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README-CN.md
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README-CN.md
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<img width="1042" alt="d48ea37adf3660e657cfb047c10edbc" src="https://user-images.githubusercontent.com/7423248/134275227-c1ddf154-f118-4b77-8949-8c4c7daf25f0.png">
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## 文件结构（目标读者：开发者）
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```
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├─archived_untest_files 废弃文件
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├─encoder encoder模型
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│ ├─data_objects
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│ └─saved_models 预训练好的模型
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├─samples 样例语音
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├─synthesizer synthesizer模型
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│ ├─models
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│ ├─saved_models 预训练好的模型
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│ └─utils 工具类库
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├─toolbox 图形化工具箱
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├─utils 工具类库
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├─vocoder vocoder模型（目前包含hifi-gan、wavrnn）
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│ ├─hifigan
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│ ├─saved_models 预训练好的模型
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│ └─wavernn
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└─web
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├─api
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│ └─Web端接口
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├─config
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│ └─ Web端配置文件
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├─static 前端静态脚本
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│ └─js
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├─templates 前端模板
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└─__init__.py Web端入口文件
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```
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### 4. 番外：语音转换Voice Conversion(PPG based)
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想像柯南拿着变声器然后发出毛利小五郎的声音吗？本项目现基于PPG-VC，引入额外两个模块（PPG extractor + PPG2Mel）, 可以实现变声功能。（文档不全，尤其是训练部分，正在努力补充中）
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#### 4.0 准备环境
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* 确保项目以上环境已经安装ok，运行`pip install -r requirements.txt` 来安装剩余的必要包。
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* 下载以下模型
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* 24K采样率专用的vocoder（hifigan）到 *vocoder\saved_mode\xxx*
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* 预训练的ppg特征encoder(ppg_extractor)到 *ppg_extractor\saved_mode\xxx*
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* 预训练的PPG2Mel到 *ppg2mel\saved_mode\xxx*
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#### 4.1 使用数据集自己训练PPG2Mel模型 (可选)
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* 下载aidatatang_200zh数据集并解压：确保您可以访问 *train* 文件夹中的所有音频文件（如.wav）
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* 进行音频和梅尔频谱图预处理：
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`python pre4ppg.py <datasets_root> -d {dataset} -n {number}`
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可传入参数：
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* `-d {dataset}` 指定数据集，支持 aidatatang_200zh, 不传默认为aidatatang_200zh
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* `-n {number}` 指定并行数，CPU 11770k在8的情况下，需要运行12到18小时！待优化
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> 假如你下载的 `aidatatang_200zh`文件放在D盘，`train`文件路径为 `D:\data\aidatatang_200zh\corpus\train` , 你的`datasets_root`就是 `D:\data\`
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* 训练合成器, 注意在上一步先下载好`ppg2mel.yaml`, 修改里面的地址指向预训练好的文件夹：
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`python ppg2mel_train.py --config .\ppg2mel\saved_models\ppg2mel.yaml --oneshotvc `
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* 如果想要继续上一次的训练，可以通过`--load .\ppg2mel\saved_models\<old_pt_file>` 参数指定一个预训练模型文件。
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#### 4.2 启动工具箱VC模式
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您可以尝试使用以下命令：
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`python demo_toolbox.py vc -d <datasets_root>`
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> 请指定一个可用的数据集文件路径，如果有支持的数据集则会自动加载供调试，也同时会作为手动录制音频的存储目录。
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## 引用及论文
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> 该库一开始从仅支持英语的[Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) 分叉出来的，鸣谢作者。
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142
run.py
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run.py
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import time
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import os
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import argparse
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import torch
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import numpy as np
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import glob
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from pathlib import Path
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from tqdm import tqdm
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from ppg_extractor import load_model
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import librosa
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import soundfile as sf
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from utils.load_yaml import HpsYaml
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from encoder.audio import preprocess_wav
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from encoder import inference as speacker_encoder
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from vocoder.hifigan import inference as vocoder
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from ppg2mel import MelDecoderMOLv2
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from utils.f0_utils import compute_f0, f02lf0, compute_mean_std, get_converted_lf0uv
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def _build_ppg2mel_model(model_config, model_file, device):
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ppg2mel_model = MelDecoderMOLv2(
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**model_config["model"]
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).to(device)
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ckpt = torch.load(model_file, map_location=device)
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ppg2mel_model.load_state_dict(ckpt["model"])
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ppg2mel_model.eval()
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return ppg2mel_model
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@torch.no_grad()
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def convert(args):
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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output_dir = args.output_dir
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os.makedirs(output_dir, exist_ok=True)
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step = os.path.basename(args.ppg2mel_model_file)[:-4].split("_")[-1]
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# Build models
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print("Load PPG-model, PPG2Mel-model, Vocoder-model...")
