# Async Text to Speech Pro V1.0

> Generate professional-quality voiceovers in seconds with Async TTS Pro model text-based control over pauses, emphasis, and timing. Voice ids can be found at https://async.com/developer/voice-library


## Overview

- **Endpoint**: `https://fal.run/async/tts-pro/v1.0`
- **Model ID**: `async/tts-pro/v1.0`
- **Category**: text-to-speech
- **Kind**: inference
**Tags**: text-to-speech, voice-clone, lipsync



## Pricing

You are going to be billed by the length of the input text, at a flat rate of **$0.01** per **1,000** characters.

For more details, see [fal.ai pricing](https://fal.ai/pricing).

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:


- **`transcript`** (`string`, _required_):
  Text to convert to speech.
  - Examples: "Hello, welcome to Async! This is a text to speech demo."

- **`voice`** (`Voice`, _required_):
  Voice to use for synthesis.

- **`output_format`** (`OutputFormat`, _optional_):
  Audio output configuration.

- **`language`** (`Enum`, _optional_):
  Force synthesis in a specific language (ISO 639-1).
  - Options: `"en"`, `"fr"`, `"es"`, `"de"`, `"it"`, `"pt"`



**Required Parameters Example**:

```json
{
  "transcript": "Hello, welcome to Async! This is a text to speech demo.",
  "voice": {
    "id": "cca0e076-94b9-4c6d-86b7-546168f11174"
  }
}
```


### Output Schema

The API returns the following output format:

- **`audio`** (`Audio`, _required_)

- **`content_type`** (`string`, _required_):
  MIME type of the audio.

- **`size_bytes`** (`integer`, _required_):
  Size of the audio in bytes.



**Example Response**:

```json
{
  "audio": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  },
  "content_type": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/async/tts-pro/v1.0 \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "transcript": "Hello, welcome to Async! This is a text to speech demo.",
     "voice": {
       "id": "cca0e076-94b9-4c6d-86b7-546168f11174"
     }
   }'
```

### Python

Ensure you have the Python client installed:

```bash
pip install fal-client
```

Then use the API client to make requests:

```python
import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "async/tts-pro/v1.0",
    arguments={
        "transcript": "Hello, welcome to Async! This is a text to speech demo.",
        "voice": {
            "id": "cca0e076-94b9-4c6d-86b7-546168f11174"
        }
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)
```

### JavaScript

Ensure you have the JavaScript client installed:

```bash
npm install --save @fal-ai/client
```

Then use the API client to make requests:

```javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("async/tts-pro/v1.0", {
  input: {
    transcript: "Hello, welcome to Async! This is a text to speech demo.",
    voice: {
      id: "cca0e076-94b9-4c6d-86b7-546168f11174"
    }
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);
```


## Additional Resources

### Documentation

- [Model Playground](https://fal.ai/models/async/tts-pro/v1.0)
- [API Documentation](https://fal.ai/models/async/tts-pro/v1.0/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=async/tts-pro/v1.0)

### fal.ai Platform

- [Platform Documentation](https://docs.fal.ai)
- [Python Client](https://docs.fal.ai/clients/python)
- [JavaScript Client](https://docs.fal.ai/clients/javascript)