Amazon Transcribe can mask, remove, or tag configured words in a live transcription. To use it, create a custom vocabulary filter, attach it to the streaming transcription request, and choose the output behavior. It filters matches from the transcript—not the audio itself—and it is not a complete detector for every profane expression.
What Amazon Transcribe’s profanity filter does
Amazon Transcribe’s custom vocabulary filter checks transcription results against a word list you provide. AWS documents offensive or profane terms as a common use case. The filter is custom: you select the words and what happens when a match is found. See AWS’s custom vocabulary filter documentation.
This is transcript-word filtering, not comprehensive audio moderation. It does not mute or censor the incoming sound, decide whether speech violates a platform’s conduct policy, or guarantee that captions are free of profanity. AWS says Transcribe converts speech to text and may return inappropriate speech verbatim; filters are controls applied to transcript results. A live application remains responsible for what it displays or passes downstream.
Choose what happens to a matched word
| Method | Effect on the transcript | Use it when |
|---|---|---|
mask |
Replaces a matched word with ***. |
You want captions to conceal the word but show that something was filtered. |
remove |
Deletes the matched word. | You want the returned transcript to omit the word entirely. |
tag |
Leaves the word in the transcript and marks it as a filter match. | A later review or processing step will decide what to do with it. Tagging alone does not hide the word from viewers. |
These methods are documented in AWS’s filter guide and streaming API reference. Select the method according to what the audience should see and what your downstream systems need to receive.
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Set up filtering for a live transcription
- Decide the language and moderation policy. Make a list of the terms your application intends to filter, including the relevant language or languages. Build it for your own policy; do not assume a general list will cover every expression or variation.
- Create a custom vocabulary filter. In Amazon Transcribe, create a vocabulary filter containing your terms; AWS supports adding words or providing a text file. Keep the filter’s language aligned with the speech you intend to transcribe.
- Start a streaming transcription session. Use the
StartStreamTranscriptionoperation. Supply the language configuration, media encoding, and the audio stream’s actual sample rate, along with the vocabulary filter name and filter method. The operation supports HTTP/2 and WebSocket bidirectional streams; AWS also provides SDKs and a Management Console workflow. The API reference describes request parameters and language-identification rules. - Choose the filter output method. Set
mask,remove, ortagbased on the behavior described above. If you use language identification, follow the API’s rules for the vocabulary-filter parameters rather than assuming a fixed-language request configuration applies unchanged. - Handle partial and finalized results separately. Transcribe returns partial results while speech is ongoing, and may revise recognized words as it receives more context. Treat partial text as provisional. Use finalized segment results when you need durable transcript records; do not assume an early partial is the final caption. AWS explains the behavior in its streaming and partial results guide.
- Test the viewer-facing output. Confirm that a configured match is handled as intended and that your application does not expose an unfiltered partial result before applying its display policy. Test representative audio and the actual language, filter, and stream configuration you plan to use.
Match the filter and stream to the audio
Language support
A filter with a language that does not match the transcription may not apply. Streaming and vocabulary-filter support can differ by language, and AWS’s supported-language information is feature-specific. Check the current Amazon Transcribe language table for the intended language and feature before relying on it in production.
Audio encoding and sample rate
AWS lists FLAC, OPUS in an Ogg container, and signed 16-bit little-endian PCM as streaming input formats. WAV is not listed as a streaming encoding in this guidance. AWS recommends lossless FLAC or PCM. Set the sample rate to match the audio actually sent to Transcribe; a mismatch between the declared rate and the input stream can undermine the request. See the streaming guide and API reference.
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Choose an integration method
You can use an AWS SDK, HTTP/2, WebSockets, or the Management Console. AWS recommends SDKs as the simpler, more reliable route for setting up streaming directly. Hand-building the lower-level transport means your application must correctly manage the bidirectional stream and its audio events. AWS’s setup guidance describes the available approaches.
A microphone is only one possible audio source; AWS’s console walkthrough demonstrates microphone capture, but a separate USB microphone is not a Transcribe requirement. The key requirement for filtering is that the application sends valid streaming audio in a supported format.
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What the filter can miss—and what it cannot do
The filter matches words on your configured list. Exact matching means that unlisted terms, spelling or inflection variants, obfuscations, homophones, and speech-recognition errors may not be caught unless your list and application account for them. This is a practical implication of exact-list matching, not an AWS guarantee about any particular expression or recognition accuracy.
AWS also describes a default filter for certain sensitive terms, but advises customers to review and supplement coverage for their language and use case. Do not treat that default as a complete profanity policy. For details on how AWS frames these controls, see its Responsible AI documentation.
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Profanity vocabulary filtering is distinct from PII redaction. The API’s content-identification and content-redaction settings address personally identifiable information; they do not replace a custom vocabulary list for filtering profanity. Consult the streaming API reference for those separate options and their constraints.
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Streaming results arrive incrementally, and partial words can change as more context becomes available. Partial-result stabilization can return text sooner, but AWS describes a trade-off: higher stability favors faster results with slightly lower accuracy, while lower stability favors accuracy. There is no single response-time or error-rate figure established here, so test the setting against your own audio and the delay your captions can tolerate. See AWS’s partial-results guide and API reference.
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Troubleshoot common problems
- Configured words are not filtered: Check that the vocabulary filter is attached to the streaming request, the filter method is set, and the filter language matches the transcription language. Verify feature availability for that language in AWS’s current language table.
- Captions briefly show a word before changing: Your application may be displaying a revisable partial result. Treat partial events as provisional and apply the intended display policy; use finalized segments for stable records.
- The wrong word is filtered—or a variant is missed: Review the exact terms and language in the custom list. Consider whether the speech uses an unlisted form or whether recognition produced a different word; the filter does not provide semantic coverage of every related expression.
- The stream fails or the audio is misrecognized: Confirm that the encoding is one of the documented streaming formats and that the declared sample rate matches the audio being sent. Check the request configuration and transport setup against the API reference.
- The transcript still contains profanity after tagging: That is expected:
tagpreserves the matched word and marks it for later handling. Usemaskorremoveif the returned caption itself must not show the word.
Or let it run in the cloud
Amazon Transcribe is for turning speech into text; it does not keep a prerecorded video live on YouTube. If your separate goal is to keep an uploaded video or playlist streaming on a YouTube channel around the clock, StreamNeo runs that stream in the cloud:
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