DeepRapper

readme
- 1. Data Preparation
- 2. Training & Generation
- - train
  - generate
- 3. Pretrained Model
创建环境
- requirement
- 建deeprapper环境
- 安装包
一、从预训练的模型生成rap
- 下载预训练模型
二、bash train.sh
- train.sh
train.py
- def main

论文解说：DeepRapper 论文

readme

DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling, by Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, Tie-Yan Liu, ACL 2021,是一个基于Transformer的RAP生成系统，它可以模拟韵律和节奏。它以相反的顺序生成歌词，具有押韵表示和韵律增强的约束，并在歌词中插入节拍符号，用于节奏/节拍建模。据我们所知，DeepRapper是第一个同时产生押韵和节奏的说唱的系统。

The input and output representation of DeepRapper model

1. Data Preparation

为每首歌准备歌词和拼音。我们提供了一些数据样本 in DeepRapper/data/.

├── data
│   └── lyrics
│       └── lyrics_samples
│           └── raw
│               └── singer01
│                   └── album01
│                       ├── song01
│                       │   ├── lyric_with_beat_global.txt
│                       │   └── mapped_final_with_beat_global.txt
│                       └── song02
│                           ├── lyric_with_beat_global.txt
│                           └── mapped_final_with_beat_global.txt

Here is a sample of lyric_with_beat_global.txt:

20_[01:12.56][BEAT]那就[BEAT]让我再沉[BEAT]沦这一世
21_[01:14.49][BEAT]不理[BEAT]解早已[BEAT]经不止一次
22_[01:16.59][BEAT]那就[BEAT]让我孤[BEAT]注最后一掷
23_[01:18.61][BEAT]不想昏[BEAT]暗之中[BEAT]度过每日
24_[01:20.60][BEAT]那就[BEAT]让我再[BEAT]沉沦这一世
25_[01:22.48][BEAT]不理[BEAT]解早已[BEAT]经不止一次
26_[01:24.58][BEAT]那就[BEAT]让我孤[BEAT]注最后一掷
27_[01:26.47][BEAT]不想昏[BEAT]暗之[BEAT]中度过每日

Here is a sample of mapped_final_with_beat_global.txt:

20_[01:12.56][BEAT] a ou [BEAT] ang o ai en [BEAT] en e i i
21_[01:14.49][BEAT] u i [BEAT] ie ao i [BEAT] in u i i i
22_[01:16.59][BEAT] a ou [BEAT] ang o u [BEAT] u ei ou i i
23_[01:18.61][BEAT] u ang en [BEAT] an i ong [BEAT] u o ei i
24_[01:20.60][BEAT] a ou [BEAT] ang o ai [BEAT] en en e i i
25_[01:22.48][BEAT] u i [BEAT] ie ao i [BEAT] in u i i i
26_[01:24.58][BEAT] a ou [BEAT] ang o u [BEAT] u ei ou i i
27_[01:26.47][BEAT] u ang en [BEAT] an i [BEAT] ong u o ei i

2. Training & Generation

我们提供了一个训练和生成的示例脚本.

train

To train run:

bash train.sh

When training, you may see the logs:

starting training
epoch 1
time: 2021-xx-xx 11:17:57.067011
51200
now time: 11:17. Step 10 of piece 0 of epoch 1, loss 9.587631130218506
now time: 11:18. Step 20 of piece 0 of epoch 1, loss 9.187388515472412

你可以在bash文件中指定参数，如number of epoch, bach size, etc.
经过训练的模型被保存在 [model_dir]/lyrics/[raw_data_dir][_reverse]/[model_sign]/final_model.
For example, in the default train.sh, the path is model/lyrics/lyrics_samples_reverse/samples/final_model.

generate

To generate by the trained DeepRapper, run

bash generate.sh

您可以在bash文件中指定参数，例如 beam width, number of samples, etc.

For 获取更多生成的样本, visit https://ai-muzic.github.io/deeprapper/.

