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| import io | |
| import os | |
| import gradio as gr | |
| import librosa | |
| import base64 | |
| import numpy as np | |
| import soundfile | |
| #from inference.infer_tool import Svc | |
| from inference.infer_tool import Svc | |
| import logging | |
| import time | |
| from tts_voices import SUPPORTED_LANGUAGES | |
| 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) | |
| #hf_token = os.environ.get('TOKEN') | |
| #hf_token1 = os.environ.get('TOKEN1') | |
| #hf_token2 = os.environ.get('TOKEN2') | |
| #hf_token_config = os.environ.get('TOKEN_config') | |
| from matplotlib import pyplot as plt | |
| import datetime | |
| import subprocess | |
| def tts_fn(_text, _gender, _lang, _rate, _volume, sid, vc_transform, auto_f0,cluster_ratio, slice_db, f0_predictor): | |
| if len( _text) > 400: | |
| return "请上传小于200字的文本", None | |
| try: | |
| _rate = f"+{int(_rate*100)}%" if _rate >= 0 else f"{int(_rate*100)}%" | |
| _volume = f"+{int(_volume*100)}%" if _volume >= 0 else f"{int(_volume*100)}%" | |
| if _lang == "Auto": | |
| _gender = "Male" if _gender == "男" else "Female" | |
| subprocess.run([r"python", "tts.py", _text, _lang, _rate, _volume, _gender]) | |
| else: | |
| subprocess.run([r"python", "tts.py", _text, _lang, _rate, _volume]) | |
| input_audio = "tts.wav" | |
| audio, sampling_rate = soundfile.read(input_audio) | |
| if np.issubdtype(audio.dtype, np.integer): | |
| audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) | |
| if len(audio.shape) > 1: | |
| audio = librosa.to_mono(audio.transpose(1, 0)) | |
| if sampling_rate != 44100: | |
| audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=44100) | |
| soundfile.write(input_audio, audio, 44100, format="wav") | |
| output_file_path = "tts_output.mp3" | |
| print (_text, _gender, _lang, _rate, _volume, sid, vc_transform, auto_f0,cluster_ratio, slice_db, f0_predictor) | |
| _audio = model.slice_inference(input_audio, sid, vc_transform, slice_db, cluster_ratio, auto_f0, 0.4,f0_predictor=f0_predictor,clip_seconds=40) | |
| soundfile.write("tts_output.mp3", _audio, 44100, format="mp3") | |
| return "Success", output_file_path | |
| except Exception as e: | |
| print(e) | |
| def f0_to_pitch(ff): | |
| f0_pitch = 69 + 12 * np.log2(ff / 441) | |
| return f0_pitch | |
| def compute_f0(wav_file1, wav_file2,tran): | |
| y1, sr1 = librosa.load(wav_file1, sr=44100) | |
| y2, sr2 = librosa.load(wav_file2, sr=44100) | |
| # Compute the f0 using the YIN pitch estimation method | |
| f0_1 = librosa.core.yin(y1, fmin=1, fmax=400) | |
| f0_2 = librosa.core.yin(y2, fmin=1, fmax=400) | |
| # 半 音 偏差 | |
| sum_y = [] | |
| if np.sum(wav_file1 == 0) / len(wav_file1) > 0.9: | |
| mistake, var_take = 0, 0 | |
| else: | |
| for i in range(min(len(f0_1), len(f0_2))): | |
| if f0_1[i] > 0 and f0_2[i] > 0: | |
| sum_y.append( | |
| abs(f0_to_pitch(f0_2[i]) - (f0_to_pitch(f0_1[i]) + tran))) | |
| num_y = 0 | |
| for x in sum_y: | |
| num_y += x | |
| len_y = len(sum_y) if len(sum_y) else 1 | |
| mistake = round(float(num_y / len_y), 2) | |
| var_take = round(float(np.std(sum_y, ddof=1)), 2) | |
| print("mistake", mistake, var_take) | |
| return f0_1, f0_2, sr1, sr2, round(mistake / 10, 2), round(var_take / 10, 2) | |
