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''' |
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按中英混合识别 |
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按日英混合识别 |
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多语种启动切分识别语种 |
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全部按中文识别 |
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全部按英文识别 |
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全部按日文识别 |
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''' |
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import os |
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import torch |
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from openvoice import se_extractor |
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from openvoice.api import BaseSpeakerTTS, ToneColorConverter |
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if torch.cuda.is_available(): |
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device = "cuda" |
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else: |
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device = "cpu" |
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|
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ckpt_base = 'checkpoints/base_speakers/EN' |
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ckpt_converter = 'checkpoints/converter' |
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base_speaker_tts = BaseSpeakerTTS(f'{ckpt_base}/config.json', device=device) |
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base_speaker_tts.load_ckpt(f'{ckpt_base}/checkpoint.pth') |
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tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device) |
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tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth') |
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def vc_en(audio_ref, style_mode): |
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text = "We have always tried to be at the intersection of technology and liberal arts, to be able to get the best of both, to make extremely advanced products from a technology point of view." |
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if style_mode=="default": |
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source_se = torch.load(f'{ckpt_base}/en_default_se.pth').to(device) |
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reference_speaker = audio_ref |
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target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, target_dir='processed', vad=True) |
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save_path = "output.wav" |
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src_path = "tmp.wav" |
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base_speaker_tts.tts(text, src_path, speaker='default', language='English', speed=1.0) |
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encode_message = "@MyShell" |
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tone_color_converter.convert( |
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audio_src_path=src_path, |
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src_se=source_se, |
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tgt_se=target_se, |
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output_path=save_path, |
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message=encode_message) |
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else: |
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source_se = torch.load(f'{ckpt_base}/en_style_se.pth').to(device) |
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reference_speaker = audio_ref |
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target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, target_dir='processed', vad=True) |
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save_path = "output.wav" |
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src_path = "tmp.wav" |
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base_speaker_tts.tts(text, src_path, speaker=style_mode, language='English', speed=1.0) |
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encode_message = "@MyShell" |
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tone_color_converter.convert( |
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audio_src_path=src_path, |
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src_se=source_se, |
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tgt_se=target_se, |
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output_path=save_path, |
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message=encode_message) |
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return "output.wav" |
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import re, logging |
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import LangSegment |
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logging.getLogger("markdown_it").setLevel(logging.ERROR) |
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logging.getLogger("urllib3").setLevel(logging.ERROR) |
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logging.getLogger("httpcore").setLevel(logging.ERROR) |
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logging.getLogger("httpx").setLevel(logging.ERROR) |
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logging.getLogger("asyncio").setLevel(logging.ERROR) |
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR) |
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) |
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import pdb |
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if os.path.exists("./gweight.txt"): |
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with open("./gweight.txt", 'r', encoding="utf-8") as file: |
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gweight_data = file.read() |
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gpt_path = os.environ.get( |
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"gpt_path", gweight_data) |
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else: |
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gpt_path = os.environ.get( |
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"gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") |
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|
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if os.path.exists("./sweight.txt"): |
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with open("./sweight.txt", 'r', encoding="utf-8") as file: |
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sweight_data = file.read() |
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sovits_path = os.environ.get("sovits_path", sweight_data) |
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else: |
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sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth") |
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cnhubert_base_path = os.environ.get( |
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"cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base" |
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) |
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bert_path = os.environ.get( |
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"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" |
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) |
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infer_ttswebui = os.environ.get("infer_ttswebui", 9872) |
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infer_ttswebui = int(infer_ttswebui) |
