Ygr avsr

.gitignore

@@ -1,5 +1,10 @@
 .DS_Store
 __pycache__
 .ipynb_checkpoints
+.vscode
+debug.py
 .idea/*
 transformers
+wandb/
+*.log
+log

examples/vllm/inference.py

@@ -7,6 +7,9 @@
 from vllm import LLM
 from vllm import LLM, SamplingParams
 
+import logging
+logger = logging.getLogger(__name__)
+
 
 torch.cuda.manual_seed(42)
 torch.manual_seed(42)
@@ -27,15 +30,15 @@ def main(
         if user_prompt is None:
             user_prompt = input("Enter your prompt: ")
 
-        print(f"User prompt:\n{user_prompt}")
+        logger.info(f"User prompt:\n{user_prompt}")
 
-        print(f"sampling params: top_p {top_p} and temperature {temperature} for this inference request")
+        logger.info(f"sampling params: top_p {top_p} and temperature {temperature} for this inference request")
         sampling_param = SamplingParams(top_p=top_p, temperature=temperature, max_tokens=max_new_tokens)
 
 
         outputs = model.generate(user_prompt, sampling_params=sampling_param)
 
-        print(f"model output:\n {user_prompt} {outputs[0].outputs[0].text}")
+        logger.info(f"model output:\n {user_prompt} {outputs[0].outputs[0].text}")
         user_prompt = input("Enter next prompt (press Enter to exit): ")
         if not user_prompt:
             break

scripts/finetune_avsr.sh

@@ -0,0 +1,28 @@
+#!/bin/bash
+# export PYTHONPATH=/root/whisper:$PYTHONPATH
+export CUDA_VISIBLE_DEVICES=0
+export CUDA_LAUNCH_BLOCKING=1
+
+cd /root/SLAM-LLM
+
+audio_encoder_path=/home/oss/maziyang.mzy/models/AudioMAE/finetuned.pth
+speech_encoder_path=/home/oss/maziyang.mzy/models/Whisper/base.pt
+llm_path=/home/oss/zhifu.gzf/ckpt/Llama-2-7b-hf
+output_dir=/nfs/maziyang.mzy/models/llama-2-hf-finetune
+
+# -m debugpy --listen 5678 --wait-for-client
+python src/llama_recipes/pipeline/finetune.py \
+--model_name avsr \
+--use_peft --peft_method lora \
+--quantization \
+--llm_name llama-2-7b-hf \
+--llm_path $llm_path \
+--encoder_name whisper \
+--encoder_path $speech_encoder_path \
+--encoder_projector linear \
+--dataset avsr_dataset \
+--avsr_dataset.file src/llama_recipes/datasets/avsr_dataset.py:get_audio_dataset \
+--batching_strategy custom \
+--num_epochs 1 \
+--batch_size_training 2 \
+--output_dir $output_dir 

scripts/finetune_avsr_debug.sh

@@ -0,0 +1,32 @@
+#!/bin/bash
+# export PYTHONPATH=/root/whisper:$PYTHONPATH
+export CUDA_VISIBLE_DEVICES=0
+export CUDA_LAUNCH_BLOCKING=1
+
+cd /root/SLAM-LLM
+
+audio_encoder_path=/home/oss/maziyang.mzy/models/AudioMAE/finetuned.pth
+speech_encoder_path=/home/oss/maziyang.mzy/models/Whisper/base.pt
+
+llm_path=/nfs/zhifu.gzf/ckpt/Llama-2-7b-hf             #/home/oss/zhifu.gzf/ckpt/Llama-2-7b-hf
+output_dir=/nfs/yangguanrou.ygr/ckpts/llama-2-hf-finetune   #/home/oss/yangguanrou.ygr/ckpts/llama-2-hf-finetune
+
+# -m debugpy --listen 5680 --wait-for-client
+python -m debugpy --listen 5680 --wait-for-client src/llama_recipes/pipeline/finetune.py \
+--model_name avsr \
+--use_peft --peft_method lora \
+--quantization \
+--llm_name llama-2-7b-hf \
+--llm_path $llm_path \
+--encoder_name whisper \
+--encoder_path $speech_encoder_path \
+--encoder_projector linear \
+--dataset avsr_dataset \
+--avsr_dataset.file src/llama_recipes/datasets/avsr_dataset.py:get_audio_dataset \
+--batching_strategy custom \
+--num_epochs 1 \
+--batch_size_training 2 \
+--output_dir $output_dir \
+--stepSize 10 \
+--log_file "/root/SLAM-LLM/log/test.log" \
+--valid_subset "LRS3/val_debug.txt" \

