import sys, os import torch import argparse import commons import utils from models import SynthesizerTrn from text.symbols import symbols from text import cleaned_text_to_sequence, get_bert from text.cleaner import clean_text import gradio as gr import webbrowser import soundfile as sf from datetime import datetime import pytz import logging logging.getLogger("numba").setLevel(logging.WARNING) logging.getLogger("markdown_it").setLevel(logging.WARNING) logging.getLogger("urllib3").setLevel(logging.WARNING) logging.getLogger("matplotlib").setLevel(logging.WARNING) logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s") logger = logging.getLogger(__name__) net_g = None models = { "AdorableDarling": "./MODELS/adorabledarling.pth", "Silverleg": "./MODELS/silverhandG_4400.pth", "Tnikki1900": "./MODELS/nikki_1900.pth", "Tnikki3400": "./MODELS/Nikki_3400.pth", "MoonLucidAloof": "./MODELS/G_2900.pth", "Rrabbitt": "./MODELS/rabbit4900.pth", "Mainlade": "./MODELS/DLM.pth", } def get_text(text, language_str, hps): norm_text, phone, tone, word2ph = clean_text(text, language_str) phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) if hps.data.add_blank: phone = commons.intersperse(phone, 0) tone = commons.intersperse(tone, 0) language = commons.intersperse(language, 0) for i in range(len(word2ph)): word2ph[i] = word2ph[i] * 2 word2ph[0] += 1 bert = get_bert(norm_text, word2ph, language_str) del word2ph assert bert.shape[-1] == len(phone) phone = torch.LongTensor(phone) tone = torch.LongTensor(tone) language = torch.LongTensor(language) return bert, phone, tone, language def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, model_dir): global net_g bert, phones, tones, lang_ids = get_text(text, "ZH", hps) with torch.no_grad(): x_tst=phones.to(device).unsqueeze(0) tones=tones.to(device).unsqueeze(0) lang_ids=lang_ids.to(device).unsqueeze(0) bert = bert.to(device).unsqueeze(0) x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) del phones speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers sf.write("tmp.wav", audio, 44100) return audio def convert_wav_to_mp3(wav_file): tz = pytz.timezone('Asia/Shanghai') now = datetime.now(tz).strftime('%m%d%H%M%S') os.makedirs('out', exist_ok=True) output_path_mp3 = os.path.join('out', f"{now}.mp3") renamed_input_path = os.path.join('in', f"in.wav") os.makedirs('in', exist_ok=True) os.rename(wav_file.name, renamed_input_path) command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libmp3lame", "-y", output_path_mp3] os.system(" ".join(command)) return output_path_mp3 def tts_generator(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, model): global net_g model_path = models[model] net_g, _, _, _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True) try: with torch.no_grad(): audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker,model_dir=model) with open('tmp.wav', 'rb') as wav_file: mp3 = convert_wav_to_mp3(wav_file) return "生成语音成功", (hps.data.sampling_rate, audio), mp3 except Exception as e: return "生成语音失败:" + str(e), None, None if __name__ == "__main__": hps = utils.get_hparams_from_file("./configs/config.json") device = "cuda:0" if torch.cuda.is_available() else "cpu" net_g = SynthesizerTrn( len(symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model).to(device) _ = net_g.eval() speaker_ids = hps.data.spk2id speakers = list(speaker_ids.keys()) with gr.Blocks() as app: with gr.Row(): with gr.Column(): gr.Markdown("测试用") text = gr.TextArea(label="Text", placeholder="Input Text Here", value="在不在?能不能借给我三百块钱买可乐", info="使用huggingface的免费CPU进行推理,因此速度不快,一次性不要输入超过500汉字") model = gr.Radio(choices=list(models.keys()), value=list(models.keys())[0], label='音声模型') #model = gr.Dropdown(choices=models,value=models[0], label='音声模型') speaker = gr.Radio(choices=speakers, value=speakers[0], label='Speaker') gr.Markdown("生成参数,效果玄学") sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='语调变化') noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.5, step=0.01, label='感情变化') noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.9, step=0.01, label='音节长度') length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成语音总长度') btn = gr.Button("生成", variant="primary") with gr.Column(): text_output = gr.Textbox(label="Message") audio_output = gr.Audio(label="试听") MP3_output = gr.File(label="下载") gr.Markdown(""" """) btn.click( tts_generator, inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, model], outputs=[text_output, audio_output,MP3_output] ) app.launch(show_error=True)