Merge pull request #173 from X-LANCE/ygr_pr2

for ctc-assisted llm-basd CASR codes pr

examples/contextual_asr/README.md

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+# CTC-Assisted LLM-Based Contextual ASR
+
+## Guides
+
+[CTC-Assisted LLM-Based Contextual ASR](https://arxiv.org/abs/2411.06437) is an LLM-based contextual ASR model that first uses CTC decoding results to filter potential relevant hotwords from pre-defined hotwords list and then incorporate them into LLM prompt input to improve recognition of hotwords.
+
+## Model Architecture
+
+We use WavLM-Large model pre-trained on 94, 000 hours of data, and fine-tuned on 960h hours of Librispeech data with CTC loss, as our speech encoder. We use the public Vicuna 7B as our large language model decoder, and a simple-structured linear projector, consisting of a 1-D convolution layer and two linear layers as our adapter. Refer to our [paper](https://arxiv.org/pdf/2411.06437) for more details.
+
+![](docs/model.png)
+
+## Checkpoints
+We only train the linear projector in this recipe.
+Encoder | Projector | LLM 
+|---|---|---|
+[CTC Fine-tuned WavLM-Large](https://drive.google.com/file/d/12ZmSSbDvx73W0eK1wpUgajapCLhqh5DI/view?usp=drive_link)(~315.45M) | [Linear](https://drive.google.com/file/d/1Zlbsnz1YUWtYtt-yNyoPK5OhR30kwLfS/view?usp=drive_link)(~15.74M) | [vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5)(~6.7B)  
+
+## Performance
+![](docs/performance.png)
+
+
+## Data preparation
+The artificial biasing list constructed in [Contextualized streaming end-to-end speech recognition with trie-based deep biasing and shallow fusion](https://arxiv.org/pdf/2104.02194) is utilized for contextual ASR testing. Refer to official [Repo](https://github.com/facebookresearch/fbai-speech/tree/main/is21_deep_bias).  
+They categorize the 5,000 most frequent words in the Librispeech training corpus as common
+words, with the remainder classified as rare words. The biasing list generated for the test set consists of two segments: rare words in the transcriptions, and distractors sampled from the 209.2K rare words vocabulary. Biasing lists of varying lengths are generated by incorporating N = {100, 500, 1000, 2000} distractors into the lists.
+
+
+
+## Decoding with checkpoints
+LLM-based ASR Inference script.
+```
+bash decode_wavlm_libri960_ft_char.sh
+```
+LLM-based Contextual ASR Inference script, with different biaisng list sizes.
+```
+bash decode_wavlm_libri960_ft_char_hotwords.sh
+```
+
+
+## Training the model
+LLM-based ASR Training script: using CTC fine-tuned Wavlm as encoder and “Transcribe speech to text.” as prompt.
+```
+bash finetune_wavlm_libri960_ft_char.sh
+```
+LLM-based Contextual ASR Training script: using CTC fine-tuned Wavlm as encoder and "Transcribe speech to text. Some hotwords might help. The hotwords are {}.” as prompt.
+```
+bash finetune_wavlm_libri960_ft_char_hotwords.sh
+```
+
+
+##  Citation
+You can refer to the paper for more results. 
+```
+@article{yang2024ctc,
+  title={CTC-Assisted LLM-Based Contextual ASR},
+  author={Yang, Guanrou and Ma, Ziyang and Gao, Zhifu and Zhang, Shiliang and Chen, Xie},
+  journal={Proc. SLT},
+  year={2024}
+}
+```

examples/contextual_asr/conf/ds_config.json

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+{
+    "train_micro_batch_size_per_gpu": 4,
+    "gradient_accumulation_steps": 1,
+    "optimizer": {
+        "type": "Adam",
+        "params": {
+            "lr": 1e-4
+        }
+    },
+    "fp16": {
+        "enabled": true
+    },
+    "zero_optimization": {
+        "stage": 3,
+        "offload_optimizer": {
+            "device": "cpu"
+        }
+    }
+}

examples/contextual_asr/conf/prompt.yaml

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+dataset_config:
+    # we put prompt here, because the hydra override in shell script only support a small subset of chars
+    # prompt: "Transcribe speech to text. Output the transcription directly without redundant content. Ensure that the output is not duplicated. "
+    prompt: "Transcribe speech to text. "

