global sr, hop_length, fp16

fp16 = args.fp16

print(f"Using device: {device}")

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_seed_v2_uvit_whisper_small_wavenet_bigvgan_pruned.pth",

"config_dit_mel_seed_uvit_whisper_small_wavenet.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,

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

inference_module = model

mel_fn = to_mel

# 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:

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(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))

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)

with torch.autocast(device_type=device.type, dtype=torch.float16 if fp16 else torch.float32):

# 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 = vocoder_fn(vc_target.float())[0]

if vc_wave.ndim == 1:

vc_wave = vc_wave.unsqueeze(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()

global model, semantic_fn, vocoder_fn, campplus_model, to_mel, mel_fn_args

global overlap_wave_len, max_context_window, sr, hop_length

model, semantic_fn, vocoder_fn, campplus_model, to_mel, mel_fn_args = load_models(args)

# streaming and chunk processing related params

max_context_window = sr // hop_length * 30

overlap_wave_len = overlap_frame_len * hop_length

description = ("Zero-shot voice conversion with in-context learning. 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=10, step=1, label="Diffusion Steps / 扩散步数", info="10 by default, 50~100 for best quality / 默认为 10，50~100 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"),

]

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, True, True, 0],

]

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,