Begin by understanding the TVmaze API documentation. The TVmaze API provides endpoints that allow access to TV show schedules and other related data. Review the documentation to identify the specific endpoints you will need (e.g., `GET /schedule`).

Choose a programming language you are comfortable with (e.g., Python, Node.js). Install necessary libraries to make HTTP requests, such as `requests` for Python or `axios` for Node.js. Set up a new project directory and initialize your project.

Write a script to fetch data from the TVmaze schedule endpoint. Use HTTP GET requests to retrieve the data. Ensure that you handle pagination if the dataset is large. For example, in Python:
```python
import requests

response = requests.get('https://api.tvmaze.com/schedule')
schedule_data = response.json()
```

Transform the data into a format that is compatible with Typesense. Typesense requires documents to be in JSON format and structured according to the schema you define. Create a schema that reflects the fields you need from the TVmaze data, such as show name, air date, and time.
```python
transformed_data = [
{
'id': str(item['id']),
'name': item['show']['name'],
'airdate': item['airdate'],
'airtime': item['airtime'],
'network': item['show']['network']['name']
}
for item in schedule_data
]
```

Install and run a Typesense server locally or on a cloud instance. Follow the Typesense documentation for installation steps. Ensure the server is running and accessible. You will need the server URL and API key for authentication.

Using the Typesense client library for your chosen language, create a collection where you will store the TVmaze schedule data. Define the schema for this collection to match the transformed data format.
```python
from typesense import Client

client = Client({
'nodes': [{
'host': 'localhost',
'port': '8108',
'protocol': 'http'
}],
'api_key': 'YOUR_API_KEY',
'connection_timeout_seconds': 2
})

schema = {
'name': 'tv_schedule',
'fields': [
{'name': 'name', 'type': 'string'},
{'name': 'airdate', 'type': 'string'},
{'name': 'airtime', 'type': 'string'},
{'name': 'network', 'type': 'string'}
],
'default_sorting_field': 'airdate'
}

client.collections.create(schema)
```

Load the transformed TVmaze data into the Typesense collection. Use the `upsert` method to ensure that data is added or updated without duplication.
```python
client.collections['tv_schedule'].documents.import_(transformed_data)
```

By following these steps, you can effectively move data from TVmaze's schedule API to a Typesense instance without relying on third-party connectors or integrations. Ensure you handle any API rate limits and errors during data fetching and indexing processes.