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is_share = os.environ.get("is_share", "False") |
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is_share = eval(is_share) |
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if "_CUDA_VISIBLE_DEVICES" in os.environ: |
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] |
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is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available() |
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import gradio as gr |
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from transformers import AutoModelForMaskedLM, AutoTokenizer |
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import numpy as np |
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import librosa |
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from feature_extractor import cnhubert |
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cnhubert.cnhubert_base_path = cnhubert_base_path |
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from module.models import SynthesizerTrn |
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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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from text.cleaner import clean_text |
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from time import time as ttime |
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from module.mel_processing import spectrogram_torch |
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from my_utils import load_audio |
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from tools.i18n.i18n import I18nAuto |
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i18n = I18nAuto() |
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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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if is_half == True: |
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bert_model = bert_model.half().to(device) |
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else: |
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bert_model = bert_model.to(device) |
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def get_bert_feature(text, word2ph): |
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with torch.no_grad(): |
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inputs = tokenizer(text, return_tensors="pt") |
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for i in inputs: |
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inputs[i] = inputs[i].to(device) |
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res = bert_model(**inputs, output_hidden_states=True) |
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] |
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assert len(word2ph) == len(text) |
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phone_level_feature = [] |
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for i in range(len(word2ph)): |
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repeat_feature = res[i].repeat(word2ph[i], 1) |
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phone_level_feature.append(repeat_feature) |
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phone_level_feature = torch.cat(phone_level_feature, dim=0) |
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return phone_level_feature.T |
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class DictToAttrRecursive(dict): |
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def __init__(self, input_dict): |
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super().__init__(input_dict) |
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for key, value in input_dict.items(): |
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if isinstance(value, dict): |
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value = DictToAttrRecursive(value) |
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self[key] = value |
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setattr(self, key, value) |
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|
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def __getattr__(self, item): |
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try: |
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return self[item] |
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except KeyError: |
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raise AttributeError(f"Attribute {item} not found") |
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|
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def __setattr__(self, key, value): |
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if isinstance(value, dict): |
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value = DictToAttrRecursive(value) |
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super(DictToAttrRecursive, self).__setitem__(key, value) |
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super().__setattr__(key, value) |
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|
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def __delattr__(self, item): |
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try: |
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del self[item] |
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except KeyError: |
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raise AttributeError(f"Attribute {item} not found") |
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ssl_model = cnhubert.get_model() |
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if is_half == True: |
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ssl_model = ssl_model.half().to(device) |
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else: |
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ssl_model = ssl_model.to(device) |
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def change_sovits_weights(sovits_path): |
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global vq_model, hps |
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dict_s2 = torch.load(sovits_path, map_location="cpu") |
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hps = dict_s2["config"] |
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hps = DictToAttrRecursive(hps) |
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hps.model.semantic_frame_rate = "25hz" |
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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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n_speakers=hps.data.n_speakers, |
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**hps.model |
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) |
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if ("pretrained" not in sovits_path): |
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del vq_model.enc_q |
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if is_half == True: |
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vq_model = vq_model.half().to(device) |
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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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print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) |
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with open("./sweight.txt", "w", encoding="utf-8") as f: |
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f.write(sovits_path) |
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change_sovits_weights(sovits_path) |
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def change_gpt_weights(gpt_path): |
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global hz, max_sec, t2s_model, config |
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hz = 50 |
