import os
import numpy as np
os.environ['HF_HUB_CACHE'] = './checkpoints/hf_cache'
import shutil
import warnings
import argparse
import torch
import yaml
warnings.simplefilter('ignore')
# load packages
import random
from modules.commons import *
import time
import torchaudio
import librosa
from modules.commons import str2bool
from hf_utils import load_custom_model_from_hf
# Load model and configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
fp16 = False
def load_models(args):
global fp16
fp16 = args.fp16
if not args.f0_condition:
dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
"DiT_seed_v2_uvit_whisper_small_wavenet_bigvgan_pruned.pth",
"config_dit_mel_seed_uvit_whisper_small_wavenet.yml")
f0_fn = None
else:
if args.checkpoint_path is None:
dit_checkpoint_path, dit_config_path = load_custom_model_from_hf("Plachta/Seed-VC",
"DiT_seed_v2_uvit_whisper_base_f0_44k_bigvgan_pruned_ft_ema_v2.pth",
"config_dit_mel_seed_uvit_whisper_base_f0_44k.yml")
else:
dit_checkpoint_path = args.checkpoint_path
dit_config_path = args.config_path
# f0 extractor
from modules.rmvpe import RMVPE
model_path = load_custom_model_from_hf("lj1995/VoiceConversionWebUI", "rmvpe.pt", None)
f0_extractor = RMVPE(model_path, is_half=False, device=device)
f0_fn = f0_extractor.infer_from_audio
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,
f0_fn,
vocoder_fn,
campplus_model,
to_mel,
mel_fn_args,
)
def adjust_f0_semitones(f0_sequence, n_semitones):
factor = 2 ** (n_semitones / 12)
return f0_sequence * factor
def crossfade(chunk1, chunk2, overlap):
fade_out = np.cos(np.linspace(0, np.pi / 2, overlap)) ** 2
fade_in = np.cos(np.linspace(np.pi / 2, 0, overlap)) ** 2
if len(chunk2) < overlap:
chunk2[:overlap] = chunk2[:overlap] * fade_in[:len(chunk2)] + (chunk1[-overlap:] * fade_out)[:len(chunk2)]
else:
chunk2[:overlap] = chunk2[:overlap] * fade_in + chunk1[-overlap:] * fade_out
return chunk2
@torch.no_grad()
def main(args):
model, semantic_fn, f0_fn, vocoder_fn, campplus_model, mel_fn, mel_fn_args = load_models(args)
sr = mel_fn_args['sampling_rate']
f0_condition = args.f0_condition
auto_f0_adjust = args.auto_f0_adjust
pitch_shift = args.semi_tone_shift
source = args.source
target_name = args.target
diffusion_steps = args.diffusion_steps
length_adjust = args.length_adjust
inference_cfg_rate = args.inference_cfg_rate
source_audio = librosa.load(source, sr=sr)[0]
ref_audio = librosa.load(target_name, sr=sr)[0]
sr = 22050 if not f0_condition else 44100
hop_length = 256 if not f0_condition else 512
max_context_window = sr // hop_length * 30
overlap_frame_len = 16
overlap_wave_len = overlap_frame_len * hop_length
# Process audio
source_audio = torch.tensor(source_audio).unsqueeze(0).float().to(device)
ref_audio = torch.tensor(ref_audio[:sr * 25]).unsqueeze(0).float().to(device)
time_vc_start = time.time()
# Resample
converted_waves_16k = torchaudio.functional.resample(source_audio, sr, 16000)
# if source audio less than 30 seconds, whisper can handle in one forward
if converted_waves_16k.size(-1) <= 16000 * 30:
S_alt = semantic_fn(converted_waves_16k)
else:
overlapping_time = 5 # 5 seconds
S_alt_list = []
buffer = None
traversed_time = 0
while traversed_time < converted_waves_16k.size(-1):
if buffer is None: # first chunk
chunk = converted_waves_16k[:, traversed_time:traversed_time + 16000 * 30]
else:
chunk = torch.cat(
[buffer, converted_waves_16k[:, traversed_time:traversed_time + 16000 * (30 - overlapping_time)]],
dim=-1)
S_alt = semantic_fn(chunk)
if traversed_time == 0:
S_alt_list.append(S_alt)
else:
S_alt_list.append(S_alt[:, 50 * overlapping_time:])
buffer = chunk[:, -16000 * overlapping_time:]
traversed_time += 30 * 16000 if traversed_time == 0 else chunk.size(-1) - 16000 * overlapping_time
S_alt = torch.cat(S_alt_list, dim=1)
ori_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
S_ori = semantic_fn(ori_waves_16k)
mel = mel_fn(source_audio.to(device).float())
mel2 = mel_fn(ref_audio.to(device).float())
target_lengths = torch.LongTensor([int(mel.size(2) * length_adjust)]).to(mel.device)
target2_lengths = torch.LongTensor([mel2.size(2)]).to(mel2.device)
feat2 = torchaudio.compliance.kaldi.fbank(ori_waves_16k,
num_mel_bins=80,
dither=0,
sample_frequency=16000)
feat2 = feat2 - feat2.mean(dim=0, keepdim=True)
style2 = campplus_model(feat2.unsqueeze(0))
if f0_condition:
F0_ori = f0_fn(ori_waves_16k[0], thred=0.03)
F0_alt = f0_fn(converted_waves_16k[0], thred=0.03)
F0_ori = torch.from_numpy(F0_ori).to(device)[None]
F0_alt = torch.from_numpy(F0_alt).to(device)[None]
voiced_F0_ori = F0_ori[F0_ori > 1]
voiced_F0_alt = F0_alt[F0_alt > 1]
log_f0_alt = torch.log(F0_alt + 1e-5)
voiced_log_f0_ori = torch.log(voiced_F0_ori + 1e-5)
voiced_log_f0_alt = torch.log(voiced_F0_alt + 1e-5)
median_log_f0_ori = torch.median(voiced_log_f0_ori)
median_log_f0_alt = torch.median(voiced_log_f0_alt)
# shift alt log f0 level to ori log f0 level
shifted_log_f0_alt = log_f0_alt.clone()
if auto_f0_adjust:
shifted_log_f0_alt[F0_alt > 1] = log_f0_alt[F0_alt > 1] - median_log_f0_alt + median_log_f0_ori
shifted_f0_alt = torch.exp(shifted_log_f0_alt)
if pitch_shift != 0:
shifted_f0_alt[F0_alt > 1] = adjust_f0_semitones(shifted_f0_alt[F0_alt > 1], pitch_shift)
else:
F0_ori = None
F0_alt = None
shifted_f0_alt = None
# Length regulation
cond, _, codes, commitment_loss, codebook_loss = model.length_regulator(S_alt, ylens=target_lengths,
n_quantizers=3,
f0=shifted_f0_alt)
prompt_condition, _, codes, commitment_loss, codebook_loss = model.length_regulator(S_ori,
ylens=target2_lengths,
n_quantizers=3,
f0=F0_ori)
max_source_window = max_context_window - mel2.size(2)
# split source condition (cond) into chunks
processed_frames = 0
generated_wave_chunks = []
# generate chunk by chunk and stream the output
while processed_frames < cond.size(1):
chunk_cond = cond[:, processed_frames:processed_frames + max_source_window]
is_last_chunk = processed_frames + max_source_window >= cond.size(1)
cat_condition = torch.cat([prompt_condition, chunk_cond], dim=1)
with torch.autocast(device_type=device.type, dtype=torch.float16 if fp16 else torch.float32):
# Voice Conversion
vc_target = model.cfm.inference(cat_condition,
torch.LongTensor([cat_condition.size(1)]).to(mel2.device),
mel2, style2, None, diffusion_steps,
inference_cfg_rate=inference_cfg_rate)
vc_target = vc_target[:, :, mel2.size(-1):]
vc_wave = vocoder_fn(vc_target.float()).squeeze()
vc_wave = vc_wave[None, :]
if processed_frames == 0:
if is_last_chunk:
output_wave = vc_wave[0].cpu().numpy()
generated_wave_chunks.append(output_wave)
break
output_wave = vc_wave[0, :-overlap_wave_len].cpu().numpy()
generated_wave_chunks.append(output_wave)
previous_chunk = vc_wave[0, -overlap_wave_len:]
processed_frames += vc_target.size(2) - overlap_frame_len
elif is_last_chunk:
output_wave = crossfade(previous_chunk.cpu().numpy(), vc_wave[0].cpu().numpy(), overlap_wave_len)
generated_wave_chunks.append(output_wave)
processed_frames += vc_target.size(2) - overlap_frame_len
break
else:
output_wave = crossfade(previous_chunk.cpu().numpy(), vc_wave[0, :-overlap_wave_len].cpu().numpy(),
overlap_wave_len)
generated_wave_chunks.append(output_wave)
previous_chunk = vc_wave[0, -overlap_wave_len:]
processed_frames += vc_target.size(2) - overlap_frame_len
vc_wave = torch.tensor(np.concatenate(generated_wave_chunks))[None, :].float()
time_vc_end = time.time()
print(f"RTF: {(time_vc_end - time_vc_start) / vc_wave.size(-1) * sr}")
source_name = os.path.basename(source).split(".")[0]
target_name = os.path.basename(target_name).split(".")[0]
os.makedirs(args.output, exist_ok=True)
torchaudio.save(os.path.join(args.output, f"vc_{source_name}_{target_name}_{length_adjust}_{diffusion_steps}_{inference_cfg_rate}.wav"), vc_wave.cpu(), sr)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--source", type=str, default="./examples/source/source_s1.wav")
parser.add_argument("--target", type=str, default="./examples/reference/s1p1.wav")
parser.add_argument("--output", type=str, default="./reconstructed")
parser.add_argument("--diffusion-steps", type=int, default=30)
parser.add_argument("--length-adjust", type=float, default=1.0)
parser.add_argument("--inference-cfg-rate", type=float, default=0.7)
parser.add_argument("--f0-condition", type=str2bool, default=False)
parser.add_argument("--auto-f0-adjust", type=str2bool, default=False)
parser.add_argument("--semi-tone-shift", type=int, default=0)
parser.add_argument("--checkpoint-path", type=str, help="Path to the checkpoint file", default=None)
parser.add_argument("--config-path", type=str, help="Path to the config file", default=None)
parser.add_argument("--fp16", type=str2bool, default=True)
args = parser.parse_args()
main(args)