Add recipe "sec_emotioncaps"

examples/sec_emotioncaps/README.md

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+# Speech Emotion Caption
+
+## Model Architecture
+
+This recipe generates high-quality, human-like speech emotion descriptions. The model is based on the **q-former projector** and the **vicuna-7b-v1.5 LLM**. The model is trained on **an unpublished datasets** dataset, which is a large-scale dataset for speech emotion captioning.
+
+![](docs/model.png)
+
+## Performance and checkpoints
+
+We only train the q-former projector in this recipe.
+
+Encoder | Projector | LLM | Similarity Score 
+---|---|---|---
+[emotion2vec_base](https://huggingface.co/emotion2vec/emotion2vec_base) | [Q-Former](to_do)| [vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) | 71.10
+
+> **Note**: The baseline model [SECap](https://github.com/thuhcsi/SECap) was tested in our environment and achieved a similarity score of 71.52. Our model's score is slightly lower.
+
+## Data preparation
+You need to prepare the data jsonl in this format.
+
+```
+{"key": "key_name", "source": "path_to_wav_file", "target": "corresponding_caption"}
+...
+```
+
+
+## Decode with checkpoints
+
+```
+bash decode_emotion2vec_qformer_vicuna_7b.sh
+```
+
+Modify the path including `speech_encoder_path`, `llm_path`, `output_dir`, `ckpt_path`, `val_data_path` and `decode_log` in the script when you run the shell script. 
+
+## Train a new model
+
+If you do have sufficient relevant data, you can train the model yourself.
+
+```
+bash finetune_emotion2vec_qformer_vicuna_7b.sh
+```
+
+##  Citation
+
+You can refer to the paper for more results. 
+
+```
+@article{ma2024embarrassingly,
+  title={An Embarrassingly Simple Approach for LLM with Strong ASR Capacity},
+  author={Ma, Ziyang and Yang, Guanrou and Yang, Yifan and Gao, Zhifu and Wang, Jiaming and Du, Zhihao and Yu, Fan and Chen, Qian and Zheng, Siqi and Zhang, Shiliang and others},
+  journal={arXiv preprint arXiv:2402.08846},
+  year={2024}
+}
+```

examples/sec_emotioncaps/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/sec_emotioncaps/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: "请用中文用一句话描述上面给出的音频中说话人的情感。"

examples/sec_emotioncaps/finetune_sec.py

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+from slam_llm.pipeline.finetune import main as train
+
+import hydra
+import logging
+from typing import Optional
+from dataclasses import dataclass, field
+from omegaconf import DictConfig, ListConfig, OmegaConf
+from sec_config import ModelConfig, TrainConfig, DataConfig, LogConfig, FSDPConfig
+
+@dataclass
+class RunConfig:
+    dataset_config: DataConfig = field(default_factory=DataConfig)
+    model_config: ModelConfig = field(default_factory=ModelConfig)
+    train_config: TrainConfig = field(default_factory=TrainConfig)
+    log_config: LogConfig = field(default_factory=LogConfig)
+    fsdp_config: FSDPConfig = field(default_factory=FSDPConfig)
+    debug: bool = field(default=False, metadata={"help": "Use pdb when true"})
+    metric: str = field(default="acc", metadata={"help": "The metric for evaluation"})
+    ckpt_path: Optional[str] = field(
+        default=None, metadata={"help": "The path to projector checkpoint"}
+    )
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(cfg: DictConfig):
+    run_config = RunConfig()
+    cfg = OmegaConf.merge(run_config, cfg)
+    def to_plain_list(cfg_item):
+        if isinstance(cfg_item, ListConfig):
+            return OmegaConf.to_container(cfg_item, resolve=True)
+        elif isinstance(cfg_item, DictConfig):
+            return {k: to_plain_list(v) for k, v in cfg_item.items()}
+        else:
+            return cfg_item
+
+    # kwargs = to_plain_list(cfg)
+    kwargs = cfg
+    log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
+
+    logging.basicConfig(level=log_level)
+
+    if kwargs.get("debug", False):
+        import pdb;
+        pdb.set_trace()
+
+    train(kwargs)
+
+
+if __name__ == "__main__":
+    main_hydra()

examples/sec_emotioncaps/inference_sec_batch.py

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+from slam_llm.pipeline.inference_batch import main as inference
+
+import hydra
+import logging
+from dataclasses import dataclass, field
+from omegaconf import DictConfig, ListConfig, OmegaConf
+from typing import Optional
+from sec_config import ModelConfig, TrainConfig, DataConfig, LogConfig, FSDPConfig
+
+
+@dataclass
+class RunConfig:
+    dataset_config: DataConfig = field(default_factory=DataConfig)
+    model_config: ModelConfig = field(default_factory=ModelConfig)
+    train_config: TrainConfig = field(default_factory=TrainConfig)
+    log_config: LogConfig = field(default_factory=LogConfig)
+    fsdp_config: FSDPConfig = field(default_factory=FSDPConfig)
+    debug: bool = field(default=False, metadata={"help": "Use pdb when true"})
+    metric: str = field(default="acc", metadata={"help": "The metric for evaluation"})
+    decode_log: str = field(
+        default="output/decode_log",
+        metadata={"help": "The prefix for the decode output"},
+    )
+    ckpt_path: str = field(
+        default="output/model.pt", metadata={"help": "The path to projector checkpoint"}
+    )
+    peft_ckpt: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The path to peft checkpoint, should be a directory including adapter_config.json"
+        },
+    )
+
+
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(cfg: DictConfig):
+    run_config = RunConfig()
+    cfg = OmegaConf.merge(run_config, cfg)
+    # kwargs = to_plain_list(cfg)
+    log_level = getattr(logging, cfg.get("log_level", "INFO").upper())
+
+    logging.basicConfig(level=log_level)
+
+    if cfg.get("debug", False):
+        import pdb
+
+        pdb.set_trace()
+
+    inference(cfg)
+
+
+if __name__ == "__main__":
+    main_hydra()