3. Pretrained Model

You can download a pretrained DeepRapper https://msramllasc.blob.core.windows.net/modelrelease/deeprapper-model.zip.

To generate by our provided pretrained DeepRapper, first unzip the pretrained DeepRapper. Then, put the unzipped directory deeprapper-model under the folder model/. 因此，完整的路径如下:

├── model
│   └── deeprapper-model
│       ├── pytorch_model.bin
│       └── config.json

最后，运行以下命令来生成:

bash generate_from_pretrain.sh

创建环境

requirement

scikit-learn==0.21.3
miditoolkit==0.1.14
fastBPE
dtw-python
nltk
keras
tqdm
matplotlib
pretty_midi
PyYAML
pypianoroll
fairseq==0.10.0
torch==1.7.1
transformers==3.5.1
pypinyin==0.39.1
jieba==0.42.1
tensorboard==2.4.0
scipy==1.3.1
librosa
pyworld
soundfile
pyOpenSSL
secrets

建deeprapper环境

conda create --name deeprapper python=3.7

安装包

conda install --yes --file requirements.txt

pip3 install -r requirements.txt

也可以一个一个装

pip3 install jieba==0.42.1

一、从预训练的模型生成rap

下载预训练模型

cd 到model目录
输入

curl https://msramllasc.blob.core.windows.net/modelrelease/deeprapper-model.zip --output deeprapper-model.zip

jar xvf deeprapper-model.zip

上述问题最主要的原因是文件没有完全下载成功 重新下就行
jar 可以解压但是是不完全的

bash generate_from_pretrain.sh

开头设置为”蛋黄的长裙蓬松的头发“

二、bash train.sh

cd 到对应的目录

bash train.sh

train.sh

#!/bin/bashpython train.py \--device '0,1' \--stride 1024 \--model_config 'config/model_config_small.json' \--model_dir 'model' \--root_path 'data/lyrics/' \--raw_data_dir 'lyrics_samples' \--batch_size 1 \--epochs 4 \--enable_final \--enable_sentence \--enable_relative_pos \--enable_beat \--reverse \--model_sign 'samples' \--with_beat \--beat_mode 0 \--tokenize \--raw

train.py

import argparse
import os
import random
import sys
from datetime import datetimeimport numpy as np
from tqdm import tqdm
from utils import swap_value

if __name__ == '__main__':main()

