The DeepL API is a language translation API that allows other computer programs to send texts and documents to DeepL's servers and receive high-quality translations. This opens a whole universe of opportunities for developers: any translation product you can imagine can now be built on top of DeepL's best-in-class translation technology.
The DeepL Python library offers a convenient way for applications written in Python to interact with the DeepL API. We intend to support all API functions with the library, though support for new features may be added to the library after they’re added to the API.
To use the DeepL Python Library, you'll need an API authentication key. To get a key, please create an account here. With a DeepL API Free account you can translate up to 500,000 characters/month for free.
The library can be installed from PyPI using pip:
pip install --upgrade deeplIf you need to modify this source code, install the dependencies using poetry:
poetry installOn Ubuntu 22.04 an error might occur: ModuleNotFoundError: No module named 'cachecontrol'. Use the workaround sudo apt install python3-cachecontrol as
explained in this bug report.
The library is tested with Python versions 3.6 to 3.11.
The requests module is used to perform HTTP requests; the minimum is version
2.0.
Starting in 2024, we will drop support for older Python versions that have reached official end-of-life. You can find the Python versions and support timelines here. To continue using this library, you should update to Python 3.8+.
Import the package and construct a Translator. The first argument is a string
containing your API authentication key as found in your
DeepL Pro Account.
Be careful not to expose your key, for example when sharing source code.
import deepl
auth_key = "f63c02c5-f056-..." # Replace with your key
translator = deepl.Translator(auth_key)
result = translator.translate_text("Hello, world!", target_lang="FR")
print(result.text) # "Bonjour, le monde !"This example is for demonstration purposes only. In production code, the authentication key should not be hard-coded, but instead fetched from a configuration file or environment variable.
Translator accepts additional options, see Configuration
for more information.
To translate text, call translate_text(). The first argument is a string
containing the text you want to translate, or a list of strings if you want to
translate multiple texts.
source_lang and target_lang specify the source and target language codes
respectively. The source_lang is optional, if it is unspecified the source
language will be auto-detected.
Language codes are case-insensitive strings according to ISO 639-1, for
example 'DE', 'FR', 'JA''. Some target languages also include the regional
variant according to ISO 3166-1, for example 'EN-US', or 'PT-BR'. The full
list of supported languages is in the
API documentation.
There are additional optional arguments to control translation, see Text translation options below.
translate_text() returns a TextResult, or a list of TextResults
corresponding to your input text(s). TextResult has two properties: text is
the translated text, and detected_source_lang is the detected source language
code.
# Translate text into a target language, in this case, French:
result = translator.translate_text("Hello, world!", target_lang="FR")
print(result.text) # "Bonjour, le monde !"
# Translate multiple texts into British English
result = translator.translate_text(
["お元気ですか？", "¿Cómo estás?"], target_lang="EN-GB"
)
print(result[0].text) # "How are you?"
print(result[0].detected_source_lang) # "JA" the language code for Japanese
print(result[1].text) # "How are you?"
print(result[1].detected_source_lang) # "ES" the language code for Spanish
# Translate into German with less and more Formality:
print(
translator.translate_text(
"How are you?", target_lang="DE", formality="less"
)
) # 'Wie geht es dir?'
print(
translator.translate_text(
"How are you?", target_lang="DE", formality="more"
)
) # 'Wie geht es Ihnen?'In addition to the input text(s) argument, the available translate_text()
arguments are:
source_lang: Specifies the source language code, but may be omitted to auto-detect the source language.target_lang: Required. Specifies the target language code.split_sentences: specify how input text should be split into sentences, default:'on'.'on''(SplitSentences.ON): input text will be split into sentences using both newlines and punctuation.'off'(SplitSentences.OFF): input text will not be split into sentences. Use this for applications where each input text contains only one sentence.'nonewlines'(SplitSentences.NO_NEWLINES): input text will be split into sentences using punctuation but not newlines.
preserve_formatting: controls automatic-formatting-correction. Set toTrueto prevent automatic-correction of formatting, default:False.formality: controls whether translations should lean toward informal or formal language. This option is only available for some target languages, see Listing available languages.'less'(Formality.LESS): use informal language.'more'(Formality.MORE): use formal, more polite language.
glossary: specifies a glossary to use with translation, either as a string containing the glossary ID, or aGlossaryInfoas returned byget_glossary().context: specifies additional context to influence translations, that is not translated itself. Characters in thecontextparameter are not counted toward billing. See the API documentation for more information and example usage.tag_handling: type of tags to parse before translation, options are'html'and'xml'.