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ppg_model = load_model(
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Path('./ppg_extractor/saved_models/24epoch.pt'),
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device,
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)
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ppg2mel_model = _build_ppg2mel_model(HpsYaml(args.ppg2mel_model_train_config), args.ppg2mel_model_file, device)
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# vocoder.load_model('./vocoder/saved_models/pretrained/g_hifigan.pt', "./vocoder/hifigan/config_16k_.json")
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vocoder.load_model('./vocoder/saved_models/24k/g_02830000.pt')
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# Data related
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ref_wav_path = args.ref_wav_path
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ref_wav = preprocess_wav(ref_wav_path)
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ref_fid = os.path.basename(ref_wav_path)[:-4]
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# TODO: specify encoder
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speacker_encoder.load_model(Path("encoder/saved_models/pretrained_bak_5805000.pt"))
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ref_spk_dvec = speacker_encoder.embed_utterance(ref_wav)
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ref_spk_dvec = torch.from_numpy(ref_spk_dvec).unsqueeze(0).to(device)
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ref_lf0_mean, ref_lf0_std = compute_mean_std(f02lf0(compute_f0(ref_wav)))
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source_file_list = sorted(glob.glob(f"{args.wav_dir}/*.wav"))
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print(f"Number of source utterances: {len(source_file_list)}.")
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total_rtf = 0.0
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cnt = 0
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for src_wav_path in tqdm(source_file_list):
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# Load the audio to a numpy array:
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src_wav, _ = librosa.load(src_wav_path, sr=16000)
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src_wav_tensor = torch.from_numpy(src_wav).unsqueeze(0).float().to(device)
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src_wav_lengths = torch.LongTensor([len(src_wav)]).to(device)
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ppg = ppg_model(src_wav_tensor, src_wav_lengths)
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lf0_uv = get_converted_lf0uv(src_wav, ref_lf0_mean, ref_lf0_std, convert=True)
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min_len = min(ppg.shape[1], len(lf0_uv))
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ppg = ppg[:, :min_len]
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lf0_uv = lf0_uv[:min_len]
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start = time.time()
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_, mel_pred, att_ws = ppg2mel_model.inference(
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ppg,
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logf0_uv=torch.from_numpy(lf0_uv).unsqueeze(0).float().to(device),
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spembs=ref_spk_dvec,
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)
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src_fid = os.path.basename(src_wav_path)[:-4]
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wav_fname = f"{output_dir}/vc_{src_fid}_ref_{ref_fid}_step{step}.wav"
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mel_len = mel_pred.shape[0]
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rtf = (time.time() - start) / (0.01 * mel_len)
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total_rtf += rtf
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cnt += 1
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# continue
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mel_pred= mel_pred.transpose(0, 1)
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y, output_sample_rate = vocoder.infer_waveform(mel_pred.cpu())
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sf.write(wav_fname, y.squeeze(), output_sample_rate, "PCM_16")
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print("RTF:")
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print(total_rtf / cnt)
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def get_parser():
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parser = argparse.ArgumentParser(description="Conversion from wave input")
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parser.add_argument(
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"--wav_dir",
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type=str,
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default=None,
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required=True,
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help="Source wave directory.",
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)
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parser.add_argument(
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"--ref_wav_path",
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type=str,
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required=True,
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help="Reference wave file path.",
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)
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parser.add_argument(
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"--ppg2mel_model_train_config", "-c",
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type=str,
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default=None,
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required=True,
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help="Training config file (yaml file)",
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)
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parser.add_argument(
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"--ppg2mel_model_file", "-m",
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type=str,
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default=None,
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required=True,
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help="ppg2mel model checkpoint file path"
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)
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parser.add_argument(
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"--output_dir", "-o",
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type=str,
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default="vc_gens_vctk_oneshot",
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help="Output folder to save the converted wave."
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)
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return parser
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def main():
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parser = get_parser()
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args = parser.parse_args()
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convert(args)
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if __name__ == "__main__":
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main()
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