run_stream_omni_s2t.py

import argparse
import torch

from stream_omni.constants import (
    IMAGE_TOKEN_INDEX,
    DEFAULT_IMAGE_TOKEN,
    DEFAULT_IM_START_TOKEN,
    DEFAULT_IM_END_TOKEN,
)
from stream_omni.conversation import conv_templates, SeparatorStyle
from stream_omni.model.builder import load_pretrained_model
from stream_omni.utils import disable_torch_init
from stream_omni.mm_utils import (
    process_images,
    tokenizer_image_token,
    get_model_name_from_path,
)

from PIL import Image

import requests
from PIL import Image
from io import BytesIO
import re


def image_parser(args):
    out = args.image_file.split(args.sep)
    return out


def load_image(image_file):
    if image_file.startswith("http") or image_file.startswith("https"):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert("RGB")
    else:
        image = Image.open(image_file).convert("RGB")
    return image


def load_images(image_files):
    out = []
    for image_file in image_files:
        image = load_image(image_file)
        out.append(image)
    return out


def eval_model(args):
    # Model
    disable_torch_init()

    model_name = args.model_name
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)

    tokenizer.model_max_length = 2048
    model.config.tokenizer_model_max_length = 2048
    qs = args.query

    if args.image_file is not None:
        image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
        if model.config.mm_use_im_start_end:
            qs = image_token_se + "\n" + qs
        else:
            qs = DEFAULT_IMAGE_TOKEN + "\n" + qs

    if "llama-2" in model_name.lower():
        conv_mode = "llava_llama_2"
    elif "mistral" in model_name.lower():
        conv_mode = "mistral_instruct"
    elif "v1.6-34b" in model_name.lower():
        conv_mode = "chatml_direct"
    elif "v1" in model_name.lower():
        conv_mode = "llava_v1"
    elif "mpt" in model_name.lower():
        conv_mode = "mpt"
    else:
        conv_mode = "llava_v0"

    if args.conv_mode is not None and conv_mode != args.conv_mode:
        print("[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format(conv_mode, args.conv_mode, args.conv_mode))
    else:
        args.conv_mode = conv_mode

    conv = conv_templates[args.conv_mode].copy()
    conv.append_message(conv.roles[0], qs)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()

    image_files = image_parser(args) if args.image_file else None
    if image_files:
        images = load_images(image_files)
        image_sizes = [x.size for x in images]
        images_tensor = process_images(images, image_processor, model.config).to(model.device, dtype=torch.float16)
    else:
        images_tensor = None
        image_sizes = None

    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).cuda()

    conv = conv_templates[args.conv_mode].copy()
    conv.append_message(conv.roles[0], " ")
    conv.append_message(conv.roles[1], None)

    prompt = conv.get_prompt()
    prefill_audio_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()

    with torch.inference_mode():

        output_ids = model.generate(
            input_ids,
            images=images_tensor,
            image_sizes=image_sizes,
            do_sample=True if args.temperature > 0 else False,
            temperature=args.temperature,
            top_p=args.top_p,
            num_beams=args.num_beams,
            max_new_tokens=args.max_new_tokens,
            use_cache=True,
            inference_type="speech_to_text",
        )

    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=False)[0].strip()
    print("\n\n")
    print(outputs)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    parser.add_argument("--model-base", type=str, default=None)
    parser.add_argument("--model-name", type=str, default="llava.llama.audio")
    parser.add_argument("--image-file", type=str, default=None)
    parser.add_argument("--query", type=str, required=True)
    parser.add_argument("--conv-mode", type=str, default=None)
    parser.add_argument("--sep", type=str, default=",")
    parser.add_argument("--temperature", type=float, default=0.0)
    parser.add_argument("--top_p", type=float, default=None)
    parser.add_argument("--num_beams", type=int, default=1)
    parser.add_argument("--max_new_tokens", type=int, default=4096)
    args = parser.parse_args()

    eval_model(args)