diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d5e92658..f3829691 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -13,9 +13,3 @@ repos: - id: ruff-format types_or: [ python, pyi ] args: [ --line-length, "120", --target-version, "py310" ] - -# - repo: https://github.com/codespell-project/codespell -# rev: v2.4.1 -# hooks: -# - id: codespell -# files: ^.*\.(py|md)$ \ No newline at end of file diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index d20daee3..6ef46eb8 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -108,7 +108,7 @@ resample_transform_dict = {} def resample(audio_tensor, sr0, sr1, device): global resample_transform_dict - key="%s-%s"%(sr0,sr1) + key = "%s-%s" % (sr0, sr1) if key not in resample_transform_dict: resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device) return resample_transform_dict[key](audio_tensor) @@ -252,7 +252,6 @@ class TTS_Config: "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", }, - } configs: dict = None v1_languages: list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"] @@ -432,7 +431,6 @@ class TTS: "aux_ref_audio_paths": [], } - self.stop_flag: bool = False self.precision: torch.dtype = torch.float16 if self.configs.is_half else torch.float32 @@ -468,7 +466,7 @@ class TTS: path_sovits = self.configs.default_configs[model_version]["vits_weights_path"] if if_lora_v3 == True and os.path.exists(path_sovits) == False: - info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重"%model_version) + info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重" % model_version) raise FileExistsError(info) # dict_s2 = torch.load(weights_path, map_location=self.configs.device,weights_only=False) @@ -507,7 +505,7 @@ class TTS: ) self.configs.use_vocoder = False else: - kwargs["version"]=model_version + kwargs["version"] = model_version vits_model = SynthesizerTrnV3( self.configs.filter_length // 2 + 1, self.configs.segment_size // self.configs.hop_length, @@ -572,7 +570,7 @@ class TTS: self.vocoder.cpu() del self.vocoder self.empty_cache() - + self.vocoder = BigVGAN.from_pretrained( "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,), use_cuda_kernel=False, @@ -595,18 +593,21 @@ class TTS: self.empty_cache() self.vocoder = Generator( - initial_channel=100, - resblock="1", - resblock_kernel_sizes=[3, 7, 11], - resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], - upsample_rates=[10, 6, 2, 2, 2], - upsample_initial_channel=512, - upsample_kernel_sizes=[20, 12, 4, 4, 4], - gin_channels=0, is_bias=True - ) + initial_channel=100, + resblock="1", + resblock_kernel_sizes=[3, 7, 11], + resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], + upsample_rates=[10, 6, 2, 2, 2], + upsample_initial_channel=512, + upsample_kernel_sizes=[20, 12, 4, 4, 4], + gin_channels=0, + is_bias=True, + ) self.vocoder.remove_weight_norm() - state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu") - print("loading vocoder",self.vocoder.load_state_dict(state_dict_g)) + state_dict_g = torch.load( + "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu" + ) + print("loading vocoder", self.vocoder.load_state_dict(state_dict_g)) self.vocoder_configs["sr"] = 48000 self.vocoder_configs["T_ref"] = 500 @@ -614,9 +615,6 @@ class TTS: self.vocoder_configs["upsample_rate"] = 480 self.vocoder_configs["overlapped_len"] = 12 - - - self.vocoder = self.vocoder.eval() if self.configs.is_half == True: self.vocoder = self.vocoder.half().to(self.configs.device) @@ -1439,7 +1437,7 @@ class TTS: ref_audio = ref_audio.to(self.configs.device).float() if ref_audio.shape[0] == 2: ref_audio = ref_audio.mean(0).unsqueeze(0) - + # tgt_sr = self.vocoder_configs["sr"] tgt_sr = 24000 if self.configs.version == "v3" else 32000 if ref_sr != tgt_sr: diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 4bee27cd..3e779344 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -106,7 +106,7 @@ cnhubert.cnhubert_base_path = cnhubert_base_path import random -from GPT_SoVITS.module.models import SynthesizerTrn, SynthesizerTrnV3,Generator +from GPT_SoVITS.module.models import SynthesizerTrn, SynthesizerTrnV3, Generator def set_seed(seed): @@ -226,9 +226,9 @@ else: resample_transform_dict = {} -def resample(audio_tensor, sr0,sr1): +def resample(audio_tensor, sr0, sr1): global resample_transform_dict - key="%s-%s"%(sr0,sr1) + key = "%s-%s" % (sr0, sr1) if key not in resample_transform_dict: resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device) return resample_transform_dict[key](audio_tensor) @@ -238,14 +238,18 @@ def resample(audio_tensor, sr0,sr1): # symbol_version-model_version-if_lora_v3 from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new -v3v4set={"v3","v4"} +v3v4set = {"v3", "v4"} + + def change_sovits_weights(sovits_path, prompt_language=None, text_language=None): global vq_model, hps, version, model_version, dict_language, if_lora_v3 version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path) - print(sovits_path,version, model_version, if_lora_v3) - is_exist=is_exist_s2gv3 if model_version=="v3"else is_exist_s2gv4 + print(sovits_path, version, model_version, if_lora_v3) + is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4 if if_lora_v3 == True and is_exist == False: - info = "GPT_SoVITS/pretrained_models/s2Gv3.pth" + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重"%model_version) + info = "GPT_SoVITS/pretrained_models/s2Gv3.pth" + i18n( + "SoVITS %s 底模缺失，无法加载相应 LoRA 权重" % model_version + ) gr.Warning(info) raise FileExistsError(info) dict_language = dict_language_v1 if version == "v1" else dict_language_v2 @@ -276,10 +280,15 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None) prompt_language_update, text_update, text_language_update, - {"__type__": "update", "visible": visible_sample_steps, "value": 32 if model_version=="v3"else 8,"choices":[4, 8, 16, 32,64,128]if model_version=="v3"else [4, 8, 16, 32]}, + { + "__type__": "update", + "visible": visible_sample_steps, + "value": 32 if model_version == "v3" else 8, + "choices": [4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32], + }, {"__type__": "update", "visible": visible_inp_refs}, {"__type__": "update", "value": False, "interactive": True if model_version not in v3v4set else False}, - {"__type__": "update", "visible": True if model_version =="v3" else False}, + {"__type__": "update", "visible": True if model_version == "v3" else False}, {"__type__": "update", "value": i18n("模型加载中，请等待"), "interactive": False}, ) @@ -304,7 +313,7 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None) ) model_version = version else: - hps.model.version=model_version + hps.model.version = model_version vq_model = SynthesizerTrnV3( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, @@ -326,7 +335,7 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None) else: path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4 print( - "loading sovits_%spretrained_G"%model_version, + "loading sovits_%spretrained_G" % model_version, vq_model.load_state_dict(load_sovits_new(path_sovits)["weight"], strict=False), ) lora_rank = dict_s2["lora_rank"] @@ -337,7 +346,7 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None) init_lora_weights=True, ) vq_model.cfm = get_peft_model(vq_model.cfm, lora_config) - print("loading sovits_%s_lora%s" % (model_version,lora_rank)) + print("loading sovits_%s_lora%s" % (model_version, lora_rank)) vq_model.load_state_dict(dict_s2["weight"], strict=False) vq_model.cfm = vq_model.cfm.merge_and_unload() # torch.save(vq_model.state_dict(),"merge_win.pth") @@ -350,10 +359,15 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None) prompt_language_update, text_update, text_language_update, - {"__type__": "update", "visible": visible_sample_steps, "value":32 if model_version=="v3"else 8,"choices":[4, 8, 16, 32,64,128]if model_version=="v3"else [4, 8, 16, 32]}, + { + "__type__": "update", + "visible": visible_sample_steps, + "value": 32 if model_version == "v3" else 8, + "choices": [4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32], + }, {"__type__": "update", "visible": visible_inp_refs}, {"__type__": "update", "value": False, "interactive": True if model_version not in v3v4set else False}, - {"__type__": "update", "visible": True if model_version =="v3" else False}, + {"__type__": "update", "visible": True if model_version == "v3" else False}, {"__type__": "update", "value": i18n("合成语音"), "interactive": True}, ) with open("./weight.json") as f: @@ -400,7 +414,7 @@ now_dir = os.getcwd() def init_bigvgan(): - global bigvgan_model,hifigan_model + global bigvgan_model, hifigan_model from BigVGAN import bigvgan bigvgan_model = bigvgan.BigVGAN.from_pretrained( @@ -411,17 +425,20 @@ def init_bigvgan(): bigvgan_model.remove_weight_norm() bigvgan_model = bigvgan_model.eval() if hifigan_model: - hifigan_model=hifigan_model.cpu() - hifigan_model=None - try:torch.cuda.empty_cache() - except:pass + hifigan_model = hifigan_model.cpu() + hifigan_model = None + try: + torch.cuda.empty_cache() + except: + pass if is_half == True: bigvgan_model = bigvgan_model.half().to(device) else: bigvgan_model = bigvgan_model.to(device) + def init_hifigan(): - global hifigan_model,bigvgan_model + global hifigan_model, bigvgan_model hifigan_model = Generator( initial_channel=100, resblock="1", @@ -430,26 +447,32 @@ def init_hifigan(): upsample_rates=[10, 6, 2, 2, 2], upsample_initial_channel=512, upsample_kernel_sizes=[20, 12, 4, 4, 4], - gin_channels=0, is_bias=True + gin_channels=0, + is_bias=True, ) hifigan_model.eval() hifigan_model.remove_weight_norm() - state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu") - print("loading vocoder",hifigan_model.load_state_dict(state_dict_g)) + state_dict_g = torch.load( + "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu" + ) + print("loading vocoder", hifigan_model.load_state_dict(state_dict_g)) if bigvgan_model: - bigvgan_model=bigvgan_model.cpu() - bigvgan_model=None - try:torch.cuda.empty_cache() - except:pass + bigvgan_model = bigvgan_model.cpu() + bigvgan_model = None + try: + torch.cuda.empty_cache() + except: + pass if is_half == True: hifigan_model = hifigan_model.half().to(device) else: hifigan_model = hifigan_model.to(device) -bigvgan_model=hifigan_model=None -if model_version=="v3": + +bigvgan_model = hifigan_model = None +if model_version == "v3": init_bigvgan() -if model_version=="v4": +if model_version == "v4": init_hifigan() @@ -831,17 +854,17 @@ def get_tts_wav( ref_audio = ref_audio.to(device).float() if ref_audio.shape[0] == 2: ref_audio = ref_audio.mean(0).unsqueeze(0) - tgt_sr=24000 if model_version=="v3"else 32000 + tgt_sr = 24000 if model_version == "v3" else 32000 if sr != tgt_sr: - ref_audio = resample(ref_audio, sr,tgt_sr) + ref_audio = resample(ref_audio, sr, tgt_sr) # print("ref_audio",ref_audio.abs().mean()) - mel2 = mel_fn(ref_audio)if model_version=="v3"else mel_fn_v4(ref_audio) + mel2 = mel_fn(ref_audio) if model_version == "v3" else mel_fn_v4(ref_audio) mel2 = norm_spec(mel2) T_min = min(mel2.shape[2], fea_ref.shape[2]) mel2 = mel2[:, :, :T_min] fea_ref = fea_ref[:, :, :T_min] - Tref=468 if model_version=="v3"else 500 - Tchunk=934 if model_version=="v3"else 1000 + Tref = 468 if model_version == "v3" else 500 + Tchunk = 934 if model_version == "v3" else 1000 if T_min > Tref: mel2 = mel2[:, :, -Tref:] fea_ref = fea_ref[:, :, -Tref:] @@ -866,13 +889,13 @@ def get_tts_wav( cfm_resss.append(cfm_res) cfm_res = torch.cat(cfm_resss, 2) cfm_res = denorm_spec(cfm_res) - if model_version=="v3": + if model_version == "v3": if bigvgan_model == None: init_bigvgan() - else:#v4 + else: # v4 if hifigan_model == None: init_hifigan() - vocoder_model=bigvgan_model if model_version=="v3"else hifigan_model + vocoder_model = bigvgan_model if model_version == "v3" else hifigan_model with torch.inference_mode(): wav_gen = vocoder_model(cfm_res) audio = wav_gen[0][0] # .cpu().detach().numpy() @@ -886,9 +909,12 @@ def get_tts_wav( t1 = ttime() print("%.3f\t%.3f\t%.3f\t%.3f" % (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3]))) audio_opt = torch.cat(audio_opt, 0) # np.concatenate - if model_version in {"v1","v2"}:opt_sr=32000 - elif model_version=="v3":opt_sr=24000 - else:opt_sr=48000#v4 + if model_version in {"v1", "v2"}: + opt_sr = 32000 + elif model_version == "v3": + opt_sr = 24000 + else: + opt_sr = 48000 # v4 if if_sr == True and opt_sr == 24000: print(i18n("音频超分中")) audio_opt, opt_sr = audio_sr(audio_opt.unsqueeze(0), opt_sr) @@ -1131,16 +1157,16 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: sample_steps = ( gr.Radio( label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"), - value=32 if model_version=="v3"else 8, - choices=[4, 8, 16, 32,64,128]if model_version=="v3"else [4, 8, 16, 32], + value=32 if model_version == "v3" else 8, + choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32], visible=True, ) if model_version in v3v4set else gr.Radio( label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"), - choices=[4, 8, 16, 32,64,128]if model_version=="v3"else [4, 8, 16, 32], + choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32], visible=False, - value=32 if model_version=="v3"else 8, + value=32 if model_version == "v3" else 8, ) ) if_sr_Checkbox = gr.Checkbox( @@ -1148,7 +1174,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: value=False, interactive=True, show_label=True, - visible=False if model_version !