Update seedvc.py

seedvc.py

@@ -1,374 +1,358 @@
-import spaces
-import gradio as gr
-import torch
-import torchaudio
-import librosa
-from modules.commons import build_model, load_checkpoint, recursive_munch
-import yaml
-from hf_utils import load_custom_model_from_hf
-import numpy as np
-from pydub import AudioSegment
-
-# Load model and configuration
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-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")
-# dit_checkpoint_path = "E:/DiT_epoch_00018_step_801000.pth"
-# dit_config_path = "configs/config_dit_mel_seed_uvit_whisper_small_encoder_wavenet.yml"
-config = yaml.safe_load(open(dit_config_path, 'r'))
-model_params = recursive_munch(config['model_params'])
-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)
-
-from modules.bigvgan import bigvgan
-
-bigvgan_model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_22khz_80band_256x', 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)
-
-ckpt_path, config_path = load_custom_model_from_hf("Plachta/FAcodec", 'pytorch_model.bin', 'config.yml')
-
-codec_config = yaml.safe_load(open(config_path))
-codec_model_params = recursive_munch(codec_config['model_params'])
-codec_encoder = build_model(codec_model_params, stage="codec")
-
-ckpt_params = torch.load(ckpt_path, map_location="cpu")
-
-for key in codec_encoder:
-    codec_encoder[key].load_state_dict(ckpt_params[key], strict=False)
-_ = [codec_encoder[key].eval() for key in codec_encoder]
-_ = [codec_encoder[key].to(device) for key in codec_encoder]
-
-# whisper
-from transformers import AutoFeatureExtractor, WhisperModel
-
-whisper_name = model_params.speech_tokenizer.whisper_name if hasattr(model_params.speech_tokenizer,
-                                                                     'whisper_name') else "openai/whisper-small"
-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)
-
-# 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": 0,
-    "fmax": None,
-    "center": False
-}
-from modules.audio import mel_spectrogram
-
-to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
-
-# f0 conditioned model
-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.pth",
-                                                "config_dit_mel_seed_uvit_whisper_base_f0_44k.yml")
-
-config = yaml.safe_load(open(dit_config_path, 'r'))
-model_params = recursive_munch(config['model_params'])
-model_f0 = build_model(model_params, stage='DiT')
-hop_length = config['preprocess_params']['spect_params']['hop_length']
-sr = config['preprocess_params']['sr']
-
-# Load checkpoints
-model_f0, _, _, _ = load_checkpoint(model_f0, None, dit_checkpoint_path,
-                                 load_only_params=True, ignore_modules=[], is_distributed=False)
-for key in model_f0:
-    model_f0[key].eval()
-    model_f0[key].to(device)
-model_f0.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
-
-# f0 extractor
-from modules.rmvpe import RMVPE
-
-model_path = load_custom_model_from_hf("lj1995/VoiceConversionWebUI", "rmvpe.pt", None)
-rmvpe = RMVPE(model_path, is_half=False, device=device)
-
-mel_fn_args_f0 = {
-    "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": 0,
-    "fmax": None,
-    "center": False
-}
-to_mel_f0 = lambda x: mel_spectrogram(x, **mel_fn_args_f0)
-bigvgan_44k_model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=False)
-
-# remove weight norm in the model and set to eval mode
-bigvgan_44k_model.remove_weight_norm()
-bigvgan_44k_model = bigvgan_44k_model.eval().to(device)
-
-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
-    chunk2[:overlap] = chunk2[:overlap] * fade_in + chunk1[-overlap:] * fade_out
-    return chunk2
-
-# streaming and chunk processing related params
-max_context_window = sr // hop_length * 30
-overlap_frame_len = 16
-overlap_wave_len = overlap_frame_len * hop_length
-bitrate = "320k"
-
-@spaces.GPU
-@torch.no_grad()
-@torch.inference_mode()
-def voice_conversion(source, target, diffusion_steps, length_adjust, inference_cfg_rate, f0_condition, auto_f0_adjust, pitch_shift):
-    inference_module = model if not f0_condition else model_f0
-    mel_fn = to_mel if not f0_condition else to_mel_f0
-    bigvgan_fn = bigvgan_model if not f0_condition else bigvgan_44k_model
-    sr = 22050 if not f0_condition else 44100