examples/contextual_asr/contextual_asr_config.py

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+from dataclasses import dataclass, field
+from typing import Optional, List
+@dataclass
+class ModelConfig:
+    file: str = "examples/contextual_asr/model/slam_model_contextual_asr.py:model_factory"
+    llm_name: str = "vicuna-13b-v1.5"
+    llm_path: str = "PATH/to/LLAMA/7B"
+    llm_type: str = "decoder_only"
+    llm_dim: int = 4096
+    encoder_name: Optional[str] = None
+    encoder_ds_rate: int = 2
+    encoder_path: Optional[str] = None
+    encoder_dim: int = 1280
+    encoder_projector: str = "linear"
+    encoder_projector_ds_rate: int = 5
+    modal: str = "audio"
+    normalize: Optional[bool] = field(default=False, metadata={
+        "help": "whether input is normalized, used for models such as wavlm"
+    })
+    encoder_type: str = field(default="finetune", metadata={
+        "help": "whether model is only pretrained or finetuned, used for models such as hubert"
+    })
+
+@dataclass
+class PeftConfig:
+    peft_method: str = "lora" # None , llama_adapter, prefix
+    r: int = 8
+    lora_alpha: int = 32
+    # target_modules: List = field(default_factory=lambda: [ "q_proj", "v_proj" ])
+    target_modules: List = field(default_factory=lambda: [ "q_proj", "v_proj","k_proj","o_proj" ])
+    bias: str = "none"
+    task_type: str = "CAUSAL_LM"
+    lora_dropout: float = 0.05
+    inference_mode: bool = False
+
+@dataclass
+class TrainConfig:
+    model_name:str = "PATH/to/LLAMA/7B"
+    enable_ddp:bool = False
+    enable_deepspeed:bool = False
+    enable_fsdp:bool = False
+    low_cpu_fsdp:bool = False
+    run_validation:bool = True
+    batch_size_training:int = 4
+    batching_strategy:str = field(default="packing", metadata={
+        "help":"alternative: padding"
+    })
+    context_length:int = 4096
+    gradient_accumulation_steps:int = 1
+    num_epochs:int = 3
+    num_workers_dataloader:int = 1
+    warmup_steps:int = 1000
+    total_steps:int = 100000
+    validation_interval:int = 1000
+    lr:float = 1e-4
+    weight_decay:float = 0.0
+    gamma:float = 0.85
+    seed:int = 42
+    use_fp16:bool = False
+    mixed_precision:bool = True
+    val_batch_size:int = 1
+    use_peft:bool = False
+    peft_config:PeftConfig = field(default_factory=PeftConfig)
+    output_dir:str = "PATH/to/save/PEFT/model"
+    freeze_layers:bool = False
+    num_freeze_layers:int = 1
+    quantization:bool = False
+    one_gpu:bool = False
+    save_model:bool = True
+    dist_checkpoint_root_folder:str = "PATH/to/save/FSDP/model" # will be used if using FSDP
+    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
+    run_test_during_validation:bool = False
+    run_test_during_validation_file:str = "test.wav"
+    run_test_during_validation_prompt:str = "<|ASR|>"
+    freeze_llm:bool = field(default=False, metadata={
+        "help": "whether to freeze llm when finetuning, should be true when use peft finetuning"
+    })
+    freeze_encoder:bool = False
+
+@dataclass
+class DataConfig:
+    dataset: str = "speech_dataset"
+    file: str = "src/slam_llm/datasets/speech_dataset.py:get_speech_dataset"
+    train_data_path: Optional[str] = None
+    val_data_path: Optional[str] = None
+    train_split: str = "train"
+    test_split:str = "validation"
+    prompt: Optional[str] = None
+    data_path: Optional[str] = None
+    max_words: Optional[int] = None
+    max_mel: Optional[float] = None
+    fix_length_audio: int = -1
+    inference_mode:bool = False
+    input_type: str = field(default="raw", metadata={
+                                "help":"Use raw when input is wav, mel when for whisper"
+                            })
+    mel_size: int = field(default=80, metadata={
+        "help": "80 for whisper large v1 and v2, 128 for v3"
+    })
+    normalize: Optional[bool] = field(default=False, metadata={
+        "help": "whether input is normalized, used for models such as wavlm"
+    })
+    infer_type: str = "bias"
+    infer_file: str = "/nfs/yangguanrou.ygr/data/fbai-speech/is21_deep_bias/my_ref/test-clean.biasing_100.tsv"
+    ctc_file: str = "/nfs/yangguanrou.ygr/data/librispeech_my_infer/wavlm_large_libri_test_other_char.txt"     
+    filter_type: str = "char"
+    phn_to_name_dict: str = "/nfs/yangguanrou.ygr/data/librispeech_my_infer/wavlm_ft_libri960_${ref_split}_phn.json"
+    common_words_5k_dir: str="/nfs/yangguanrou.ygr/data/fbai-speech/is21_deep_bias/words/common_words_5k.txt"
+    probability_threshold: float = 0.9
+    word_num: int = 15
+    filter_infer_sentence: bool = False
+    filter_infer_sentence_few: bool = False
+    first: int = 1
+
+@dataclass
+class FSDPConfig:
+    mixed_precision: bool = True
+    use_fp16: bool = False
+    # sharding_strategy = "FULL_SHARD" #ShardingStrategy = ShardingStrategy.FULL_SHARD
+    sharding_strategy: str = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
+    checkpoint_type: str = "SHARDED_STATE_DICT"  # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size.
+    fsdp_activation_checkpointing: bool = True
+    fsdp_cpu_offload: bool = False
+    pure_bf16: bool = False
+    optimizer: str = "AdamW"
+
+@dataclass
+class LogConfig:
+    use_wandb: bool = False
+    wandb_dir: str = "/root/test_wandb"
+    wandb_entity_name: str = "project_name"
+    wandb_project_name: str = "project_name"
+    wandb_exp_name: str = "exp_name"
+    log_file: str = "/root/test.log"
+    log_interval: int = 5