="v3" else True, + visible=False if model_version != "v3" else True, ) gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"), "h3")) with gr.Row(): diff --git a/GPT_SoVITS/inference_webui_fast.py b/GPT_SoVITS/inference_webui_fast.py index 311994b8..0b5cbd16 100644 --- a/GPT_SoVITS/inference_webui_fast.py +++ b/GPT_SoVITS/inference_webui_fast.py @@ -262,15 +262,17 @@ SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root) from process_ckpt import get_sovits_version_from_path_fast -v3v4set={"v3","v4"} +v3v4set = {"v3", "v4"} + + def change_sovits_weights(sovits_path, prompt_language=None, text_language=None): global version, model_version, dict_language, if_lora_v3 version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path) # print(sovits_path,version, model_version, if_lora_v3) - is_exist=is_exist_s2gv3 if model_version=="v3"else is_exist_s2gv4 + is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4 path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4 if if_lora_v3 == True and is_exist == False: - info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重"%model_version) + info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重" % model_version) gr.Warning(info) raise FileExistsError(info) dict_language = dict_language_v1 if version == "v1" else dict_language_v2 diff --git a/GPT_SoVITS/module/data_utils.py b/GPT_SoVITS/module/data_utils.py index 1bda2b31..11f6b092 100644 --- a/GPT_SoVITS/module/data_utils.py +++ b/GPT_SoVITS/module/data_utils.py @@ -470,6 +470,7 @@ class TextAudioSpeakerCollateV3: # return ssl_padded, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, wav_padded, wav_lengths,mel_lengths return ssl_padded, spec_padded, mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, mel_lengths + class TextAudioSpeakerLoaderV4(torch.utils.data.Dataset): """ 1) loads audio, speaker_id, text pairs @@ -596,7 +597,7 @@ class TextAudioSpeakerLoaderV4(torch.utils.data.Dataset): audio_norm, self.filter_length, self.sampling_rate, self.hop_length, self.win_length, center=False ) spec = torch.squeeze(spec, 0) - spec1 = spectrogram_torch(audio_norm, 1280,32000, 320, 1280,center=False) + spec1 = spectrogram_torch(audio_norm, 1280, 32000, 320, 1280, center=False) mel = spec_to_mel_torch(spec1, 1280, 100, 32000, 0, None) mel = self.norm_spec(torch.squeeze(mel, 0)) return spec, mel @@ -643,7 +644,7 @@ class TextAudioSpeakerCollateV4: mel_lengths = torch.LongTensor(len(batch)) spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) - mel_padded = torch.FloatTensor(len(batch), batch[0][2].size(0), max_spec_len*2) + mel_padded = torch.FloatTensor(len(batch), batch[0][2].size(0), max_spec_len * 2) ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len) text_padded = torch.LongTensor(len(batch), max_text_len) # wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) diff --git a/GPT_SoVITS/module/mel_processing.py b/GPT_SoVITS/module/mel_processing.py index 7a17c54b..62c7b40e 100644 --- a/GPT_SoVITS/module/mel_processing.py +++ b/GPT_SoVITS/module/mel_processing.py @@ -39,24 +39,36 @@ hann_window = {} def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): if torch.min(y) < -1.2: - print('min value is ', torch.min(y)) + print("min value is ", torch.min(y)) if torch.max(y) > 1.2: - print('max value is ', torch.max(y)) + print("max value is ", torch.max(y)) global hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) + dtype_device = str(y.dtype) + "_" + str(y.device) # wnsize_dtype_device = str(win_size) + '_' + dtype_device - key = "%s-%s-%s-%s-%s" %(dtype_device,n_fft, sampling_rate, hop_size, win_size) + key = "%s-%s-%s-%s-%s" % (dtype_device, n_fft, sampling_rate, hop_size, win_size) # if wnsize_dtype_device not in hann_window: if key not in hann_window: # hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) hann_window[key] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') + y = torch.nn.functional.pad( + y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect" + ) y = y.squeeze(1) # spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[key], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) + spec = torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[key], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=False, + ) spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-8) return spec @@ -64,9 +76,9 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False) def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): global mel_basis - dtype_device = str(spec.dtype) + '_' + str(spec.device) + dtype_device = str(spec.dtype) + "_" + str(spec.device) # fmax_dtype_device = str(fmax) + '_' + dtype_device - key = "%s-%s-%s-%s-%s-%s"%(dtype_device,n_fft, num_mels, sampling_rate, fmin, fmax) + key = "%s-%s-%s-%s-%s-%s" % (dtype_device, n_fft, num_mels, sampling_rate, fmin, fmax) # if fmax_dtype_device not in mel_basis: if key not in mel_basis: mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) @@ -78,17 +90,25 @@ def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): return spec - def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): if torch.min(y) < -1.2: - print('min value is ', torch.min(y)) + print("min value is ", torch.min(y)) if torch.max(y) > 1.2: - print('max value is ', torch.max(y)) + print("max value is ", torch.max(y)) global mel_basis, hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) + dtype_device = str(y.dtype) + "_" + str(y.device) # fmax_dtype_device = str(fmax) + '_' + dtype_device - fmax_dtype_device = "%s-%s-%s-%s-%s-%s-%s-%s"%(dtype_device,n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax) + fmax_dtype_device = "%s-%s-%s-%s-%s-%s-%s-%s" % ( + dtype_device, + n_fft, + num_mels, + sampling_rate, + hop_size, + win_size, + fmin, + fmax, + ) # wnsize_dtype_device = str(win_size) + '_' + dtype_device wnsize_dtype_device = fmax_dtype_device if fmax_dtype_device not in mel_basis: @@ -97,11 +117,23 @@ def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, if wnsize_dtype_device not in hann_window: hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') + y = torch.nn.functional.pad( + y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect" + ) y = y.squeeze(1) - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) + spec = torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[wnsize_dtype_device], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=False, + ) spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-8) diff --git a/GPT_SoVITS/module/models.py b/GPT_SoVITS/module/models.py index 21f60d99..3e37f0ff 100644 --- a/GPT_SoVITS/module/models.py +++ b/GPT_SoVITS/module/models.py @@ -414,7 +414,8 @@ class Generator(torch.nn.Module): upsample_rates, upsample_initial_channel, upsample_kernel_sizes, - gin_channels=0,is_bias=False, + gin_channels=0, + is_bias=False, ): super(Generator, self).