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)