Create Whisper/API.md

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+# API Documentation for `Lenylvt/Whisper-API`
+This documentation outlines how to use the Whisper API through Python and JavaScript.
+
+## API Endpoint
+
+The API can be accessed using the `gradio_client` Python library [docs](https://www.gradio.app/guides/getting-started-with-the-python-client) or the `@gradio/client` JavaScript package [docs](https://www.gradio.app/guides/getting-started-with-the-js-client).
+
+## Python Usage
+
+### Step 1: Installation
+
+To begin, ensure the `gradio_client` library is installed.
+
+```python
+pip install gradio_client
+```
+
+### Step 2: Making a Request
+
+Identify the API endpoint for the function you wish to utilize. Replace the placeholders in the following code snippet with your actual input data. If accessing a private Space, you may need to include your Hugging Face token.
+
+**API Name**: `/predict`
+
+```python
+from gradio_client import Client
+
+client = Client("Lenylvt/Whisper-API")
+result = client.predict(
+    "https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav",  # filepath in 'Upload Audio' Audio component
+    "base",  # Model size in 'Model Size' Dropdown component (e.g., 'base', 'small', 'medium', 'large', 'large-v2', 'large-v3')
+    api_name="/predict"
+)
+print(result)
+```
+
+**Return Type(s):**
+
+- A `str` representing the output in the 'output' Textbox component.
+
+## JavaScript Usage
+
+### Step 1: Installation
+
+For JavaScript usage, make sure the `@gradio/client` package is included in your project.
+
+```bash
+npm i -D @gradio/client
+```
+
+### Step 2: Making a Request
+
+Similar to Python, locate the API endpoint that fits your requirements. Replace the placeholder values with your own data. Include your Hugging Face token if you are accessing a private Space.
+
+**API Name**: `/predict`
+
+```javascript
+import { client } from "@gradio/client";
+const response_0 = await fetch("https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav");
+const exampleAudio = await response_0.blob();
+                        
+const app = await client("Lenylvt/Whisper-API");
+const result = await app.predict("/predict", [
+    exampleAudio,  // blob in 'Upload Audio' Audio component        
+    "base",        // string in 'Model Size' Dropdown component
+]);
+
+console.log(result.data);
+```
+
+**Return Type(s):**
+
+- A `string` representing the output in the 'output' Textbox component.