__init__() self.num_kernels = len(resblock_kernel_sizes) @@ -1173,7 +1174,7 @@ class SynthesizerTrnV3(nn.Module): quantized = F.interpolate(quantized, scale_factor=2, mode="nearest") ##BCT x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge) fea = self.bridge(x) - fea = F.interpolate(fea, scale_factor=(1.875 if self.version=="v3"else 2), mode="nearest") ##BCT + fea = F.interpolate(fea, scale_factor=(1.875 if self.version == "v3" else 2), mode="nearest") ##BCT fea, y_mask_ = self.wns1( fea, mel_lengths, ge ) ##If the 1-minute fine-tuning works fine, no need to manually adjust the learning rate. @@ -1196,9 +1197,9 @@ class SynthesizerTrnV3(nn.Module): ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask) y_lengths = torch.LongTensor([int(codes.size(2) * 2)]).to(codes.device) if speed == 1: - sizee = int(codes.size(2) * (3.875 if self.version=="v3"else 4)) + sizee = int(codes.size(2) * (3.875 if self.version == "v3" else 4)) else: - sizee = int(codes.size(2) * (3.875 if self.version=="v3"else 4) / speed) + 1 + sizee = int(codes.size(2) * (3.875 if self.version == "v3" else 4) / speed) + 1 y_lengths1 = torch.LongTensor([sizee]).to(codes.device) text_lengths = torch.LongTensor([text.size(-1)]).to(text.device) @@ -1207,7 +1208,7 @@ class SynthesizerTrnV3(nn.Module): quantized = F.interpolate(quantized, scale_factor=2, mode="nearest") ##BCT x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge, speed) fea = self.bridge(x) - fea = F.interpolate(fea, scale_factor=(1.875 if self.version=="v3"else 2), mode="nearest") ##BCT + fea = F.interpolate(fea, scale_factor=(1.875 if self.version == "v3" else 2), mode="nearest") ##BCT ####more wn paramter to learn mel fea, y_mask_ = self.wns1(fea, y_lengths1, ge) return fea, ge diff --git a/GPT_SoVITS/process_ckpt.py b/GPT_SoVITS/process_ckpt.py index 4a2a1bac..1c458a4d 100644 --- a/GPT_SoVITS/process_ckpt.py +++ b/GPT_SoVITS/process_ckpt.py @@ -28,18 +28,18 @@ def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path from io import BytesIO -def my_save2(fea, path,cfm_version): +def my_save2(fea, path, cfm_version): bio = BytesIO() torch.save(fea, bio) bio.seek(0) data = bio.getvalue() - byte=b"03" if cfm_version=="v3"else b"04" + byte = b"03" if cfm_version == "v3" else b"04" data = byte + data[2:] with open(path, "wb") as f: f.write(data) -def savee(ckpt, name, epoch, steps, hps, cfm_version=None,lora_rank=None): +def savee(ckpt, name, epoch, steps, hps, cfm_version=None, lora_rank=None): try: opt = OrderedDict() opt["weight"] = {} @@ -51,7 +51,7 @@ def savee(ckpt, name, epoch, steps, hps, cfm_version=None,lora_rank=None): opt["info"] = "%sepoch_%siteration" % (epoch, steps) if lora_rank: opt["lora_rank"] = lora_rank - my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name),cfm_version) + my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), cfm_version) else: my_save(opt, "%s/%s.pth" % (hps.save_weight_dir, name)) return "Success." diff --git a/GPT_SoVITS/s2_train_v3_lora.py b/GPT_SoVITS/s2_train_v3_lora.py index ddeec4fc..4d8d23db 100644 --- a/GPT_SoVITS/s2_train_v3_lora.py +++ b/GPT_SoVITS/s2_train_v3_lora.py @@ -31,7 +31,6 @@ from module.data_utils import ( TextAudioSpeakerLoaderV3, TextAudioSpeakerCollateV4, TextAudioSpeakerLoaderV4, - ) from module.models import ( SynthesizerTrnV3 as SynthesizerTrn, @@ -88,8 +87,8 @@ def run(rank, n_gpus, hps): if torch.cuda.is_available(): torch.cuda.set_device(rank) - TextAudioSpeakerLoader=TextAudioSpeakerLoaderV3 if hps.model.version=="v3"else TextAudioSpeakerLoaderV4 - TextAudioSpeakerCollate=TextAudioSpeakerCollateV3 if hps.model.version=="v3"else TextAudioSpeakerCollateV4 + TextAudioSpeakerLoader = TextAudioSpeakerLoaderV3 if hps.model.version == "v3" else TextAudioSpeakerLoaderV4 + TextAudioSpeakerCollate = TextAudioSpeakerCollateV3 if hps.model.version == "v3" else TextAudioSpeakerCollateV4 train_dataset = TextAudioSpeakerLoader(hps.data) ######## train_sampler = DistributedBucketSampler( train_dataset, @@ -365,7 +364,8 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade hps.name + "_e%s_s%s_l%s" % (epoch, global_step, lora_rank), epoch, global_step, - hps,cfm_version=hps.model.version, + hps, + cfm_version=hps.model.version, lora_rank=lora_rank, ), ) diff --git a/tools/uvr5/lib/lib_v5/dataset.py b/tools/uvr5/lib/lib_v5/dataset.py index cfd01a17..1a30eec7 100644 --- a/tools/uvr5/lib/lib_v5/dataset.py +++ b/tools/uvr5/lib/lib_v5/dataset.py @@ -32,18 +32,10 @@ def make_pair(mix_dir, inst_dir): input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"] X_list = sorted( - [ - os.path.join(mix_dir, fname) - for fname in os.listdir(mix_dir) - if os.path.splitext(fname)[1] in input_exts - ] + [os.path.join(mix_dir, fname) for fname in os.listdir(mix_dir) if os.path.splitext(fname)[1] in input_exts] ) y_list = sorted( - [ - os.path.join(inst_dir, fname) - for fname in os.listdir(inst_dir) - if os.path.splitext(fname)[1] in input_exts - ] + [os.path.join(inst_dir, fname) for fname in os.listdir(inst_dir) if os.path.splitext(fname)[1] in input_exts] ) filelist = list(zip(X_list, y_list)) @@ -65,14 +57,10 @@ def train_val_split(dataset_dir, split_mode, val_rate, val_filelist): train_filelist = filelist[:-val_size] val_filelist = filelist[-val_size:] else: - train_filelist = [ - pair for pair in filelist if list(pair) not in val_filelist - ] + train_filelist = [pair for pair in filelist if list(pair) not in val_filelist] elif split_mode == "subdirs": if len(val_filelist) != 0: - raise ValueError( - "The `val_filelist` option is not available in `subdirs` mode" - ) + raise ValueError("The `val_filelist` option is not available in `subdirs` mode") train_filelist = make_pair( os.path.join(dataset_dir, "training/mixtures"), @@ -91,9 +79,7 @@ def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha): perm = np.random.permutation(len(X)) for i, idx in enumerate(tqdm(perm)): if np.random.uniform() < reduction_rate: - y[idx] = spec_utils.reduce_vocal_aggressively( - X[idx], y[idx], reduction_mask - ) + y[idx] = spec_utils.reduce_vocal_aggressively(X[idx], y[idx], reduction_mask) if np.random.uniform() < 0.5: # swap channel @@ -152,9 +138,7 @@ def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset): patch_list = [] - patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format( - cropsize, sr, hop_length, n_fft, offset - ) + patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format(cropsize, sr, hop_length, n_fft, offset) os.makedirs(patch_dir, exist_ok=True) for i, (X_path, y_path) in enumerate(tqdm(filelist)): diff --git a/tools/uvr5/lib/lib_v5/layers.py b/tools/uvr5/lib/lib_v5/layers.py index 4fc1b5cb..2b9101e0 100644 --- a/tools/uvr5/lib/lib_v5/layers.py +++ b/tools/uvr5/lib/lib_v5/layers.py @@ -63,9 +63,7 @@ class Encoder(nn.Module): class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): super(Decoder, self).