examples/sec_emotioncaps/model/slam_model_sec.py

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+import torch
+import os
+import logging
+from slam_llm.models.slam_model import (
+    slam_model,
+    setup_tokenizer,
+    setup_encoder,
+    setup_encoder_projector,
+    setup_llm,
+)
+from slam_llm.utils.train_utils import print_model_size
+
+logger = logging.getLogger(__name__)
+
+def model_factory(train_config, model_config, **kwargs):
+    # return necessary components for training
+    tokenizer = setup_tokenizer(train_config, model_config, **kwargs)
+
+    encoder = setup_encoder(train_config, model_config, **kwargs)
+
+    # llm
+    llm = setup_llm(train_config, model_config, **kwargs)
+
+    # projector
+    encoder_projector = setup_encoder_projector(
+        train_config, model_config, **kwargs
+    )
+    model = slam_model_asr(
+        encoder,
+        llm,
+        encoder_projector,
+        tokenizer,
+        train_config,
+        model_config,
+        **kwargs,
+    )
+
+    ckpt_path = kwargs.get(
+        "ckpt_path", None
+    )  # FIX(MZY): load model ckpt(mainly projector, related to model_checkpointing/checkpoint_handler.py: save_model_checkpoint_peft)
+    if ckpt_path is not None:
+        logger.info("loading other parts from: {}".format(ckpt_path))
+        ckpt_dict = torch.load(ckpt_path, map_location="cpu")
+        model.load_state_dict(ckpt_dict, strict=False)
+
+    print_model_size(
+        model,
+        train_config,
+        (
+            int(os.environ["RANK"])
+            if train_config.enable_fsdp or train_config.enable_ddp
+            else 0
+        ),
+    )
+    return model, tokenizer
+
+
+class slam_model_asr(slam_model):
+    def __init__(
+        self,
+        encoder,
+        llm,
+        encoder_projector,
+        tokenizer,
+        train_config,
+        model_config,
+        **kwargs,
+    ):
+        super().__init__(
+            encoder,
+            llm,
+            encoder_projector,
+            tokenizer,
+            train_config,
+            model_config,
+            **kwargs,
+        )
+
+
+    @torch.no_grad()
+    def inference(
+        self,
+        wav_path=None,
+        prompt=None,
+        generation_config=None,
+        logits_processor=None,
+        stopping_criteria=None,
+        prefix_allowed_tokens_fn=None,
+        synced_gpus=None,
+        assistant_model=None,
+        streamer=None,
+        negative_prompt_ids=None,
+        negative_prompt_attention_mask=None,
+        **kwargs,
+    ):
+        # inference for asr model
+
+        device = kwargs.get("device", "cuda")
+        if os.path.exists(wav_path):  # Audio-Text QA
+            import whisper
+
+            audio_raw = whisper.load_audio(wav_path)
+            audio_raw = whisper.pad_or_trim(audio_raw)
+
+            mel_size = getattr(
+                self.dataset_config, "mel_size", 80
+            )  # 80 for large v1 and v2, 128 for large v3
+            audio_mel = (
+                whisper.log_mel_spectrogram(audio_raw, n_mels=mel_size)
+                .permute(1, 0)[None, :, :]
+                .to(device)
+            )
+
+            encoder_outs = self.encoder.extract_variable_length_features(
+                audio_mel.permute(0, 2, 1)
+            )
+
+            if self.model_config.encoder_projector == "q-former":
+                audio_mel_post_mask = torch.ones(
+                    encoder_outs.size()[:-1], dtype=torch.long
+                ).to(encoder_outs.device)
+                encoder_outs = self.encoder_projector(encoder_outs, audio_mel_post_mask)
+            if self.model_config.encoder_projector == "linear":
+                encoder_outs = self.encoder_projector(encoder_outs)
+        else:  # Text QA
+            encoder_outs = torch.empty(
+                1, 0, self.llm.model.embed_tokens.embedding_dim
+            ).to(device)
+
+        prompt = "USER: {}\n ASSISTANT:".format(prompt)
+        prompt_ids = self.tokenizer.encode(prompt)
+        prompt_length = len(prompt_ids)
+        prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(device)
+
+        if hasattr(self.llm.model, "embed_tokens"):
+            inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
+        elif hasattr(self.llm.model.model, "embed_tokens"):
+            inputs_embeds = self.llm.model.model.embed_tokens(prompt_ids)
+        else:
+            inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)
+
+        inputs_embeds = torch.cat(
+            (encoder_outs, inputs_embeds[None, :, :]), dim=1
+        )  # [audio,prompt]
+
+        attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(
+            inputs_embeds.device
+        )
+
+        # generate
+        model_outputs = self.generate(
+            inputs_embeds=inputs_embeds, attention_mask=attention_mask, **kwargs
+        )
+
+        return model_outputs