The following options are only used if tag_handling is 'xml':
outline_detection: specifyFalseto disable automatic tag detection, default isTrue.splitting_tags: list of XML tags that should be used to split text into sentences. Tags may be specified as an array of strings (['tag1', 'tag2']), or a comma-separated list of strings ('tag1,tag2'). The default is an empty list.non_splitting_tags: list of XML tags that should not be used to split text into sentences. Format and default are the same as forsplitting_tags.ignore_tags: list of XML tags that containing content that should not be translated. Format and default are the same as forsplitting_tags.
For a detailed explanation of the XML handling options, see the API documentation.
To translate documents, you may call either translate_document() using file IO
objects, or translate_document_from_filepath() using file paths. For both
functions, the first and second arguments correspond to the input and output
files respectively.
Just as for the translate_text() function, the source_lang and
target_lang arguments specify the source and target language codes.
There are additional optional arguments to control translation, see Document translation options below.
# Translate a formal document from English to German
input_path = "/path/to/Instruction Manual.docx"
output_path = "/path/to/Bedienungsanleitung.docx"
try:
# Using translate_document_from_filepath() with file paths
translator.translate_document_from_filepath(
input_path,
output_path,
target_lang="DE",
formality="more"
)
# Alternatively you can use translate_document() with file IO objects
with open(input_path, "rb") as in_file, open(output_path, "wb") as out_file:
translator.translate_document(
in_file,
out_file,
target_lang="DE",
formality="more"
)
except deepl.DocumentTranslationException as error:
# If an error occurs during document translation after the document was
# already uploaded, a DocumentTranslationException is raised. The
# document_handle property contains the document handle that may be used to
# later retrieve the document from the server, or contact DeepL support.
doc_id = error.document_handle.id
doc_key = error.document_handle.key
print(f"Error after uploading ${error}, id: ${doc_id} key: ${doc_key}")
except deepl.DeepLException as error:
# Errors during upload raise a DeepLException
print(error)translate_document() and translate_document_from_filepath() are convenience
functions that wrap multiple API calls: uploading, polling status until the
translation is complete, and downloading. If your application needs to execute
these steps individually, you can instead use the following functions directly:
translate_document_upload(),translate_document_get_status()(ortranslate_document_wait_until_done()), andtranslate_document_download()
In addition to the input file, output file, source_lang and target_lang
arguments, the available translate_document() and
translate_document_from_filepath() arguments are:
formality: same as in Text translation options.glossary: same as in Text translation options.output_format: (translate_document()only) file extension of desired format of translated file, for example:'pdf'. If unspecified, by default the translated file will be in the same format as the input file.
Glossaries allow you to customize your translations using user-defined terms. Multiple glossaries can be stored with your account, each with a user-specified name and a uniquely-assigned ID.
You can create a glossary with your desired terms and name using
create_glossary(). Each glossary applies to a single source-target language
pair. Note: Glossaries are only supported for some language pairs, see
Listing available glossary languages
for more information. The entries should be specified as a dictionary.
If successful, the glossary is created and stored with your DeepL account, and
a GlossaryInfo object is returned including the ID, name, languages and entry
count.
# Create an English to German glossary with two terms:
entries = {"artist": "Maler", "prize": "Gewinn"}
my_glossary = translator.create_glossary(
"My glossary",
source_lang="EN",
target_lang="DE",
entries=entries,
)
print(
f"Created '{my_glossary.name}' ({my_glossary.glossary_id}) "
f"{my_glossary.source_lang}->{my_glossary.target_lang} "
f"containing {my_glossary.entry_count} entries"
)
# Example: Created 'My glossary' (559192ed-8e23-...) EN->DE containing 2 entriesYou can also upload a glossary downloaded from the DeepL website using
create_glossary_from_csv(). Instead of supplying the entries as a dictionary,
specify the CSV data as csv_data either as a file-like object or string or
bytes containing file content:
# Open the CSV file assuming UTF-8 encoding. If your file contains a BOM,
# consider using encoding='utf-8-sig' instead.
with open('/path/to/glossary_file.csv', 'r', encoding='utf-8') as csv_file:
csv_data = csv_file.read() # Read the file contents as a string
my_csv_glossary = translator.create_glossary_from_csv(
"CSV glossary",
source_lang="EN",
target_lang="DE",
csv_data=csv_data,
)The API documentation explains the expected CSV format in detail.