scripts/finetune_avsr_debug_1214.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+# export PYTHONPATH=/root/whisper:$PYTHONPATH
+export CUDA_VISIBLE_DEVICES=0
+export CUDA_LAUNCH_BLOCKING=1
+export OMP_NUM_THREADS=1
+
+cd /root/SLAM-LLM
+
+
+llm_path=/nfs/zhifu.gzf/ckpt/Llama-2-7b-hf
+output_dir=/nfs/yangguanrou.ygr/llama-2-hf-finetune
+
+# -m debugpy --listen 5678 --wait-for-client
+python -m debugpy --listen 5679 --wait-for-client src/llama_recipes/pipeline/finetune.py \
+--model_name avsr \
+--freeze_llm \
+--llm_name llama-2-7b-hf \
+--llm_path $llm_path \
+--encoder_name whisper \
+--encoder_ds_rate 2 \
+--encoder_path $speech_encoder_path \
+--encoder_projector linear \
+--encoder_projector_ds_rate 5 \
+--dataset avsr_dataset \
+--avsr_dataset.file src/llama_recipes/datasets/avsr_dataset.py:get_audio_dataset \
+--batching_strategy custom \
+--num_epochs 1 \
+--batch_size_training 4 \
+--lr 1e-5 \
+--output_dir $output_dir \
+--metric acc \
+--log_file "/root/SLAM-LLM/log/test.log" \
+
+
+# --avsr_dataset.file src/llama_recipes/datasets/avsr_dataset.py:get_audio_dataset \

scripts/finetune_avsr_debug_1218.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+# export PYTHONPATH=/root/whisper:$PYTHONPATH
+export CUDA_VISIBLE_DEVICES=0
+export CUDA_LAUNCH_BLOCKING=1
+export OMP_NUM_THREADS=1
+
+cd /root/SLAM-LLM
+
+
+llm_path=/nfs/zhifu.gzf/ckpt/Llama-2-7b-hf
+output_dir=/nfs/yangguanrou.ygr/llama-2-hf-finetune
+
+# -m debugpy --listen 5678 --wait-for-client
+python src/llama_recipes/pipeline/finetune.py \
+--model_name avsr \
+--freeze_llm \
+--llm_name llama-2-7b-hf \
+--llm_path $llm_path \
+--encoder_name whisper \
+--encoder_ds_rate 2 \
+--encoder_path $speech_encoder_path \
+--encoder_projector linear \
+--encoder_projector_ds_rate 5 \
+--dataset avsr_dataset \
+--avsr_dataset.file src/llama_recipes/datasets/avsr_dataset.py:get_audio_dataset \
+--batching_strategy custom \
+--num_epochs 1 \
+--batch_size_training 4 \
+--lr 1e-5 \
+--output_dir $output_dir \
+--metric acc \
+--log_file "/root/SLAM-LLM/log/test.log" \
+
+
+# --avsr_dataset.file src/llama_recipes/datasets/avsr_dataset.py:get_audio_dataset \

