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https://github.com/RVC-Boss/GPT-SoVITS.git
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完善api.py对于v4模型的兼容
完善api.py对于v4模型的兼容。
This commit is contained in:
Karasukaigan
parent
fa971a4e09
commit
ffb520ee54
92
api.py
92
api.py
@ -163,7 +163,7 @@ from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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from feature_extractor import cnhubert
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from io import BytesIO
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from module.models import SynthesizerTrn, SynthesizerTrnV3
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from module.models import SynthesizerTrn, SynthesizerTrnV3, Generator
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from peft import LoraConfig, get_peft_model
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from text import cleaned_text_to_sequence
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@ -214,6 +214,38 @@ def init_bigvgan():
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else:
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bigvgan_model = bigvgan_model.to(device)
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def init_vocoder(version: str):
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global bigvgan_model
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from BigVGAN import bigvgan
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if version == "v3":
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bigvgan_model = bigvgan.BigVGAN.from_pretrained(
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"%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
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use_cuda_kernel=False,
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) # if True, RuntimeError: Ninja is required to load C++ extensions
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# remove weight norm in the model and set to eval mode
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bigvgan_model.remove_weight_norm()
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bigvgan_model = bigvgan_model.eval()
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elif version == "v4":
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bigvgan_model = Generator(
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initial_channel=100,
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resblock="1",
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resblock_kernel_sizes=[3, 7, 11],
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resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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upsample_rates=[10, 6, 2, 2, 2],
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upsample_initial_channel=512,
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upsample_kernel_sizes=[20, 12, 4, 4, 4],
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gin_channels=0, is_bias=True
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)
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bigvgan_model.remove_weight_norm()
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state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu")
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bigvgan_model.load_state_dict(state_dict_g)
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if is_half == True:
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bigvgan_model = bigvgan_model.half().to(device)
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else:
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bigvgan_model = bigvgan_model.to(device)
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resample_transform_dict = {}
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@ -253,6 +285,20 @@ mel_fn = lambda x: mel_spectrogram_torch(
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},
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)
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mel_fn_v4 = lambda x: mel_spectrogram_torch(
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x,
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**{
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"n_fft": 1280,
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"win_size": 1280,
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"hop_size": 320,
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"num_mels": 100,
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"sampling_rate": 32000,
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"fmin": 0,
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"fmax": None,
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"center": False,
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},
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)
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sr_model = None
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@ -297,10 +343,8 @@ def get_sovits_weights(sovits_path):
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path_sovits_v4 = "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth"
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version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
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if if_lora_v3 == True and not os.path.exists(path_sovits_v3):
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logger.info("SoVITS V3 底模缺失，无法加载相应 LoRA 权重")
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if model_version == "v4" and not os.path.exists(path_sovits_v4):
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logger.info("SoVITS V4 底模缺失，无法加载相应 LoRA 权重")
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if (if_lora_v3 == True and not os.path.exists(path_sovits_v3)) or (model_version == "v4" and not os.path.exists(path_sovits_v4)):
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logger.info(f"SoVITS {model_version.upper()} 底模缺失，无法加载相应 LoRA 权重")
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dict_s2 = load_sovits_new(sovits_path)
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hps = dict_s2["config"]
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@ -312,13 +356,9 @@ def get_sovits_weights(sovits_path):
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hps.model.version = "v1"
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else:
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hps.model.version = "v2"
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if model_version == "v3":
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hps.model.version = "v3"
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if model_version == "v4":
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hps.model.version = "v4"
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model_params_dict = vars(hps.model)
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if model_version != "v3" and model_version != "v4":
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if model_version not in {"v3", "v4"}:
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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@ -326,14 +366,16 @@ def get_sovits_weights(sovits_path):
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**model_params_dict,
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)
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else:
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model_params_dict["version"]=model_version
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vq_model = SynthesizerTrnV3(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**model_params_dict,
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)
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init_bigvgan()
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model_version = hps.model.version
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# init_bigvgan()
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init_vocoder(model_version)
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logger.info(f"模型版本: {model_version}")
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if "pretrained" not in sovits_path:
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try:
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@ -345,7 +387,7 @@ def get_sovits_weights(sovits_path):
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else:
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vq_model = vq_model.to(device)
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vq_model.eval()
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if if_lora_v3 == False or model_version != "v4":
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if model_version not in {"v3", "v4"}:
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vq_model.load_state_dict(dict_s2["weight"], strict=False)
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else:
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if model_version == "v4":
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@ -763,7 +805,7 @@ def get_tts_wav(
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prompt_semantic = codes[0, 0]
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prompt = prompt_semantic.unsqueeze(0).to(device)
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if version != "v3" and version != "v4":
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if version not in {"v3", "v4"}:
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refers = []
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if inp_refs:
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for path in inp_refs:
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@ -814,8 +856,7 @@ def get_tts_wav(
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)
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pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
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t3 = ttime()
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if version != "v3" and version != "v4":
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if version not in {"v3", "v4"}:
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audio = (
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vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed)
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.detach()
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@ -834,16 +875,18 @@ def get_tts_wav(
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if sr != 24000:
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ref_audio = resample(ref_audio, sr)
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# print("ref_audio",ref_audio.abs().mean())
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mel2 = mel_fn(ref_audio)
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mel2 = mel_fn_v4(ref_audio) if version == "v4" else mel_fn(ref_audio)
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mel2 = norm_spec(mel2)
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T_min = min(mel2.shape[2], fea_ref.shape[2])
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mel2 = mel2[:, :, :T_min]
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fea_ref = fea_ref[:, :, :T_min]
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if T_min > 468:
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mel2 = mel2[:, :, -468:]
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fea_ref = fea_ref[:, :, -468:]
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T_min = 468
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chunk_len = 934 - T_min
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T_ref = 500 if version == "v4" else 468
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T_chunk = 1000 if version == "v4" else 934
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if T_min > T_ref:
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mel2 = mel2[:, :, -T_ref:]
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fea_ref = fea_ref[:, :, -T_ref:]
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T_min = T_ref
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chunk_len = T_chunk - T_min
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# print("fea_ref",fea_ref,fea_ref.shape)
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# print("mel2",mel2)
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mel2 = mel2.to(dtype)
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@ -871,7 +914,8 @@ def get_tts_wav(
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cmf_res = torch.cat(cfm_resss, 2)
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cmf_res = denorm_spec(cmf_res)
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if bigvgan_model == None:
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init_bigvgan()
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# init_bigvgan()
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init_vocoder(version)
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with torch.inference_mode():
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wav_gen = bigvgan_model(cmf_res)
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audio = wav_gen[0][0].cpu().detach().numpy()
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@ -905,7 +949,7 @@ def get_tts_wav(
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if not stream_mode == "normal":
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if media_type == "wav":
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sr = 48000 if if_sr else 24000
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sr = hps.data.sampling_rate if version != "v3" and version != "v4" else sr
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sr = hps.data.sampling_rate if version != "v3" else sr
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audio_bytes = pack_wav(audio_bytes, sr)
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yield audio_bytes.getvalue()
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