Functions to get, list, and delete stored glossaries are also provided:
get_glossary()takes a glossary ID and returns aGlossaryInfoobject for a stored glossary, or raises an exception if no such glossary is found.list_glossaries()returns a list ofGlossaryInfoobjects corresponding to all of your stored glossaries.delete_glossary()takes a glossary ID orGlossaryInfoobject and deletes the stored glossary from the server, or raises an exception if no such glossary is found.
# Retrieve a stored glossary using the ID
glossary_id = "559192ed-8e23-..."
my_glossary = translator.get_glossary(glossary_id)
# Find and delete glossaries named 'Old glossary'
glossaries = translator.list_glossaries()
for glossary in glossaries:
if glossary.name == "Old glossary":
translator.delete_glossary(glossary)The GlossaryInfo object does not contain the glossary entries, but instead
only the number of entries in the entry_count property.
To list the entries contained within a stored glossary, use
get_glossary_entries() providing either the GlossaryInfo object or glossary
ID:
entries = translator.get_glossary_entries(my_glossary)
print(entries) # "{'artist': 'Maler', 'prize': 'Gewinn'}"You can use a stored glossary for text translation by setting the glossary
argument to either the glossary ID or GlossaryInfo object. You must also
specify the source_lang argument (it is required when using a glossary):
text = "The artist was awarded a prize."
with_glossary = translator.translate_text(
text, source_lang="EN", target_lang="DE", glossary=my_glossary,
)
print(with_glossary) # "Der Maler wurde mit einem Gewinn ausgezeichnet."
# For comparison, the result without a glossary:
without_glossary = translator.translate_text(text, target_lang="DE")
print(without_glossary) # "Der Künstler wurde mit einem Preis ausgezeichnet."Using a stored glossary for document translation is the same: set the glossary
argument and specify the source_lang argument:
translator.translate_document(
in_file, out_file, source_lang="EN", target_lang="DE", glossary=my_glossary,
)The translate_document(), translate_document_from_filepath() and
translate_document_upload() functions all support the glossary argument.
To check account usage, use the get_usage() function.
The returned Usage object contains three usage subtypes: character,
document and team_document. Depending on your account type, some usage
subtypes may be invalid; this can be checked using the valid property. For API
accounts:
usage.character.validisTrue,usage.document.validandusage.team_document.validareFalse.
Each usage subtype (if valid) has count and limit properties giving the
amount used and maximum amount respectively, and the limit_reached property
that checks if the usage has reached the limit. The top level Usage object has
the any_limit_reached property to check all usage subtypes.
usage = translator.get_usage()
if usage.any_limit_reached:
print('Translation limit reached.')
if usage.character.valid:
print(
f"Character usage: {usage.character.count} of {usage.character.limit}")
if usage.document.valid:
print(f"Document usage: {usage.document.count} of {usage.document.limit}")You can request the list of languages supported by DeepL for text and documents
using the get_source_languages() and get_target_languages() functions. They
both return a list of Language objects.
The name property gives the name of the language in English, and the code
property gives the language code. The supports_formality property only appears
for target languages, and indicates whether the target language supports the
optional formality parameter.
print("Source languages:")
for language in translator.get_source_languages():
print(f"{language.name} ({language.code})") # Example: "German (DE)"
print("Target languages:")
for language in translator.get_target_languages():
if language.supports_formality:
print(f"{language.name} ({language.code}) supports formality")
# Example: "Italian (IT) supports formality"
else:
print(f"{language.name} ({language.code})")
# Example: "Lithuanian (LT)"Glossaries are supported for a subset of language pairs. To retrieve those
languages use the get_glossary_languages() function, which returns an array
of GlossaryLanguagePair objects. Each has source_lang and target_lang
properties indicating that that pair of language codes is supported.
glossary_languages = translator.get_glossary_languages()
for language_pair in glossary_languages:
print(f"{language_pair.source_lang} to {language_pair.target_lang}")
# Example: "EN to DE", "DE to EN", etc.You can also find the list of supported glossary language pairs in the API documentation.
Note that glossaries work for all target regional-variants: a glossary for the
target language English ("EN") supports translations to both American English
("EN-US") and British English ("EN-GB").
