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 from emotion_extract import extract_wav 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, sid=0): 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([sid]) 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, sid=0): if len(text) > 150: return "Error: Text is too long", None audio, _ = tts(text, emotion, sid) return "Success", (hps.data.sampling_rate, audio) def tts2(text, sid=0): if len(text) > 150: return "Error: Text is too long", None audio, randsample = tts(text, "random_sample", sid) return str(randsample), (hps.data.sampling_rate, audio) def tts3(text, sample, sid=0): if len(text) > 150: return "Error: Text is too long", None try: audio, _ = tts(text, int(sample), sid) return "Success", (hps.data.sampling_rate, audio) except: return "输入参数不为整数或其他错误", None def tts4(refer_wav_path, text, sid=0): audio, _ = tts(text, refer_wav_path, sid) return "Success", (hps.data.sampling_rate, audio) app = gr.Blocks() with app: with gr.Tabs(): with gr.TabItem("使用预制情感合成"): tts_spk_id = gr.Dropdown(label="speaker", choices=list(range(hps.data.n_speakers)), value=0) 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_spk_id], [tts_output1, tts_output2]) with gr.TabItem("随机抽取训练集样本作为情感参数"): tts_spk_id = gr.Dropdown(label="speaker", choices=list(range(hps.data.n_speakers)), value=0) 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_spk_id], [tts_output1, tts_output2]) with gr.TabItem("使用情感样本id作为情感参数"): tts_spk_id = gr.Dropdown(label="speaker", choices=list(range(hps.data.n_speakers)), value=0) 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_spk_id], [tts_output1, tts_output2]) with gr.TabItem("使用参考音频作为情感参数"): tts_spk_id = gr.Dropdown(label="speaker", choices=list(range(hps.data.n_speakers)), value=0) tts_refer_wav = gr.File(label="参考音频") 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="Message") tts_output2 = gr.Audio(label="Output") tts_submit.click(tts4, [tts_refer_wav, tts_input1, tts_spk_id], [tts_output1, tts_output2]) app.launch()