target.shape}")
vc_wave = vocoder_fn(vc_target).squeeze()
output_len = return_length * sr // 50
tail_len = skip_tail * sr // 50
output = vc_wave[-output_len - tail_len: -tail_len]
return output
def load_models(args):
global fp16
fp16 = args.fp16
print(f"Using fp16: {fp16}")
if args.checkpoint_path is None or args.checkpoint_path == "":
dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
"DiT_uvit_tat_xlsr_ema.pth",
"config_dit_mel_seed_uvit_xlsr_tiny.yml")
else:
dit_checkpoint_path = args.checkpoint_path
dit_config_path = args.config_path
config = yaml.safe_load(open(dit_config_path, "r"))
model_params = recursive_munch(config["model_params"])
model_params.dit_type = 'DiT'
model = build_model(model_params, stage="DiT")
hop_length = config["preprocess_params"]["spect_params"]["hop_length"]
sr = config["preprocess_params"]["sr"]
# Load checkpoints
model, _, _, _ = load_checkpoint(
model,
None,
dit_checkpoint_path,
load_only_params=True,
ignore_modules=[],
is_distributed=False,
)
for key in model:
model[key].eval()
model[key].to(device)
model.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
# Load additional modules
from modules.campplus.DTDNN import CAMPPlus
campplus_ckpt_path = load_custom_model_from_hf(
"funasr/campplus", "campplus_cn_common.bin", config_filename=None
)
campplus_model = CAMPPlus(feat_dim=80, embedding_size=192)
campplus_model.load_state_dict(torch.load(campplus_ckpt_path, map_location="cpu"))
campplus_model.eval()
campplus_model.to(device)
vocoder_type = model_params.vocoder.type
if vocoder_type == 'bigvgan':
from modules.bigvgan import bigvgan
bigvgan_name = model_params.vocoder.name
bigvgan_model = bigvgan.BigVGAN.from_pretrained(bigvgan_name, use_cuda_kernel=False)
# remove weight norm in the model and set to eval mode
bigvgan_model.remove_weight_norm()
bigvgan_model = bigvgan_model.eval().to(device)
vocoder_fn = bigvgan_model
elif vocoder_type == 'hifigan':
from modules.hifigan.generator import HiFTGenerator
from modules.hifigan.f0_predictor import ConvRNNF0Predictor
hift_config = yaml.safe_load(open('configs/hifigan.yml', 'r'))
hift_gen = HiFTGenerator(**hift_config['hift'], f0_predictor=ConvRNNF0Predictor(**hift_config['f0_predictor']))
hift_path = load_custom_model_from_hf("FunAudioLLM/CosyVoice-300M", 'hift.pt', None)
hift_gen.load_state_dict(torch.load(hift_path, map_location='cpu'))
hift_gen.eval()
hift_gen.to(device)
vocoder_fn = hift_gen
elif vocoder_type == "vocos":
vocos_config = yaml.safe_load(open(model_params.vocoder.vocos.config, 'r'))
vocos_path = model_params.vocoder.vocos.path
vocos_model_params = recursive_munch(vocos_config['model_params'])
vocos = build_model(vocos_model_params, stage='mel_vocos')
vocos_checkpoint_path = vocos_path
vocos, _, _, _ = load_checkpoint(vocos, None, vocos_checkpoint_path,
load_only_params=True, ignore_modules=[], is_distributed=False)
_ = [vocos[key].eval().to(device) for key in vocos]
_ = [vocos[key].to(device) for key in vocos]
total_params = sum(sum(p.numel() for p in vocos[key].parameters() if p.requires_grad) for key in vocos.keys())
print(f"Vocoder model total parameters: {total_params / 1_000_000:.2f}M")
vocoder_fn = vocos.decoder
else:
raise ValueError(f"Unknown vocoder type: {vocoder_type}")
speech_tokenizer_type = model_params.speech_tokenizer.type
if speech_tokenizer_type == 'whisper':
# whisper
from transformers import AutoFeatureExtractor, WhisperModel
whisper_name = model_params.speech_tokenizer.name
