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Zero
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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 __future__ import print_function
import argparse
import logging
logging.getLogger('matplotlib').setLevel(logging.WARNING)
import os
import torch
from torch.utils.data import DataLoader
import torchaudio
from hyperpyyaml import load_hyperpyyaml
from tqdm import tqdm
from inspiremusic.cli.model import InspireMusicModel
from inspiremusic.dataset.dataset import Dataset
from inspiremusic.utils.common import MUSIC_STRUCTURE_LABELS
def get_args():
parser = argparse.ArgumentParser(description='inference only with flow model')
parser.add_argument('--config', required=True, help='config file')
parser.add_argument('--prompt_data', required=True, help='prompt data file')
parser.add_argument('--flow_model', required=True, help='flow model file')
parser.add_argument('--llm_model', default=None,required=False, help='llm model file')
parser.add_argument('--music_tokenizer', required=True, help='music tokenizer model file')
parser.add_argument('--wavtokenizer', required=True, help='wavtokenizer model file')
parser.add_argument('--chorus', default="random",required=False, help='chorus tag generation mode, eg. random, verse, chorus, intro.')
parser.add_argument('--sample_rate', type=int, default=48000, required=False,
help='sampling rate of generated audio')
parser.add_argument('--min_generate_audio_seconds', type=float, default=10.0, required=False,
help='the minimum generated audio length in seconds')
parser.add_argument('--max_generate_audio_seconds', type=float, default=30.0, required=False,
help='the maximum generated audio length in seconds')
parser.add_argument('--gpu',
type=int,
default=-1,
help='gpu id for this rank, -1 for cpu')
parser.add_argument('--result_dir', required=True, help='asr result file')
args = parser.parse_args()
print(args)
return args
def main():
args = get_args()
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s')
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
# Init inspiremusic models from configs
use_cuda = args.gpu >= 0 and torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
with open(args.config, 'r') as f:
configs = load_hyperpyyaml(f)
model = InspireMusicModel(None, configs['flow'], configs['hift'], configs['wavtokenizer'])
model.load(args.llm_model, args.flow_model, args.music_tokenizer, args.wavtokenizer)
if args.llm_model is None:
model.llm = None
else:
model.llm = model.llm.to(torch.float32)
if args.flow_model is None:
model.flow = None
test_dataset = Dataset(args.prompt_data, data_pipeline=configs['data_pipeline'], mode='inference', shuffle=True, partition=False)
test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0)
del configs
os.makedirs(args.result_dir, exist_ok=True)
fn = os.path.join(args.result_dir, 'wav.scp')
f = open(fn, 'w')
with torch.no_grad():
for _, batch in tqdm(enumerate(test_data_loader)):
utts = batch["utts"]
assert len(utts) == 1, "inference mode only support batchsize 1"
if "semantic_token" in batch:
token = batch["semantic_token"].to(device)
token_len = batch["semantic_token_len"].to(device)
else:
if audio_token is None:
token = None
token_len = None
else:
token = audio_token.view(audio_token.size(0),-1,4)[:,:,0]
token_len = audio_token_len / 4
text_token = batch["text_token"].to(device)
text_token_len = batch["text_token_len"].to(device)
text = batch["text"]
if "time_start" not in batch.keys():
batch["time_start"] = torch.randint(0, args.min_generate_audio_seconds, (1,)).to(torch.float64)
if "time_end" not in batch.keys():
batch["time_end"] = torch.randint(args.min_generate_audio_seconds, args.max_generate_audio_seconds, (1,)).to(torch.float64)
elif (batch["time_end"].numpy()[0] - batch["time_start"].numpy()[0]) < args.min_generate_audio_seconds:
batch["time_end"] = torch.randint(int(batch["time_start"].numpy()[0] + args.min_generate_audio_seconds), int(batch["time_start"].numpy()[0] + args.max_generate_audio_seconds), (1,)).to(torch.float64)
if "chorus" not in batch.keys():
batch["chorus"] = torch.randint(1, 5, (1,))
if args.chorus == "random":
batch["chorus"] = torch.randint(1, 5, (1,))
elif args.chorus == "intro":
batch["chorus"] = torch.Tensor([0])
elif "verse" in args.chorus:
batch["chorus"] = torch.Tensor([1])
elif args.chorus == "chorus":
batch["chorus"] = torch.Tensor([2])
elif args.chorus == "outro":
batch["chorus"] = torch.Tensor([4])
time_start = batch["time_start"].to(device)
time_end = batch["time_end"].to(device)
chorus = batch["chorus"].to(torch.int)
text_prompt = f"<|{batch['time_start'].numpy()[0]}|><|{MUSIC_STRUCTURE_LABELS[chorus.numpy()[0]]}|><|{batch['text'][0]}|><|{batch['time_end'].numpy()[0]}|>"
chorus = chorus.to(device)
model_input = {"text": text, "audio_token": token, "audio_token_len": token_len,
"text_token": text_token, "text_token_len": text_token_len,
"embeddings": [time_start, time_end, chorus], "raw_text":text}
music_audios = []
for model_output in model.inference(**model_input):
music_audios.append(model_output['music_audio'])
music_key = utts[0]
music_fn = os.path.join(args.result_dir, '{}.wav'.format(music_key))
torchaudio.save(music_fn, music_audios[0], sample_rate=args.sample_rate)
f.write('{} {}\n'.format(music_key, music_fn))
f.flush()
f.close()
logging.info('Result wav.scp saved in {}'.format(fn))
if __name__ == '__main__':
main() |