Last Updated: July 25, 2025 Use the code samples below to quickly get started developing with the Salad Transcription Python SDK. For a complete list of supported parameters and request fields, refer to the Request Options below and API Reference.

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Submit a local file for transcription

from salad_cloud_transcription_sdk import SaladCloudTranscriptionSdk
from salad_cloud_transcription_sdk.models import TranscriptionRequest, TranscriptionJobInput

sdk = SaladCloudTranscriptionSdk(api_key="<YOUR_API_KEY>")

request = TranscriptionRequest(
    options=TranscriptionJobInput(
        language_code="en",
        return_as_file=False,
    ),
)

job = sdk.transcribe(
    source="<LOCAL_FILE_PATH>",
    organization_name="<ORG_NAME>",
    request=request,
    auto_poll=True
)

print("Transcript text:\n", job.output['text'])

​
Submit a remote file for transcription

sdk = SaladCloudTranscriptionSdk(api_key="<YOUR_API_KEY>")

request = TranscriptionRequest(
    options=TranscriptionJobInput(language_code="en",return_as_file=False),
)

job = sdk.transcribe(
    source="https://example.com/audio.mp3",
    organization_name="<ORG_NAME>",
    request=request,
    auto_poll=True
)

print(job.output['text'])

​
Poll for job status

import time
from salad_cloud_transcription_sdk import SaladCloudTranscriptionSdk
from salad_cloud_sdk.models import Status
from salad_cloud_transcription_sdk.models import TranscriptionRequest, TranscriptionJobInput

sdk = SaladCloudTranscriptionSdk(api_key="<YOUR_API_KEY>")

# Setup the request
request_object = TranscriptionRequest(
    options=TranscriptionJobInput(,
        return_as_file=False,
    ),
    metadata={"project": "example_project"}
)

job = sdk.transcribe(
    source="<FILE_PATH>",
    organization_name="<ORG_NAME>",
    request=TranscriptionRequest(
        options=TranscriptionJobInput(language_code="en")
    ),
    auto_poll=False
)

while True:
    job = sdk.get_transcription_job("<ORG_NAME>", job.id_)
    if job.status in [
        Status.SUCCEEDED.value,
        Status.FAILED.value,
        Status.CANCELLED.value,
        ]:
        break
    time.sleep(5)

if job.status == Status.SUCCEEDED.value:
    print(job.output)

​
Use the Lite model engine

from salad_cloud_transcription_sdk import SaladCloudTranscriptionSdk
from salad_cloud_transcription_sdk.models import (
    TranscriptionEngine,
    TranscriptionRequest,
    TranscriptionJobInput
)

sdk = SaladCloudTranscriptionSdk(api_key="<YOUR_API_KEY>")

request = TranscriptionRequest(
    options=TranscriptionJobInput(
        sentence_level_timestamps=True,
    ),
)

job = sdk.transcribe(
    source="<FILE_PATH>",
    organization_name="<ORG_NAME>",
    request=request,
    engine=TranscriptionEngine.Lite,
    auto_poll=True
)

​
Add a custom vocabulary and summary (Full engine)

request = TranscriptionRequest(
    options=TranscriptionJobInput(
        language_code="en",
        custom_vocabulary=["SaladCloud", "GPU", "transcription"],
        summarize=100  # Target ~100 words
    ),
)

job = sdk.transcribe(
    source="<FILE_PATH>",
    organization_name="<ORG_NAME>",
    request=request,
    auto_poll=True
)

print("Summary:\n", job.output['summary'])

​
Download transcription results as a file (return_as_file)

When processing large jobs (over ~1 MB of response data), or if you prefer a downloadable JSON file instead of inline results, you can set return_as_file=True. Even if you don’t set it, responses exceeding 1 MB will automatically be returned as a file link. Refer to the Features Guide and API Reference for details. 
from salad_cloud_transcription_sdk import SaladCloudTranscriptionSdk
from salad_cloud_transcription_sdk.models import TranscriptionRequest, TranscriptionJobInput

sdk = SaladCloudTranscriptionSdk(api_key="<YOUR_API_KEY>")

# Configure the job to return the transcription as a file URL
request = TranscriptionRequest(
    options=TranscriptionJobInput(
        language_code="en",
        return_as_file=True,  # Force output as file
    ),
    metadata={"project": "file_download_example"},
)

job = sdk.transcribe(
    source="<FILE_PATH>",
    organization_name="<ORG_NAME>",
    request=request,
    auto_poll=True
)

# The job output will contain a signed Salad S4 URL
print("Download your transcription from:", job.output['url'])

​
Request Options

Below is a list of parameters supported by the Python SDK’s TranscriptionJobInput. For full details, refer to the API Reference.

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return_as_file (bool)

Set to "True" to receive the transcription output as a downloadable file URL (useful for large responses). Default: "False", which returns the transcription inline in the API response.
Note: If the response exceeds 1 MB, it will always be returned as a file link, even if return_as_file is False.

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language_code (str)

Transcription supports multiple. The SDK auto-detects the source language, but specifying might improve accuracy.

​
translate (str)

Enables translation to English by setting: 
translate="to_eng"
When translation is enabled: Original transcription is not returned. Other features (timestamps, SRT, diarization) can still be combined.

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sentence_level_timestamps (bool)

Returns timestamps per sentence. Default: false.

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word_level_timestamps (bool)

Returns timestamps per word. Default: false.

​
diarization (bool)

Enables speaker separation and identification at the word level. Default: false.

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sentence_diarization (bool)

Adds speaker attribution per sentence. Default: false.

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srt (bool)

Generates srt captions for use as subtitles. Default: false.

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summarize (int)

Returns a summary of the transcription, capped at the specified number of words. Available in Full model engine only. Default: 0 (no summary).

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llm_translation (list of TranslationLanguage)

Uses the built-in LLM to translate the transcription into multiple target languages. Supported languages: English, French, German, Italian, Portuguese, Hindi, Spanish, Thai. Available in Full model engine only.

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srt_translation (list of TranslationLanguage)

Uses the LLM to translate the generated SRT captions into multiple target languages (same languages as llm_translation). Available in Full model engine only.

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custom_vocabulary (str)

Comma-separated list of domain-specific terms or phrases to improve recognition accuracy. Currently in preview. Available in Full model engine only.

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custom_prompt (str)

Custom prompt to guide the LLM in generating summaries, insights, or other LLM-powered outputs. Available in Full model engine only. 
custom_prompt="what is the text about?"

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multichannel (bool)

Enables multichannel audio processing, allowing the model to handle separate audio channels independently. Default: false.