cli_inference.py

# Copyright (2023) Tsinghua University, Bytedance Ltd. and/or its affiliates
#
# 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 torch
import argparse
from model import SALMONN

if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--ckpt_path", type=str, default='./salomnn_7b.bin')
    parser.add_argument("--whisper_path", type=str, default='whisper-large-v2')
    parser.add_argument("--beats_path", type=str, default='BEATs_iter3_plus_AS2M_finetuned_on_AS2M_cpt2.pt')
    parser.add_argument("--vicuna_path", type=str, default='vicuna-7b-v1.5')
    parser.add_argument("--low_resource", action='store_true', default=False)
    parser.add_argument("--debug", action="store_true", default=False)

    args = parser.parse_args()

    model = SALMONN(
        ckpt=args.ckpt_path,
        whisper_path=args.whisper_path,
        beats_path=args.beats_path,
        vicuna_path=args.vicuna_path
    ).to(torch.float16).cuda()

    prompt = 'First describe the music in general in terms of mood, theme, tempo, melody, instruments and chord progression. Then provide a detailed music analysis by describing each functional segment and its time boundaries.'
    prompt_tmp = 'This is a Pop music of 69 beat-per-minute (BPM). First describe the music in general in terms of mood, theme, tempo, melody, instruments and chord progression. Then provide a detailed music analysis by describing each functional segment and its time boundaries. Please note that the music boundaries are [0, 41, 58, 83, 100].'
    model.eval()
    while True:
        print("=====================================")
        wav_path = input("Your Wav Path:\n")
        prompt = input("Your Prompt:\n")
        try:
            print("Output:")
            # for environment with cuda>=117
            with torch.cuda.amp.autocast(dtype=torch.float16):
                print(model.generate(wav_path, prompt=prompt, repetition_penalty=1.5, num_beams=10, top_p=.7, temperature=.2)[0])
        except Exception as e:
            print(e)
            if args.debug:
                import pdb

                pdb.set_trace()