-    # Load audio
-    source_audio = librosa.load(source, sr=sr)[0]
-    ref_audio = librosa.load(target, sr=sr)[0]
-
-    # 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)
-
-    # Resample
-    ref_waves_16k = torchaudio.functional.resample(ref_audio, sr, 16000)
-    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:
-        alt_inputs = whisper_feature_extractor([converted_waves_16k.squeeze(0).cpu().numpy()],
-                                               return_tensors="pt",
-                                               return_attention_mask=True,
-                                               sampling_rate=16000)
-        alt_input_features = whisper_model._mask_input_features(
-            alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
-        alt_outputs = whisper_model.encoder(
-            alt_input_features.to(whisper_model.encoder.dtype),
-            head_mask=None,
-            output_attentions=False,
-            output_hidden_states=False,
-            return_dict=True,
-        )
-        S_alt = alt_outputs.last_hidden_state.to(torch.float32)
-        S_alt = S_alt[:, :converted_waves_16k.size(-1) // 320 + 1]
-    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)
-            alt_inputs = whisper_feature_extractor([chunk.squeeze(0).cpu().numpy()],
-                                                   return_tensors="pt",
-                                                   return_attention_mask=True,
-                                                   sampling_rate=16000)
-            alt_input_features = whisper_model._mask_input_features(
-                alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
-            alt_outputs = whisper_model.encoder(
-                alt_input_features.to(whisper_model.encoder.dtype),
-                head_mask=None,
-                output_attentions=False,
-                output_hidden_states=False,
-                return_dict=True,
-            )
-            S_alt = alt_outputs.last_hidden_state.to(torch.float32)
-            S_alt = S_alt[:, :chunk.size(-1) // 320 + 1]
-            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)
-    ori_inputs = whisper_feature_extractor([ori_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[:, :ori_waves_16k.size(-1) // 320 + 1]
-
-    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(ref_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 = rmvpe.infer_from_audio(ref_waves_16k[0], thred=0.5)
-        F0_alt = rmvpe.infer_from_audio(converted_waves_16k[0], thred=0.5)
-
-        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 = inference_module.length_regulator(S_alt, ylens=target_lengths, n_quantizers=3, f0=shifted_f0_alt)
-    prompt_condition, _, codes, commitment_loss, codebook_loss = inference_module.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)
-        # Voice Conversion
-        vc_target = inference_module.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 = bigvgan_fn(vc_target)[0]
-        if processed_frames == 0:
-            if is_last_chunk:
-                output_wave = vc_wave[0].cpu().numpy()
-                generated_wave_chunks.append(output_wave)
-                output_wave = (output_wave * 32768.0).astype(np.int16)
-                mp3_bytes = AudioSegment(
-                    output_wave.tobytes(), frame_rate=sr,
-                    sample_width=output_wave.dtype.itemsize, channels=1
-                ).export(format="mp3", bitrate=bitrate).read()
-                yield mp3_bytes, (sr, np.concatenate(generated_wave_chunks))
-                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
-            output_wave = (output_wave * 32768.0).astype(np.int16)
-            mp3_bytes = AudioSegment(
-                output_wave.tobytes(), frame_rate=sr,
-                sample_width=output_wave.dtype.itemsize, channels=1
-            ).export(format="mp3", bitrate=bitrate).read()
-            yield mp3_bytes, None
-        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
-            output_wave = (output_wave * 32768.0).astype(np.int16)
-            mp3_bytes = AudioSegment(
-                output_wave.tobytes(), frame_rate=sr,
-                sample_width=output_wave.dtype.itemsize, channels=1
-            ).export(format="mp3", bitrate=bitrate).read()
