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| import gradio as gr | |
| import torch | |
| import commons | |
| import utils | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text import text_to_sequence | |
| import numpy as np | |
| def get_text(text, hps): | |
| text_norm = text_to_sequence(text, hps.data.text_cleaners) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_norm | |
| hps = utils.get_hparams_from_file("./configs/vtubers.json") | |
| 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) | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint("./nene_final.pth", net_g, None) | |
| all_emotions = np.load("all_emotions.npy") | |
| emotion_dict = { | |
| "小声": 2077, | |
| "激动": 111, | |
| "平静1": 434, | |
| "平静2": 3554 | |
| } | |
| import random | |
| def tts(txt, emotion): | |
| stn_tst = get_text(txt, hps) | |
| randsample = None | |
| with torch.no_grad(): | |
| x_tst = stn_tst.unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) | |
| sid = torch.LongTensor([0]) | |
| if type(emotion) ==int: | |
| emo = torch.FloatTensor(all_emotions[emotion]).unsqueeze(0) | |
| elif emotion == "random": | |
| emo = torch.randn([1,1024]) | |
| elif emotion == "random_sample": | |
| randint = random.randint(0, all_emotions.shape[0]) | |
| emo = torch.FloatTensor(all_emotions[randint]).unsqueeze(0) | |
| randsample = randint | |
| elif emotion.endswith("wav"): | |
| import emotion_extract | |
| emo = torch.FloatTensor(emotion_extract.extract_wav(emotion)) | |
| else: | |
| emo = torch.FloatTensor(all_emotions[emotion_dict[emotion]]).unsqueeze(0) | |
| audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=1, emo=emo)[0][0,0].data.float().numpy() | |
| return audio, randsample | |
| def tts1(text, emotion): | |
| if len(text) > 150: | |
| return "Error: Text is too long", None | |
| audio, _ = tts(text, emotion) | |
| return "Success", (hps.data.sampling_rate, audio) | |
| def tts2(text): | |
| if len(text) > 150: | |
| return "Error: Text is too long", None | |
| audio, randsample = tts(text, "random_sample") | |
| return str(randsample), (hps.data.sampling_rate, audio) | |
| def tts3(text, sample): | |
| if len(text) > 150: | |
| return "Error: Text is too long", None | |
| try: | |
| audio, _ = tts(text, int(sample)) | |
| return "Success", (hps.data.sampling_rate, audio) | |
| except: | |
| return "输入参数不为整数或其他错误", None | |
| app = gr.Blocks() | |
| with app: | |
| with gr.Tabs(): | |
| with gr.TabItem("使用预制情感合成"): | |
| tts_input1 = gr.TextArea(label="日语文本", value="こんにちは。私わあやちねねです。") | |
| tts_input2 = gr.Dropdown(label="情感", choices=list(emotion_dict.keys()), value="平静1") | |
| tts_submit = gr.Button("合成音频", variant="primary") | |
| tts_output1 = gr.Textbox(label="Message") | |
| tts_output2 = gr.Audio(label="Output") | |
| tts_submit.click(tts1, [tts_input1, tts_input2], [tts_output1, tts_output2]) | |
| with gr.TabItem("随机抽取训练集样本作为情感参数"): | |
| tts_input1 = gr.TextArea(label="日语文本", value="こんにちは。私わあやちねねです。") | |
| tts_submit = gr.Button("合成音频", variant="primary") | |
| tts_output1 = gr.Textbox(label="随机样本id（可用于第三个tab中合成）") | |
| tts_output2 = gr.Audio(label="Output") | |
| tts_submit.click(tts2, [tts_input1], [tts_output1, tts_output2]) | |
| with gr.TabItem("使用情感样本id作为情感参数"): | |
| tts_input1 = gr.TextArea(label="日语文本", value="こんにちは。私わあやちねねです。") | |
| tts_input2 = gr.Number(label="情感样本id", value=2004) | |
| tts_submit = gr.Button("合成音频", variant="primary") | |
| tts_output1 = gr.Textbox(label="Message") | |
| tts_output2 = gr.Audio(label="Output") | |
| tts_submit.click(tts3, [tts_input1, tts_input2], [tts_output1, tts_output2]) | |
| with gr.TabItem("使用参考音频作为情感参数"): | |
| tts_input1 = gr.TextArea(label="text", value="暂未实现") | |
| app.launch() | |