__init__() self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) self.dropout = nn.Dropout2d(0.1) if dropout else None @@ -91,24 +89,14 @@ class ASPPModule(nn.Module): Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), ) self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) + self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) + self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) + self.conv5 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.bottleneck = nn.Sequential(Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)) def forward(self, x): _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) + feat1 = F.interpolate(self.conv1(x), size=(h, w), mode="bilinear", align_corners=True) feat2 = self.conv2(x) feat3 = self.conv3(x) feat4 = self.conv4(x) diff --git a/tools/uvr5/lib/lib_v5/layers_123812KB.py b/tools/uvr5/lib/lib_v5/layers_123812KB.py index 4fc1b5cb..2b9101e0 100644 --- a/tools/uvr5/lib/lib_v5/layers_123812KB.py +++ b/tools/uvr5/lib/lib_v5/layers_123812KB.py @@ -63,9 +63,7 @@ class Encoder(nn.Module): class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): super(Decoder, self).__init__() self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) self.dropout = nn.Dropout2d(0.1) if dropout else None @@ -91,24 +89,14 @@ class ASPPModule(nn.Module): Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), ) self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) + self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) + self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) + self.conv5 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.bottleneck = nn.Sequential(Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)) def forward(self, x): _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) + feat1 = F.interpolate(self.conv1(x), size=(h, w), mode="bilinear", align_corners=True) feat2 = self.conv2(x) feat3 = self.conv3(x) feat4 = self.conv4(x) diff --git a/tools/uvr5/lib/lib_v5/layers_123821KB.py b/tools/uvr5/lib/lib_v5/layers_123821KB.py index 4fc1b5cb..2b9101e0 100644 --- a/tools/uvr5/lib/lib_v5/layers_123821KB.py +++ b/tools/uvr5/lib/lib_v5/layers_123821KB.py @@ -63,9 +63,7 @@ class Encoder(nn.Module): class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): super(Decoder, self).__init__() self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) self.dropout = nn.Dropout2d(0.1) if dropout else None @@ -91,24 +89,14 @@ class ASPPModule(nn.Module): Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), ) self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) + self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) + self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) + self.conv5 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.bottleneck = nn.Sequential(Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)) def forward(self, x): _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) + feat1 = F.interpolate(self.conv1(x), size=(h, w), mode="bilinear", align_corners=True) feat2 = self.conv2(x) feat3 = self.conv3(x) feat4 = self.conv4(x) diff --git a/tools/uvr5/lib/lib_v5/layers_33966KB.py b/tools/uvr5/lib/lib_v5/layers_33966KB.py index 9b127bc6..43977778 100644 --- a/tools/uvr5/lib/lib_v5/layers_33966KB.py +++ b/tools/uvr5/lib/lib_v5/layers_33966KB.py @@ -63,9 +63,7 @@ class Encoder(nn.Module): class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): super(Decoder, self).__init__() self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) self.dropout = nn.Dropout2d(0.1) if dropout else None @@ -91,30 +89,16 @@ class ASPPModule(nn.Module): Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), ) self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv7 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) + self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) + self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) + self.conv5 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.conv6 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.conv7 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.bottleneck = nn.Sequential(Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)) def forward(self, x): _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) + feat1 = F.interpolate(self.conv1(x), size=(h, w), mode="bilinear", align_corners=True) feat2 = self.conv2(x) feat3 = self.conv3(x) feat4 = self.conv4(x) diff --git a/tools/uvr5/lib/lib_v5/layers_537227KB.py b/tools/uvr5/lib/lib_v5/layers_537227KB.py index 9b127bc6..43977778 100644 --- a/tools/uvr5/lib/lib_v5/layers_537227KB.py +++ b/tools/uvr5/lib/lib_v5/layers_537227KB.py @@ -63,9 +63,7 @@ class Encoder(nn.Module): class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): super(Decoder, self).__init__() self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) self.dropout = nn.Dropout2d(0.1) if dropout else None @@ -91,30 +89,16 @@ class ASPPModule(nn.Module): Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), ) self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv7 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) + self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) + self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) + self.conv5 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.conv6 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.conv7 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.bottleneck = nn.Sequential(Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)) def forward(self, x): _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) + feat1 = F.interpolate(self.conv1(x), size=(h, w), mode="bilinear", align_corners=True) feat2 = self.conv2(x) feat3 = self.conv3(x) feat4 = self.conv4(x) diff --git a/tools/uvr5/lib/lib_v5/layers_537238KB.py b/tools/uvr5/lib/lib_v5/layers_537238KB.py index 9b127bc6..43977778 100644 --- a/tools/uvr5/lib/lib_v5/layers_537238KB.py +++ b/tools/uvr5/lib/lib_v5/layers_537238KB.py @@ -63,9 +63,7 @@ class Encoder(nn.Module): class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): super(Decoder, self).