scripts/finetune_speech_pretraining_my.sh

@@ -0,0 +1,89 @@
+#!/bin/bash
+#export PYTHONPATH=/root/whisper:$PYTHONPATH
+export CUDA_VISIBLE_DEVICES=1
+export CUDA_LAUNCH_BLOCKING=1
+export OMP_NUM_THREADS=1
+
+# debug setting for multiple gpus
+# export NCCL_DEBUG=INFO
+# export NCCL_DEBUG_SUBSYS=ALL
+# export TORCH_DISTRIBUTED_DEBUG=INFO
+
+cd /root/SLAM-LLM
+
+speech_encoder_path=/nfs/zhifu.gzf/ckpt/Whisper/large-v2.pt
+# speech_encoder_path=/nfs/maziyang.mzy/models/Whisper/large-v2-qwen.pt
+llm_path=/nfs/zhifu.gzf/ckpt/Llama-2-7b-hf
+output_dir=/nfs/maziyang.mzy/exps/llama-2-hf-finetune-asr-ds5-proj2048
+
+# -m debugpy --listen 5678 --wait-for-client
+if [[ $CUDA_VISIBLE_DEVICES != *","* ]]; then
+python -m debugpy --listen 5678 --wait-for-client src/llama_recipes/pipeline/finetune.py \
+--model_name asr \
+--freeze_encoder \
+--freeze_llm \
+--llm_name llama-2-7b-hf \
+--llm_path $llm_path \
+--encoder_name whisper \
+--encoder_ds_rate 2 \
+--encoder_path $speech_encoder_path \
+--encoder_projector linear \
+--encoder_projector_ds_rate 5 \
+--dataset custom_dataset \
+--custom_dataset.file src/llama_recipes/datasets/speech_dataset.py:get_audio_dataset \
+--custom_dataset.train_data_path /nfs/beinian.lzr/workspace/datasets/speech_llm/train_dataset/data_wav_json/asr/librispeech_train_960h_wav_speech_llm_train_data.json \
+--custom_dataset.val_data_path /nfs/beinian.lzr/workspace/datasets/data/16k/opendata/librispeech/dev_other/librispeech_dev_other.jsonl \
+--batching_strategy custom \
+--num_epochs 100 \
+--batch_size_training 4 \
+--val_batch_size 4 \
+--lr 1e-5 \
+--output_dir $output_dir \
+--run_test_during_validation \
+--run_test_during_validation_file "/cpfs01/shared/Group-speech/beinian.lzr/data/open_data/librispeech_audio/audio/se_librispeech_1001-134707-0000.wav" \
+--run_test_during_validation_prompt "<|ASR|>" \
+--metric acc \
+--log_file "/root/SLAM-LLM/log/test.log" \
+# --ckpt_path "/nfs/maziyang.mzy/models/llama-2-hf-finetune/echat/7/model.pt" \
+# --peft_ckpt "/nfs/maziyang.mzy/models/llama-2-hf-finetune/echat/7" \
+# --use_peft --peft_method lora \
+
+else
+torchrun \
+--nnodes 1 \
+--nproc_per_node 4 \
+src/llama_recipes/pipeline/finetune.py \
+--model_name asr \
+--freeze_encoder \
+--freeze_llm \
+--use_fp16 \
+--enable_fsdp \
+--llm_name llama-2-7b-hf \
+--llm_path $llm_path \
+--encoder_name whisper \
+--encoder_ds_rate 2 \
+--encoder_path $speech_encoder_path \
+--encoder_projector linear \
+--encoder_projector_ds_rate 5 \
+--dataset custom_dataset \
+--custom_dataset.file src/llama_recipes/datasets/speech_dataset.py:get_audio_dataset \
+--custom_dataset.train_data_path /nfs/maziyang.mzy/data/librispeech/librispeech_train_960h_wav_speech_llm_train_data.json \
+--custom_dataset.val_data_path /nfs/maziyang.mzy/data/librispeech/librispeech_dev_other.jsonl \
+--batching_strategy custom \
+--num_epochs 100 \
+--batch_size_training 8 \
+--val_batch_size 8 \
+--num_workers_dataloader 4 \
+--lr 1e-5 \
+--output_dir $output_dir \
+--run_test_during_validation \
+--run_test_during_validation_file "/nfs/beinian.lzr/workspace/datasets/data/16k/opendata/librispeech/test_other/wav/1688-142285-0000.wav" \
+--run_test_during_validation_prompt "<|ASR|>" \
+--metric acc \
+--log_file "/root/SLAM-LLM/log/test.log" \
+# --ckpt_path "/nfs/maziyang.mzy/models/llama-2-hf-finetune/echat/7/model.pt" \
+# --peft_ckpt "/nfs/maziyang.mzy/models/llama-2-hf-finetune/echat/7" \
+# --use_peft --peft_method lora \
+fi
+
+# {"key": "1001-134707-0000_ASR", "prompt": "<ASR>", "source": "/cpfs01/shared/Group-speech/beinian.lzr/data/open_data/librispeech_audio/audio/se_librispeech_1001-134707-0000.wav", "target": "1 little recks the laborer. How near his work is holding him to God, The loving laborer through space and time, after all, not to create, only or found only.", "target_len": 157, "source_len": 1581, "text-type": "Transcribe", "audio_language": "en", "text_language": "en", "task-type": "<ASR>"}

