import argparse, pickle import logging import os, random import numpy as np import torch import torchaudio import devicetorch from data.tokenizer import ( AudioTokenizer, TextTokenizer, tokenize_audio, tokenize_text ) from models import voicecraft import argparse, time, tqdm # this script only works for the musicgen architecture def get_args(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument("--manifest_fn", type=str, default="path/to/eval_metadata_file") parser.add_argument("--audio_root", type=str, default="path/to/audio_folder") parser.add_argument("--exp_dir", type=str, default="path/to/model_folder") parser.add_argument("--left_margin", type=float, default=0.08, help="extra space on the left to the word boundary") parser.add_argument("--right_margin", type=float, default=0.08, help="extra space on the right to the word boundary") parser.add_argument("--seed", type=int, default=1) parser.add_argument("--codec_audio_sr", type=int, default=16000, help='the sample rate of audio that the codec is trained for') parser.add_argument("--codec_sr", type=int, default=50, help='the sample rate of the codec codes') parser.add_argument("--top_k", type=int, default=-1, help="sampling param") parser.add_argument("--top_p", type=float, default=0.8, help="sampling param") parser.add_argument("--temperature", type=float, default=1.0, help="sampling param") parser.add_argument("--output_dir", type=str, default=None) parser.add_argument("--device", type=str, default="cuda") parser.add_argument("--signature", type=str, default=None, help="path to the encodec model") parser.add_argument("--stop_repetition", type=int, default=2, help="used for inference, when the number of consecutive repetition of a token is bigger than this, stop it") parser.add_argument("--kvcache", type=int, default=1, help='if true, use kv cache, which is 4-8x faster than without') parser.add_argument("--silence_tokens", type=str, default="[1388,1898,131]", help="note that if you are not using the pretrained encodec 6f79c6a8, make sure you specified it yourself, rather than using the default") return parser.parse_args() @torch.no_grad() def inference_one_sample(model, model_args, phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_text, mask_interval, device, decode_config): # phonemize text_tokens = [phn2num[phn] for phn in tokenize_text( text_tokenizer, text=target_text.strip() ) if phn in phn2num ] text_tokens = torch.LongTensor(text_tokens).unsqueeze(0) text_tokens_lens = torch.LongTensor([text_tokens.shape[-1]]) encoded_frames = tokenize_audio(audio_tokenizer, audio_fn) original_audio = encoded_frames[0][0].transpose(2,1) # [1,T,K] assert original_audio.ndim==3 and original_audio.shape[0] == 1 and original_audio.shape[2] == model_args.n_codebooks, original_audio.shape logging.info(f"with direct encodec encoding before input, original audio length: {original_audio.shape[1]} codec frames, which is {original_audio.shape[1]/decode_config['codec_sr']:.2f} sec.") # forward stime = time.time() encoded_frames = model.inference( text_tokens.to(device), text_tokens_lens.to(device), original_audio[...,:model_args.n_codebooks].to(device), # [1,T,8] mask_interval=mask_interval.unsqueeze(0).to(device), top_k=decode_config['top_k'], top_p=decode_config['top_p'], temperature=decode_config['temperature'], stop_repetition=decode_config['stop_repetition'], kvcache=decode_config['kvcache'], silence_tokens=eval(decode_config['silence_tokens']) if type(decode_config['silence_tokens']) == str else decode_config['silence_tokens'], ) # output is [1,K,T] logging.info(f"inference on one sample take: {time.time() - stime:.4f} sec.") if type(encoded_frames) == tuple: encoded_frames = encoded_frames[0] logging.info(f"generated encoded_frames.shape: {encoded_frames.shape}, which is {encoded_frames.shape[-1]/decode_config['codec_sr']} sec.") # decode (both original and generated) original_sample = audio_tokenizer.decode( [(original_audio.transpose(2,1), None)] # [1,T,8] -> [1,8,T] ) generated_sample = audio_tokenizer.decode( [(encoded_frames, None)] ) return original_sample, generated_sample def get_model(exp_dir, device=None): with open(os.path.join(exp_dir, "args.pkl"), "rb") as f: model_args = pickle.load(f) logging.info("load model weights...") model = voicecraft.VoiceCraft(model_args) ckpt_fn = os.path.join(exp_dir, "best_bundle.pth") ckpt = torch.load(ckpt_fn, map_location='cpu')['model'] phn2num = torch.load(ckpt_fn, map_location='cpu')['phn2num'] model.load_state_dict(ckpt) del ckpt logging.info("done loading weights...") if device == None: device = devicetorch(torch) # device = torch.device("cpu") # if torch.cuda.is_available(): # device = torch.device("cuda:0") model.to(device) model.eval() return model, model_args, phn2num def get_mask_interval(ali_fn, word_span_ind, editType): with open(ali_fn, "r") as rf: data = [l.strip().split(",") for l in rf.readlines()] data = data[1:] tmp = word_span_ind.split(",") s, e = int(tmp[0]), int(tmp[-1]) start = None for j, item in enumerate(data): if j == s and item[3] == "words": if editType == 'insertion': start = float(item[1]) else: start = float(item[0]) if j == e and item[3] == "words": if editType == 'insertion': end = float(item[0]) else: end = float(item[1]) assert start != None break return (start, end) if __name__ == "__main__": def seed_everything(seed): os.environ['PYTHONHASHSEED'] = str(seed) random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if device == "cuda": torch.cuda.manual_seed(seed) elif device == "mps": torch.mps.manual_seed(seed) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True formatter = ( "%(asctime)s [%(levelname)s] %(filename)s:%(lineno)d || %(message)s" ) logging.basicConfig(format=formatter, level=logging.INFO) args = get_args() # args.device = 'cpu' args.allowed_repeat_tokens = eval(args.allowed_repeat_tokens) seed_everything(args.seed) # load model stime = time.time() logging.info(f"loading model from {args.exp_dir}") model, model_args, phn2num = get_model(args.exp_dir) if not os.path.isfile(model_args.exp_dir): model_args.exp_dir = args.exp_dir logging.info(f"loading model done, took {time.time() - stime:.4f} sec") # setup text and audio tokenizer text_tokenizer = TextTokenizer(backend="espeak") audio_tokenizer = AudioTokenizer(signature=args.signature) # will also put the neural codec model on gpu with open(args.manifest_fn, "r") as rf: manifest = [l.strip().split("\t") for l in rf.readlines()] manifest = manifest[1:] # wav_fn txt_fn alingment_fn num_words word_span_ind audio_fns = [] target_texts = [] mask_intervals = [] edit_types = [] new_spans = [] orig_spans = [] os.makedirs(args.output_dir, exist_ok=True) if args.crop_concat: mfa_temp = f"{args.output_dir}/mfa_temp" os.makedirs(mfa_temp, exist_ok=True) for item in manifest: audio_fn = os.path.join(args.audio_root, item[0]) temp = torchaudio.info(audio_fn) audio_dur = temp.num_frames/temp.sample_rate audio_fns.append(audio_fn) target_text = item[2].split("|")[-1] edit_types.append(item[5].split("|")) new_spans.append(item[4].split("|")) orig_spans.append(item[3].split("|")) target_texts.append(target_text) # the last transcript is the target # mi needs to be created from word_ind_span and alignment_fn, along with args.left_margin and args.right_margin mis = [] all_ind_intervals = item[3].split("|") editTypes = item[5].split("|") smaller_indx = [] alignment_fn = os.path.join(args.audio_root, "aligned", item[0].replace(".wav", ".csv")) if not os.path.isfile(alignment_fn): alignment_fn = alignment_fn.replace("/aligned/", "/aligned_csv/") assert os.path.isfile(alignment_fn), alignment_fn for ind_inter,editType in zip(all_ind_intervals, editTypes): # print(ind_inter) mi = get_mask_interval(alignment_fn, ind_inter, editType) mi = (max(mi[0] - args.left_margin, 1/args.codec_sr), min(mi[1] + args.right_margin, audio_dur)) # in seconds mis.append(mi) smaller_indx.append(mi[0]) ind = np.argsort(smaller_indx) mis = [mis[id] for id in ind] mask_intervals.append(mis) for i, (audio_fn, target_text, mask_interval) in enumerate(tqdm.tqdm(zip(audio_fns, target_texts, mask_intervals))): orig_mask_interval = mask_interval mask_interval = [[round(cmi[0]*args.codec_sr), round(cmi[1]*args.codec_sr)] for cmi in mask_interval] # logging.info(f"i: {i}, mask_interval: {mask_interval}") mask_interval = torch.LongTensor(mask_interval) # [M,2] orig_audio, new_audio = inference_one_sample(model, model_args, phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_text, mask_interval, args.device, vars(args)) # save segments for comparison orig_audio, new_audio = orig_audio[0].cpu(), new_audio[0].cpu() # logging.info(f"length of the resynthesize orig audio: {orig_audio.shape}") save_fn_new = f"{args.output_dir}/{os.path.basename(audio_fn)[:-4]}_new_seed{args.seed}.wav" torchaudio.save(save_fn_new, new_audio, args.codec_audio_sr) save_fn_orig = f"{args.output_dir}/{os.path.basename(audio_fn)[:-4]}_orig.wav" if not os.path.isfile(save_fn_orig): orig_audio, orig_sr = torchaudio.load(audio_fn) if orig_sr != args.codec_audio_sr: orig_audio = torchaudio.transforms.Resample(orig_sr, args.codec_audio_sr)(orig_audio) torchaudio.save(save_fn_orig, orig_audio, args.codec_audio_sr)