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dict_s1 = torch.load(gpt_path, map_location="cpu") |
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config = dict_s1["config"] |
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max_sec = config["data"]["max_sec"] |
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t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) |
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t2s_model.load_state_dict(dict_s1["weight"]) |
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if is_half == True: |
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t2s_model = t2s_model.half() |
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t2s_model = t2s_model.to(device) |
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t2s_model.eval() |
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total = sum([param.nelement() for param in t2s_model.parameters()]) |
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print("Number of parameter: %.2fM" % (total / 1e6)) |
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with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path) |
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change_gpt_weights(gpt_path) |
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def get_spepc(hps, filename): |
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audio = load_audio(filename, int(hps.data.sampling_rate)) |
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audio = torch.FloatTensor(audio) |
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audio_norm = audio |
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audio_norm = audio_norm.unsqueeze(0) |
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spec = spectrogram_torch( |
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audio_norm, |
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hps.data.filter_length, |
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hps.data.sampling_rate, |
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hps.data.hop_length, |
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hps.data.win_length, |
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center=False, |
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) |
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return spec |
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dict_language = { |
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i18n("中文"): "all_zh", |
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i18n("英文"): "en", |
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i18n("日文"): "all_ja", |
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i18n("中英混合"): "zh", |
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i18n("日英混合"): "ja", |
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i18n("多语种混合"): "auto", |
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} |
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def clean_text_inf(text, language): |
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phones, word2ph, norm_text = clean_text(text, language) |
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phones = cleaned_text_to_sequence(phones) |
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return phones, word2ph, norm_text |
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dtype=torch.float16 if is_half == True else torch.float32 |
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def get_bert_inf(phones, word2ph, norm_text, language): |
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language=language.replace("all_","") |
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if language == "zh": |
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bert = get_bert_feature(norm_text, word2ph).to(device) |
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else: |
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bert = torch.zeros( |
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(1024, len(phones)), |
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dtype=torch.float16 if is_half == True else torch.float32, |
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).to(device) |
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return bert |
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splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } |
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|
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def get_first(text): |
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pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" |
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text = re.split(pattern, text)[0].strip() |
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return text |
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def get_phones_and_bert(text,language): |
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if language in {"en","all_zh","all_ja"}: |
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language = language.replace("all_","") |
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if language == "en": |
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LangSegment.setfilters(["en"]) |
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formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) |
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else: |
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|
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formattext = text |
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while " " in formattext: |
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formattext = formattext.replace(" ", " ") |
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phones, word2ph, norm_text = clean_text_inf(formattext, language) |
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if language == "zh": |
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bert = get_bert_feature(norm_text, word2ph).to(device) |
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else: |
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bert = torch.zeros( |
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(1024, len(phones)), |
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dtype=torch.float16 if is_half == True else torch.float32, |
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).to(device) |
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elif language in {"zh", "ja","auto"}: |
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textlist=[] |
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langlist=[] |
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LangSegment.setfilters(["zh","ja","en","ko"]) |
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if language == "auto": |
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for tmp in LangSegment.getTexts(text): |
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if tmp["lang"] == "ko": |
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langlist.append("zh") |
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textlist.append(tmp["text"]) |
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else: |
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langlist.append(tmp["lang"]) |
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textlist.append(tmp["text"]) |
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else: |
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for tmp in LangSegment.getTexts(text): |
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if tmp["lang"] == "en": |
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langlist.append(tmp["lang"]) |
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else: |