If you use this library in an application, please identify the application with
deepl.Translator.set_app_info, which needs the name and version of the app:
translator = deepl.Translator(...).set_app_info("sample_python_plugin", "1.0.2")This information is passed along when the library makes calls to the DeepL API.
Both name and version are required. Please note that setting the User-Agent header
via deepl.http_client.user_agent will override this setting, if you need to use this,
please manually identify your Application in the User-Agent header.
All module functions may raise deepl.DeepLException or one of its subclasses.
If invalid arguments are provided, they may raise the standard exceptions
ValueError and TypeError.
Logging can be enabled to see the HTTP requests sent and responses received by
the library. Enable and control logging using Python's logging module, for
example:
import logging
logging.basicConfig()
logging.getLogger('deepl').setLevel(logging.DEBUG)You can override the URL of the DeepL API by specifying the server_url
argument when constructing a deepl.Translator. This may be useful for testing
purposes. You do not need to specify the URL to distinguish API Free and API
Pro accounts, the library selects the correct URL automatically.
server_url = "http://user:pass@localhost:3000"
translator = deepl.Translator(..., server_url=server_url)You can configure a proxy by specifying the proxy argument when constructing a
deepl.Translator:
proxy = "http://user:[email protected]:3128"
translator = deepl.Translator(..., proxy=proxy)The proxy argument is passed to the underlying requests session, see the
documentation for requests; a dictionary of schemes to
proxy URLs is also accepted.
You can control how requests performs SSL verification by specifying the
verify_ssl option when constructing a deepl.Translator, for example to
disable SSL certificate verification:
translator = deepl.Translator(..., verify_ssl=False)This option is passed to the underlying requests session as the verify
option, see the documentation for requests.
This SDK will automatically retry failed HTTP requests (if the failures could
be transient, e.g. a HTTP 429 status code). This behaviour can be configured
in http_client.py, for example by default the number of retries is 5. This
can be changed to 3 as follows:
import deepl
deepl.http_client.max_network_retries = 3
t = deepl.Translator(...)
t.translate_text(...)You can configure the timeout min_connection_timeout the same way, as well
as set a custom user_agent, see the next section.
By default, we send some basic information about the platform the client library is running on with each request, see here for an explanation. This data is completely anonymous and only used to improve our product, not track any individual users. If you do not wish to send this data, you can opt-out when creating your deepl.Translator object by setting the send_platform_info flag like so:
translator = deepl.Translator(..., send_platform_info=False)You can also customize the user_agent by setting its value explicitly before constructing your deepl.Translator object.
deepl.http_client.user_agent = 'my custom user agent'
translator = deepl.Translator(os.environ["DEEPL_AUTH_KEY"])The library can be run on the command line supporting all API functions. Use the
--help option for usage information:
python3 -m deepl --helpThe CLI requires your DeepL authentication key specified either as the
DEEPL_AUTH_KEY environment variable, through the keyring module, or
using the --auth-key option, for example:
python3 -m deepl --auth-key=YOUR_AUTH_KEY usageNote that the --auth-key argument must appear before the command argument.
To use the keyring module, set the
DEEPL_AUTH_KEY field in the service deepl to your API key.
The recognized commands are:
| Command | Description |
|---|---|
| text | translate text(s) |
| document | translate document(s) |
| usage | print usage information for the current billing period |
| languages | print available languages |
| glossary | create, list, and remove glossaries |
For example, to translate text:
python3 -m deepl --auth-key=YOUR_AUTH_KEY text --to=DE "Text to be translated."Wrap text arguments in quotes to prevent the shell from splitting sentences into words.
If you experience problems using the library, or would like to request a new feature, please open an issue.
We welcome Pull Requests, please read the contributing guidelines.
Execute the tests using pytest. The tests communicate with the DeepL API using
the auth key defined by the DEEPL_AUTH_KEY environment variable.
Be aware that the tests make DeepL API requests that contribute toward your API usage.
The test suite may instead be configured to communicate with the mock-server
provided by deepl-mock. Although most test cases work for either,
some test cases work only with the DeepL API or the mock-server and will be
otherwise skipped. The test cases that require the mock-server trigger server
errors and test the client error-handling. To execute the tests using
deepl-mock, run it in another terminal while executing the tests. Execute the
tests using pytest with the DEEPL_MOCK_SERVER_PORT and DEEPL_SERVER_URL
environment variables defined referring to the mock-server.
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