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import sys, os |
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import torch |
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import argparse |
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import commons |
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import utils |
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from models import SynthesizerTrn |
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from text.symbols import symbols |
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from text import cleaned_text_to_sequence, get_bert |
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from text.cleaner import clean_text |
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import gradio as gr |
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import soundfile as sf |
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from datetime import datetime |
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import pytz |
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net_g = None |
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models = { |
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"V1": "./MODELS/v1100.pth", |
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"V2": "./MODELS/180_3000.pth", |
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"V3":"./MODELS/v3_8000.pth" |
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} |
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def get_text(text, language_str, hps): |
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norm_text, phone, tone, word2ph = clean_text(text, language_str) |
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) |
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if hps.data.add_blank: |
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phone = commons.intersperse(phone, 0) |
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tone = commons.intersperse(tone, 0) |
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language = commons.intersperse(language, 0) |
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for i in range(len(word2ph)): |
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word2ph[i] = word2ph[i] * 2 |
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word2ph[0] += 1 |
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bert = get_bert(norm_text, word2ph, language_str) |
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del word2ph |
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assert bert.shape[-1] == len(phone) |
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phone = torch.LongTensor(phone) |
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tone = torch.LongTensor(tone) |
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language = torch.LongTensor(language) |
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return bert, phone, tone, language |
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def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, model_dir): |
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global net_g |
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bert, phones, tones, lang_ids = get_text(text, "ZH", hps) |
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with torch.no_grad(): |
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x_tst=phones.to(device).unsqueeze(0) |
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tones=tones.to(device).unsqueeze(0) |
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lang_ids=lang_ids.to(device).unsqueeze(0) |
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bert = bert.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) |
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del phones |
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device) |
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audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio |
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, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() |
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del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers |
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sf.write("tmp.wav", audio, 44100) |
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return audio |
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tz = pytz.timezone('Asia/Shanghai') |
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def convert_wav_to_mp3(wav_file): |
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global tz |
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now = datetime.now(tz).strftime('%m%d%H%M%S') |
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os.makedirs('out', exist_ok=True) |
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output_path_mp3 = os.path.join('out', f"{now}.mp3") |
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renamed_input_path = os.path.join('in', f"in.wav") |
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os.makedirs('in', exist_ok=True) |
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os.rename(wav_file.name, renamed_input_path) |
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command = ["ffmpeg", "-i", renamed_input_path, "-acodec", "libmp3lame", "-y", output_path_mp3] |
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os.system(" ".join(command)) |
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return output_path_mp3 |
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def tts_generator(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, model): |
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global net_g,speakers |
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model_path = models[model] |
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net_g, _, _, _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True) |
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text = text[:500] |
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try: |
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with torch.no_grad(): |
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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) |
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with open('tmp.wav', 'rb') as wav_file: |
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mp3 = convert_wav_to_mp3(wav_file) |
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return "生成语音成功", (hps.data.sampling_rate, audio), mp3 |
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except Exception as e: |
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return "生成语音失败：" + str(e), None, None |
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if __name__ == "__main__": |
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hps = utils.get_hparams_from_file("./configs/config.json") |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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net_g = SynthesizerTrn( |
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len(symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model).to(device) |
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_ = net_g.eval() |
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speaker_ids = hps.data.spk2id |
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speaker = list(speaker_ids.keys())[0] |
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theme='remilia/Ghostly' |
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with gr.Blocks(theme=theme) as app: |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("""**测试用**""") |
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text = gr.TextArea(label="✨输入需要生成语音的文字", placeholder="输入文字", |
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value="漩涡帮可不是吃素的，我是碰巧路过听人说，他们要整一个全金属和尚", |
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info="使用huggingface的免费CPU进行推理，因此速度不快，最多生成500字，多余的会被忽略。字数越多越耗时，请耐心等待，只会说中文", |
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) |
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model = gr.Radio(choices=list(models.keys()), value=list(models.keys())[0], label='📢音声模型') |
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with gr.Accordion(label="💡展开设置生成参数", open=False): |
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sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label='SDP/DP混合比',info='可控制一定程度的语调变化') |
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noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.5, step=0.01, label='感情变化') |
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noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.9, step=0.01, label='音节长度') |
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length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成语音总长度',info='数值越大，语速越慢') |
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btn = gr.Button("🪄生成", variant="primary") |
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with gr.Column(): |
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audio_output = gr.Audio(label="🔊试听") |
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MP3_output = gr.File(label="💾下载") |
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text_output = gr.Textbox(label="❗调试信息") |
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gr.Markdown(""" |
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""") |
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btn.click( |
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tts_generator, |
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inputs=[text, sdp_ratio, noise_scale, noise_scale_w, length_scale, model], |
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outputs=[text_output, audio_output,MP3_output] |
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) |
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gr.Examples( |
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fn=tts_generator, |
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examples=[ |
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[ |
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"我？当警察，上次我说这话的时候才六岁" |
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], |
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[ |
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"但对我来说，回忆中的夜之城反而笼罩在一种暖暖淡淡的，有奶油质感的颜色中。" |
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], |
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[ |
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"与我打卡过的北京其他几家社区图书馆一样，环境那叫一个整洁优雅，工作日那叫一个人烟稀少。书虽不多，但好书不少，而且崭新得烫手。" |
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], |
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[ |
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"《神笔狗良》冒险解谜涂色游戏，对小朋友来说或许有点幼稚但对我来说刚刚好！" |
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], |
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[ |
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"不知道有没有使用过不同读取速度内存卡的姐妹，游戏加载和运行速度会差很多吗？" |
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] |
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, |
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], |
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inputs=[text], |
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outputs=[audio_output] |
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) |
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app.launch(show_error=True) |
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