def main

def main():parser = argparse.ArgumentParser()# path to dataparser.add_argument('--model_dir', default='model', type=str, required=False, help='directory of learned models')parser.add_argument('--root_path', default='data/lyrics/', type=str, required=False, help='root path')parser.add_argument('--raw_data_dir', default='lyric_with_final_small', type=str, required=False, help='directory of raw data')parser.add_argument('--model_sign', default='1a', type=str, required=False, help='model sign, to identify each model')parser.add_argument('--writer_dir', default='tensorboard_summary/', type=str, required=False, help='directory of tensorboard logs')# path to dictionaryparser.add_argument('--tokenizer_path', default='tokenizations/chinese_dicts.txt', type=str, required=False, help='vocabulary of tokens')parser.add_argument('--finalizer_path', default='tokenizations/finals.txt', type=str, required=False, help='vocabulary of finals')parser.add_argument('--sentencer_path', default='tokenizations/sentences.txt', type=str, required=False, help='vocabulary of sentence numbers')parser.add_argument('--poser_path', default='tokenizations/sentences.txt', type=str, required=False, help='vocabulary of intra-sentence positions')parser.add_argument('--beater_path', default='tokenizations/beats.txt', type=str, required=False, help='vocabulary of beats')# hyperparameters for trainingparser.add_argument('--device', default='0', type=str, required=False, help='choose gpus')parser.add_argument('--init_device', default=0, type=int, required=False, help='set the main gpu number')parser.add_argument('--model_config', default='config/model_config_small.json', type=str, required=False,help='model configurations')parser.add_argument('--epochs', default=5, type=int, required=False, help='number of epochs')parser.add_argument('--start_epoch', default=0, type=int, required=False, help='the initial epoch')parser.add_argument('--batch_size', default=8, type=int, required=False, help='batch size')parser.add_argument('--lr', default=1.5e-4, type=float, required=False, help='learning rate')parser.add_argument('--warmup_steps', default=2000, type=int, required=False, help='warm up steps')parser.add_argument('--log_step', default=10, type=int, required=False,help='steps of each printing of logs')parser.add_argument('--stride', default=1024, type=int, required=False, help='windows of context in training')parser.add_argument('--gradient_accumulation', default=1, type=int, required=False, help='steps of gradient accumulation')parser.add_argument('--fp16', action='store_true', help='mixed precision')parser.add_argument('--fp16_opt_level', default='O1', type=str, required=False)parser.add_argument('--max_grad_norm', default=1.0, type=float, required=False)parser.add_argument('--num_pieces', default=1, type=int, required=False, help='number of pieces of data')parser.add_argument('--min_length', default=0, type=int, required=False, help='min length of the lyrics')parser.add_argument('--pretrained_model', default='', type=str, required=False, help='path to the pretrianed model')# ways to process dataparser.add_argument('--encoder_json', default="tokenizations/encoder.json", type=str, help="encoder.json", required=False)parser.add_argument('--vocab_bpe', default="tokenizations/vocab.bpe", type=str, help="vocab.bpe", required=False)parser.add_argument('--raw', action='store_true', help='whether the preprocessing is done', required=False)parser.add_argument('--tokenize', action='store_true', help='whether the tokenization is done', required=False)parser.add_argument('--segment', action='store_true', help='do Chinese Word Segmentation or not', required=False)parser.add_argument('--bpe_token', action='store_true', help='use subword', required=False)parser.add_argument('--enable_final', action='store_true', help='whether to use final embedding', required=False)parser.add_argument('--enable_sentence', action='store_true', help='whether to use sentence embedding', required=False)parser.add_argument('--enable_relative_pos', action='store_true', help='whether to use intra-sentence positional embedding', required=False)parser.add_argument('--enable_beat', action='store_true', help='whether to use beat embedding', required=False)parser.add_argument('--reverse', action='store_true', help='whether to use reverse language model', required=False)parser.add_argument('--with_beat', action='store_true', help='whether to generate beat', required=False)parser.add_argument('--beat_mode', default=0, type=int, help='beat mode：0.no control；1.global control；2.local control', required=False)args = parser.parse_args()print('args:\n' + args.