whisper_model = WhisperModel.from_pretrained(whisper_name, torch_dtype=torch.float16).to(device)
del whisper_model.decoder
whisper_feature_extractor = AutoFeatureExtractor.from_pretrained(whisper_name)
def semantic_fn(waves_16k):
ori_inputs = whisper_feature_extractor([waves_16k.squeeze(0).cpu().numpy()],
return_tensors="pt",
return_attention_mask=True)
ori_input_features = whisper_model._mask_input_features(
ori_inputs.input_features, attention_mask=ori_inputs.attention_mask).to(device)
with torch.no_grad():
ori_outputs = whisper_model.encoder(
ori_input_features.to(whisper_model.encoder.dtype),
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
)
S_ori = ori_outputs.last_hidden_state.to(torch.float32)
S_ori = S_ori[:, :waves_16k.size(-1) // 320 + 1]
return S_ori
elif speech_tokenizer_type == 'cnhubert':
from transformers import (
Wav2Vec2FeatureExtractor,
HubertModel,
)
hubert_model_name = config['model_params']['speech_tokenizer']['name']
hubert_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(hubert_model_name)
hubert_model = HubertModel.from_pretrained(hubert_model_name)
hubert_model = hubert_model.to(device)
hubert_model = hubert_model.eval()
hubert_model = hubert_model.half()
def semantic_fn(waves_16k):
ori_waves_16k_input_list = [
waves_16k[bib].cpu().numpy()
for bib in range(len(waves_16k))
]
ori_inputs = hubert_feature_extractor(ori_waves_16k_input_list,
return_tensors="pt",
return_attention_mask=True,
padding=True,
sampling_rate=16000).to(device)
with torch.no_grad():
ori_outputs = hubert_model(
ori_inputs.input_values.half(),
)
S_ori = ori_outputs.last_hidden_state.float()
return S_ori
elif speech_tokenizer_type == 'xlsr':
from transformers import (
Wav2Vec2FeatureExtractor,
Wav2Vec2Model,
)
model_name = config['model_params']['speech_tokenizer']['name']
output_layer = config['model_params']['speech_tokenizer']['output_layer']
wav2vec_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
wav2vec_model = Wav2Vec2Model.from_pretrained(model_name)
wav2vec_model.encoder.layers = wav2vec_model.encoder.layers[:output_layer]
wav2vec_model = wav2vec_model.to(device)
wav2vec_model = wav2vec_model.eval()
wav2vec_model = wav2vec_model.half()
def semantic_fn(waves_16k):
ori_waves_16k_input_list = [
waves_16k[bib].cpu().numpy()
for bib in range(len(waves_16k))
]
ori_inputs = wav2vec_feature_extractor(ori_waves_16k_input_list,
return_tensors="pt",
return_attention_mask=True,
padding=True,
sampling_rate=16000).to(device)
with torch.no_grad():
ori_outputs = wav2vec_model(
ori_inputs.input_values.half(),
)
S_ori = ori_outputs.last_hidden_state.float()
return S_ori
else:
raise ValueError(f"Unknown speech tokenizer type: {speech_tokenizer_type}")
# Generate mel spectrograms
mel_fn_args = {
"n_fft": config['preprocess_params']['spect_params']['n_fft'],
"win_size": config['preprocess_params']['spect_params']['win_length'],
"hop_size": config['preprocess_params']['spect_params']['hop_length'],
"num_mels": config['preprocess_params']['spect_params']['n_mels'],
"sampling_rate": sr,
"fmin": config['preprocess_params']['spect_params'].get('fmin', 0),
"fmax": None if config['preprocess_params']['spect_params'].get('fmax', "None") == "None" else 8000,
"center": False
}
from modules.audio import mel_spectrogram
to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
return (
model,
semantic_fn,
vocoder_fn,
campplus_model,
to_mel,
mel_fn_args,
)
def printt(strr, *args):
if len(args) == 0:
