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| 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 | |
| net_g = None | |
| models = { | |
| "AdorableDarling": "./MODELS/adorabledarling.pth", | |
| "Silverleg": "./MODELS/J8900.pth", | |
| "Takemura": "./MODELS/take2.pth", | |
| "LucidMoon": "./MODELS/lucid.pth", | |
| "Rrabbitt": "./MODELS/rabbit4900.pth", | |
| "MistyNikki": "./MODELS/nikki9400.pth", | |
| "LightHammer": "./MODELS/hammer.pth", | |
| "VivaciousViolet": "./MODELS/vv.pth", | |
| "AlluWin": "./MODELS/AW.pth", | |
| "ArasakaAI": "Arasaka.pth", | |
| "DLM": "./MODELS/DLM.pth", | |
| "BadGirlDLM": "./MODELS/BG1300.pth", | |
| "BadBoyDLM": "./MODELS/BAD1100.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, sdp_ratio, noise_scale, noise_scale_w, length_scale, model): | |
| global net_g,speakers | |
| 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 | |
| speaker = list(speaker_ids.keys())[0] | |
| css=''' | |
| #main {background-color: #ffffff;opacity: 0.8;background-image: repeating-linear-gradient(45deg, #edffe1 25%, transparent 25%, transparent 75%, #edffe1 75%, #edffe1), repeating-linear-gradient(45deg, #edffe1 25%, #ffffff 25%, #ffffff 75%, #edffe1 75%, #edffe1); | |
| background-position: 0 0, 40px 40px;background-size: 80px 80px;} | |
| #btn {background-color:transparent;border-radius: 5px;} | |
| ''' | |
| with gr.Blocks(theme=gr.themes.Soft()) as app: | |
| with gr.Row(elem_id=""): | |
| 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') | |
| gr.Markdown(value="生成参数") | |
| 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",elem_id="btn") | |
| 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, sdp_ratio, noise_scale, noise_scale_w, length_scale, model], | |
| outputs=[text_output, audio_output,MP3_output] | |
| ) | |
| app.launch(show_error=True) | |