Update README.md

README.md

@@ -1,85 +1,85 @@
----
-license: mit
----
-
-# LongVA-7B-Qwen2-Audio
-
-
-LongVA-7B-Qwen2-Audio is an extension of [LongVA-7B](https://github.com/EvolvingLMMs-Lab/LongVA), further trained using the [LLaVA-NeXT-Audio](https://huggingface.co/datasets/ColorfulAI/LLaVA-NeXT-Audio) dataset.
-
-
-## Usage
-
-*Please refer to [M4](https://github.com/patrick-tssn/M4) to install relvevant packages*
-
-```python
-
-import os
-from PIL import Image
-import numpy as np
-import torchaudio
-import torch
-from decord import VideoReader, cpu
-import whisper
-# fix seed
-torch.manual_seed(0)
-
-from intersuit.model.builder import load_pretrained_model
-from intersuit.mm_utils import tokenizer_image_speech_tokens, process_images
-from intersuit.constants import IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX
-
-import warnings
-warnings.filterwarnings("ignore")
-
-model_path = "ColorfulAI/LongVA-7B-Qwen2-Audio"
-video_path = "local_demo/assets/water.mp4"
-audio_path = "local_demo/wav/infer.wav"
-max_frames_num = 16 # you can change this to several thousands so long you GPU memory can handle it :)
-gen_kwargs = {"do_sample": True, "temperature": 0.5, "top_p": None, "num_beams": 1, "use_cache": True, "max_new_tokens": 1024}
-tokenizer, model, image_processor, _ = load_pretrained_model(model_path, None, "llava_qwen", device_map="cuda:0")
-
-query = "Give a detailed caption of the video as if I am blind."
-query = None # comment this to use ChatTTS to convert the query to audio
-
-#video input
-prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image><|im_end|>\n<|im_start|>user\n<speech>\n<|im_end|>\n<|im_start|>assistant\n"
-input_ids = tokenizer_image_speech_tokens(prompt, tokenizer, IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(model.device)
-vr = VideoReader(video_path, ctx=cpu(0))
-total_frame_num = len(vr)
-uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
-frame_idx = uniform_sampled_frames.tolist()
-frames = vr.get_batch(frame_idx).asnumpy()
-video_tensor = image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].to(model.device, dtype=torch.float16)
-
-#audio input
-# process speech for input question
-if query is not None:
-    import ChatTTS
-    chat = ChatTTS.Chat()
-    chat.load(source='local', compile=True)
-    audio_path = "./local_demo/wav/" + "infer.wav"
-    if os.path.exists(audio_path): os.remove(audio_path) # refresh
-    if not os.path.exists(audio_path):
-        wav = chat.infer(query)
-        try:
-            torchaudio.save(audio_path, torch.from_numpy(wav).unsqueeze(0), 24000)
-        except:
-            torchaudio.save(audio_path, torch.from_numpy(wav), 24000)
-    print(f"Human: {query}")
-    
-else:
-    print("Human: <audio>")
-    
-speech = whisper.load_audio(audio_path)
-speech = whisper.pad_or_trim(speech)
-speech = whisper.log_mel_spectrogram(speech, n_mels=128).permute(1, 0).to(device=model.device, dtype=torch.float16)
-speech_length = torch.LongTensor([speech.shape[0]]).to(model.device)
-
-with torch.inference_mode():
-    output_ids = model.generate(input_ids, images=[video_tensor],  modalities=["video"], speeches=speech.unsqueeze(0), speech_lengths=speech_length, **gen_kwargs)
-outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
-print(f"Agent: {outputs}")
-
-```
-
-
+---
+license: mit
+---
+
+# LongVA-7B-Qwen2-Audio
+
+
+LongVA-7B-Qwen2-Audio is an extension of [LongVA-7B](https://github.com/EvolvingLMMs-Lab/LongVA), further trained using the [LLaVA-NeXT-Audio](https://huggingface.co/datasets/ColorfulAI/LLaVA-NeXT-Audio) dataset for 0.4 epochs.
+
+
+## Usage
+
+*Please refer to [M4](https://github.com/patrick-tssn/M4) to install relvevant packages*
+
+```python
+
+import os
+from PIL import Image
+import numpy as np
+import torchaudio
+import torch
+from decord import VideoReader, cpu
+import whisper
+# fix seed
+torch.manual_seed(0)
+
+from intersuit.model.builder import load_pretrained_model
+from intersuit.mm_utils import tokenizer_image_speech_tokens, process_images
+from intersuit.constants import IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX
+
+import warnings
+warnings.filterwarnings("ignore")
+
+model_path = "ColorfulAI/LongVA-7B-Qwen2-Audio"
+video_path = "local_demo/assets/water.mp4"
+audio_path = "local_demo/wav/infer.wav"
+max_frames_num = 16 # you can change this to several thousands so long you GPU memory can handle it :)
+gen_kwargs = {"do_sample": True, "temperature": 0.5, "top_p": None, "num_beams": 1, "use_cache": True, "max_new_tokens": 1024}
+tokenizer, model, image_processor, _ = load_pretrained_model(model_path, None, "llava_qwen", device_map="cuda:0")
+
+query = "Give a detailed caption of the video as if I am blind."
+query = None # comment this to use ChatTTS to convert the query to audio
+
+#video input
+prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image><|im_end|>\n<|im_start|>user\n<speech>\n<|im_end|>\n<|im_start|>assistant\n"
+input_ids = tokenizer_image_speech_tokens(prompt, tokenizer, IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(model.device)
+vr = VideoReader(video_path, ctx=cpu(0))
+total_frame_num = len(vr)
+uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
+frame_idx = uniform_sampled_frames.tolist()
+frames = vr.get_batch(frame_idx).asnumpy()
+video_tensor = image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].to(model.device, dtype=torch.float16)
+
+#audio input
+# process speech for input question
+if query is not None:
+    import ChatTTS
+    chat = ChatTTS.Chat()
+    chat.load(source='local', compile=True)
+    audio_path = "./local_demo/wav/" + "infer.wav"
+    if os.path.exists(audio_path): os.remove(audio_path) # refresh
+    if not os.path.exists(audio_path):
+        wav = chat.infer(query)
+        try:
+            torchaudio.save(audio_path, torch.from_numpy(wav).unsqueeze(0), 24000)
+        except:
+            torchaudio.save(audio_path, torch.from_numpy(wav), 24000)
+    print(f"Human: {query}")
+    
+else:
+    print("Human: <audio>")
+    
+speech = whisper.load_audio(audio_path)
+speech = whisper.pad_or_trim(speech)
+speech = whisper.log_mel_spectrogram(speech, n_mels=128).permute(1, 0).to(device=model.device, dtype=torch.float16)
+speech_length = torch.LongTensor([speech.shape[0]]).to(model.device)
+
+with torch.inference_mode():
+    output_ids = model.generate(input_ids, images=[video_tensor],  modalities=["video"], speeches=speech.unsqueeze(0), speech_lengths=speech_length, **gen_kwargs)
+outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+print(f"Agent: {outputs}")
+
+```
+
+