__repr__())# basic settings# set envs and import related packagesos.environ["CUDA_VISIBLE_DEVICES"] = args.deviceimport torchimport transformersfrom torch.nn import DataParallelfrom torch.utils.tensorboard import SummaryWriterfrom prepare_train_data import build_files_separate, read_lyrics, prepare_lyrics, get_shuffled_samplesfrom tokenizations.bpe_tokenizer import get_encoderfrom module import GPT2Config, GPT2Model, GPT2LMHeadModel# choose tokenizerif args.segment:from tokenizations import tokenization_bert_word_level as tokenization_bertelse:from tokenizations import tokenization_bert# set tokenizerif args.bpe_token:full_tokenizer = get_encoder(args.encoder_json, args.vocab_bpe)full_tokenizer.max_len = 999999else:full_tokenizer = tokenization_bert.BertTokenizer(vocab_file=args.tokenizer_path,do_lower_case=False)full_finalizer = tokenization_bert.BertTokenizer(vocab_file=args.finalizer_path,tokenize_chinese_chars=False,do_lower_case=False)full_sentencer = tokenization_bert.BertTokenizer(vocab_file=args.sentencer_path,tokenize_chinese_chars=False,do_lower_case=False)full_poser = tokenization_bert.BertTokenizer(vocab_file=args.poser_path,tokenize_chinese_chars=False,do_lower_case=False)full_beater = tokenization_bert.BertTokenizer(vocab_file=args.beater_path,tokenize_chinese_chars=False,do_lower_case=False)# run tokenizeing# dataset root keykey = args.root_path.rstrip('/').split('/')[-1]# processed data root pathprocessed_path = os.path.join(args.root_path, args.raw_data_dir, 'processed')tokenized_path = os.path.join(processed_path, 'tokenized')reverse_str = '_reverse' if args.reverse else ''tokenized_data_path = os.path.join(tokenized_path, f'tokenized{reverse_str}')finalized_data_path = os.path.join(tokenized_path, f'finalized{reverse_str}')sentenced_data_path = os.path.join(tokenized_path, f'sentenced{reverse_str}')posed_data_path = os.path.join(tokenized_path, f'posed{reverse_str}')beated_data_path = os.path.join(tokenized_path, f'beated{reverse_str}')if args.tokenize:# prepare dataif args.raw:print('Processing from raw data...')prepare_fn = {'lyrics': prepare_lyrics}prepare_fn[key](ins_path=os.path.join(args.root_path, args.raw_data_dir, 'raw'),  # demo: data/lyrics/lyrics_22w/rawout_path=processed_path,  # demo: data/lyrics/lyrics_22w/processedwith_beat=args.with_beat,beat_mode=args.beat_mode)print('Loading processed data for training...')read_fn = {'lyrics': read_lyrics,}train_lines, train_finals, train_sentences, train_pos, train_beats = read_fn[key](processed_path, reverse=args.reverse)print('Tokenizing processed data for training...')build_files_separate(num_pieces=args.num_pieces,stride=args.stride,min_length=args.min_length,lines=train_lines,finals=train_finals,sentences=train_sentences,pos=train_pos,beats=train_beats,tokenized_data_path=tokenized_data_path,finalized_data_path=finalized_data_path,sentenced_data_path=sentenced_data_path,posed_data_path=posed_data_path,beated_data_path=beated_data_path,full_tokenizer=full_tokenizer,full_finalizer=full_finalizer,full_sentencer=full_sentencer,full_poser=full_poser,full_beater=full_beater,enable_final=args.enable_final,enable_sentence=args.enable_sentence,enable_pos=args.enable_relative_pos,enable_beat=args.enable_beat,segment=args.segment)print('End')# Training settings# calculate total training stepsfull_len = 0print('calculating total steps')for i in tqdm(range(args.num_pieces)):with open(os.path.join(tokenized_data_path, 'tokenized_train_{}.txt'.format(i)), 'r') as f:full_len += len([int(item) for item in f.read().strip().split()])total_steps = int(full_len / args.stride * args.epochs / args.batch_size / args.gradient_accumulation)print('total steps = {}'.format(total_steps))# build modelmodel_config = GPT2Config.from_json_file(args.model_config)print('config:\n' + model_config.to_json_string())if not args.pretrained_model:model = GPT2LMHeadModel(config=model_config)else:model = GPT2LMHeadModel.from_pretrained(args.pretrained_model)model.train()# set whether to use cudagpu_count = torch.cuda.device_count()if gpu_count > 0:device_ids = [int(i) for i in range(gpu_count)]swap_value(device_ids, 0, args.init_device)device = f'cuda:{device_ids[0]}'else:device = 'cpu'print('using device:', device)model.to(device)# check parameters number of the built modelnum_parameters = 0parameters = model.parameters()for parameter in parameters:num_parameters += parameter.numel()print('number of parameters: {}'.format(num_parameters))# set optimizeroptimizer = transformers.AdamW(model.parameters(), lr=args.lr, correct_bias=True)# change WarmupLinearSchedule to get_linear_schedule_with_warmup for current version of Transformersscheduler = transformers.get_linear_schedule_with_warmup(optimizer,num_warmup_steps=args.warmup_steps,num_training_steps=total_steps)# set whether to use 16-bits parameters to save GPU memory if your GPU support the operations of 16-bits numberif args.fp16:try:from apex import ampexcept ImportError:raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)# set whether to use multi GPUsmulti_gpu = Falseif gpu_count > 1:print("Let's use", gpu_count, "GPUs!", device_ids)model = DataParallel(model, device_ids=device_ids)multi_gpu = True# set log infolog_dir = os.path.join(args.writer_dir, key, f'{args.raw_data_dir}{reverse_str}', args.model_sign)tb_writer = SummaryWriter(log_dir=log_dir)assert args.log_step % args.gradient_accumulation == 0output_dir = os.path.join(args.model_dir, key, f'{args.raw_data_dir}{reverse_str}', args.model_sign)if not os.path.exists(output_dir):os.makedirs(output_dir)print('starting training')overall_step = 0running_loss = 0for epoch in range(args.start_epoch, args.epochs):print('epoch {}'.format(epoch + 1))now = datetime.now()print('time: {}'.format(now))# shuffle pieces of datax = np.linspace(0, args.num_pieces - 1, args.num_pieces, dtype=np.int32)random.shuffle(x)piece_num = 0# enumerate data piecesfor i in x:# prepare training sentenceswith open(os.path.join(tokenized_data_path, 'tokenized_train_{}.txt'.format(i)), 'r') as f:line = f.read().strip()tokens = line.split()# print(len(tokens))tokens = [int(token) for token in tokens]# tokens = torch.Tensor(tokens)if args.enable_final:with open(os.path.join(finalized_data_path, 'tokenized_train_{}.txt'.format(i)), 'r') as f:final = f.read().strip()finals = final.split()# print(len(finals))finals = [int(final) for final in finals]# finals = torch.Tensor(finals)if args.enable_sentence:with open(os.path.join(sentenced_data_path, 'tokenized_train_{}.txt'.format(i)), 'r') as f:sentence = f.read().strip()sentences = sentence.split()# print(len(sentences))sentences = [int(sentence) for sentence in sentences]# sentences = torch.Tensor(sentences)if args.enable_relative_pos:with open(os.path.join(posed_data_path, 'tokenized_train_{}.txt'.format(i)), 'r') as f:p = f.read().strip()pos = p.split()# print(len(sentences))pos = [int(p) for p in pos]# sentences = torch.Tensor(sentences)if args.enable_beat:with open(os.path.join(beated_data_path, 'tokenized_train_{}.txt'.format(i)), 'r') as f:beat = f.read().strip()beats = beat.split()# print(len(sentences))beats = [int(beat) for beat in beats]# sentences = torch.Tensor(sentences)# print('training: ', len(tokens), len(finals), len(sentences))start_point = 0samples_token, samples_final, samples_sentence, samples_pos, samples_beat = [], [], [], [], []n_ctx = model_config.n_ctx  # the length of a sentence for trainingstride = args.strideprint(len(tokens))while start_point < len(tokens) - stride:samples_token.append(tokens[start_point: start_point + stride])if args.enable_final:samples_final.append(finals[start_point: start_point + stride])if args.enable_sentence:samples_sentence.append(sentences[start_point: start_point + stride])if args.enable_relative_pos:samples_pos.append(pos[start_point: start_point + stride])if args.enable_beat:samples_beat.append(beats[start_point: start_point + stride])start_point += strideif start_point < len(tokens):samples_token.append(tokens[len(tokens) - stride:])if args.enable_final:samples_final.append(finals[len(tokens) - stride:])if args.enable_sentence:samples_sentence.append(sentences[len(tokens) - stride:])if args.enable_relative_pos:samples_pos.append(pos[len(tokens) - stride:])if args.enable_beat:samples_beat.append(beats[len(tokens) - stride:])samples_token, samples_final, samples_sentence, samples_pos, samples_beat = get_shuffled_samples(samples_token, samples_final,samples_sentence, samples_pos, samples_beat)
