import os import gradio as gr import torch import torchaudio from peft import ( LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer, LlamaConfig from utils.prompter import Prompter import datetime import time,json device = "cuda" if torch.cuda.is_available() else "cpu" base_model = "/fs/nexus-projects/brain_project/Llama-2-7b-chat-hf-qformer/" prompter = Prompter('alpaca_short') tokenizer = LlamaTokenizer.from_pretrained(base_model) model = LlamaForCausalLM.from_pretrained(base_model, device_map="auto", torch_dtype=torch.float32) config = LoraConfig( r=8, lora_alpha=16, target_modules=["q_proj", "v_proj"], lora_dropout=0.0, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) temp, top_p, top_k = 0.1, 0.95, 500 # change it to your model path ### Stage 4 ckpt eval_mdl_path = '/fs/gamma-projects/audio/gama/new_data_no_aggr/stage4_all_mix_new/checkpoint-46800/pytorch_model.bin' ### Stage 5 ckpt # eval_mdl_path = '/fs/gamma-projects/audio/gama/new_data/stage5_all_mix_all/checkpoint-900/pytorch_model.bin' state_dict = torch.load(eval_mdl_path, map_location='cpu') msg = model.load_state_dict(state_dict, strict=False) model.is_parallelizable = True model.model_parallel = True # unwind broken decapoda-research config model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk model.config.bos_token_id = 1 model.config.eos_token_id = 2 model.eval() eval_log = [] cur_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") log_save_path = './inference_log/' if os.path.exists(log_save_path) == False: os.mkdir(log_save_path) log_save_path = log_save_path + cur_time + '.json' SAMPLE_RATE = 16000 AUDIO_LEN = 1.0 def load_audio(filename): waveform, sr = torchaudio.load(filename) audio_info = 'Original input audio length {:.2f} seconds, number of channels: {:d}, sampling rate: {:d}.'.format(waveform.shape[1]/sr, waveform.shape[0], sr) waveform = waveform - waveform.mean() fbank = torchaudio.compliance.kaldi.fbank(waveform, htk_compat=True, sample_frequency=sr, use_energy=False, window_type='hanning', num_mel_bins=128, dither=0.0, frame_shift=10) target_length = 1024 n_frames = fbank.shape[0] p = target_length - n_frames if p > 0: m = torch.nn.ZeroPad2d((0, 0, 0, p)) fbank = m(fbank) elif p < 0: fbank = fbank[0:target_length, :] # normalize the fbank fbank = (fbank + 5.081) / 4.4849 return fbank, audio_info def predict(audio_path, question): print('audio path, ', audio_path) begin_time = time.time() instruction = question prompt = prompter.generate_prompt(instruction, None) print('Input prompt: ', prompt) inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].to(device) if audio_path != 'empty': cur_audio_input, audio_info = load_audio(audio_path) cur_audio_input = cur_audio_input.unsqueeze(0) if torch.cuda.is_available() == False: pass else: # cur_audio_input = cur_audio_input.half().to(device) cur_audio_input = cur_audio_input.to(device) else: cur_audio_input = None audio_info = 'Audio is not provided, answer pure language question.' generation_config = GenerationConfig( do_sample=True, temperature=0.1, top_p=0.95, max_new_tokens=400, bos_token_id=model.config.bos_token_id, eos_token_id=model.config.eos_token_id, pad_token_id=model.config.pad_token_id, num_return_sequences=1 ) # Without streaming with torch.no_grad(): generation_output = model.generate( input_ids=input_ids.to(device), audio_input=cur_audio_input, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=400, ) s = generation_output.sequences[0] output = tokenizer.decode(s)[len(prompt)+6:-4] # trim and end_time = time.time() print(output) cur_res = {'audio_id': audio_path, 'input': instruction, 'output': output} eval_log.append(cur_res) with open(log_save_path, 'w') as outfile: json.dump(eval_log, outfile, indent=1) print('eclipse time: ', end_time - begin_time, ' seconds.') return audio_info, output link = "https://github.com/Sreyan88/GAMA" text = "[Github]" paper_link = "https://sreyan88.github.io/gamaaudio/" paper_text = "[Paper]" demo = gr.Interface(fn=predict, inputs=[gr.Audio(type="filepath"), gr.Textbox(value='Describe the audio.', label='Edit the textbox to ask your own questions!')], outputs=[gr.Textbox(label="Audio Meta Information"), gr.Textbox(label="GAMA Output")], cache_examples=True, title="Quick Demo of GAMA", description="GAMA is a novel Large Large Audio-Language Model that is capable of understanding audio inputs and answer any open-ended question about it." + f"{paper_text} " + f"{text}
" + "GAMA is authored by members of the GAMMA Lab at the University of Maryland, College Park and Adobe, USA.
" + "**GAMA is not an ASR model and has limited ability to recognize the speech content. It primarily focuses on perception and understanding of non-speech sounds.**
" + "Input an audio and ask quesions! Audio will be converted to 16kHz and padded or trim to 10 seconds.") demo.launch(debug=True, share=True)