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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.
import os
import time
from tqdm import tqdm
from hyperpyyaml import load_hyperpyyaml
from inspiremusic.cli.frontend import InspireMusicFrontEnd
from inspiremusic.cli.model import InspireMusicModel
from inspiremusic.utils.file_utils import logging
import torch
class InspireMusic:
def __init__(self, model_dir, load_jit=True, load_onnx=False, fast = False, fp16=True, hub="modelscope"):
instruct = True if '-Instruct' in model_dir else False
if model_dir is None:
model_dir = f"pretrained_models/InspireMusic-1.5B-Long"
if not os.path.isfile(f"{model_dir}/llm.pt"):
model_name = model_dir.split("/")[-1]
if hub == "modelscope":
from modelscope import snapshot_download
if model_name == "InspireMusic-Base":
snapshot_download(f"iic/InspireMusic", local_dir=model_dir)
else:
snapshot_download(f"iic/{model_name}", local_dir=model_dir)
assert os.path.exists(f'{model_dir}/inspiremusic.yaml')
with open('{}/inspiremusic.yaml'.format(model_dir), 'r') as f:
configs = load_hyperpyyaml(f)
self.frontend = InspireMusicFrontEnd(configs,
configs['get_tokenizer'],
'{}/llm.pt'.format(model_dir),
'{}/flow.pt'.format(model_dir),
'{}/music_tokenizer/'.format(model_dir),
'{}/wavtokenizer/'.format(model_dir),
instruct,
fast,
fp16,
configs['allowed_special'])
self.model = InspireMusicModel(configs['llm'], configs['flow'], configs['hift'], configs['wavtokenizer'], fast, fp16)
self.model.load('{}/llm.pt'.format(model_dir),
'{}/flow.pt'.format(model_dir),
'{}/music_tokenizer/'.format(model_dir),
'{}/wavtokenizer/model.pt'.format(model_dir))
del configs
@torch.inference_mode()
def inference(self, task, text, audio, time_start, time_end, chorus, stream=False, sr=24000):
if task == "text-to-music":
for i in tqdm(self.frontend.text_normalize(text, split=True)):
model_input = self.frontend.frontend_text_to_music(i, time_start, time_end, chorus)
start_time = time.time()
logging.info('prompt text {}'.format(i))
for model_output in self.model.inference(**model_input, stream=stream):
music_audios_len = model_output['music_audio'].shape[1] / sr
logging.info('yield music len {}, rtf {}'.format(music_audios_len, (time.time() - start_time) / music_audios_len))
yield model_output
start_time = time.time()
elif task == "continuation":
if text is None:
if audio is not None:
for i in tqdm(audio):
model_input = self.frontend.frontend_continuation(None, i, time_start, time_end, chorus, sr, max_audio_length)
start_time = time.time()
logging.info('prompt text {}'.format(i))
for model_output in self.model.continuation_inference(**model_input, stream=stream):
music_audios_len = model_output['music_audio'].shape[1] / sr
logging.info('yield music len {}, rtf {}'.format(music_audios_len, (time.time() - start_time) / music_audios_len))
yield model_output
start_time = time.time()
else:
if audio is not None:
for i in tqdm(self.frontend.text_normalize(text, split=True)):
model_input = self.frontend.frontend_continuation(i, audio, time_start, time_end, chorus, sr, max_audio_length)
start_time = time.time()
logging.info('prompt text {}'.format(i))
for model_output in self.model.continuation_inference(**model_input, stream=stream):
music_audios_len = model_output['music_audio'].shape[1] / sr
logging.info('yield music len {}, rtf {}'.format(music_audios_len, (time.time() - start_time) / music_audios_len))
yield model_output
start_time = time.time()
else:
print("Please input text or audio.")
else:
print("Currently only support text-to-music and music continuation tasks.")
@torch.inference_mode()
def cli_inference(self, text, audio_prompt, time_start, time_end, chorus, task, stream=False, duration_to_gen=30, sr=24000):
if task == "text-to-music":
model_input = self.frontend.frontend_text_to_music(text, time_start, time_end, chorus)
logging.info('prompt text {}'.format(text))
elif task == "continuation":
model_input = self.frontend.frontend_continuation(text, audio_prompt, time_start, time_end, chorus, sr)
logging.info('prompt audio length: {}'.format(len(audio_prompt)))
start_time = time.time()
for model_output in self.model.inference(**model_input, duration_to_gen=duration_to_gen, task=task):
music_audios_len = model_output['music_audio'].shape[1] / sr
logging.info('yield music len {}, rtf {}'.format(music_audios_len, (time.time() - start_time) / music_audios_len))
yield model_output
start_time = time.time()
@torch.inference_mode()
def inference_zero_shot(self, text, prompt_text, prompt_audio_16k, stream=False, sr=24000):
prompt_text = self.frontend.text_normalize(prompt_text, split=False)
for i in tqdm(self.frontend.text_normalize(text, split=True)):
model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_audio_16k)
start_time = time.time()
logging.info('prompt text {}'.format(i))
for model_output in self.model.inference(**model_input, stream=stream):
audio_len = model_output['music_audio'].shape[1] / sr
logging.info('yield audio len {}, rtf {}'.format(audio_len, (time.time() - start_time) / audio_len))
yield model_output
start_time = time.time()
@torch.inference_mode()
def inference_instruct(self, text, spk_id, instruct_text, stream=False, sr=24000):
if self.frontend.instruct is False:
raise ValueError('{} do not support instruct inference'.format(self.model_dir))
instruct_text = self.frontend.text_normalize(instruct_text, split=False)
for i in tqdm(self.frontend.text_normalize(text, split=True)):
model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
start_time = time.time()
logging.info('prompt text {}'.format(i))
for model_output in self.model.inference(**model_input, stream=stream):
audio_len = model_output['music_audio'].shape[1] / sr
logging.info('yield audio len {}, rtf {}'.format(audio_len, (time.time() - start_time) / audio_len))
yield model_output
start_time = time.time()
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