# coding=utf-8 import logging import sys import os logging.getLogger('numba').setLevel(logging.WARNING) logging.basicConfig( format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=os.environ.get("LOGLEVEL", "INFO").upper(), stream=sys.stdout, ) logger = logging.getLogger("APP") import time import os import gradio as gr import utils import argparse import commons from models import SynthesizerTrn from text import text_to_sequence import torch from torch import no_grad, LongTensor from gradio_client import utils as client_utils limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces audio_postprocess_ori = gr.Audio.postprocess def audio_postprocess(self, y): data = audio_postprocess_ori(self, y) if data is None: return None return client_utils.encode_url_or_file_to_base64(data["name"]) gr.Audio.postprocess = audio_postprocess def get_text(text, hps): text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) if hps.data.add_blank: text_norm = commons.intersperse(text_norm, 0) text_norm = LongTensor(text_norm) return text_norm, clean_text def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale): start = time.perf_counter() if not len(text): return None text = text.replace('\n', ' ').replace('\r', '').replace(" ", "") if len(text) > 200 and limitation: return None if language == "中文": text = f"[ZH]{text}[ZH]" elif language == "日语": text = f"[JA]{text}[JA]" else: text = f"{text}" stn_tst, clean_text = get_text(text, hps_ms) with no_grad(): x_tst = stn_tst.unsqueeze(0).to(device) x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) speaker_id = LongTensor([speaker_id]).to(device) audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0, 0].data.cpu().float().numpy() logger.info(f"gen: {(text[:100], language, speaker_id, noise_scale, noise_scale_w, length_scale)}") return (22050, audio) def search_speaker(search_value): for s in speakers: if search_value == s: return s for s in speakers: if search_value in s: return s if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--device', type=str, default='cpu') args = parser.parse_args() device = torch.device(args.device) hps_ms = utils.get_hparams_from_file(r'./model/config.json') net_g_ms = SynthesizerTrn( len(hps_ms.symbols), hps_ms.data.filter_length // 2 + 1, hps_ms.train.segment_size // hps_ms.data.hop_length, n_speakers=hps_ms.data.n_speakers, **hps_ms.model) _ = net_g_ms.eval().to(device) speakers = hps_ms.speakers speakers = [f"{i}.{s}" for i, s in enumerate(speakers)] model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None) app = gr.Interface( fn=vits, inputs=[ gr.Textbox(label="Text (200 words limitation)", lines=5, value="可莉不知道哦!", elem_id=f"input-text"), gr.Radio(label="language", choices=["中文", "日语", "中日混合(格式参考下面的example)"], value="中文"), gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[329]), gr.Slider(label="noise_scale (控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.1, interactive=True), gr.Slider(label="noise_scale_w (控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.7, interactive=True), gr.Slider(label="length_scale (控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True), ], outputs=gr.Audio(label="Output Audio", elem_id=f"tts-audio"), examples=[ ["可莉不知道哦!", "中文", speakers[329], 0.1, 0.6, 1.2], ["该做什么好呢?", "中文", speakers[104], 0.1, 0.8, 1.2], ["我给你讲个故事吧!", "中文", speakers[122], 0.1, 0.8, 1.2], ["おはようございます~", "日语", speakers[335], 0.1, 0.6, 1.2], ["[ZH]我会用日语说早上好啦![ZH][JA]おはようございます~[JA]", "中日混合", speakers[317], 0.1, 0.6, 1.2], ], title="VITS Genshin", description="", cache_examples=False ) app.queue(concurrency_count=1) app.launch()