Inference Logoinference.sh
apps/infsh/chatterbox

chatterbox

Converts written text into spoken audio.

run in browser run via API
run with your agent
# install belt
$curl -fsSL https://cli.inference.sh | sh
# view schema & details
$belt app get infsh/chatterbox
# run
$belt app run infsh/chatterbox

api reference

about

converts written text into spoken audio.

1. calling the api

install the client

the client provides a convenient way to interact with the api.

bash
1pip install inferencesh

setup your api key

set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.

bash
1export INFERENCE_API_KEY="inf_your_key"

run and get result

submit a request and wait for the final result. best for batch processing or when you don't need progress updates.

python
1from inferencesh import inference23client = inference()456result = client.run({7        "app": "infsh/chatterbox",8        "input": {}9    })1011print(result["output"])

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.

python
1from inferencesh import inference23client = inference()456# stream=True yields updates as they arrive7for update in client.run({8        "app": "infsh/chatterbox",9        "input": {}10    }, stream=True):11    if update.get("progress"):12        print(f"progress: {update['progress']}%")13    if update.get("output"):14        print(f"output: {update['output']}")

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.

python
1# local file paths are automatically uploaded2result = client.run({3    "app": "infsh/chatterbox",4    "input": {5        "image": "/path/to/local/image.png",  # detected & uploaded6        "audio": "https://example.com/audio.mp3",  # url passed through7    }8})

manual upload

you can also upload files manually and use the returned url.

python
1# upload and get a hosted URL2file = client.files.upload("/path/to/file.png")3print(file.uri)  # https://cloud.inference.sh/...

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

python
1result = client.run({2    "app": "infsh/chatterbox",3    "input": {},4    "webhook": "https://your-server.com/webhook"5}, wait=False)

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "id": "task_abc123",3  "status": 9,4  "output": { ... },5  "error": "",6  "session_id": null,7  "created_at": "2024-01-15T10:30:00Z",8  "updated_at": "2024-01-15T10:30:05Z"9}
idstring— task id
statusnumber— terminal status (9=completed, 10=failed, 11=cancelled)
outputobject— task output (when completed)
errorstring— error message (when failed)
session_idstring— session id (if using sessions)
created_atstring— iso timestamp
updated_atstring— iso timestamp

5. schema

input

modelstring

model to use: 'original' (works without voice ref), 'turbo' (fastest, requires voice ref, supports [laugh] tags), or 'multilingual' (23+ languages)

default: "original"
options:"turbo""multilingual""original"
modestring

mode of operation: 'tts' (text-to-speech) or 'vc' (voice conversion, original model only)

default: "tts"
options:"tts""vc"
textstring*

text to convert to speech. turbo model supports tags like [laugh], [cough], [chuckle]

voice_referencestring(file)

reference audio file for voice cloning. required for turbo model.

languagestring

language code for multilingual model (ar, da, de, el, en, es, fi, fr, he, hi, it, ja, ko, ms, nl, no, pl, pt, ru, sv, sw, tr, zh)

default: "en"
source_audiostring(file)

source audio for voice conversion (vc mode with original model only)

target_voice_stylestring(file)

target voice style for voice conversion (vc mode with original model only)

exaggerationnumber

expressiveness control (original/multilingual). higher (0.7+) = dramatic, lower (0.3-) = neutral

default: 0.5
cfg_weightnumber

voice matching strength (original/multilingual). lower (0.3) = natural variation, higher (0.7+) = closer match

default: 0.5

output

audio_outputstring(file)*

generated audio file

ready to run chatterbox?

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