__init__() self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) self.dropout = nn.Dropout2d(0.1) if dropout else None @@ -91,30 +89,16 @@ class ASPPModule(nn.Module): Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), ) self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv6 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.conv7 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) + self.conv3 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[0], dilations[0], activ=activ) + self.conv4 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[1], dilations[1], activ=activ) + self.conv5 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.conv6 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.conv7 = SeperableConv2DBNActiv(nin, nin, 3, 1, dilations[2], dilations[2], activ=activ) + self.bottleneck = nn.Sequential(Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)) def forward(self, x): _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) + feat1 = F.interpolate(self.conv1(x), size=(h, w), mode="bilinear", align_corners=True) feat2 = self.conv2(x) feat3 = self.conv3(x) feat4 = self.conv4(x) diff --git a/tools/uvr5/lib/lib_v5/layers_new.py b/tools/uvr5/lib/lib_v5/layers_new.py index 44153b6a..7d7005c0 100644 --- a/tools/uvr5/lib/lib_v5/layers_new.py +++ b/tools/uvr5/lib/lib_v5/layers_new.py @@ -40,9 +40,7 @@ class Encoder(nn.Module): class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): + def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): super(Decoder, self).__init__() self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) @@ -72,23 +70,15 @@ class ASPPModule(nn.Module): Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ), ) self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ) - self.conv3 = Conv2DBNActiv( - nin, nout, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = Conv2DBNActiv( - nin, nout, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = Conv2DBNActiv( - nin, nout, 3, 1, dilations[2], dilations[2], activ=activ - ) + self.conv3 = Conv2DBNActiv(nin, nout, 3, 1, dilations[0], dilations[0], activ=activ) + self.conv4 = Conv2DBNActiv(nin, nout, 3, 1, dilations[1], dilations[1], activ=activ) + self.conv5 = Conv2DBNActiv(nin, nout, 3, 1, dilations[2], dilations[2], activ=activ) self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ) self.dropout = nn.Dropout2d(0.1) if dropout else None def forward(self, x): _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) + feat1 = F.interpolate(self.conv1(x), size=(h, w), mode="bilinear", align_corners=True) feat2 = self.conv2(x) feat3 = self.conv3(x) feat4 = self.conv4(x) @@ -106,12 +96,8 @@ class LSTMModule(nn.Module): def __init__(self, nin_conv, nin_lstm, nout_lstm): super(LSTMModule, self).__init__() self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0) - self.lstm = nn.LSTM( - input_size=nin_lstm, hidden_size=nout_lstm // 2, bidirectional=True - ) - self.dense = nn.Sequential( - nn.Linear(nout_lstm, nin_lstm), nn.BatchNorm1d(nin_lstm), nn.ReLU() - ) + self.lstm = nn.LSTM(input_size=nin_lstm, hidden_size=nout_lstm // 2, bidirectional=True) + self.dense = nn.Sequential(nn.Linear(nout_lstm, nin_lstm), nn.BatchNorm1d(nin_lstm), nn.ReLU()) def forward(self, x): N, _, nbins, nframes = x.size() diff --git a/tools/uvr5/lib/lib_v5/model_param_init.py b/tools/uvr5/lib/lib_v5/model_param_init.py index b995c0bf..79b94d33 100644 --- a/tools/uvr5/lib/lib_v5/model_param_init.py +++ b/tools/uvr5/lib/lib_v5/model_param_init.py @@ -1,5 +1,4 @@ import json -import os import pathlib default_param = {} @@ -48,9 +47,7 @@ class ModelParameters(object): import zipfile with zipfile.ZipFile(config_path, "r") as zip: - self.param = json.loads( - zip.read("param.json"), object_pairs_hook=int_keys - ) + self.param = json.loads(zip.read("param.json"), object_pairs_hook=int_keys) elif ".json" == pathlib.Path(config_path).suffix: with open(config_path, "r") as f: self.param = json.loads(f.read(), object_pairs_hook=int_keys) @@ -65,5 +62,5 @@ class ModelParameters(object): "stereo_n", "reverse", ]: - if not k in self.param: + if k not in self.param: self.param[k] = False diff --git a/tools/uvr5/lib/lib_v5/nets.py b/tools/uvr5/lib/lib_v5/nets.py index 5da3948c..42d7807a 100644 --- a/tools/uvr5/lib/lib_v5/nets.py +++ b/tools/uvr5/lib/lib_v5/nets.py @@ -3,8 +3,6 @@ import torch import torch.nn.functional as F from torch import nn -from . import spec_utils - class BaseASPPNet(nn.Module): def __init__(self, nin, ch, dilations=(4, 8, 16)): diff --git a/tools/uvr5/lib/lib_v5/nets_537227KB.py b/tools/uvr5/lib/lib_v5/nets_537227KB.py index 823b44fb..9bb1df1e 100644 --- a/tools/uvr5/lib/lib_v5/nets_537227KB.py +++ b/tools/uvr5/lib/lib_v5/nets_537227KB.py @@ -1,4 +1,3 @@ -import numpy as np import torch import torch.nn.functional as F from torch import nn diff --git a/tools/uvr5/lib/lib_v5/nets_537238KB.py b/tools/uvr5/lib/lib_v5/nets_537238KB.py index 823b44fb..9bb1df1e 100644 --- a/tools/uvr5/lib/lib_v5/nets_537238KB.py +++ b/tools/uvr5/lib/lib_v5/nets_537238KB.py @@ -1,4 +1,3 @@ -import numpy as np import torch import torch.nn.functional as F from torch import nn diff --git a/tools/uvr5/lib/lib_v5/nets_new.py b/tools/uvr5/lib/lib_v5/nets_new.py index 1c0f4fa9..ba1a5599 100644 --- a/tools/uvr5/lib/lib_v5/nets_new.py +++ b/tools/uvr5/lib/lib_v5/nets_new.py @@ -6,9 +6,7 @@ from . import layers_new class BaseNet(nn.Module): - def __init__( - self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6)) - ): + def __init__(self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6))): super(BaseNet, self).__init__() self.enc1 = layers_new.Conv2DBNActiv(nin, nout, 3, 1, 1) self.enc2 = layers_new.Encoder(nout, nout * 2, 3, 2, 1) @@ -56,21 +54,15 @@ class CascadedNet(nn.Module): layers_new.