mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2026-04-29 21:00:42 +08:00
feat:Added batch tts option
This commit is contained in:
Kaning123
parent
cb2b844f45
commit
fb50fc090f
@ -1,3 +1,7 @@
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{
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"running_on" : "local"
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"running_on" : "local",
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"Default":{
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"GPT_Path": "不训练直接推v3底模！",
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"SoVITS_Path": "不训练直接推v2ProPlus底模！"
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}
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}
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@ -24,6 +24,7 @@ class CNHubert(nn.Module):
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super().__init__()
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if base_path is None:
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base_path = cnhubert_base_path
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print(f"Loading CN-Hubert from \"{base_path}\"")
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if os.path.exists(base_path):
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...
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else:
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@ -69,6 +70,7 @@ class CNHubert(nn.Module):
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def get_model():
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print("cnhubert_base_path:", cnhubert_base_path)
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model = CNHubert()
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model.eval()
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return model
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@ -12,6 +12,7 @@ import sys
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import json
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from pathlib import Path
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import uuid
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from scipy.io.wavfile import write
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@ -31,6 +32,7 @@ with open(merge_dir_txt2(get_my_dir(), "config.json"), "r", encoding="utf-8") as
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config_json = f.read()
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config_json = json.loads(config_json)
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running_on = config_json["running_on"]
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Default = config_json["Default"]
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ROOT_DIR = str(get_parent_dir(get_my_dir()))
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sys.path.append(get_my_dir())
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@ -124,6 +126,7 @@ with open("./weight.json", "r", encoding="utf-8") as file:
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if isinstance(sovits_path, list):
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sovits_path = sovits_path[0]
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# print(2333333)
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# print(os.environ["gpt_path"])
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# print(gpt_path)
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@ -150,7 +153,7 @@ import numpy as np
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from feature_extractor import cnhubert
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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cnhubert.cnhubert_base_path = cnhubert_base_path
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cnhubert.cnhubert_base_path = merge_dir_txt2(ROOT_DIR, cnhubert_base_path)
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import random
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@ -184,6 +187,12 @@ language = os.environ.get("language", "Auto")
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language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
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i18n = I18nAuto(language=language)
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if gpt_path in [None, "",]:
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gpt_path = str(merge_dir_txt2(ROOT_DIR, name2gpt_path[i18n(Default["GPT_Path"])]))
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if sovits_path in [None, "",]:
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sovits_path = str(merge_dir_txt2(ROOT_DIR, name2sovits_path[i18n(Default["SoVITS_Path"])]))
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# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
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if torch.cuda.is_available():
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@ -214,8 +223,8 @@ dict_language_v2 = {
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}
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dict_language = dict_language_v1 if version == "v1" else dict_language_v2
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
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tokenizer = AutoTokenizer.from_pretrained(str(merge_dir_txt2(ROOT_DIR,bert_path)))
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bert_model = AutoModelForMaskedLM.from_pretrained(str(merge_dir_txt2(ROOT_DIR,bert_path)))
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if is_half == True:
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bert_model = bert_model.half().to(device)
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else:
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@ -428,6 +437,7 @@ except:
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def change_gpt_weights(gpt_path):
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print("gpt_path:", gpt_path)
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if "！" in gpt_path or "!" in gpt_path:
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gpt_path = name2gpt_path[gpt_path]
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global hz, max_sec, t2s_model, config
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@ -1205,7 +1215,204 @@ def get_tts_wav(
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else:
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return opt_sr, (audio_opt * 32767).astype(np.int16)
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def batched_tts_wav(
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ref_wav_path,
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prompt_text,
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prompt_language,
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texts,
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text_language,
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how_to_cut=i18n("不切"),
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top_k=20,
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top_p=0.6,
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temperature=0.6,
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ref_free=False,
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speed=1,
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if_freeze=False,
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inp_refs=None,
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sample_steps=8,
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if_sr=False,
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pause_second=0.3,
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SaveSvEmb=False,
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SaveRefers=False,
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SaveSvEmbName="sv_emb.voice",
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SaveRefersName="refers.voice",
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SaveGE=False,
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SaveGEName="ge.voice",
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InjectSvEmb=False,
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InjectRefers=False,
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InjectSvEmbName="sv_emb.voice",
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InjectRefersName="refers.voice",
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EnableAudioLoad=True,
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SaveOutputAsUndecoded=False,
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SaveOutputAsUndecodedName="output.voice",
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AddRandomSaltToSaveOutputAsUndecodedName=False,
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ReturnWay = "yield", # "yield" or "return"
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):
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count = 0
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out = []
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SaveDir = merge_dir_txt2(ROOT_DIR,"output","tts_output",f"batch_{uuid.uuid4()}")
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if not os.path.exists(SaveDir):
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os.makedirs(SaveDir,exist_ok=True)
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for text in texts:
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if text in [None, " ", ""]:
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gr.Warning(i18n(f"输入文本第{count}行中有空行，已跳过"))
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continue
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else:
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unparsed = get_tts_wav(
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ref_wav_path,
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prompt_text,
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prompt_language,
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text,
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text_language,
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how_to_cut,
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top_k,
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top_p,
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temperature,
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ref_free,
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speed,
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if_freeze,
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inp_refs,
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sample_steps,
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if_sr,
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pause_second,
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SaveSvEmb,
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SaveRefers,
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SaveSvEmbName,
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SaveRefersName,
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SaveGE,
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SaveGEName,
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InjectSvEmb,
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InjectRefers,
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InjectSvEmbName,
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InjectRefersName,
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EnableAudioLoad,
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SaveOutputAsUndecoded,
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SaveOutputAsUndecodedName,
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AddRandomSaltToSaveOutputAsUndecodedName,
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"yield",
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)
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unparsed = list(unparsed)
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print(unparsed)
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a = text.strip().replace(' ','_').replace('\n','_')
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wav_path = os.path.join(SaveDir,f"tts_output_{a}_{str(uuid.uuid4())}.wav")
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write(wav_path, unparsed[0][0], unparsed[0][1])
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out.append(wav_path)
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count += 1
