VoiceCraft/inference_speech_editing_sc...

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2024-03-21 19:02:20 +01:00
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("--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 = 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)
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'
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)