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0), ) - self.stg1_high_band_net = BaseNet( - 2, nout // 4, self.nin_lstm // 2, nout_lstm // 2 - ) + self.stg1_high_band_net = BaseNet(2, nout // 4, self.nin_lstm // 2, nout_lstm // 2) self.stg2_low_band_net = nn.Sequential( BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm), layers_new.Conv2DBNActiv(nout, nout // 2, 1, 1, 0), ) - self.stg2_high_band_net = BaseNet( - nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2 - ) + self.stg2_high_band_net = BaseNet(nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2) - self.stg3_full_band_net = BaseNet( - 3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm - ) + self.stg3_full_band_net = BaseNet(3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm) self.out = nn.Conv2d(nout, 2, 1, bias=False) self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False) diff --git a/tools/uvr5/lib/lib_v5/spec_utils.py b/tools/uvr5/lib/lib_v5/spec_utils.py index da072e4b..4d987cd8 100644 --- a/tools/uvr5/lib/lib_v5/spec_utils.py +++ b/tools/uvr5/lib/lib_v5/spec_utils.py @@ -27,9 +27,7 @@ def crop_center(h1, h2): return h1 -def wave_to_spectrogram( - wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False -): +def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False): if reverse: wave_left = np.flip(np.asfortranarray(wave[0])) wave_right = np.flip(np.asfortranarray(wave[1])) @@ -43,7 +41,7 @@ def wave_to_spectrogram( wave_left = np.asfortranarray(wave[0]) wave_right = np.asfortranarray(wave[1]) - spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length) + spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length) spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length) spec = np.asfortranarray([spec_left, spec_right]) @@ -51,9 +49,7 @@ def wave_to_spectrogram( return spec -def wave_to_spectrogram_mt( - wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False -): +def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False): import threading if reverse: @@ -103,21 +99,13 @@ def combine_spectrograms(specs, mp): raise ValueError("Too much bins") # lowpass fiter - if ( - mp.param["pre_filter_start"] > 0 - ): # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']: + if mp.param["pre_filter_start"] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']: if bands_n == 1: - spec_c = fft_lp_filter( - spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"] - ) + spec_c = fft_lp_filter(spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"]) else: gp = 1 - for b in range( - mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"] - ): - g = math.pow( - 10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0 - ) + for b in range(mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]): + g = math.pow(10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0) gp = g spec_c[:, b, :] *= g @@ -189,9 +177,7 @@ def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32): else: e += fade_size - mag[:, :, s + fade_size : e - fade_size] += ref[ - :, :, s + fade_size : e - fade_size - ] + mag[:, :, s + fade_size : e - fade_size] += ref[:, :, s + fade_size : e - fade_size] old_e = e return mag @@ -207,9 +193,7 @@ def cache_or_load(mix_path, inst_path, mp): mix_basename = os.path.splitext(os.path.basename(mix_path))[0] inst_basename = os.path.splitext(os.path.basename(inst_path))[0] - cache_dir = "mph{}".format( - hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest() - ) + cache_dir = "mph{}".format(hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode("utf-8")).hexdigest()) mix_cache_dir = os.path.join("cache", cache_dir) inst_cache_dir = os.path.join("cache", cache_dir) @@ -230,31 +214,27 @@ def cache_or_load(mix_path, inst_path, mp): if d == len(mp.param["band"]): # high-end band X_wave[d], _ = librosa.load( - mix_path, - sr = bp["sr"], - mono = False, - dtype = np.float32, - res_type = bp["res_type"] + mix_path, sr=bp["sr"], mono=False, dtype=np.float32, res_type=bp["res_type"] ) y_wave[d], _ = librosa.load( inst_path, - sr = bp["sr"], - mono = False, - dtype = np.float32, - res_type = bp["res_type"], + sr=bp["sr"], + mono=False, + dtype=np.float32, + res_type=bp["res_type"], ) else: # lower bands X_wave[d] = librosa.resample( X_wave[d + 1], - orig_sr = mp.param["band"][d + 1]["sr"], - target_sr = bp["sr"], - res_type = bp["res_type"], + orig_sr=mp.param["band"][d + 1]["sr"], + target_sr=bp["sr"], + res_type=bp["res_type"], ) y_wave[d] = librosa.resample( y_wave[d + 1], - orig_sr = mp.param["band"][d + 1]["sr"], - target_sr = bp["sr"], - res_type = bp["res_type"], + orig_sr=mp.param["band"][d + 1]["sr"], + target_sr=bp["sr"], + res_type=bp["res_type"], ) X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d]) @@ -302,9 +282,7 @@ def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse): if reverse: return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)]) elif mid_side: - return np.asfortranarray( - [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)] - ) + return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]) elif mid_side_b2: return np.asfortranarray( [ @@ -326,9 +304,7 @@ def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2): global wave_left wave_left = librosa.istft(**kwargs) - thread = threading.Thread( - target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length} - ) + thread = threading.Thread(target=run_thread, kwargs={"stft_matrix": spec_left, "hop_length": hop_length}) thread.start() wave_right = librosa.istft(spec_right, hop_length=hop_length) thread.join() @@ -336,9 +312,7 @@ def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2): if reverse: return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)]) elif mid_side: - return np.asfortranarray( - [np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)] - ) + return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]) elif mid_side_b2: return np.asfortranarray( [ @@ -357,21 +331,15 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None): for d in range(1, bands_n + 1): bp = mp.param["band"][d] - spec_s = np.ndarray( - shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex - ) + spec_s = np.ndarray(shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex) h = bp["crop_stop"] - bp["crop_start"] - spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[ - :, offset : offset + h, : - ] + spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[:, offset : offset + h, :] offset += h if d == bands_n: # higher if extra_bins_h: # if --high_end_process bypass max_bin = bp["n_fft"] // 2 - spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[ - :, :extra_bins_h, : - ] + spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[:, :extra_bins_h, :] if bp["hpf_start"] > 0: spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1) if bands_n == 1: @@ -405,9 +373,9 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None): mp.param["mid_side_b2"], mp.param["reverse"], ), - orig_sr = bp["sr"], - target_sr = sr, - res_type = "sinc_fastest", + orig_sr=bp["sr"], + target_sr=sr, + res_type="sinc_fastest", ) else: # mid spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1) @@ -456,10 +424,7 @@ def mirroring(a, spec_m, input_high_end, mp): np.abs( spec_m[ :, - mp.param["pre_filter_start"] - - 10 - - input_high_end.shape[1] : mp.param["pre_filter_start"] - - 10, + mp.param["pre_filter_start"] - 10 - input_high_end.shape[1] : mp.param["pre_filter_start"] - 