print(strr)
else:
print(strr % args)
class Config:
def __init__(self):
self.device = device
if __name__ == "__main__":
import json
import multiprocessing
import re
import threading
import time
import traceback
from multiprocessing import Queue, cpu_count
import argparse
import librosa
import numpy as np
import FreeSimpleGUI as sg
import sounddevice as sd
import torch
import torch.nn.functional as F
import torchaudio.transforms as tat
current_dir = os.getcwd()
n_cpu = cpu_count()
class GUIConfig:
def __init__(self) -> None:
self.reference_audio_path: str = ""
# self.index_path: str = ""
self.diffusion_steps: int = 10
self.sr_type: str = "sr_model"
self.block_time: float = 0.25 # s
self.threhold: int = -60
self.crossfade_time: float = 0.05
self.extra_time_ce: float = 2.5
self.extra_time: float = 0.5
self.extra_time_right: float = 2.0
self.I_noise_reduce: bool = False
self.O_noise_reduce: bool = False
self.inference_cfg_rate: float = 0.7
self.sg_hostapi: str = ""
self.wasapi_exclusive: bool = False
self.sg_input_device: str = ""
self.sg_output_device: str = ""
class GUI:
def __init__(self, args) -> None:
self.gui_config = GUIConfig()
self.config = Config()
self.function = "vc"
self.delay_time = 0
self.hostapis = None
self.input_devices = None
self.output_devices = None
self.input_devices_indices = None
self.output_devices_indices = None
self.stream = None
self.model_set = load_models(args)
from funasr import AutoModel
self.vad_model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
self.update_devices()
self.launcher()
def load(self):
try:
os.makedirs("configs/inuse", exist_ok=True)
if not os.path.exists("configs/inuse/config.json"):
shutil.copy("configs/config.json", "configs/inuse/config.json")
with open("configs/inuse/config.json", "r") as j:
data = json.load(j)
data["sr_model"] = data["sr_type"] == "sr_model"
data["sr_device"] = data["sr_type"] == "sr_device"
if data["sg_hostapi"] in self.hostapis:
self.update_devices(hostapi_name=data["sg_hostapi"])
if (
data["sg_input_device"] not in self.input_devices
or data["sg_output_device"] not in self.output_devices
):
self.update_devices()
data["sg_hostapi"] = self.hostapis[0]
data["sg_input_device"] = self.input_devices[
self.input_devices_indices.index(sd.default.device[0])
]
data["sg_output_device"] = self.output_devices[
self.output_devices_indices.index(sd.default.device[1])
]
else:
data["sg_hostapi"] = self.hostapis[0]
data["sg_input_device"] = self.input_devices[
self.input_devices_indices.index(sd.default.device[0])
]
data["sg_output_device"] = self.output_devices[
self.output_devices_indices.index(sd.default.device[1])
]
except:
with open("configs/inuse/config.json", "w") as j:
data = {
"sg_hostapi": self.hostapis[0],
"sg_wasapi_exclusive": False,
"sg_input_device": self.input_devices[
self.input_devices_indices.index(sd.default.device[0])
],
"sg_output_device": self.output_devices[
self.output_devices_indices.index(sd.default.device[1])
],
"sr_type": "sr_model",
"block_time": 0.3,
"crossfade_length": 0.04,
"extra_time_ce": 2.5,
"extra_time": 0.5,
"extra_time_right": 0.02,
"diffusion_steps": 10,
"inference_cfg_rate": 0.7,
"max_prompt_length": 3.0,
}
data["sr_model"] = data["sr_type"] == "sr_model"
data["sr_device"] = data["sr_type"] == "sr_device"
return data
def launcher(self):
self.config = Config()
data = self.load()
sg.theme("LightBlue3")
layout = [
[
sg.Frame(
title="Load reference audio",
layout=[
[
sg.Input(