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langlist.append(language) |
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textlist.append(tmp["text"]) |
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print(textlist) |
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print(langlist) |
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phones_list = [] |
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bert_list = [] |
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norm_text_list = [] |
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for i in range(len(textlist)): |
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lang = langlist[i] |
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phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) |
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bert = get_bert_inf(phones, word2ph, norm_text, lang) |
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phones_list.append(phones) |
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norm_text_list.append(norm_text) |
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bert_list.append(bert) |
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bert = torch.cat(bert_list, dim=1) |
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phones = sum(phones_list, []) |
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norm_text = ''.join(norm_text_list) |
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return phones,bert.to(dtype),norm_text |
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def merge_short_text_in_array(texts, threshold): |
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if (len(texts)) < 2: |
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return texts |
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result = [] |
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text = "" |
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for ele in texts: |
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text += ele |
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if len(text) >= threshold: |
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result.append(text) |
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text = "" |
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if (len(text) > 0): |
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if len(result) == 0: |
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result.append(text) |
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else: |
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result[len(result) - 1] += text |
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return result |
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|
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False): |
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if prompt_text is None or len(prompt_text) == 0: |
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ref_free = True |
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t0 = ttime() |
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prompt_language = dict_language[prompt_language] |
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text_language = dict_language[text_language] |
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if not ref_free: |
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prompt_text = prompt_text.strip("\n") |
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if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." |
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print(i18n("实际输入的参考文本:"), prompt_text) |
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text = text.strip("\n") |
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if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text |
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|
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print(i18n("实际输入的目标文本:"), text) |
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zero_wav = np.zeros( |
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int(hps.data.sampling_rate * 0.3), |
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dtype=np.float16 if is_half == True else np.float32, |
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) |
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with torch.no_grad(): |
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wav16k, sr = librosa.load(ref_wav_path, sr=16000) |
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if (wav16k.shape[0] > 240000 or wav16k.shape[0] < 48000): |
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raise OSError(i18n("参考音频在3~15秒范围外，请更换！")) |
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wav16k = torch.from_numpy(wav16k) |
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zero_wav_torch = torch.from_numpy(zero_wav) |
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if is_half == True: |
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wav16k = wav16k.half().to(device) |
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zero_wav_torch = zero_wav_torch.half().to(device) |
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else: |
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wav16k = wav16k.to(device) |
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zero_wav_torch = zero_wav_torch.to(device) |
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wav16k = torch.cat([wav16k, zero_wav_torch]) |
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ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ |
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"last_hidden_state" |
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].transpose( |
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1, 2 |
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) |
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codes = vq_model.extract_latent(ssl_content) |
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|
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prompt_semantic = codes[0, 0] |
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t1 = ttime() |
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|
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if (how_to_cut == i18n("凑四句一切")): |
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text = cut1(text) |
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elif (how_to_cut == i18n("凑50字一切")): |
|
text = cut2(text) |
|
elif (how_to_cut == i18n("按中文句号。切")): |
|
text = cut3(text) |
|
elif (how_to_cut == i18n("按英文句号.切")): |
|
text = cut4(text) |
|
elif (how_to_cut == i18n("按标点符号切")): |
|
text = cut5(text) |
|
while "\n\n" in text: |
|
text = text.replace("\n\n", "\n") |
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print(i18n("实际输入的目标文本(切句后):"), text) |
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texts = text.split("\n") |
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texts = merge_short_text_in_array(texts, 5) |
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audio_opt = [] |
|
if not ref_free: |
|
phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language) |
|
|
|
for text in texts: |
|
|
|
if (len(text.strip()) == 0): |
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continue |
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if (text[-1] not in splits): text += "。" if text_language != "en" else "." |
|
print(i18n("实际输入的目标文本(每句):"), text) |
|
phones2,bert2,norm_text2=get_phones_and_bert(text, text_language) |
|
print(i18n("前端处理后的文本(每句):"), norm_text2) |
|
if not ref_free: |
|
bert = torch.cat([bert1, bert2], 1) |
|
all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0) |
|
else: |
|
bert = bert2 |
|
all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0) |
