add speech transcription doc (#107)

Author: gty1829

docs/.vuepress/notes/en/guide.ts

@@ -30,7 +30,8 @@ export const Guide: ThemeNote = defineNoteConfig({
                 'prompted_vqa',
                 'mathquestion_extract',
                 'knowledge_cleaning',
-                'quick_general_text_evaluation'
+                'quick_general_text_evaluation',
+                'speech_transcription',
             ],
         },
         // {

docs/.vuepress/notes/zh/guide.ts

@@ -30,7 +30,8 @@ export const Guide: ThemeNote = defineNoteConfig({
                 "prompted_vqa",
                 "mathquestion_extract",
                 'knowledge_cleaning',
-                'quick_general_text_evaluation'
+                'quick_general_text_evaluation',
+                'speech_transcription',
             ],
         },
         //         {

docs/en/notes/guide/quickstart/speech_transcription.md

@@ -0,0 +1,71 @@
+---
+title: Case 9. Speech transcription
+createTime: 2025/08/22 16:38:49
+permalink: /en/guide/5pdipkiv/
+icon: fad:headphones
+---
+
+
+This example demonstrates how to use the SpeechTranscriptor operator for speech-to-text transcription.
+
+## Speech Transcription
+### Step 1: Install the Dataflow Environment
+```bash
+pip install open-dataflow[vllm]
+```
+
+### Step 2: Create a New Dataflow Working Directory
+```shell
+mkdir run_dataflow
+cd run_dataflow
+```
+
+### Step 3: Initialize Dataflow
+```shell
+dataflow init
+```
+After this step, you should see:
+```shell
+run_dataflow/gpu_pipelines/speechtranscription_pipeline.py
+```
+
+### Step 4: Prepare the data to be translated.
+```python
+self.storage = FileStorage(
+    first_entry_file_name="../example_data/SpeechTranscription/pipeline_speechtranscription.jsonl", # your data path
+    cache_path="./cache",
+    file_name_prefix="dataflow_cache_step",
+    cache_type="jsonl",
+)
+```
+
+Data format is as follows
+```jsonl
+{"raw_content": "../example_data/SpeechTranscription/audio/test.wav"}
+{"raw_content": "https://raw.githubusercontent.com/FireRedTeam/FireRedASR/main/examples/wav/IT0011W0001.wav"}
+```
+
+### Step 5: Launch serving
+```python
+self.llm_serving = LocalModelLALMServing_vllm(
+    hf_model_name_or_path='Qwen/Qwen2-Audio-7B-Instruct',   # your model path
+    vllm_tensor_parallel_size=4,
+    vllm_max_tokens=8192,
+)
+```
+
+### Step 6: Speech transcription operator
+```python
+self.speech_transcriptor = SpeechTranscriptor(
+    llm_serving = self.llm_serving,                  
+    system_prompt="You are a professional translator; your task is to transcribe speech into text and then translate it into English."  # model system prompt
+)
+```
+
+### Step 7: Run the operator
+```python
+self.speech_transcriptor.run(
+    storage=self.storage.step(),
+    input_key="raw_content"
+)
+```

docs/zh/notes/guide/quickstart/speech_transcription.md

@@ -0,0 +1,71 @@
+---
+title: 案例9. 语音转文字
+createTime: 2025/08/22 16:37:30
+permalink: /zh/guide/du2akut8/
+icon: fad:headphones
+---
+
+
+本示例展示使用SpeechTranscriptor算子进行语音转文字。
+
+## 语音转文字
+### 第一步:安装DataFlow环境
+```bash
+pip install open-dataflow[vllm]
+```
+
+### 第二步:创建新的dataflow工作文件夹
+```shell
+mkdir run_dataflow
+cd run_dataflow
+```
+
+### 第三步:初始化Dataflow
+```shell
+dataflow init
+```
+这时你会看见
+```shell
+run_dataflow/gpu_pipelines/speechtranscription_pipeline.py
+```
+
+### 第四步:准备需要翻译的数据
+```python
+self.storage = FileStorage(
+    first_entry_file_name="../example_data/SpeechTranscription/pipeline_speechtranscription.jsonl", # 数据路径写在这里
+    cache_path="./cache",
+    file_name_prefix="dataflow_cache_step",
+    cache_type="jsonl",
+)
+```
+
+数据格式如下
+```jsonl
+{"raw_content": "../example_data/SpeechTranscription/audio/test.wav"}
+{"raw_content": "https://raw.githubusercontent.com/FireRedTeam/FireRedASR/main/examples/wav/IT0011W0001.wav"}
+```
+
+### 第五步:启动serving
+```python
+self.llm_serving = LocalModelLALMServing_vllm(
+    hf_model_name_or_path='Qwen/Qwen2-Audio-7B-Instruct',   # 填入模型路径
+    vllm_tensor_parallel_size=4,
+    vllm_max_tokens=8192,
+)
+```
+
+### 第六步:语音转文字算子
+```python
+self.speech_transcriptor = SpeechTranscriptor(
+    llm_serving = self.llm_serving,  
+    system_prompt="你是一个专业的翻译员，你需要将语音转录为文本。"  # 模型系统提示词
+)
+```
+
+### 第七步:执行算子
+```python
+self.speech_transcriptor.run(
+    storage=self.storage.step(),
+    input_key="raw_content"
+)
+```