default_text=data.get("reference_audio_path", ""),
key="reference_audio_path",
),
sg.FileBrowse(
"choose an audio file",
initial_folder=os.path.join(
os.getcwd(), "examples/reference"
),
file_types=((". wav"), (". mp3"), (". flac"), (". m4a"), (". ogg"), (". opus")),
),
],
],
)
],
[
sg.Frame(
layout=[
[
sg.Text("Device type"),
sg.Combo(
self.hostapis,
key="sg_hostapi",
default_value=data.get("sg_hostapi", ""),
enable_events=True,
size=(20, 1),
),
sg.Checkbox(
"WASAPI Exclusive Device",
key="sg_wasapi_exclusive",
default=data.get("sg_wasapi_exclusive", False),
enable_events=True,
),
],
[
sg.Text("Input Device"),
sg.Combo(
self.input_devices,
key="sg_input_device",
default_value=data.get("sg_input_device", ""),
enable_events=True,
size=(45, 1),
),
],
[
sg.Text("Output Device"),
sg.Combo(
self.output_devices,
key="sg_output_device",
default_value=data.get("sg_output_device", ""),
enable_events=True,
size=(45, 1),
),
],
[
sg.Button("Reload devices", key="reload_devices"),
sg.Radio(
"Use model sampling rate",
"sr_type",
key="sr_model",
default=data.get("sr_model", True),
enable_events=True,
),
sg.Radio(
"Use device sampling rate",
"sr_type",
key="sr_device",
default=data.get("sr_device", False),
enable_events=True,
),
sg.Text("Sampling rate:"),
sg.Text("", key="sr_stream"),
],
],
title="Sound Device",
)
],
[
sg.Frame(
layout=[
# [
# sg.Text("Activation threshold"),
# sg.Slider(
# range=(-60, 0),
# key="threhold",
# resolution=1,
# orientation="h",
# default_value=data.get("threhold", -60),
# enable_events=True,
# ),
# ],
[
sg.Text("Diffusion steps"),
sg.Slider(
range=(1, 30),
key="diffusion_steps",
resolution=1,
orientation="h",
default_value=data.get("diffusion_steps", 10),
enable_events=True,
),
],
[
sg.Text("Inference cfg rate"),
sg.Slider(
range=(0.0, 1.0),
key="inference_cfg_rate",
resolution=0.1,
orientation="h",
default_value=data.get("inference_cfg_rate", 0.7),
enable_events=True,
),
],
[
sg.Text("Max prompt length (s)"),
sg.Slider(
range=(1.0, 20.0),
key="max_prompt_length",
resolution=0.5,
orientation="h",
default_value=data.get("max_prompt_length", 3.0),
enable_events=True,
),
],
],
title="Regular settings",
),
sg.Frame(
layout=[
[
sg.Text("Block time"),
sg.Slider(
range=(0.04, 3.0),
key="block_time",
resolution=0.02,
orientation="h",
default_value=data.get("block_time", 1.0),
enable_events=True,
),
],
[
sg.Text("Crossfade length"),
sg.Slider(
range=(0.02, 0.5),
key="crossfade_length",
resolution=0.02,
orientation="h",
default_value=data.get("crossfade_length", 0.1),
enable_events=True,
),
],
[
sg.Text("Extra content encoder context time (left)"),
sg.Slider(
range=(0.5, 10.0),
key="extra_time_ce",
resolution=0.1,
orientation="h",
default_value=data.get("extra_time_ce", 5.0),
enable_events=True,
),
],
[
sg.Text("Extra DiT context time (left)"),
sg.Slider(
range=(0.5, 10.0),
key="extra_time",
resolution=0.1,
orientation="h",
default_value=data.get("extra_time", 5.0),
enable_events=True,
),
],
[
sg.Text("Extra context time (right)"),
sg.Slider(
range=(0.02, 10.0),
key="extra_time_right",
resolution=0.02,
orientation="h",
default_value=data.get("extra_time_right", 2.0),
enable_events=True,
),
],
],
title="Performance settings",
),
],
[
sg.Button("Start Voice Conversion", key="start_vc"),
sg.Button("Stop Voice Conversion", key="stop_vc"),
sg.Radio(
"Input listening",
"function",
key="im",
default=False,
enable_events=True,
),
sg.Radio(
"Voice Conversion",
"function",
key="vc",
default=True,