10, :, ] ), @@ -467,19 +432,14 @@ def mirroring(a, spec_m, input_high_end, mp): ) mirror = mirror * np.exp(1.0j * np.angle(input_high_end)) - return np.where( - np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror - ) + return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror) if "mirroring2" == a: mirror = np.flip( np.abs( spec_m[ :, - mp.param["pre_filter_start"] - - 10 - - input_high_end.shape[1] : mp.param["pre_filter_start"] - - 10, + mp.param["pre_filter_start"] - 10 - input_high_end.shape[1] : mp.param["pre_filter_start"] - 10, :, ] ), @@ -528,7 +488,6 @@ def istft(spec, hl): if __name__ == "__main__": import argparse - import sys import time import cv2 @@ -573,10 +532,10 @@ if __name__ == "__main__": if d == len(mp.param["band"]): # high-end band wave[d], _ = librosa.load( args.input[i], - sr = bp["sr"], - mono = False, - dtype = np.float32, - res_type = bp["res_type"], + sr=bp["sr"], + mono=False, + dtype=np.float32, + res_type=bp["res_type"], ) if len(wave[d].shape) == 1: # mono to stereo @@ -584,9 +543,9 @@ if __name__ == "__main__": else: # lower bands wave[d] = librosa.resample( wave[d + 1], - orig_sr = mp.param["band"][d + 1]["sr"], - target_sr = bp["sr"], - res_type = bp["res_type"], + orig_sr=mp.param["band"][d + 1]["sr"], + target_sr=bp["sr"], + res_type=bp["res_type"], ) spec[d] = wave_to_spectrogram( diff --git a/tools/uvr5/lib/utils.py b/tools/uvr5/lib/utils.py index 5e8cd22f..0166d52b 100644 --- a/tools/uvr5/lib/utils.py +++ b/tools/uvr5/lib/utils.py @@ -27,9 +27,7 @@ def inference(X_spec, device, model, aggressiveness, data): data : dic configs """ - def _execute( - X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True - ): + def _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True): model.eval() with torch.no_grad(): preds = [] @@ -39,9 +37,7 @@ def inference(X_spec, device, model, aggressiveness, data): total_iterations = sum(iterations) for i in tqdm(range(n_window)): start = i * roi_size - X_mag_window = X_mag_pad[ - None, :, :, start : start + data["window_size"] - ] + X_mag_window = X_mag_pad[None, :, :, start : start + data["window_size"]] X_mag_window = torch.from_numpy(X_mag_window) if is_half: X_mag_window = X_mag_window.half() @@ -76,9 +72,7 @@ def inference(X_spec, device, model, aggressiveness, data): is_half = True else: is_half = False - pred = _execute( - X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half - ) + pred = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half) pred = pred[:, :, :n_frame] if data["tta"]: @@ -88,9 +82,7 @@ def inference(X_spec, device, model, aggressiveness, data): X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant") - pred_tta = _execute( - X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half - ) + pred_tta = _execute(X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half) pred_tta = pred_tta[:, :, roi_size // 2 :] pred_tta = pred_tta[:, :, :n_frame] diff --git a/webui.py b/webui.py index cddbb029..8a9e0856 100644 --- a/webui.py +++ b/webui.py @@ -147,7 +147,9 @@ if torch.cuda.is_available() or ngpu != 0: # mem.append(psutil.virtual_memory().total/ 1024 / 1024 / 1024) # 实测使用系统内存作为显存不会爆显存 -v3v4set={"v3","v4"} +v3v4set = {"v3", "v4"} + + def set_default(): global \ default_batch_size, \ @@ -589,6 +591,7 @@ def close_denoise(): p_train_SoVITS = None process_name_sovits = i18n("SoVITS训练") + def open1Ba( batch_size, total_epoch, @@ -641,7 +644,9 @@ def open1Ba( yield ( process_info(process_name_sovits, "opened"), {"__type__": "update", "visible": False}, - {"__type__": "update", "visible": True},{"__type__": "update"},{"__type__": "update"} + {"__type__": "update", "visible": True}, + {"__type__": "update"}, + {"__type__": "update"}, ) print(cmd) p_train_SoVITS = Popen(cmd, shell=True) @@ -651,13 +656,17 @@ def open1Ba( yield ( process_info(process_name_sovits, "finish"), {"__type__": "update", "visible": True}, - {"__type__": "update", "visible": False},SoVITS_dropdown_update,GPT_dropdown_update + {"__type__": "update", "visible": False}, + SoVITS_dropdown_update, + GPT_dropdown_update, ) else: yield ( process_info(process_name_sovits, "occupy"), {"__type__": "update", "visible": False}, - {"__type__": "update", "visible": True},{"__type__": "update"},{"__type__": "update"} + {"__type__": "update", "visible": True}, + {"__type__": "update"}, + {"__type__": "update"}, ) @@ -726,7 +735,9 @@ def open1Bb( yield ( process_info(process_name_gpt, "opened"), {"__type__": "update", "visible": False}, - {"__type__": "update", "visible": True},{"__type__": "update"},{"__type__": "update"} + {"__type__": "update", "visible": True}, + {"__type__": "update"}, + {"__type__": "update"}, ) print(cmd) p_train_GPT = Popen(cmd, shell=True) @@ -736,13 +747,17 @@ def open1Bb( yield ( process_info(process_name_gpt, "finish"), {"__type__": "update", "visible": True}, - {"__type__": "update", "visible": False},SoVITS_dropdown_update,GPT_dropdown_update + {"__type__": "update", "visible": False}, + SoVITS_dropdown_update, + GPT_dropdown_update, ) else: yield ( process_info(process_name_gpt, "occupy"), {"__type__": "update", "visible": False}, - {"__type__": "update", "visible": True},{"__type__": "update"},{"__type__": "update"} + {"__type__": "update", "visible": True}, + {"__type__": "update"}, + {"__type__": "update"}, ) @@ -1291,6 +1306,7 @@ def close1abc(): {"__type__": "update", "visible": False}, ) + def switch_version(version_): os.environ["version"] = version_ global version @@ -1492,7 +1508,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: with gr.Row(): exp_name = gr.Textbox(label=i18n("*实验/模型名"), value="xxx", interactive=True) gpu_info = gr.Textbox(label=i18n("显卡信息"), value=gpu_info, visible=True, interactive=False) - version_checkbox = gr.Radio(label=i18n("版本"), value=version, choices=["v1", "v2", "v4"])#, "v3" + version_checkbox = gr.Radio(label=i18n("版本"), value=version, choices=["v1", "v2", "v4"]) # , "v3" with gr.Row(): pretrained_s2G = gr.Textbox( label=i18n("预训练SoVITS-G模型路径"), @@ -1915,7 +1931,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: if_grad_ckpt, lora_rank, ], - [info1Ba, button1Ba_open, button1Ba_close,SoVITS_dropdown,GPT_dropdown], + [info1Ba, button1Ba_open, button1Ba_close, SoVITS_dropdown, GPT_dropdown], ) button1Bb_open.click( open1Bb, @@ -1930,7 +1946,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: gpu_numbers1Bb, pretrained_s1, ], - [info1Bb, button1Bb_open, button1Bb_close,SoVITS_dropdown,GPT_dropdown], + [info1Bb, button1Bb_open, button1Bb_close, SoVITS_dropdown, GPT_dropdown], ) version_checkbox.change( switch_version,