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import torch
import whisperx
import gradio as gr
from peft import PeftModel
from configs import get_config_phase2
from transformers import AutoTokenizer, AutoProcessor, CLIPVisionModel, AutoModelForCausalLM
config = get_config_phase2()
clip_model = CLIPVisionModel.from_pretrained(config.get("clip_model_name"))
base_model = AutoModelForCausalLM.from_pretrained(
config.get("phi2_model_name"),
low_cpu_mem_usage=True,
return_dict=True,
torch_dtype=torch.float16,
trust_remote_code=True
)
ckpts = "ckpts/Qlora_adaptor/"
phi2_model = PeftModel.from_pretrained(base_model, ckpts)
phi2_model = phi2_model.merge_and_unload().to(config.get("device"))
projection_layer = torch.nn.Linear(config.get("clip_embed"), config.get("phi_embed"))
projection_layer.load_state_dict(torch.load('./ckpts/model_phase2.pth', map_location=config.get("device")))
# tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.get("phi2_model_name"), trust_remote_code=True)
processor = AutoProcessor.from_pretrained(config.get("clip_model_name"), trust_remote_code=True)
audio_model = whisperx.load_model('tiny', 'cpu', compute_type="float32")
def generate_answers(img=None, aud = None, q = None, max_tokens = 30):
batch_size = 1
start_iq = tokenizer.encode("<iQ>")
end_iq = tokenizer.encode("</iQ>")
start_iq_embeds = torch.tensor(start_iq).repeat(batch_size, 1)
end_iq_embeds = torch.tensor(end_iq).repeat(batch_size, 1)
start_iq_embeds = phi2_model.model.embed_tokens(start_iq_embeds.to(config.get("device")))
end_iq_embeds = phi2_model.model.embed_tokens(end_iq_embeds.to(config.get("device")))
inputs_embeddings = []
inputs_embeddings.append(start_iq_embeds)
predicted_caption = torch.full((batch_size, max_tokens), 50256, dtype=torch.long, device=config.get('device'))
if img is not None:
images = processor(images=img, return_tensors="pt")['pixel_values'].to(config.get('device'))
images = {'pixel_values': images.to(config.get("device"))}
clip_outputs = clip_model(**images)
# remove cls token
images = clip_outputs.last_hidden_state[:, 1:, :]
image_embeddings = projection_layer(images).to(torch.float16)
inputs_embeddings.append(image_embeddings)
if aud is not None:
trans = audio_model.transcribe(aud)
audio_res = ""
for seg in trans['segments']:
audio_res += seg['text']
audio_res = audio_res.strip()
audio_tokens = tokenizer(audio_res,return_tensors="pt", return_attention_mask=False)['input_ids']
audio_embeds = phi2_model.model.embed_tokens(audio_tokens.to(config.get("device")))
inputs_embeddings.append(audio_embeds)
if q!='':
ques = tokenizer(q, return_tensors="pt", return_attention_mask=False)['input_ids']
q_embeds = phi2_model.model.embed_tokens(ques.to(config.get("device")))
inputs_embeddings.append(q_embeds)
inputs_embeddings.append(end_iq_embeds)
# Combine embeddings
combined_embeds = torch.cat(inputs_embeddings, dim=1)
print("----------",combined_embeds.shape)
for pos in range(max_tokens - 1):
model_output_logits = phi2_model.forward(inputs_embeds = combined_embeds)['logits']
print("-=-=-=-", model_output_logits.shape)
predicted_word_token_logits = model_output_logits[:, -1, :].unsqueeze(1)
predicted_word_token = torch.argmax(predicted_word_token_logits, dim = -1)
predicted_caption[:, pos] = predicted_word_token.view(1,-1).to('cpu')
print(predicted_caption)
next_token_embeds = phi2_model.model.embed_tokens(predicted_word_token)
combined_embeds = torch.cat([combined_embeds, next_token_embeds], dim=1)
del next_token_embeds
del predicted_word_token
del predicted_word_token_logits
del combined_embeds
predicted_captions_decoded = tokenizer.batch_decode(predicted_caption,ignore_index = 50256)[0]
predicted_captions_decoded = predicted_captions_decoded.replace("<|endoftext|>","")
return predicted_captions_decoded
with gr.Blocks() as demo:
gr.Markdown(
"""
# TAI2T Model(Text, Audio, Image to Text Model)
Multimodel GPT with inputs as Image, Audio, Text with output as Text.
"""
)
with gr.Row():
with gr.Column():
image = gr.Image(label='Image', type="pil", value=None)
audio_q = gr.Audio(label="Audio Question", value=None, sources=['microphone', 'upload'], type='filepath')
question = gr.Text(label ='Question?', value=None)
max_tokens = gr.Slider(1, 50, value=10, step=1, label="Max tokens")
with gr.Row():
answer = gr.Text(label ='Answer')
with gr.Row():
submit = gr.Button("Submit")
submit.click(generate_answers, inputs=[image, audio_q, question, max_tokens], outputs=[answer])
clear_btn = gr.ClearButton([image, audio_q, question, max_tokens, answer])
if __name__ == "__main__":
demo.launch(share=True, debug=True) |