-            yield mp3_bytes, (sr, np.concatenate(generated_wave_chunks))
-            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
-            output_wave = (output_wave * 32768.0).astype(np.int16)
-            mp3_bytes = AudioSegment(
-                output_wave.tobytes(), frame_rate=sr,
-                sample_width=output_wave.dtype.itemsize, channels=1
-            ).export(format="mp3", bitrate=bitrate).read()
-            yield mp3_bytes, None
-
-
-if __name__ == "__main__":
-    description = ("State-of-the-Art zero-shot voice conversion/singing voice conversion. For local deployment please check [GitHub repository](https://github.com/Plachtaa/seed-vc) "
-                   "for details and updates.<br>Note that any reference audio will be forcefully clipped to 25s if beyond this length.<br> "
-                   "If total duration of source and reference audio exceeds 30s, source audio will be processed in chunks.<br> "
-                   "无需训练的 zero-shot 语音/歌声转换模型，若需本地部署查看[GitHub页面](https://github.com/Plachtaa/seed-vc)<br>"
-                   "请注意，参考音频若超过 25 秒，则会被自动裁剪至此长度。<br>若源音频和参考音频的总时长超过 30 秒，源音频将被分段处理。")
-    inputs = [
-        gr.Audio(type="filepath", label="Source Audio / 源音频"),
-        gr.Audio(type="filepath", label="Reference Audio / 参考音频"),
-        gr.Slider(minimum=1, maximum=200, value=25, step=1, label="Diffusion Steps / 扩散步数", info="25 by default, 50~100 for best quality / 默认为 25，50~100 为最佳质量"),
-        gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Length Adjust / 长度调整", info="<1.0 for speed-up speech, >1.0 for slow-down speech / <1.0 加速语速，>1.0 减慢语速"),
-        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="Inference CFG Rate", info="has subtle influence / 有微小影响"),
-        gr.Checkbox(label="Use F0 conditioned model / 启用F0输入", value=False, info="Must set to true for singing voice conversion / 歌声转换时必须勾选"),
-        gr.Checkbox(label="Auto F0 adjust / 自动F0调整", value=True,
-                    info="Roughly adjust F0 to match target voice. Only works when F0 conditioned model is used. / 粗略调整 F0 以匹配目标音色，仅在勾选 '启用F0输入' 时生效"),
-        gr.Slider(label='Pitch shift / 音调变换', minimum=-24, maximum=24, step=1, value=0, info="Pitch shift in semitones, only works when F0 conditioned model is used / 半音数的音高变换，仅在勾选 '启用F0输入' 时生效"),
-    ]
-
-    examples = [["examples/source/yae_0.wav", "examples/reference/dingzhen_0.wav", 25, 1.0, 0.7, False, True, 0],
-                ["examples/source/jay_0.wav", "examples/reference/azuma_0.wav", 25, 1.0, 0.7, False, True, 0],
-                ["examples/source/Wiz Khalifa,Charlie Puth - See You Again [vocals]_[cut_28sec].wav",
-                 "examples/reference/kobe_0.wav", 50, 1.0, 0.7, True, False, -6],
-                ["examples/source/TECHNOPOLIS - 2085 [vocals]_[cut_14sec].wav",
-                 "examples/reference/trump_0.wav", 50, 1.0, 0.7, True, False, -12],
-                ]
-
-    outputs = [gr.Audio(label="Stream Output Audio / 流式输出", streaming=True, format='mp3'),
-               gr.Audio(label="Full Output Audio / 完整输出", streaming=False, format='wav')]
-
-    gr.Interface(fn=voice_conversion,
-                 description=description,
-                 inputs=inputs,
-                 outputs=outputs,
-                 title="Seed Voice Conversion",
-                 examples=examples,
-                 cache_examples=False,
-                 ).launch()
+import torch
+import torchaudio
+import librosa
+from modules.commons import build_model, load_checkpoint, recursive_munch
+import yaml
+from hf_utils import load_custom_model_from_hf
+import numpy as np
+
+# Загрузка моделей и конфигураций
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Загрузка конфигурации и модели DiT
+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"
+)
+
+config = yaml.safe_load(open(dit_config_path, 'r'))
+model_params = recursive_munch(config['model_params'])
+model = build_model(model_params, stage='DiT')
+hop_length = config['preprocess_params']['spect_params']['hop_length']
+sr = config['preprocess_params']['sr']
+
+# Загрузка контрольных точек модели
+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)
+
+# Загрузка дополнительной модели CAMPPlus
+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)
+
