import argparse, pickle import logging import os, random import numpy as np import torch import torchaudio 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("--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=0, 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("--crop_concat", type=int, default=0) parser.add_argument("--stop_repetition", type=int, default=-1, 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("--sample_batch_size", type=int, default=1, help="batch size for sampling, NOTE that it's not running inference for several samples, but duplicate one input sample batch_size times, and during inference, we only return the shortest generation") 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, device, decode_config, prompt_end_frame): # 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]]) # encode audio encoded_frames = tokenize_audio(audio_tokenizer, audio_fn, offset=0, num_frames=prompt_end_frame) 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"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() if decode_config['sample_batch_size'] <= 1: logging.info(f"running inference with batch size 1") concat_frames, gen_frames = model.inference_tts( text_tokens.to(device), text_tokens_lens.to(device), original_audio[...,:model_args.n_codebooks].to(device), # [1,T,8] 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] else: logging.info(f"running inference with batch size {decode_config['sample_batch_size']}, i.e. return the shortest among {decode_config['sample_batch_size']} generations.") concat_frames, gen_frames = model.inference_tts_batch( text_tokens.to(device), text_tokens_lens.to(device), original_audio[...,:model_args.n_codebooks].to(device), # [1,T,8] 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'], batch_size = decode_config['sample_batch_size'], 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.") logging.info(f"generated encoded_frames.shape: {gen_frames.shape}, which is {gen_frames.shape[-1]/decode_config['codec_sr']} sec.") # for timestamp, codes in enumerate(gen_frames[0].transpose(1,0)): # logging.info(f"{timestamp}: {codes.tolist()}") # decode (both original and generated) concat_sample = audio_tokenizer.decode( [(concat_frames, None)] # [1,T,8] -> [1,8,T] ) gen_sample = audio_tokenizer.decode( [(gen_frames, None)] ) #Empty cuda cache between runs if torch.cuda.is_available(): torch.cuda.empty_cache() # return return concat_sample, gen_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 = torch.device("cpu") if torch.cuda.is_available(): device = torch.device("cuda:0") model.to(device) model.eval() return model, model_args, phn2num if __name__ == "__main__": def seed_everything(seed): os.environ['PYTHONHASHSEED'] = str(seed) random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.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' seed_everything(args.seed) os.makedirs(args.output_dir, exist_ok=True) # load model with open(args.manifest_fn, "r") as rf: manifest = [l.strip().split("\t") for l in rf.readlines()] manifest = manifest[1:] manifest = [[item[0], item[2], item[3], item[1], item[5]] for item in manifest] stime = time.time() logging.info(f"loading model from {args.exp_dir}") model, model_args, phn2num = get_model(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 audio_fns = [] texts = [] prompt_end_frames = [] new_audio_fns = [] text_to_syn = [] for item in manifest: audio_fn = os.path.join(args.audio_root, item[0]) audio_fns.append(audio_fn) temp = torchaudio.info(audio_fn) prompt_end_frames.append(round(float(item[2])*temp.sample_rate)) texts.append(item[1]) new_audio_fns.append(item[-2]) all_text = item[1].split(" ") start_ind = int(item[-1].split(",")[0]) text_to_syn.append(" ".join(all_text[start_ind:])) for i, (audio_fn, text, prompt_end_frame, new_audio_fn, to_syn) in enumerate(tqdm.tqdm((zip(audio_fns, texts, prompt_end_frames, new_audio_fns, text_to_syn)))): output_expected_sr = args.codec_audio_sr concated_audio, gen_audio = inference_one_sample(model, model_args, phn2num, text_tokenizer, audio_tokenizer, audio_fn, text, args.device, vars(args), prompt_end_frame) # save segments for comparison concated_audio, gen_audio = concated_audio[0].cpu(), gen_audio[0].cpu() if output_expected_sr != args.codec_audio_sr: gen_audio = torchaudio.transforms.Resample(output_expected_sr, args.codec_audio_sr)(gen_audio) concated_audio = torchaudio.transforms.Resample(output_expected_sr, args.codec_audio_sr)(concated_audio) seg_save_fn_gen = f"{args.output_dir}/gen_{new_audio_fn[:-4]}_{i}_seed{args.seed}.wav" seg_save_fn_concat = f"{args.output_dir}/concat_{new_audio_fn[:-4]}_{i}_seed{args.seed}.wav" torchaudio.save(seg_save_fn_gen, gen_audio, args.codec_audio_sr) torchaudio.save(seg_save_fn_concat, concated_audio, args.codec_audio_sr)