src/llama_recipes/configs/datasets.py

@@ -32,9 +32,34 @@ class custom_dataset:
     file: str = "examples/custom_dataset.py"
     train_split: str = "train"
     test_split: str = "validation"
-    data_path: str = NotImplemented
-    train_data_path: str = NotImplemented
-    val_data_path: str = NotImplemented
-    max_words: int = NotImplemented
-    max_mel: int = NotImplemented
-    fix_length_audio: int = -1
+    data_path: str = None
+    max_words: int = None
+    train_data_path: str = None
+    val_data_path: str = None
+    max_words: int = None
+    max_mel: int = None
+    fix_length_audio: int = -1
+
+
+
+@dataclass
+class avsr_dataset:
+    dataset: str = "avsr_dataset"
+    file: str = "examples/avsr_dataset.py"
+    train_split: str = "train"
+    test_split: str = "val"
+    data_path: str = "/nfs/yangguanrou.ygr/" #"/home/oss/yangguanrou.ygr/"
+    h5file: str =  "/nfs/yangguanrou.ygr/LRS3/LRS3.h5"       # "/home/oss/yangguanrou.ygr/LRS3/LRS3.h5"
+    noiseFile : str = "/nfs/yangguanrou.ygr/AVSR/LRS3/Noise.h5" #"/home/oss/yangguanrou.ygr/AVSR/LRS3/Noise.h5"
+    noiseProb: float = 0.
+    noiseSNR: float = 5
+    stepSize: int = 16384
+    # charToIx={" ": 1, "'": 22, "1": 30, "0": 29, "3": 37, "2": 32, "5": 34, "4": 38, "7": 36, "6": 35, "9": 31, "8": 33, "A": 5, "C": 17,
+    #                      "B": 20, "E": 2, "D": 12, "G": 16, "F": 19, "I": 6, "H": 9, "K": 24, "J": 25, "M": 18, "L": 11, "O": 4, "N": 7, "Q": 27,
+    #                      "P": 21, "S": 8, "R": 10, "U": 13, "T": 3, "W": 15, "V": 23, "Y": 14, "X": 26, "Z": 28, "<EOS>": 39}
+    charToIx : str = "x"   #应该没用了  TypeError: Object of type NotImplementedType is not JSON serializable 但这个是上面的问题
+    modal: str = "AV"
+    pretrain_subset: str = "LRS3/pretrain.txt"
+    train_subset: str = "LRS3/train.txt"
+    valid_subset: str = "LRS3/val.txt"
+    test_subset: str = "LRS3/test.txt"

src/llama_recipes/configs/model.py

@@ -9,4 +9,33 @@ class model_config:
     encoder_ds_rate: int = 2
     encoder_path: str = None
     encoder_projector: str = "linear"
-    encoder_projector_ds_rate: int = 5
+    encoder_projector_ds_rate: int = 5
+
+    DMODEL: int = 512
+    FRONTEND_DMODEL: int = 1024   #这个是专门指moco的
+    TX_ATTENTION_HEADS: int = 8
+    TX_NUM_LAYERS: int = 6
+    PE_MAX_LENGTH: int = 500
+    AUDIO_FEATURE_SIZE: int = 1024
+    VIDEO_FEATURE_SIZE: int = 2048
+    TX_FEEDFORWARD_DIM: int= 2048
+    TX_DROPOUT: int = 0.1
+    CHAR_NUM_CLASSES: int = 40
+
+    WORD_NUM_CLASSES: int = 500
+    FRAME_LENGTH: int = 29
+    MOCO_FRONTEND_FILE: str = "/nfs/yangguanrou.ygr/AVSR/pretrain_model/moco_frontend.pt" #"/home/oss/yangguanrou.ygr/AVSR/pretrain_model/moco_frontend.pt"
+    WAV2VEC_FILE: str = "/nfs/yangguanrou.ygr/AVSR/pretrain_model/wav2vec_vox_new.pt" #"/home/oss/yangguanrou.ygr/AVSR/pretrain_model/wav2vec_vox_new.pt"
+    MAIN_REQ_INPUT_LENGTH: int = 80
+    modal: str = "AV"
+    TRAIN_LRS3_MODEL_FILE: str = "/nfs/yangguanrou.ygr/AVSR/train-step_0108-wer_0.058.ckpt"  # "/home/oss/yangguanrou.ygr/AVSR/train-step_0108-wer_0.058.ckpt"  #单一模态是这个
+    TRAINED_AO_FILE : str = "/nfs/yangguanrou.ygr/AVSR/check/train-step_0604-wer_0.054.ckpt"  #"/home/oss/yangguanrou.ygr/AVSR/check/train-step_0604-wer_0.054.ckpt"
+    TRAINED_VO_FILE: str = "/nfs/yangguanrou.ygr/AVSR/check/train-step_1191-wer_0.674.ckpt"  #"/home/oss/yangguanrou.ygr/AVSR/check/train-step_1191-wer_0.674.ckpt"
+
+
+
+
+
+
+
+

src/llama_recipes/configs/training.py

@@ -36,8 +36,10 @@ class train_config:
     dist_checkpoint_folder: str="fine-tuned" # will be used if using FSDP
     save_optimizer: bool=False # will be used if using FSDP
     use_fast_kernels: bool = False # Enable using SDPA from PyTroch Accelerated Transformers, make use Flash Attention and Xformer memory-efficient kernels
+    log_file: str="PATH/to/Log_File"
     run_test_during_validation: bool = False
     run_test_during_validation_file: str = "test.wav"
     run_test_during_validation_prompt: str = "<|ASR|>"
     freeze_llm: bool = False
     freeze_encoder: bool = False
+    log_interval: int = 5