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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
import time
from inspiremusic.utils.audio_utils import trim_audio, fade_out, process_audio
from inspiremusic.utils.common import MUSIC_STRUCTURE_LABELS
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def get_args():
parser = argparse.ArgumentParser(description='inference only with your 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', default=None, required=False, help='flow model file')
parser.add_argument('--llm_model', default=None,required=False, help='flow 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('--fast', action='store_true', required=False, help='True: fast inference mode, without flow matching for fast inference. False: normal inference mode, with flow matching for high quality.')
parser.add_argument('--fp16', default=True, type=bool, required=False, help='inference with fp16 model')
parser.add_argument('--fade_out', default=True, type=bool, required=False, help='add fade out effect to generated audio')
parser.add_argument('--fade_out_duration', default=1.0, type=float, required=False, help='fade out duration in seconds')
parser.add_argument('--trim', default=False, type=bool, required=False, help='trim the silence ending of generated audio')
parser.add_argument('--format', type=str, default="wav", required=False,
choices=["wav", "mp3", "m4a", "flac"],
help='sampling rate of input audio')
parser.add_argument('--sample_rate', type=int, default=24000, required=False,
help='sampling rate of input audio')
parser.add_argument('--output_sample_rate', type=int, default=48000, required=False, choices=[24000, 48000],
help='sampling rate of generated output 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=0,
help='gpu id for this rank, -1 for cpu')
parser.add_argument('--task',
default='text-to-music',
choices=['text-to-music', 'continuation', "reconstruct", "super_resolution"],
help='choose inference task type. text-to-music: text-to-music task. continuation: music continuation task. reconstruct: reconstruction of original music. super_resolution: convert original 24kHz music into 48kHz music.')
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)
if args.fast:
args.output_sample_rate = 24000
min_generate_audio_length = int(args.output_sample_rate * args.min_generate_audio_seconds)
max_generate_audio_length = int(args.output_sample_rate * args.max_generate_audio_seconds)
assert args.min_generate_audio_seconds <= args.max_generate_audio_seconds
# 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(configs['llm'], configs['flow'], configs['hift'], configs['wavtokenizer'], args.fast, args.fp16)
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')
caption_fn = os.path.join(args.result_dir, 'captions.txt')
caption_f = open(caption_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"
text_token = batch["text_token"].to(device)
text_token_len = batch["text_token_len"].to(device)
if "time_start" not in batch.keys():
batch["time_start"] = torch.randint(0, args.min_generate_audio_seconds, (1,)).to(torch.float64)
if batch["time_start"].numpy()[0] > 300:
batch["time_start"] = torch.Tensor([0]).to(torch.float64)
if "time_end" not in batch.keys():
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)
else:
if (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)
elif (batch["time_end"].numpy()[0] - batch["time_start"].numpy()[0]) > args.max_generate_audio_seconds:
batch["time_end"] = torch.Tensor([(batch["time_start"].numpy()[0] + args.max_generate_audio_seconds)]).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])
else:
batch["chorus"] = batch["chorus"]
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)
if batch["acoustic_token"] is None:
audio_token = None
audio_token_len = None
else:
audio_token = batch["acoustic_token"].to(device)
audio_token_len = batch["acoustic_token_len"].to(device)
text = batch["text"]
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
if args.task in ['text-to-music', 'continuation']:
# text to music, music continuation
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,
"sample_rate": args.output_sample_rate,
"duration_to_gen": args.max_generate_audio_seconds,
"task": args.task}
elif args.task in ['reconstruct', 'super_resolution']:
# audio reconstruction, audio super resolution
model_input = {"text": text, "audio_token": audio_token,
"audio_token_len": audio_token_len,
"text_token": text_token,
"text_token_len": text_token_len,
"embeddings": [time_start, time_end, chorus],
"raw_text": text,
"sample_rate": args.output_sample_rate,
"duration_to_gen": args.max_generate_audio_seconds,
"task": args.task}
else:
# zero-shot
model_input = {'text' : text,
'text_len' : text_token_len,
'prompt_text' : text_token,
'prompt_text_len' : text_token_len,
'llm_prompt_audio_token' : token,
'llm_prompt_audio_token_len' : token_len,
'flow_prompt_audio_token' : audio_token,
'flow_prompt_audio_token_len': audio_token_len,
'prompt_audio_feat' : audio_feat,
'prompt_audio_feat_len' : audio_feat_len,
"embeddings" : [time_start,
time_end,
chorus]}
music_key = utts[0]
music_audios = []
music_fn = os.path.join(args.result_dir, f'{music_key}.{args.format}')
bench_start = time.time()
for model_output in model.inference(**model_input):
music_audios.append(model_output['music_audio'])
bench_end = time.time()
if args.trim:
music_audio = trim_audio(music_audios[0],
sample_rate=args.output_sample_rate,
threshold=0.05,
min_silence_duration=0.8)
else:
music_audio = music_audios[0]
if music_audio.shape[0] != 0:
if music_audio.shape[1] > max_generate_audio_length:
music_audio = music_audio[:, :max_generate_audio_length]
if music_audio.shape[1] >= min_generate_audio_length:
try:
if args.fade_out:
music_audio = fade_out(music_audio, args.output_sample_rate, args.fade_out_duration)
music_audio = music_audio.repeat(2, 1)
if args.format in ["wav", "flac"]:
torchaudio.save(music_fn, music_audio, sample_rate=args.output_sample_rate, encoding="PCM_S", bits_per_sample=24)
elif args.format in ["mp3", "m4a"]:
torchaudio.backend.sox_io_backend.save(filepath=music_fn, src=music_audio, sample_rate=args.output_sample_rate, format=args.format)
else:
logging.info(f"Format is not supported. Please choose from wav, mp3, m4a, flac.")
except Exception as e:
logging.info(f"Error saving file: {e}")
raise
audio_duration = music_audio.shape[1] / args.output_sample_rate
rtf = (bench_end - bench_start) / audio_duration
logging.info(f"processing time: {int(bench_end - bench_start)}s, audio length: {int(audio_duration)}s, rtf: {rtf}, text prompt: {text_prompt}")
f.write('{} {}\n'.format(music_key, music_fn))
f.flush()
caption_f.write('{}\t{}\n'.format(music_key, text_prompt))
caption_f.flush()
else:
logging.info(f"Generate audio length {music_audio.shape[1]} is shorter than min_generate_audio_length.")
else:
logging.info(f"Generate audio is empty, dim = {music_audio.shape[0]}.")
f.close()
logging.info('Result wav.scp saved in {}'.format(fn))
if __name__ == '__main__':
main()
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