+# Загрузка модели BigVGAN
+from modules.bigvgan import bigvgan
+
+bigvgan_model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_22khz_80band_256x', use_cuda_kernel=False)
+bigvgan_model.remove_weight_norm()
+bigvgan_model = bigvgan_model.eval().to(device)
+
+# Загрузка модели FAcodec
+ckpt_path, config_path = load_custom_model_from_hf("Plachta/FAcodec", 'pytorch_model.bin', 'config.yml')
+
+codec_config = yaml.safe_load(open(config_path))
+codec_model_params = recursive_munch(codec_config['model_params'])
+codec_encoder = build_model(codec_model_params, stage="codec")
+
+ckpt_params = torch.load(ckpt_path, map_location="cpu")
+
+for key in codec_encoder:
+    codec_encoder[key].load_state_dict(ckpt_params[key], strict=False)
+_ = [codec_encoder[key].eval() for key in codec_encoder]
+_ = [codec_encoder[key].to(device) for key in codec_encoder]
+
+# Загрузка модели Whisper
+from transformers import AutoFeatureExtractor, WhisperModel
+
+whisper_name = model_params.speech_tokenizer.whisper_name if hasattr(model_params.speech_tokenizer, 'whisper_name') else "openai/whisper-small"
+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)
+
+# Функция для генерации мел-спектрограммы
+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": 0,
+    "fmax": None,
+    "center": False
+}
+from modules.audio import mel_spectrogram
+
+to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
+
+# Модель с F0 условием
+dit_checkpoint_path_f0, dit_config_path_f0 = load_custom_model_from_hf(
+    "Plachta/Seed-VC",
+    "DiT_seed_v2_uvit_whisper_base_f0_44k_bigvgan_pruned_ft_ema.pth",
+    "config_dit_mel_seed_uvit_whisper_base_f0_44k.yml"
+)
+
+config_f0 = yaml.safe_load(open(dit_config_path_f0, 'r'))
+model_params_f0 = recursive_munch(config_f0['model_params'])
+model_f0 = build_model(model_params_f0, stage='DiT')
+hop_length_f0 = config_f0['preprocess_params']['spect_params']['hop_length']
+sr_f0 = config_f0['preprocess_params']['sr']
+
+# Загрузка контрольных точек модели с F0
+model_f0, _, _, _ = load_checkpoint(
+    model_f0, None, dit_checkpoint_path_f0,
+    load_only_params=True, ignore_modules=[], is_distributed=False
+)
+for key in model_f0:
+    model_f0[key].eval()
+    model_f0[key].to(device)
+model_f0.cfm.estimator.setup_caches(max_batch_size=1, max_seq_length=8192)
+
+# Загрузка F0-экстрактора RMVPE
+from modules.rmvpe import RMVPE
+
+model_path = load_custom_model_from_hf("lj1995/VoiceConversionWebUI", "rmvpe.pt", None)
+rmvpe = RMVPE(model_path, is_half=False, device=device)
+
+# Параметры мел-спектрограммы для F0
+mel_fn_args_f0 = {
+    "n_fft": config_f0['preprocess_params']['spect_params']['n_fft'],
+    "win_size": config_f0['preprocess_params']['spect_params']['win_length'],
+    "hop_size": config_f0['preprocess_params']['spect_params']['hop_length'],
+    "num_mels": config_f0['preprocess_params']['spect_params']['n_mels'],
+    "sampling_rate": sr_f0,
+    "fmin": 0,
+    "fmax": None,
+    "center": False
+}
+to_mel_f0 = lambda x: mel_spectrogram(x, **mel_fn_args_f0)
+
+# Загрузка модели BigVGAN для 44kHz
+bigvgan_44k_model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=False)
+bigvgan_44k_model.remove_weight_norm()
+bigvgan_44k_model = bigvgan_44k_model.eval().to(device)
+
+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
+    chunk2[:overlap] = chunk2[:overlap] * fade_in + chunk1[-overlap:] * fade_out
+    return chunk2
+
+# Параметры для обработки потоков и чанков
+max_context_window = sr // hop_length * 30
+overlap_frame_len = 16
+overlap_wave_len = overlap_frame_len * hop_length
+bitrate = "320k"
+
+@torch.no_grad()
+def voice_conversion(source, target, diffusion_steps, length_adjust, inference_cfg_rate, f0_condition, auto_f0_adjust, pitch_shift):
+    """
+    Функция для голосового преобразования.
+
+    Параметры:
+    - source (str): Путь к исходному аудио файлу.
+    - target (str): Путь к целевому аудио файлу (голос, на который нужно преобразовать).
+    - diffusion_steps (int): Количество шагов диффузии.
+    - length_adjust (float): Коэффициент регулировки длины.
+    - inference_cfg_rate (float): Коэффициент CFG для инференса.
+    - f0_condition (bool): Использовать ли условие F0.
+    - auto_f0_adjust (bool): Автоматически ли корректировать F0.
+    - pitch_shift (int): Сдвиг тона в полутонах.
