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Running
on
Zero
File size: 4,076 Bytes
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# Copyright (c) 2024 Alibaba Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from torch.utils.data import DataLoader
from inspiremusic.dataset.dataset import Dataset
import numpy as np
import librosa
def audio_process_dataset_and_dataloader(args, configs):
input_dataset = Dataset(args.input_data, data_pipeline=configs['data_pipeline'], mode='processing', shuffle=True, partition=True)
# do not use persistent_workers=True, as whisper tokenizer opens tiktoken file each time when the for loop starts
input_data_loader = DataLoader(input_dataset,
batch_size=None,
pin_memory=args.pin_memory,
num_workers=args.num_workers,
prefetch_factor=args.prefetch)
return input_dataset, input_data_loader
def is_silent(wav_path, threshold=0.01, frame_length=2048, hop_length=512):
y, sr = librosa.load(wav_path, sr=None)
rms = librosa.feature.rms(y=y, frame_length=frame_length, hop_length=hop_length)[0]
silent_frames = np.sum(rms < threshold) / len(rms)
silence_fraction_threshold = 0.95
return silent_frames >= silence_fraction_threshold
def rich_captions(text=None, tags=None, lyrics=None, chorus="verse", start_time=0.0, end_time=30.0):
if text is None and tags is None and lyrics is None:
return None
else:
if start_time is None:
start_time = 0.0
if end_time is None:
end_time = 30.0
if chorus is None:
chorus = "verse"
captions = f"<|{start_time:.1f}|><|{chorus}|>"
if tags is not None:
captions += f"<|{tags}|>"
if text is not None:
captions += f"<|{text}|>"
if lyrics is not None:
captions += f"<|lyrics|><|{lyrics}|>"
captions += f"<|{end_time:.1f}|>"
return captions
def process_tags(infile, outfile, timefile = None):
key_list = []
with open(infile, "r") as f:
for line in f:
sec = line.strip()
key_list.append(sec)
f.close()
if timefile is None:
with open(outfile, 'w') as f:
for k in key_list:
parts = k.rsplit('_', 1)
text = parts[0].replace('_', ' ') + ', ' + parts[1]
caption = rich_captions(text, None, None)
if caption is not None:
f.write("%s\t%s\n" %(k, caption))
f.close()
else:
times = {}
with open(timefile, "r") as f:
for line in f:
sec = line.strip().split("\t")
if len(sec) == 2 :
times[sec[0]] = sec[1]
f.close()
with open(outfile, 'w') as f:
for k in key_list:
parts = k.rsplit('_', 1)
text = parts[0].replace('_', ' ') + ', ' + parts[1]
if k in times.keys():
caption = rich_captions(text, None, None, "verse", 0.0, float(times[k]))
if caption is not None:
f.write("%s\t%s\n" %(k, caption))
f.close()
def process_trans(infile, outfile):
trans = {}
with open(infile, "r") as f:
for line in f:
sec = line.strip().split("\t")
if len(sec) == 2:
trans[sec[0]] = sec[1]
else:
print(line)
f.close()
with open(outfile, 'w') as f:
for k, v in trans.items():
f.write("%s\t%s\n" %(k, rich_captions(v)))
f.close() |