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| import argparse | |
| import logging | |
| import os | |
| import re | |
| import gradio.processing_utils as gr_pu | |
| import gradio as gr | |
| import librosa | |
| import numpy as np | |
| import soundfile | |
| from scipy.io import wavfile | |
| import tempfile | |
| import edge_tts | |
| import utils | |
| from inference.infer_tool import Svc | |
| 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) | |
| sampling_rate = 44100 | |
| tts_voice = { | |
| "中文男": "zh-CN-YunxiNeural", | |
| "中文女": "zh-CN-XiaoyiNeural", | |
| "英文男": "en-US-EricNeural", | |
| "英文女": "en-US-AnaNeural" | |
| } | |
| hubert_dict = { | |
| "vec768l12": utils.get_speech_encoder("vec768l12", device="cpu"), | |
| "vec256l9": utils.get_speech_encoder("vec256l9", device="cpu") | |
| } | |
| def create_fn(model, spk): | |
| def svc_fn(input_audio, vc_transform, auto_f0, f0p): | |
| if input_audio is None: | |
| return 0, None | |
| sr, audio = input_audio | |
| audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) | |
| audio = librosa.resample(audio, orig_sr=sr, target_sr=sampling_rate) | |
| if len(audio.shape) > 1: | |
| audio = librosa.to_mono(audio.transpose(1, 0)) | |
| temp_path = "temp.wav" | |
| soundfile.write(temp_path, audio, sampling_rate, format="wav") | |
| model.hubert_model = hubert_dict[model.speech_encoder] | |
| out_audio = model.slice_inference(raw_audio_path=temp_path, | |
| spk=spk, | |
| slice_db=-40, | |
| cluster_infer_ratio=0, | |
| noice_scale=0.4, | |
| clip_seconds=10, | |
| tran=vc_transform, | |
| f0_predictor=f0p, | |
| auto_predict_f0=auto_f0) | |
| model.clear_empty() | |
| os.remove(temp_path) | |
| return sampling_rate, out_audio | |
| async def tts_fn(input_text, gender, tts_rate, vc_transform, auto_f0, f0p): | |
| if input_text == '': | |
| return 0, None | |
| input_text = re.sub(r"[\n\,\(\) ]", "", input_text) | |
| voice = tts_voice[gender] | |
| ratestr = "+{:.0%}".format(tts_rate) if tts_rate >= 0 else "{:.0%}".format(tts_rate) | |
| communicate = edge_tts.Communicate(text=input_text, voice=voice, rate=ratestr) | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: | |
| temp_path = tmp_file.name | |
| await communicate.save(temp_path) | |
| audio, sr = librosa.load(temp_path) | |
| audio = librosa.resample(audio, orig_sr=sr, target_sr=sampling_rate) | |
| os.remove(temp_path) | |
| temp_path = "temp.wav" | |
| wavfile.write(temp_path, sampling_rate, (audio * np.iinfo(np.int16).max).astype(np.int16)) | |
| sr, audio = gr_pu.audio_from_file(temp_path) | |
| input_audio = (sampling_rate, audio) | |
| return svc_fn(input_audio, vc_transform, auto_f0, f0p) | |
| return svc_fn, tts_fn | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--device', type=str, default='cpu') | |
| parser.add_argument('--api', action="store_true", default=False) | |
| parser.add_argument("--share", action="store_true", default=True, help="share gradio app") | |
| args = parser.parse_args() | |
| models = [] | |
| for f in os.listdir("models"): | |
| name = f | |
| model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config_{f}.json", device=args.device) | |
| cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else f"models/{f}/cover.jpg" | |
| models.append((name, cover, create_fn(model, name))) | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| "# <center> 罪恶都市角色语音生成\n" | |
| "## <center> 模型作者：B站[Cyber蝈蝈总](https://space.bilibili.com/37706580)\n" | |
| "#### <center> 传送门[GTASA](https://huggingface.co/spaces/GroveStreet/GTA_SOVITS)；[GTAV](https://huggingface.co/spaces/GroveStreet/GTAV_SOVITS)\n" | |
| "<center>使用此资源创作的作品请标明出处" | |
| ) | |
| with gr.Tabs(): | |
| for (name, cover, (svc_fn, tts_fn)) in models: | |
| with gr.TabItem(name): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| vc_transform = gr.Number(label="音高调整 (正负半音，12为1个八度)", value=0) | |
| f0_predictor = gr.Radio(label="f0预测器 (推荐rmvpe)", | |
| choices=['crepe', 'harvest', 'rmvpe'], value='rmvpe') | |
| auto_f0 = gr.Checkbox(label="自动音高预测 (文本转语音或讲话可选,会导致唱歌跑调)", | |
| value=False) | |
| with gr.Tabs(): | |
| with gr.TabItem('语音转语音'): | |
| svc_input = gr.Audio( | |
| label="上传干声 (已支持无限长音频，处理时间约为原音频时间的5倍)") | |
| svc_submit = gr.Button("生成", variant="primary") | |
| with gr.TabItem('文本转语音'): | |
| tts_input = gr.Textbox(label='说话内容', value='', | |
| placeholder='已支持无限长内容，处理时间约为说完原内容时间的5倍') | |
| with gr.Row(): | |
| gender = gr.Radio(label='说话人性别 (男音调低，女音调高)', value='中文男', | |
| choices=['中文男', '中文女', '英文男', '英文女']) | |
| tts_rate = gr.Number(label='语速 (正负, 单位百分比)', value=0) | |
| tts_submit = gr.Button("生成", variant="primary") | |
| with gr.Column(): | |
| gr.Image(cover, width=400, height=400) | |
| vc_output = gr.Audio(label="输出音频") | |
| svc_submit.click(svc_fn, [svc_input, vc_transform, auto_f0, f0_predictor], vc_output) | |
| tts_submit.click(tts_fn, [tts_input, gender, tts_rate, vc_transform, auto_f0, f0_predictor], | |
| vc_output) | |
| app.queue(api_open=args.api).launch(share=args.share) | |