enable_events=True,
),
sg.Text("Algorithm delay (ms):"),
sg.Text("0", key="delay_time"),
sg.Text("Inference time (ms):"),
sg.Text("0", key="infer_time"),
],
]
self.window = sg.Window("Seed-VC - GUI", layout=layout, finalize=True)
self.event_handler()
def event_handler(self):
global flag_vc
while True:
event, values = self.window.read()
if event == sg.WINDOW_CLOSED:
self.stop_stream()
exit()
if event == "reload_devices" or event == "sg_hostapi":
self.gui_config.sg_hostapi = values["sg_hostapi"]
self.update_devices(hostapi_name=values["sg_hostapi"])
if self.gui_config.sg_hostapi not in self.hostapis:
self.gui_config.sg_hostapi = self.hostapis[0]
self.window["sg_hostapi"].Update(values=self.hostapis)
self.window["sg_hostapi"].Update(value=self.gui_config.sg_hostapi)
if (
self.gui_config.sg_input_device not in self.input_devices
and len(self.input_devices) > 0
):
self.gui_config.sg_input_device = self.input_devices[0]
self.window["sg_input_device"].Update(values=self.input_devices)
self.window["sg_input_device"].Update(
value=self.gui_config.sg_input_device
)
if self.gui_config.sg_output_device not in self.output_devices:
self.gui_config.sg_output_device = self.output_devices[0]
self.window["sg_output_device"].Update(values=self.output_devices)
self.window["sg_output_device"].Update(
value=self.gui_config.sg_output_device
)
if event == "start_vc" and not flag_vc:
if self.set_values(values) == True:
printt("cuda_is_available: %s", torch.cuda.is_available())
self.start_vc()
settings = {
"reference_audio_path": values["reference_audio_path"],
# "index_path": values["index_path"],
"sg_hostapi": values["sg_hostapi"],
"sg_wasapi_exclusive": values["sg_wasapi_exclusive"],
"sg_input_device": values["sg_input_device"],
"sg_output_device": values["sg_output_device"],
"sr_type": ["sr_model", "sr_device"][
[
values["sr_model"],
values["sr_device"],
].index(True)
],
# "threhold": values["threhold"],
"diffusion_steps": values["diffusion_steps"],
"inference_cfg_rate": values["inference_cfg_rate"],
"max_prompt_length": values["max_prompt_length"],
"block_time": values["block_time"],
"crossfade_length": values["crossfade_length"],
"extra_time_ce": values["extra_time_ce"],
"extra_time": values["extra_time"],
"extra_time_right": values["extra_time_right"],
}
with open("configs/inuse/config.json", "w") as j:
json.dump(settings, j)
if self.stream is not None:
self.delay_time = (
self.stream.latency[-1]
+ values["block_time"]
+ values["crossfade_length"]
+ values["extra_time_right"]
+ 0.01
)
self.window["sr_stream"].update(self.gui_config.samplerate)
self.window["delay_time"].update(
int(np.round(self.delay_time * 1000))
)
# Parameter hot update
# if event == "threhold":
# self.gui_config.threhold = values["threhold"]
elif event == "diffusion_steps":
self.gui_config.diffusion_steps = values["diffusion_steps"]
elif event == "inference_cfg_rate":
self.gui_config.inference_cfg_rate = values["inference_cfg_rate"]
elif event in ["vc", "im"]:
self.function = event
elif event == "stop_vc" or event != "start_vc":
# Other parameters do not support hot update
self.stop_stream()
def set_values(self, values):
if len(values["reference_audio_path"].strip()) == 0:
sg.popup("Choose an audio file")
return False
pattern = re.compile("[^\x00-\x7F]+")
if pattern.findall(values["reference_audio_path"]):
sg.popup("audio file path contains non-ascii characters")
return False
self.set_devices(values["sg_input_device"], values["sg_output_device"])