| def same_auth(username, password): | |
| now = datetime.datetime.utcnow() + datetime.timedelta(hours=8) | |
| print(username, password,now.strftime("%Y-%m-%d %H:%M:%S")) | |
| username = username.replace("https://","").replace("http://","").replace("/","") | |
| return username == base64.b64decode( b'c292aXRzNC5ub2dpemFrYTQ2LmNj' ).decode() or username == base64.b64decode( b'c292aXRzNC1kZXYubm9naXpha2E0Ni5jYw==' ).decode() or password == base64.b64decode( b'c292aXRzNC1kZXYubm9naXpha2E0Ni5jYw==' ).decode() or password == base64.b64decode( b'c292aXRzNC5ub2dpemFrYTQ2LmNj' ).decode() | |
| def vc_fn(output_format,sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db,f0_predictor,clip_seconds=50): | |
| start_time = time.time() | |
| if input_audio is None: | |
| return "You need to upload an audio ", None | |
| audio, sampling_rate = soundfile.read(input_audio) | |
| duration = audio.shape[0] / sampling_rate | |
| if duration > 280: | |
| return "请上传小于280s的音频，需要转换长音频请使用tgbot", None , None | |
| if np.issubdtype(audio.dtype, np.integer): | |
| audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) | |
| if len(audio.shape) > 1: | |
| audio = librosa.to_mono(audio.transpose(1, 0)) | |
| if sampling_rate != 44100: | |
| audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=44100) | |
| out_wav_path = "temp.wav" | |
| soundfile.write(out_wav_path, audio, 44100, format="wav") | |
| now = datetime.datetime.utcnow() + datetime.timedelta(hours=8) | |
| print(sid, vc_transform, auto_f0,cluster_ratio, slice_db,f0_predictor,now.strftime("%Y-%m-%d %H:%M:%S")) | |
| _audio = model.slice_inference(out_wav_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, 0.4,f0_predictor=f0_predictor,clip_seconds=clip_seconds,loudness_envelope_adjustment = 0) | |
| out_wav_path1 = 'output_'+f'{sid}_{vc_transform}.{output_format}' | |
| soundfile.write(out_wav_path1, _audio, 44100, format=output_format) | |
| used_time = round(time.time() - start_time, 2) | |
| out_str = ("Success! total use time:{}s".format(used_time)) | |
| return out_str ,out_wav_path1 | |
| def change_audio(audio,vc): | |
| new_audio = audio | |
| return new_audio,vc | |
| def loadmodel(model_): | |
| global model | |
| model_name = os.path.splitext(os.path.basename(model_))[0] | |
| model.unload_model() | |
| if os.path.exists("./kmeans/" + model_name + ".pt") == True: | |
| model = Svc(model_, "configs/" + model_name + ".json", cluster_model_path="./kmeans/" + model_name + ".pt") | |
| else: | |
| model = Svc(model_, "configs/" + model_name + ".json") | |
| spks = list(model.spk2id.keys()) | |
| print(model_, "configs/" + model_name + ".json", "./kmeans/" + model_name + ".pt") | |
| return "success",update_dropdown(spks) | |
| def update_dropdown(new_choices): | |
| global model | |
| spks = list(model.spk2id.keys()) | |
| new_choices = gr.Dropdown.update(choices=spks) | |
| return new_choices | |
| sid ="" | |
| import pyzipper | |
| hf_token1 = os.environ.get('TOKEN1').encode("utf-8") | |
| with pyzipper.AESZipFile('./N.zip') as zf: | |
| zf.pwd = hf_token1 | |
| zf.extractall() | |
| with pyzipper.AESZipFile('./N_2.zip') as zf: | |
| zf.pwd = hf_token1 | |
| zf.extractall() | |