#             print(len(samples_token), len(samples_final), len(samples_sentence), len(samples_))# enumerate batch datafor step in range(len(samples_token) // args.batch_size):  # drop last#  prepare batch databatch_token = samples_token[step * args.batch_size: (step + 1) * args.batch_size]batch_inputs_token = torch.Tensor(batch_token).long().to(device)if samples_final is not None:batch_final = samples_final[step * args.batch_size: (step + 1) * args.batch_size]batch_inputs_final = torch.Tensor(batch_final).long().to(device)else:batch_inputs_final = Noneif samples_sentence is not None:batch_sentence = samples_sentence[step * args.batch_size: (step + 1) * args.batch_size]batch_inputs_sentence = torch.Tensor(batch_sentence).long().to(device)else:batch_inputs_sentence = Noneif samples_pos is not None:batch_pos = samples_pos[step * args.batch_size: (step + 1) * args.batch_size]batch_inputs_pos = torch.Tensor(batch_pos).long().to(device)else:batch_inputs_pos = Noneif samples_beat is not None:batch_beat = samples_beat[step * args.batch_size: (step + 1) * args.batch_size]batch_inputs_beat = torch.Tensor(batch_beat).long().to(device)else:batch_inputs_beat = None#  forward pass# Notes: Using Transformers, the labels are shifted inside the model,#           i.e. you can set labels = input_idsoutputs = model.forward(input_ids=batch_inputs_token,sentence_ids=batch_inputs_sentence,final_ids=batch_inputs_final,pos_ids=batch_inputs_pos,beat_ids=batch_inputs_beat,labels=batch_inputs_token)loss, logits = outputs[:2]#  get lossif multi_gpu:loss = loss.mean()'''running_loss += lossoverall_step += 1'''if args.gradient_accumulation > 1:loss = loss / args.gradient_accumulation#  loss backwardif args.fp16:with amp.scale_loss(loss, optimizer) as scaled_loss:scaled_loss.backward()torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)else:loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)#  optimizer stepif (overall_step + 1) % args.gradient_accumulation == 0:running_loss += loss.item()optimizer.step()optimizer.zero_grad()scheduler.step()# log info of training processif (overall_step + 1) % args.log_step == 0:loss_log = running_loss * args.gradient_accumulation / (args.log_step / args.gradient_accumulation)tb_writer.add_scalar('loss', loss_log, overall_step)print('now time: {}:{}. Step {} of piece {} of epoch {}, loss {}'.format(datetime.now().hour,datetime.now().minute,step + 1, piece_num,epoch + 1, loss_log))running_loss = 0overall_step += 1piece_num += 1# save model per epochprint('saving model for epoch {}'.format(epoch + 1))if not os.path.exists(os.path.join(output_dir, 'model_epoch{}'.format(epoch + 1))):os.mkdir(os.path.join(output_dir, 'model_epoch{}'.format(epoch + 1)))model_to_save = model.module if hasattr(model, 'module') else modelmodel_to_save.save_pretrained(os.path.join(output_dir, 'model_epoch{}'.format(epoch + 1)))# torch.save(scheduler.state_dict(), output_dir + 'model_epoch{}/scheduler.pt'.format(epoch + 1))# torch.save(optimizer.state_dict(), output_dir + 'model_epoch{}/optimizer.pt'.format(epoch + 1))print('epoch {} finished'.format(epoch + 1))then = datetime.now()print('time: {}'.format(then))print('time for one epoch: {}'.format(then - now))# save final modelprint('training finished')if not os.path.exists(os.path.join(output_dir, 'final_model')):os.mkdir(os.path.join(output_dir, 'final_model'))model_to_save = model.module if hasattr(model, 'module') else modelmodel_to_save.save_pretrained(os.path.join(output_dir, 'final_model'))# torch.save(scheduler.state_dict(), output_dir + 'final_model/scheduler.pt')# torch.save(optimizer.state_dict(), output_dir + 'final_model/optimizer.pt')

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