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from typing import List, Union
import logging
import os
import sys
import joblib
import fire
import fairseq
from fairseq import checkpoint_utils
import soundfile as sf
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm.auto import tqdm
from einops import rearrange
import re
import numpy as np
from functools import partial
import torch.multiprocessing as mp
import torchaudio
import glob
import tqdm
import argparse
from torchaudio.functional import resample
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
logger = logging.getLogger('generate_pseudo_language')
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
class FeatureReader(object):
def __init__(self, ckpt_path, layer, max_chunk=1600000, fp16=False, sampling_rate=16000):
(
model,
cfg,
task,
) = checkpoint_utils.load_model_ensemble_and_task([ckpt_path])
self.model = model[0].eval().to(DEVICE)
self.task = task
self.layer = layer
self.max_chunk = max_chunk
self.fp16 = fp16
if fp16:
self.model.half()
self.layer_shift = 0
self.target_sample_hz = sampling_rate
logger.info(f"TASK CONFIG:\n{self.task.cfg}")
def read_audio(self, path):
wav, sr = torchaudio.load(path)
if sr != self.target_sample_hz:
wav = resample(wav, sr, self.target_sample_hz)
return wav
@torch.no_grad()
def get_feats(self, waveform):
x = waveform
with torch.no_grad():
if self.fp16:
x = x.half().cuda()
else:
x = x.float().cuda()
if self.task.cfg.normalize:
x = F.layer_norm(x, x.shape)
x = x.view(1, -1)
feat = []
for start in range(0, x.size(1), self.max_chunk):
x_chunk = x[:, start: start + self.max_chunk]
feat_chunk, _ = self.model.extract_features(
source=x_chunk,
padding_mask=None,
mask=False,
output_layer=self.layer + self.layer_shift,
)
feat.append(feat_chunk)
if len(feat) == 0:
return torch.zeros(0, 0)
return torch.cat(feat, 1).squeeze(0)
class ApplyKmeans(object):
def __init__(self, km_path):
self.km_model = joblib.load(km_path)
self.C_np = self.km_model.cluster_centers_.transpose()
self.Cnorm_np = (self.C_np ** 2).sum(0, keepdims=True)
self.C = torch.from_numpy(self.C_np)
self.Cnorm = torch.from_numpy(self.Cnorm_np)
if torch.cuda.is_available():
self.C = self.C.cuda()
self.Cnorm = self.Cnorm.cuda()
def __call__(self, x):
if isinstance(x, torch.Tensor):
self.C = self.C.to(x)
self.Cnorm = self.Cnorm.to(x)
dist = (
x.pow(2).sum(1, keepdim=True)
- 2 * torch.matmul(x, self.C)
+ self.Cnorm
)
return dist.argmin(dim=1).cpu().numpy()
else:
dist = (
(x ** 2).sum(1, keepdims=True)
- 2 * np.matmul(x, self.C_np)
+ self.Cnorm_np
)
return np.argmin(dist, axis=1)
class Speech2Unit(torch.nn.Module):
def __init__(
self,
ckpt_dir,
layer=11,
max_chunk=1600000,
fp16=False,
sampling_rate=16000,
):
"""
Args:
ckpt_dir(str): path to hubert model dir(e.g. hubert_base_ls960.pt)
layer(int): feat from which layer of hubert models defauly by 9
max_chunk(int): default by 1600000
fp16(bool): default by False
sampling_rate(int): sampling_rate default by 16000
"""
super().__init__()
ckpt_path = os.path.join(ckpt_dir, "mhubert_base_vp_en_es_fr_it3.pt")
km_path = os.path.join(ckpt_dir, "mhubert_base_vp_en_es_fr_it3_L11_km1000.bin")
self.feature_reader = FeatureReader(ckpt_path, layer, max_chunk, fp16, sampling_rate)
self.apply_kmeans = ApplyKmeans(km_path)
@staticmethod
def merge_duplicates(cluster_ids):
dup_cluster_list = []
duration_list = []
count = 1
for i in range(0, len(cluster_ids)):
if i + 1 < len(cluster_ids) and cluster_ids[i] == cluster_ids[i+1]:
count += 1
else:
dup_cluster_list.append(cluster_ids[i])
duration_list.append(count)
count = 1
return dup_cluster_list, duration_list
def __call__(self, path, merged=True):
waveform = self.feature_reader.read_audio(path).to(DEVICE)
feat = self.feature_reader.get_feats(waveform)
cluster_ids = self.apply_kmeans(feat).tolist()
dup_cluster_list, duration_list = self.merge_duplicates(cluster_ids)
merged_units = "<sosp>" + "".join([f"<{str(x)}>" for x in dup_cluster_list]) + "<eosp>"
unmerged_units = "<sosp>" + "".join([f"<{str(x)}>" for x in cluster_ids]) + "<eosp>"
if merged:
return merged_units
else:
return unmerged_units
# return {"continuous":feat, "units":dup_cluster_list, "duration":duration_list, "unmerged_units":cluster_ids}
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--wav", type=str)
args = parser.parse_args()
ckpt_dir = "/data1/speech/anhnmt2/Speech2Speech/LLaMA-Omni/utils/speech2unit/models"
s2u = Speech2Unit(
ckpt_dir=ckpt_dir
)
units = s2u(args.wav)
print(units) |