Merge pull request #176 from X-LANCE/yxdu

Yxdu

Author: ddlBoJack

examples/st_covost2/README.md

@@ -2,13 +2,49 @@
 
 
 ## Model Stracture
-<img src="image/framework.jpg" alt="示例图片" style="width:75%;">
+<img src="image/framework.jpg" alt="Photo" style="width:75%;">
 
 
 ## Multitask 
-<img src="image/prompt.png" alt="示例图片" style="width:50%;">
+<img src="image/prompt.png" alt="Photo" style="width:50%;">
 
 
+## Installation
+```
+conda create -n cotst python=3.10
+conda activate cotst
+
+git clone https://github.com/ddlBoJack/SLAM-LLM.git
+cd SLAM-LLM
+
+pip install -e .
+sudo apt install ffmpeg
+pip install -U openai-whisper
+pip install wandb
+pip install soundfile
+pip install evaluate
+pip install transformers
+pip install datasets
+pip install sacrebleu
+pip install jiwer
+pip install librosa
+pip install torch==2.4.0
+pip install torchaudio==2.4.0
+pip install torchvision==0.19.0
+```
+
+## Infer Demo
+It is recommended to run on a single GPU for the first execution. Later, remove CUDA_VISIBLE_DEVICES=0, and it will automatically utilize all GPUs.
+
+This demo will automatically download the model and dataset from Hugging Face, totaling approximately 100GB. Each card requires 128GB of RAM and 24GB of GPU memory.
+
+#supported translation languages are Chinese (zh), German (de), and Japanese (ja).
+
+
+```
+CUDA_VISIBLE_DEVICES=0 bash examples/st_covost2/scripts/infer_enzh.sh zh
+```
+
 
 ## Download Model 
 We only train the q-former projector in this recipe.
@@ -46,31 +82,25 @@ You can find the test jsonl in "test_st.jsonl"
 Here, we have designed a three-step training process, where each training session uses the checkpoint obtained from the previous training session.
 ```
 #In this step, we perform ASR pretraining to acquire speech recognition capabilities.
-bash asr_pretrain.sh
+bash examples/st_covost2/scripts/asr_pretrain.sh
 
-#In this phase, we conduct multimodal machine translation training to enhance the final performance.
-bash mmt.sh
 
-#monolingual SRT training and multitask training.
-bash srt.sh
-bash zsrt.sh
+#monolingual MMT,SRT training and multitask training. 
+#You can change the task type by modifying the value of **source** in the script.
+bash examples/st_covost2/scripts/all.sh
 ```
 
 
-## Infer Stage
-You can try our pre-trained model.
-
-```
-bash infer_enzh.sh
-```
-
 ##  Citation
 You can refer to the paper for more results. 
 ```
-@article{du2024cot,
-  title={CoT-ST: Enhancing LLM-based Speech Translation with Multimodal Chain-of-Thought},
-  author={Yexing Du, Ziyang Ma, Yifan Yang, Keqi Deng, Xie Chen, Bo Yang, Yang Xiang, Ming Liu, Bing Qin},
-  journal={arXiv preprint arXiv:2409.19510},
-  year={2024}
+@misc{du2024cotstenhancingllmbasedspeech,
+      title={CoT-ST: Enhancing LLM-based Speech Translation with Multimodal Chain-of-Thought}, 
+      author={Yexing Du and Ziyang Ma and Yifan Yang and Keqi Deng and Xie Chen and Bo Yang and Yang Xiang and Ming Liu and Bing Qin},
+      year={2024},
+      eprint={2409.19510},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2409.19510}, 
 }
 ```

examples/st_covost2/asr_config.py

@@ -24,8 +24,10 @@ class ModelConfig:
     encoder_type: str = field(default="finetune", metadata={
         "help": "whether model is only pretrained or finetuned, used for models such as hubert"
     })
-    ckpt_path: Optional[str] = None
     query_len: Optional[str] = None
+    qformer_layers: int = 8
+
+
 
 
 @dataclass
@@ -93,7 +95,7 @@ class DataConfig:
     train_data_path: Optional[str] = None
     val_data_path: Optional[str] = None
     train_split: str = "train"
-    test_split:str = "validation"
+    test_split:str = "test"
     prompt: Optional[str] = None
     data_path: Optional[str] = None
     max_words: Optional[int] = None
@@ -127,7 +129,7 @@ class FSDPConfig:
 class LogConfig:
     use_wandb: bool = False
     wandb_dir: str = "test_wandb"
-    wandb_entity_name: str = "SLAM"
+    wandb_entity_name: str = "sdinger"
     wandb_project_name: str = "project_name"
     wandb_exp_name: str = "exp_name"
     log_file: str = "./test.log"

examples/st_covost2/change_dir.py

@@ -0,0 +1,34 @@
+import os
+import json
+
+# 定义输入文件夹路径
+folder_path = ""
+
+# 定义关键词替换规则
+old_keyword = ""  # 需要替换的关键词
+new_keyword = "/code_dir"  # 替换成的关键词
+
+# 遍历文件夹及其子文件夹
+for root, _, files in os.walk(folder_path):
+    for file_name in files:
+        if file_name.endswith(".jsonl"):
+            file_path = os.path.join(root, file_name)
+
+            # 读取和处理 JSONL 文件
+            with open(file_path, "r", encoding="utf-8") as file:
+                lines = file.readlines()
+
+            updated_lines = []
+            for line in lines:
+                data = json.loads(line)
+                if "audio" in data and old_keyword in data["audio"]:
+                    data["audio"] = data["audio"].replace(old_keyword, new_keyword)
+                updated_lines.append(json.dumps(data, ensure_ascii=False))
+
+            # 写入修改后的内容到原文件
+            with open(file_path, "w", encoding="utf-8") as file:
+                file.write("\n".join(updated_lines))
+
+            print(f"关键词替换完成，修改内容已写回文件: {file_path}")
+
+print("所有文件处理完成。")

examples/st_covost2/conf/prompt.yaml

@@ -1,4 +1,4 @@
 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: "<en>"
+    # prompt: "<en>"