import os import sys sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xcodec_mini_infer')) sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xcodec_mini_infer', 'descriptaudiocodec')) import argparse import torch import numpy as np import json from omegaconf import OmegaConf import torchaudio from torchaudio.transforms import Resample import soundfile as sf import uuid from tqdm import tqdm from einops import rearrange from codecmanipulator import CodecManipulator from mmtokenizer import _MMSentencePieceTokenizer from transformers import AutoTokenizer, AutoModelForCausalLM, LogitsProcessor, LogitsProcessorList import glob import time import copy from collections import Counter from models.soundstream_hubert_new import SoundStream from vocoder import build_codec_model, process_audio from post_process_audio import replace_low_freq_with_energy_matched import re parser = argparse.ArgumentParser() # Model Configuration: parser.add_argument("--stage1_model", type=str, default="m-a-p/YuE-s1-7B-anneal-en-cot", help="The model checkpoint path or identifier for the Stage 1 model.") parser.add_argument("--max_new_tokens", type=int, default=3000, help="The maximum number of new tokens to generate in one pass during text generation.") parser.add_argument("--run_n_segments", type=int, default=2, help="The number of segments to process during the generation.") # Prompt parser.add_argument("--genre_txt", type=str, required=True, help="The file path to a text file containing genre tags that describe the musical style or characteristics (e.g., instrumental, genre, mood, vocal timbre, vocal gender). This is used as part of the generation prompt.") parser.add_argument("--lyrics_txt", type=str, required=True, help="The file path to a text file containing the lyrics for the music generation. These lyrics will be processed and split into structured segments to guide the generation process.") parser.add_argument("--use_audio_prompt", action="store_true", help="If set, the model will use an audio file as a prompt during generation. The audio file should be specified using --audio_prompt_path.") parser.add_argument("--audio_prompt_path", type=str, default="", help="The file path to an audio file to use as a reference prompt when --use_audio_prompt is enabled.") parser.add_argument("--prompt_start_time", type=float, default=0.0, help="The start time in seconds to extract the audio prompt from the given audio file.") parser.add_argument("--prompt_end_time", type=float, default=30.0, help="The end time in seconds to extract the audio prompt from the given audio file.") # Output parser.add_argument("--output_dir", type=str, default="./output", help="The directory where generated outputs will be saved.") parser.add_argument("--keep_intermediate", action="store_true", help="If set, intermediate outputs will be saved during processing.") parser.add_argument("--disable_offload_model", action="store_true", help="If set, the model will not be offloaded from the GPU to CPU after Stage 1 inference.") parser.add_argument("--cuda_idx", type=int, default=0) # Config for xcodec and upsampler parser.add_argument('--basic_model_config', default='./xcodec_mini_infer/final_ckpt/config.yaml', help='YAML files for xcodec configurations.') parser.add_argument('--resume_path', default='./xcodec_mini_infer/final_ckpt/ckpt_00360000.pth', help='Path to the xcodec checkpoint.') parser.add_argument('--config_path', type=str, default='./xcodec_mini_infer/decoders/config.yaml', help='Path to Vocos config file.') parser.add_argument('--vocal_decoder_path', type=str, default='./xcodec_mini_infer/decoders/decoder_131000.pth', help='Path to Vocos decoder weights.') parser.add_argument('--inst_decoder_path', type=str, default='./xcodec_mini_infer/decoders/decoder_151000.pth', help='Path to Vocos decoder weights.') parser.add_argument('-r', '--rescale', action='store_true', help='Rescale output to avoid clipping.') args = parser.parse_args() if args.use_audio_prompt and not args.audio_prompt_path: raise FileNotFoundError("Please offer audio prompt filepath using '--audio_prompt_path', when you enable 'use_audio_prompt'!") model = args.stage1_model cuda_idx = args.cuda_idx max_new_tokens = args.max_new_tokens stage1_output_dir = os.path.join(args.output_dir, f"stage1") os.makedirs(stage1_output_dir, exist_ok=True) # load tokenizer and model device = torch.device(f"cuda:{cuda_idx}" if torch.cuda.is_available() else "cpu") # Now you can use `device` to move your tensors or models to the GPU (if available) print(f"Using device: {device}") mmtokenizer = _MMSentencePieceTokenizer("./mm_tokenizer_v0.2_hf/tokenizer.model") codectool = CodecManipulator("xcodec", 0, 1) model_config = OmegaConf.load(args.basic_model_config) codec_model = eval(model_config.generator.name)(**model_config.generator.config).to(device) parameter_dict = torch.load(args.resume_path, map_location='cpu') codec_model.load_state_dict(parameter_dict['codec_model']) codec_model.to(device) codec_model.eval() class BlockTokenRangeProcessor(LogitsProcessor): def __init__(self, start_id, end_id): self.blocked_token_ids = list(range(start_id, end_id)) def __call__(self, input_ids, scores): scores[:, self.blocked_token_ids] = -float("inf") return scores def load_audio_mono(filepath, sampling_rate=16000): audio, sr = torchaudio.load(filepath) # Convert to mono audio = torch.mean(audio, dim=0, keepdim=True) # Resample if needed if sr != sampling_rate: resampler = Resample(orig_freq=sr, new_freq=sampling_rate) audio = resampler(audio) return audio def split_lyrics(lyrics): pattern = r"\[(\w+)\](.*?)\n(?=\[|\Z)" segments = re.findall(pattern, lyrics, re.DOTALL) structured_lyrics = [f"[{seg[0]}]\n{seg[1].strip()}\n\n" for seg in segments] return structured_lyrics # Call the function and print the result stage1_output_set = [] # Tips: # genre tags support instrumental，genre，mood，vocal timbr and vocal gender # all kinds of tags are needed with open(args.genre_txt) as f: genres = f.strip() with open(args.lyrics_txt) as f: lyrics = split_lyrics(f) # intruction full_lyrics = "\n".join(lyrics) prompt_texts = [f"Generate music from the given lyrics segment by segment.\n[Genre] {genres}\n{full_lyrics}"] prompt_texts += lyrics random_id = uuid.uuid4() output_seq = None # Here is suggested decoding config top_p = 0.93 temperature = 1.0 repetition_penalty = 1.2 # special tokens start_of_segment = mmtokenizer.tokenize('[start_of_segment]') end_of_segment = mmtokenizer.tokenize('[end_of_segment]') raw_output = None # Format text prompt run_n_segments = min(args.run_n_segments+1, len(lyrics)) print(list(enumerate(tqdm(prompt_texts[:run_n_segments])))) for i, p in enumerate(tqdm(prompt_texts[:run_n_segments])): section_text = p.replace('[start_of_segment]', '').replace('[end_of_segment]', '') guidance_scale = 1.5 if i <=1 else 1.2 if i==0: continue if i==1: if args.use_audio_prompt: audio_prompt = load_audio_mono(args.audio_prompt_path) audio_prompt.unsqueeze_(0) with torch.no_grad(): raw_codes = codec_model.encode(audio_prompt.to(device), target_bw=0.5) raw_codes = raw_codes.transpose(0, 1) raw_codes = raw_codes.cpu().numpy().astype(np.int16) # Format audio prompt code_ids = codectool.npy2ids(raw_codes[0]) audio_prompt_codec = code_ids[int(args.prompt_start_time *50): int(args.prompt_end_time *50)] # 50 is tps of xcodec audio_prompt_codec_ids = [mmtokenizer.soa] + codectool.sep_ids + audio_prompt_codec + [mmtokenizer.eoa] sentence_ids = mmtokenizer.tokenize("[start_of_reference]") + audio_prompt_codec_ids + mmtokenizer.tokenize("[end_of_reference]") head_id = mmtokenizer.tokenize(prompt_texts[0]) + sentence_ids else: head_id = mmtokenizer.tokenize(prompt_texts[0]) prompt_ids = head_id + start_of_segment + mmtokenizer.tokenize(section_text) + [mmtokenizer.soa] + codectool.sep_ids else: prompt_ids = end_of_segment + start_of_segment + mmtokenizer.tokenize(section_text) + [mmtokenizer.soa] + codectool.sep_ids prompt_ids = torch.as_tensor(prompt_ids).unsqueeze(0).to(device) input_ids = torch.cat([raw_output, prompt_ids], dim=1) if i > 1 else prompt_ids # Use window slicing in case output sequence exceeds the context of model max_context = 16384-max_new_tokens-1 if input_ids.shape[-1] > max_context: print(f'Section {i}: output length {input_ids.shape[-1]} exceeding context length {max_context}, now using the last {max_context} tokens.') input_ids = input_ids[:, -(max_context):] with torch.no_grad(): output_seq = model.generate( input_ids=input_ids, max_new_tokens=max_new_tokens, min_new_tokens=100, do_sample=True, top_p=top_p, temperature=temperature, repetition_penalty=repetition_penalty, eos_token_id=mmtokenizer.eoa, pad_token_id=mmtokenizer.eoa, logits_processor=LogitsProcessorList([BlockTokenRangeProcessor(0, 32002), BlockTokenRangeProcessor(32016, 32016)]), guidance_scale=guidance_scale, ) if output_seq[0][-1].item() != mmtokenizer.eoa: tensor_eoa = torch.as_tensor([[mmtokenizer.eoa]]).to(model.device) output_seq = torch.cat((output_seq, tensor_eoa), dim=1) if i > 1: raw_output = torch.cat([raw_output, prompt_ids, output_seq[:, input_ids.shape[-1]:]], dim=1) else: raw_output = output_seq print(len(raw_output)) # save raw output and check sanity ids = raw_output[0].cpu().numpy() soa_idx = np.where(ids == mmtokenizer.soa)[0].tolist() eoa_idx = np.where(ids == mmtokenizer.eoa)[0].tolist() if len(soa_idx)!