self.gui_config.sg_hostapi = values["sg_hostapi"]
self.gui_config.sg_wasapi_exclusive = values["sg_wasapi_exclusive"]
self.gui_config.sg_input_device = values["sg_input_device"]
self.gui_config.sg_output_device = values["sg_output_device"]
self.gui_config.reference_audio_path = values["reference_audio_path"]
self.gui_config.sr_type = ["sr_model", "sr_device"][
[
values["sr_model"],
values["sr_device"],
].index(True)
]
# self.gui_config.threhold = values["threhold"]
self.gui_config.diffusion_steps = values["diffusion_steps"]
self.gui_config.inference_cfg_rate = values["inference_cfg_rate"]
self.gui_config.max_prompt_length = values["max_prompt_length"]
self.gui_config.block_time = values["block_time"]
self.gui_config.crossfade_time = values["crossfade_length"]
self.gui_config.extra_time_ce = values["extra_time_ce"]
self.gui_config.extra_time = values["extra_time"]
self.gui_config.extra_time_right = values["extra_time_right"]
return True
def start_vc(self):
torch.cuda.empty_cache()
self.reference_wav, _ = librosa.load(
self.gui_config.reference_audio_path, sr=self.model_set[-1]["sampling_rate"]
)
self.gui_config.samplerate = (
self.model_set[-1]["sampling_rate"]
if self.gui_config.sr_type == "sr_model"
else self.get_device_samplerate()
)
self.gui_config.channels = self.get_device_channels()
self.zc = self.gui_config.samplerate // 50 # 44100 // 100 = 441
self.block_frame = (
int(
np.round(
self.gui_config.block_time
* self.gui_config.samplerate
/ self.zc
)
)
* self.zc
)
self.block_frame_16k = 320 * self.block_frame // self.zc
self.crossfade_frame = (
int(
np.round(
self.gui_config.crossfade_time
* self.gui_config.samplerate
/ self.zc
)
)
* self.zc
)
self.sola_buffer_frame = min(self.crossfade_frame, 4 * self.zc)
self.sola_search_frame = self.zc
self.extra_frame = (
int(
np.round(
self.gui_config.extra_time_ce
* self.gui_config.samplerate
/ self.zc
)
)
* self.zc
)
self.extra_frame_right = (
int(
np.round(
self.gui_config.extra_time_right
* self.gui_config.samplerate
/ self.zc
)
)
* self.zc
)
self.input_wav: torch.Tensor = torch.zeros(
self.extra_frame
+ self.crossfade_frame
+ self.sola_search_frame
+ self.block_frame
+ self.extra_frame_right,
device=self.config.device,
dtype=torch.float32,
) # 2 * 44100 + 0.08 * 44100 + 0.01 * 44100 + 0.25 * 44100
self.input_wav_denoise: torch.Tensor = self.input_wav.clone()
self.input_wav_res: torch.Tensor = torch.zeros(
320 * self.input_wav.shape[0] // self.zc,
device=self.config.device,
dtype=torch.float32,
) # input wave 44100 -> 16000
self.rms_buffer: np.ndarray = np.zeros(4 * self.zc, dtype="float32")
self.sola_buffer: torch.Tensor = torch.zeros(
self.sola_buffer_frame, device=self.config.device, dtype=torch.float32
)
self.nr_buffer: torch.Tensor = self.sola_buffer.clone()
self.output_buffer: torch.Tensor = self.input_wav.clone()
self.skip_head = self.extra_frame // self.zc
self.skip_tail = self.extra_frame_right // self.zc
self.return_length = (
self.block_frame + self.sola_buffer_frame + self.sola_search_frame
) // self.zc
self.fade_in_window: torch.Tensor = (
torch.sin(
0.5
* np.pi
* torch.linspace(
0.0,
1.0,
steps=self.sola_buffer_frame,
device=self.config.device,
dtype=torch.float32,
)
)
** 2
)
self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
self.resampler = tat.Resample(
orig_freq=self.gui_config.samplerate,
new_freq=16000,
dtype=torch.float32,
).to(self.config.device)
if self.model_set[-1]["sampling_rate"] != self.gui_config.samplerate:
self.resampler2 = tat.Resample(
orig_freq=self.model_set[-1]["sampling_rate"],
new_freq=self.gui_config.samplerate,
dtype=torch.float32,
).to(self.config.device)
else:
self.resampler2 = None
self.vad_cache = {}
self.vad_chunk_size = min(500, 1000 * self.gui_config.block_time)
self.vad_speech_detected = False
self.set_speech_detected_false_at_end_flag = False
self.start_stream()
def start_stream(self):
global flag_vc
if not flag_vc:
flag_vc = True
if (
"WASAPI" in self.gui_config.sg_hostapi
and self.gui_config.sg_wasapi_exclusive
):
extra_settings = sd.WasapiSettings(exclusive=True)
else:
extra_settings = None
self.stream = sd.Stream(
callback=self.audio_callback,
blocksize=self.block_frame,
samplerate=self.gui_config.samplerate,
channels=self.gui_config.channels,
dtype="float32",
extra_settings=extra_settings,
)
self.stream.start()
def stop_stream(self):
global flag_vc
if flag_vc:
flag_vc = False
if self.stream is not None:
self.stream.abort()
self.stream.close()
self.stream = None
def audio_callback(
self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
):
"""
Audio block callback function
"""
global flag_vc
print(indata.shape)
start_time = time.perf_counter()
indata = librosa.to_mono(indata.T)
# VAD first
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
torch.cuda.synchronize()
start_event.record()
indata_16k = librosa.resample(indata, orig_sr=self.gui_config.samplerate, target_sr=16000)
res = self.vad_model.generate(input=indata_16k, cache=self.vad_cache, is_final=False, chunk_size=self.vad_chunk_size)
res_value = res[0]["value"]
print(res_value)
if len(res_value) % 2 == 1 and not self.vad_speech_detected:
self.vad_speech_detected = True
elif len(res_value) % 2 == 1 and self.vad_speech_detected:
self.set_speech_detected_false_at_end_flag = True
end_event.record()
torch.cuda.synchronize() # Wait for the events to be recorded!
elapsed_time_ms = start_event.elapsed_time(end_event)
print(f"Time taken for VAD: {elapsed_time_ms}ms")
# if self.gui_config.threhold > -60:
# indata = np.append(self.rms_buffer, indata)
# rms = librosa.feature.rms(
# y=indata, frame_length=4 * self.zc, hop_length=self.zc
# )[:, 2:]
# self.rms_buffer[:] = indata[-4 * self.zc :]
# indata = indata[2 * self.zc - self.zc // 2 :]
# db_threhold = (
# librosa.amplitude_to_db(rms, ref=1.0)[0] < self.gui_config.threhold
# )
# for i in range(db_threhold.shape[0]):
# if db_threhold[i]:
# indata[i * self.zc : (i + 1) * self.zc] = 0
# indata = indata[self.zc // 2 :]
self.input_wav[: -self.block_frame] = self.input_wav[
self.block_frame :
].clone()
self.input_wav[-indata.shape[0] :] = torch.from_numpy(indata).to(
self.config.device
)
self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[
self.block_frame_16k :
].clone()
self.input_wav_res[-320 * (indata.shape[0] // self.zc + 1) :] = (
# self.resampler(self.input_wav[-indata.shape[0] - 2 * self.zc :])[
# 320:
# ]
torch.from_numpy(librosa.resample(self.input_wav[-indata.shape[0] - 2 * self.zc :].cpu().numpy(), orig_sr=self.gui_config.samplerate, target_sr=16000)[320:])
)
print(f"preprocess time: {time.perf_counter() - start_time:.2f}")
# infer
if self.function == "vc":
if self.gui_config.extra_time_ce - self.gui_config.extra_time < 0:
raise ValueError("Content encoder extra context must be greater than DiT extra context!")
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
torch.cuda.synchronize()
start_event.record()
infer_wav = custom_infer(