+
+    Возвращает:
+    - tuple: (частота дискретизации, numpy массив аудио данных)
+    """
+    inference_module = model_f0 if f0_condition else model
+    mel_fn = to_mel_f0 if f0_condition else to_mel
+    bigvgan_fn = bigvgan_44k_model if f0_condition else bigvgan_model
+    sr_used = sr_f0 if f0_condition else sr
+
+    # Загрузка аудио
+    source_audio, _ = librosa.load(source, sr=sr_used)
+    ref_audio, _ = librosa.load(target, sr=sr_used)
+
+    # Ограничение длины целевого аудио
+    ref_audio = ref_audio[:sr_used * 25]
+
+    # Преобразование аудио в тензоры
+    source_audio = torch.tensor(source_audio).unsqueeze(0).float().to(device)
+    ref_audio = torch.tensor(ref_audio).unsqueeze(0).float().to(device)
+
+    # Ресемплирование для Whisper
+    ref_waves_16k = torchaudio.functional.resample(ref_audio, sr_used, 16000)
+    converted_waves_16k = torchaudio.functional.resample(source_audio, sr_used, 16000)
+
+    # Извлечение признаков с помощью Whisper
+    if converted_waves_16k.size(-1) <= 16000 * 30:
+        alt_inputs = whisper_feature_extractor([converted_waves_16k.squeeze(0).cpu().numpy()],
+                                               return_tensors="pt",
+                                               return_attention_mask=True,
+                                               sampling_rate=16000)
+        alt_input_features = whisper_model._mask_input_features(
+            alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
+        alt_outputs = whisper_model.encoder(
+            alt_input_features.to(whisper_model.encoder.dtype),
+            head_mask=None,
+            output_attentions=False,
+            output_hidden_states=False,
+            return_dict=True,
+        )
+        S_alt = alt_outputs.last_hidden_state.to(torch.float32)
+        S_alt = S_alt[:, :converted_waves_16k.size(-1) // 320 + 1]
+    else:
+        # Обработка длинного аудио в чанках
+        overlapping_time = 5  # секунд
+        S_alt_list = []
+        buffer = None
+        traversed_time = 0
+        while traversed_time < converted_waves_16k.size(-1):
+            if buffer is None:
+                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)
+            alt_inputs = whisper_feature_extractor([chunk.squeeze(0).cpu().numpy()],
+                                                   return_tensors="pt",
+                                                   return_attention_mask=True,
+                                                   sampling_rate=16000)
+            alt_input_features = whisper_model._mask_input_features(
+                alt_inputs.input_features, attention_mask=alt_inputs.attention_mask).to(device)
+            alt_outputs = whisper_model.encoder(
+                alt_input_features.to(whisper_model.encoder.dtype),
+                head_mask=None,
+                output_attentions=False,
+                output_hidden_states=False,
+                return_dict=True,
+            )
+            S_alt = alt_outputs.last_hidden_state.to(torch.float32)
+            S_alt = S_alt[:, :chunk.size(-1) // 320 + 1]
+            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)
+
+    # Извлечение признаков из референсного аудио
+    ori_waves_16k = torchaudio.functional.resample(ref_audio, sr_used, 16000)
+    ori_inputs = whisper_feature_extractor([ori_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[:, :ori_waves_16k.size(-1) // 320 + 1]
+
+    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)
+
+    # Извлечение стиля с помощью CAMPPlus
+    feat2 = torchaudio.compliance.kaldi.fbank(ref_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:
+        # Извлечение F0 с помощью RMVPE
+        F0_ori = rmvpe.infer_from_audio(ref_waves_16k[0], thred=0.5)
+        F0_alt = rmvpe.infer_from_audio(converted_waves_16k[0], thred=0.5)
+
+        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)
+
+        # Корректировка F0
+        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
+
+    # Регулировка длины
+    cond, _, codes, commitment_loss, codebook_loss = inference_module.length_regulator(
+        S_alt, ylens=target_lengths, n_quantizers=3, f0=shifted_f0_alt
+    )
+    prompt_condition, _, codes, commitment_loss, codebook_loss = inference_module.length_regulator(
+        S_ori, ylens=target2_lengths, n_quantizers=3, f0=F0_ori
+    )
+
+    max_source_window = max_context_window - mel2.size(2)
+    processed_frames = 0
+    generated_wave_chunks = []
+
+    # Генерация аудио по частям
+    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)
+        vc_target = inference_module.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 = bigvgan_fn(vc_target)[0]
+        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
+
+    # Объединение всех чанков в одно аудио
+    full_output_wave = np.concatenate(generated_wave_chunks)
+
+    # Нормализация аудио
+    max_val = np.max(np.abs(full_output_wave))
+    if max_val > 1.0:
+        full_output_wave = full_output_wave / max_val
+
+    return sr_used, full_output_wave