| model = Svc("./N/58v1.pth", "configs/58v1.json" , cluster_model_path="./kmeans/58v1.pt") | |
| modelPaths = [] | |
| for dirpath, dirnames, filenames in os.walk("./N/"): | |
| for filename in filenames: | |
| modelPaths.append(os.path.join(dirpath, filename)) | |
| app = gr.Blocks(theme='NoCrypt/miku') | |
| with app: | |
| with gr.Tabs(): | |
| with gr.TabItem(" "): | |
| #gr.Markdown( | |
| #'<div align="center">' | |
| #f'<img style="width:auto;height:300px;" src="cover.png">' | |
| #'</div>') | |
| gr.Markdown(value=base64.b64decode( b'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').decode()) | |
| with gr.Tabs(): | |
| with gr.TabItem("单个音频上传"): | |
| vc_input3 = gr.Audio(label="上传音频<280s无BGM无和声的干声", type="filepath", source="upload",value="examples/1.mp3") | |
| with gr.TabItem("文字转语音（实验性）"): | |
| gr.Markdown("文字转语音（TTS）说明：使用edge_tts服务生成音频，并转换为So-VITS模型音色。") | |
| auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好（仅限转换语音，歌声不要勾选此项会究极跑调）", value=False) | |
| with gr.Row(): | |
| text_input = gr.Textbox(label = "在此输入需要转译的文字（建议打开自动f0预测）限定200字以内,可尝试不同f0预测器") | |
| with gr.Row(): | |
| tts_gender = gr.Radio(label = "说话人性别", choices = ["男","女"], value = "女") | |
| tts_lang = gr.Dropdown(label = "选择语言，Auto为根据输入文字自动识别", choices=SUPPORTED_LANGUAGES, value = "Auto") | |
| with gr.Row(): | |
| tts_rate = gr.Slider(label = "TTS语音变速（倍速相对值）", minimum = -1, maximum = 3, value = 0, step = 0.1) | |
| tts_volume = gr.Slider(label = "TTS语音音量（相对值）", minimum = -1, maximum = 1.5, value = 0, step = 0.1) | |
| vc_tts_submit = gr.Button("文本转语音", variant="primary") | |
| spks = list(model.spk2id.keys()) | |
| sid = gr.Dropdown(label="音色", choices=spks, value="HOSHINO_MINAMI") | |
| sid.change(fn=update_dropdown,inputs=[sid],outputs=[sid]) | |
| sid.update(interactive=True) | |
| with gr.Accordion(label="↓切换模型(默认58位成员v1,音色具有抽奖性质，可切换尝试。也有特化的个人模型可选择)", open=False): | |
| modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value") | |
| btnMod = gr.Button("载入模型") | |
| statusa = gr.TextArea() | |
| btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa,sid]) | |
| with gr.Row(): | |
| slice_db = gr.Slider(label="切片阈值(较嘈杂时-30，保留呼吸声时-50)",maximum=-30, minimum=-70, step=1, value=-40) | |
| vc_transform = gr.Slider(label="变调（整数，可以正负，半音数量，升高八度就是12）",maximum=16, minimum=-16, step=1, value=0) | |
| f0_predictor = gr.Radio(label="f0预测器(如遇哑音可以尝试更换f0)凭干声干净程度选择。推荐fcpe和rmvpe", choices=["pm","dio","harvest","fcpe","rmvpe"], value="fcpe") | |
| with gr.Row(): | |
| cluster_ratio = gr.Slider(label="聚类模型混合比例，0-1之间，默认为0不启用聚类，能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）",maximum=1, minimum=0, step=0.1, value=0) | |
| output_format = gr.Radio(label="音频输出格式(MP3会导致时间轴多27ms,需合成请选flac)", choices=["flac", "mp3"], value = "mp3")#格式 | |
| vc_submit = gr.Button("音频转换", variant="primary") | |
| vc_output1 = gr.Textbox(label="音高平均偏差半音数量，体现转换音频的跑调情况（一般小于0.5）") | |
| vc_output2 = gr.Audio(label="Output Audio") | |
| vc_submit.click(vc_fn, [output_format,sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db,f0_predictor], [vc_output1, vc_output2]) | |
| vc_tts_submit.click(tts_fn, [text_input, tts_gender, tts_lang, tts_rate, tts_volume, sid, vc_transform,auto_f0,cluster_ratio, slice_db, f0_predictor], [vc_output1, vc_output2]) | |
| app.launch() | |