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if ReturnWay == "yield":
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yield SaveDir
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else:
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return SaveDir
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def read_tts_batch_file(file_path):
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ret = []
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with open(file_path, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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for l in lines:
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if l.strip() in [None, " ", ""]:
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continue
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else:
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ret.append(l)
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return ret
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def batch_tts(
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ref_wav_path,
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prompt_text,
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prompt_language,
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text_paths,
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text_language,
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how_to_cut=i18n("不切"),
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top_k=20,
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top_p=0.6,
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temperature=0.6,
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ref_free=False,
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speed=1,
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if_freeze=False,
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inp_refs=None,
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sample_steps=8,
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if_sr=False,
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pause_second=0.3,
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SaveSvEmb=False,
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SaveRefers=False,
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SaveSvEmbName="sv_emb.voice",
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SaveRefersName="refers.voice",
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SaveGE=False,
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SaveGEName="ge.voice",
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InjectSvEmb=False,
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InjectRefers=False,
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InjectSvEmbName="sv_emb.voice",
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InjectRefersName="refers.voice",
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EnableAudioLoad=True,
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SaveOutputAsUndecoded=False,
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SaveOutputAsUndecodedName="output.voice",
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AddRandomSaltToSaveOutputAsUndecodedName=False,
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ReturnWay = "yield", # "yield" or "return"
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):
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print(text_paths)
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text_list = []
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for i in text_paths:
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text_list.extend(read_tts_batch_file(i))
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out = batched_tts_wav(
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ref_wav_path,
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prompt_text,
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prompt_language,
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text_list,
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text_language,
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how_to_cut,
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top_k,
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top_p,
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temperature,
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ref_free,
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speed,
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if_freeze,
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inp_refs,
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sample_steps,
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if_sr,
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pause_second,
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SaveSvEmb,
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SaveRefers,
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SaveSvEmbName,
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SaveRefersName,
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SaveGE,
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SaveGEName,
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InjectSvEmb,
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InjectRefers,
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InjectSvEmbName,
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InjectRefersName,
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EnableAudioLoad,
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SaveOutputAsUndecoded,
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SaveOutputAsUndecodedName,
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AddRandomSaltToSaveOutputAsUndecodedName,
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"yield"
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)
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out = list(out)
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if ReturnWay == "yield":
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yield out
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else:
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return out
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def close_serv():
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if running_on == "local":
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sys.exit(0)
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@ -1540,6 +1747,25 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css
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show_label=True,
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visible=False if model_version != "v3" else True,
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)
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with gr.Row():
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gr.Markdown(html_center(i18n("批量语音合成参数"), "h3"))
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with gr.Column(scale=13):
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txt_paths = gr.File(label=i18n("批量语音合成文本文件，每行一个文本"),
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file_types=[".txt"],
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interactive=True,
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file_count="multiple",
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scale=13)
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with gr.Column(scale=7):
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out = gr.File(label=i18n("批量合成输出的语音文件"),
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file_types=[".wav"],
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file_count="directory",)
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start_batch_btn = gr.Button(i18n("开始批量合成"),
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variant="primary",
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size="lg",
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interactive=True,
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scale=25)
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gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"), "h3"))
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with gr.Row():
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with gr.Column(scale=13):
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@ -1648,7 +1874,51 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css
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],
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[output],
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api_name="get_tts_wav",
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)
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start_batch_btn.click(
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batch_tts,
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[
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inp_ref,
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prompt_text,
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prompt_language,
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txt_paths,
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text_language,
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how_to_cut,
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top_k,
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top_p,
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temperature,
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ref_text_free,
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speed,
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if_freeze,
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inp_refs,
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sample_steps,
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if_sr_Checkbox,
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pause_second_slider,
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SaveSvEmb,
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SaveRefers,
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SaveSvEmbName,
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SaveRefersName,
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SaveGE,
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SaveGEName,
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InjectSvEmb,
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InjectRefers,
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InjectSvEmbName,
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InjectRefersName,
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EnableAudioLoad,
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SaveOutputAsUndecoded,
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SaveOutputAsUndecodedName,
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AddRandomSaltToSaveOutputAsUndecodedName,
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],
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[out],
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api_name="batch_tts",
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)
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SoVITS_dropdown.change(
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change_sovits_weights,
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[SoVITS_dropdown, prompt_language, text_language],
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@ -1,9 +1,10 @@
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import sys
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import os
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import torch
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from pathlib import Path
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sys.path.append(f"{os.getcwd()}/GPT_SoVITS/eres2net")
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sv_path = "GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt"
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sys.path.append(f"{str(Path(os.path.dirname(os.path.abspath(__file__))).parent)}/GPT_SoVITS/eres2net")
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sv_path = f"{str(Path(os.path.dirname(os.path.abspath(__file__))).parent)}/GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt"
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from ERes2NetV2 import ERes2NetV2
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import kaldi as Kaldi
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