|
|
|
bert = bert.to(device).unsqueeze(0) |
|
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) |
|
prompt = prompt_semantic.unsqueeze(0).to(device) |
|
t2 = ttime() |
|
with torch.no_grad(): |
|
|
|
pred_semantic, idx = t2s_model.model.infer_panel( |
|
all_phoneme_ids, |
|
all_phoneme_len, |
|
None if ref_free else prompt, |
|
bert, |
|
|
|
top_k=top_k, |
|
top_p=top_p, |
|
temperature=temperature, |
|
early_stop_num=hz * max_sec, |
|
) |
|
t3 = ttime() |
|
|
|
pred_semantic = pred_semantic[:, -idx:].unsqueeze( |
|
0 |
|
) |
|
refer = get_spepc(hps, ref_wav_path) |
|
if is_half == True: |
|
refer = refer.half().to(device) |
|
else: |
|
refer = refer.to(device) |
|
|
|
audio = ( |
|
vq_model.decode( |
|
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer |
|
) |
|
.detach() |
|
.cpu() |
|
.numpy()[0, 0] |
|
) |
|
max_audio=np.abs(audio).max() |
|
if max_audio>1:audio/=max_audio |
|
audio_opt.append(audio) |
|
audio_opt.append(zero_wav) |
|
t4 = ttime() |
|
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) |
|
yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype( |
|
np.int16 |
|
) |
|
|
|
|
|
def split(todo_text): |
|
todo_text = todo_text.replace("……", "。").replace("——", "，") |
|
if todo_text[-1] not in splits: |
|
todo_text += "。" |
|
i_split_head = i_split_tail = 0 |
|
len_text = len(todo_text) |
|
todo_texts = [] |
|
while 1: |
|
if i_split_head >= len_text: |
|
break |
|
if todo_text[i_split_head] in splits: |
|
i_split_head += 1 |
|
todo_texts.append(todo_text[i_split_tail:i_split_head]) |
|
i_split_tail = i_split_head |
|
else: |
|
i_split_head += 1 |
|
return todo_texts |
|
|
|
|
|
def cut1(inp): |
|
inp = inp.strip("\n") |
|
inps = split(inp) |
|
split_idx = list(range(0, len(inps), 4)) |
|
split_idx[-1] = None |
|
if len(split_idx) > 1: |
|
opts = [] |
|
for idx in range(len(split_idx) - 1): |
|
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) |
|
else: |
|
opts = [inp] |
|
return "\n".join(opts) |
|
|
|
|
|
def cut2(inp): |
|
inp = inp.strip("\n") |
|
inps = split(inp) |
|
if len(inps) < 2: |
|
return inp |
|
opts = [] |
|
summ = 0 |
|
tmp_str = "" |
|
for i in range(len(inps)): |
|
summ += len(inps[i]) |
|
tmp_str += inps[i] |
|
if summ > 50: |
|
summ = 0 |
|
opts.append(tmp_str) |
|
tmp_str = "" |
|
if tmp_str != "": |
|
opts.append(tmp_str) |
|
|
|
if len(opts) > 1 and len(opts[-1]) < 50: |
|
opts[-2] = opts[-2] + opts[-1] |
|
opts = opts[:-1] |
|
return "\n".join(opts) |
|
|
|
|
|
def cut3(inp): |
|
inp = inp.strip("\n") |
|
return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) |
|
|
|
|
|
def cut4(inp): |
|
inp = inp.strip("\n") |
|
return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) |
|
|
|
|
|
|
|
def cut5(inp): |
|
|
|
|
|
inp = inp.strip("\n") |
|
punds = r'[,.;?!、，。？！;：…]' |
|
items = re.split(f'({punds})', inp) |
|
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])] |
|
|
|
if len(items)%2 == 1: |
|
mergeitems.append(items[-1]) |
|
opt = "\n".join(mergeitems) |
|
return opt |
|
|
|
|
|
def custom_sort_key(s): |
|
|
|
parts = re.split('(\d+)', s) |
|
|
|
parts = [int(part) if part.isdigit() else part for part in parts] |
|
return parts |
|
|
|
|
|
def change_choices(): |
|
SoVITS_names, GPT_names = get_weights_names() |
|
return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"} |
|
|
|
|
|
pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth" |
|
pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" |
|
SoVITS_weight_root = "SoVITS_weights" |
|
GPT_weight_root = "GPT_weights" |
|
os.makedirs(SoVITS_weight_root, exist_ok=True) |
|
os.makedirs(GPT_weight_root, exist_ok=True) |
|
|
|
|
|
def get_weights_names(): |
|
SoVITS_names = [pretrained_sovits_name] |
|
for name in os.listdir(SoVITS_weight_root): |
|
if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name)) |
|
GPT_names = [pretrained_gpt_name] |
|
for name in os.listdir(GPT_weight_root): |
|
if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name)) |
|
return SoVITS_names, GPT_names |
|
|
|
|
|
SoVITS_names, GPT_names = get_weights_names() |
|
|
|
with gr.Blocks(title="GPT-SoVITS WebUI") as app: |
|
gr.Markdown( |
|
value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.") |
|
) |
|
with gr.Group(): |
|
gr.Markdown(value=i18n("模型切换")) |
|
with gr.Row(): |
|
GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True) |
|
SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True) |
|
refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") |
|
refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) |
|
SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], []) |
|
GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], []) |
|
gr.Markdown(value=i18n("*请上传并填写参考信息")) |
|
with gr.Row(): |
|
inp_training_audio = gr.Audio(label="请上传您完整的1分钟训练音频", type="filepath") |
|
style_control = gr.Dropdown(label="请选择一种语音情感", info="🙂default😊friendly🤫whispering😄cheerful😱terrified😡angry😢sad", choices=["default", "friendly", "whispering", "cheerful", "terrified", "angry", "sad"], value="default") |
|
btn_style = gr.Button("一键生成情感参考音频吧💕", variant="primary") |
|
out_ref_audio = gr.Audio(label="为您生成的情感参考音频", type="filepath", interactive=False) |
|
inp_ref = out_ref_audio |
|
with gr.Column(): |
|
ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=False, show_label=True) |
|
gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。")) |
|
prompt_text = gr.Textbox(label=i18n("参考音频的文本"), interactive=False, value="We have always tried to be at the intersection of technology and liberal arts, to be able to get the best of both, to make extremely advanced products from a technology point of view.") |
|
prompt_language = gr.Dropdown( |
|
label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("英文"), interactive=False |
|
) |
|
gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式")) |
|
with gr.Row(): |
|
text = gr.Textbox(label=i18n("需要合成的文本"), value="") |
|
text_language = gr.Dropdown( |
|
label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文") |
|
) |
|
how_to_cut = gr.Radio( |
|
label=i18n("怎么切"), |
|
choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ], |
|
value=i18n("凑四句一切"), |
|
interactive=True, |
|
) |
|
with gr.Row(): |
|
gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)：")) |
|
top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True) |
|
top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True) |
|
temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True) |
|
inference_button = gr.Button(i18n("合成语音"), variant="primary") |
|
output = gr.Audio(label=i18n("输出的语音")) |
|
|
|
inference_button.click( |
|
get_tts_wav, |
|
[inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free], |
|
[output], |
|
) |
|
|
|
gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。")) |
|
with gr.Row(): |
|
text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="") |
|
button1 = gr.Button(i18n("凑四句一切"), variant="primary") |
|
button2 = gr.Button(i18n("凑50字一切"), variant="primary") |
|
button3 = gr.Button(i18n("按中文句号。切"), variant="primary") |
|
button4 = gr.Button(i18n("按英文句号.切"), variant="primary") |
|
button5 = gr.Button(i18n("按标点符号切"), variant="primary") |
|
text_opt = gr.Textbox(label=i18n("切分后文本"), value="") |
|
button1.click(cut1, [text_inp], [text_opt]) |
|
button2.click(cut2, [text_inp], [text_opt]) |
|
button3.click(cut3, [text_inp], [text_opt]) |
|
button4.click(cut4, [text_inp], [text_opt]) |
|
button5.click(cut5, [text_inp], [text_opt]) |
|
btn_style.click(vc_en, [inp_training_audio, style_control], [out_ref_audio]) |
|
gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")) |
|
|
|
app.queue(concurrency_count=511, max_size=1022).launch( |
|
server_name="0.0.0.0", |
|
inbrowser=True, |
|
share=True, |
|
server_port=infer_ttswebui, |
|
quiet=True, |
|
) |
|
|