=len(eoa_idx): raise ValueError(f'invalid pairs of soa and eoa, Num of soa: {len(soa_idx)}, Num of eoa: {len(eoa_idx)}') vocals = [] instrumentals = [] range_begin = 1 if args.use_audio_prompt else 0 for i in range(range_begin, len(soa_idx)): codec_ids = ids[soa_idx[i]+1:eoa_idx[i]] if codec_ids[0] == 32016: codec_ids = codec_ids[1:] codec_ids = codec_ids[:2 * (codec_ids.shape[0] // 2)] vocals_ids = codectool.ids2npy(rearrange(codec_ids,"(n b) -> b n", b=2)[0]) vocals.append(vocals_ids) instrumentals_ids = codectool.ids2npy(rearrange(codec_ids,"(n b) -> b n", b=2)[1]) instrumentals.append(instrumentals_ids) vocals = np.concatenate(vocals, axis=1) instrumentals = np.concatenate(instrumentals, axis=1) vocal_save_path = os.path.join(stage1_output_dir, f"cot_{genres.replace(' ', '-')}_tp{top_p}_T{temperature}_rp{repetition_penalty}_maxtk{max_new_tokens}_vocal_{random_id}".replace('.', '@')+'.npy') inst_save_path = os.path.join(stage1_output_dir, f"cot_{genres.replace(' ', '-')}_tp{top_p}_T{temperature}_rp{repetition_penalty}_maxtk{max_new_tokens}_instrumental_{random_id}".replace('.', '@')+'.npy') np.save(vocal_save_path, vocals) np.save(inst_save_path, instrumentals) stage1_output_set.append(vocal_save_path) stage1_output_set.append(inst_save_path) # offload model if not args.disable_offload_model: model.cpu() del model torch.cuda.empty_cache() print("Converting to Audio...") # convert audio tokens to audio def save_audio(wav: torch.Tensor, path, sample_rate: int, rescale: bool = False): folder_path = os.path.dirname(path) if not os.path.exists(folder_path): os.makedirs(folder_path) limit = 0.99 max_val = wav.abs().max() wav = wav * min(limit / max_val, 1) if rescale else wav.clamp(-limit, limit) torchaudio.save(str(path), wav, sample_rate=sample_rate, encoding='PCM_S', bits_per_sample=16) # reconstruct tracks recons_output_dir = os.path.join(args.output_dir, "recons") recons_mix_dir = os.path.join(recons_output_dir, 'mix') os.makedirs(recons_mix_dir, exist_ok=True) tracks = [] for npy in stage1_output_set: codec_result = np.load(npy) decodec_rlt=[] with torch.no_grad(): decoded_waveform = codec_model.decode(torch.as_tensor(codec_result.astype(np.int16), dtype=torch.long).unsqueeze(0).permute(1, 0, 2).to(device)) decoded_waveform = decoded_waveform.cpu().squeeze(0) decodec_rlt.append(torch.as_tensor(decoded_waveform)) decodec_rlt = torch.cat(decodec_rlt, dim=-1) save_path = os.path.join(recons_output_dir, os.path.splitext(os.path.basename(npy))[0] + ".mp3") tracks.append(save_path) save_audio(decodec_rlt, save_path, 16000) # mix tracks for inst_path in tracks: try: if (inst_path.endswith('.wav') or inst_path.endswith('.mp3')) \ and 'instrumental' in inst_path: # find pair vocal_path = inst_path.replace('instrumental', 'vocal') if not os.path.exists(vocal_path): continue # mix recons_mix = os.path.join(recons_mix_dir, os.path.basename(inst_path).replace('instrumental', 'mixed')) vocal_stem, sr = sf.read(inst_path) instrumental_stem, _ = sf.read(vocal_path) mix_stem = (vocal_stem + instrumental_stem) / 1 sf.write(recons_mix, mix_stem, sr) except Exception as e: print(e) # vocoder to upsample audios vocal_decoder, inst_decoder = build_codec_model(args.config_path, args.vocal_decoder_path, args.inst_decoder_path) vocoder_output_dir = os.path.join(args.output_dir, 'vocoder') vocoder_stems_dir = os.path.join(vocoder_output_dir, 'stems') vocoder_mix_dir = os.path.join(vocoder_output_dir, 'mix') os.makedirs(vocoder_mix_dir, exist_ok=True) os.makedirs(vocoder_stems_dir, exist_ok=True) for npy in stage1_output_set: if 'instrumental' in npy: # Process instrumental instrumental_output = process_audio( npy, os.path.join(vocoder_stems_dir, 'instrumental.mp3'), args.rescale, args, inst_decoder, codec_model ) else: # Process vocal vocal_output = process_audio( npy, os.path.join(vocoder_stems_dir, 'vocal.mp3'), args.rescale, args, vocal_decoder, codec_model ) # mix tracks try: mix_output = instrumental_output + vocal_output vocoder_mix = os.path.join(vocoder_mix_dir, os.path.basename(recons_mix)) save_audio(mix_output, vocoder_mix, 44100, args.rescale) print(f"Created mix: {vocoder_mix}") except RuntimeError as e: print(e) print(f"mix {vocoder_mix} failed! inst: {instrumental_output.shape}, vocal: {vocal_output.shape}") # Post process replace_low_freq_with_energy_matched( a_file=recons_mix, # 16kHz b_file=vocoder_mix, # 48kHz c_file=os.path.join(args.output_dir, os.path.basename(recons_mix)), cutoff_freq=5500.0 ) print("All process Done")