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Get media transcription, translation, and language identification insights

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Important

The deadline for migrating Azure Video Indexer content due to the Azure Media Services retirement has passed. See the retirement guide for more information.

Media transcription, translation, and language identification

Transcription, translation, and language identification detects, transcribes, and translates the speech in media files into over 50 languages.

Azure AI Video Indexer (VI) processes the speech in the audio file to extract the transcription that is then translated into many languages. When selecting to translate into a specific language, both the transcription and the insights like keywords, topics, labels or OCR are translated into the specified language. Transcription can be used as is or be combined with speaker insights that map and assign the transcripts into speakers. Multiple speakers can be detected in an audio file. An ID is assigned to each speaker and is displayed under their transcribed speech.

Language identification (LID) recognizes the supported dominant spoken language in the video file. For more information, see Applying LID.

Multi-language identification (MLID) automatically recognizes the spoken languages in different segments in the audio file and sends each segment to be transcribed in the identified languages. At the end of this process, all transcriptions are combined into the same file. For more information, see Applying MLID. The resulting insights are generated in a categorized list in a JSON file that includes the ID, language, transcribed text, duration, and confidence score.

When indexing media files with multiple speakers, Azure AI Video Indexer performs speaker diarization that identifies each speaker in a video and attributes each transcribed line to a speaker. The speakers are given a unique identity such as Speaker #1 and Speaker #2. This allows for the identification of speakers during conversations and can be useful in various scenarios such as doctor-patient conversations, agent-customer interactions, and court proceedings.

Media transcription, translation, and language identification use cases

  • Promoting accessibility by making content available for people with hearing disabilities using Azure AI Video Indexer to generate speech to text transcription and translation into multiple languages.
  • Improving content distribution to a diverse audience in different regions and languages by delivering content in multiple languages using Azure AI Video Indexer’s transcription and translation capabilities.
  • Enhancing and improving manual closed captioning and subtitles generation by using Azure AI Video Indexer’s transcription and translation capabilities and by using the closed captions generated by Azure AI Video Indexer in one of the supported formats.
  • Using language identification (LID) or multi language identification (MLID) to transcribe videos in unknown languages to allow Azure AI Video Indexer to automatically identify the languages appearing in the video and generate the transcription accordingly.

View the insight JSON with the web portal

Once you have uploaded and indexed a video, insights are available in JSON format for download using the web portal.

  1. Select the Library tab.
  2. Select media you want to work with.
  3. Select Download and the Insights (JSON). The JSON file opens in a new browser tab.
  4. Look for the key pair described in the example response.

Use the API

  1. Use the Get Video Index request. We recommend passing &includeSummarizedInsights=false.
  2. Look for the key pairs described in the example response.

Example response

All the languages detected in the video are under sourceLanauge and each instance in the transcription sectin includes the trascribed language.

    "insights": {
      "version": "1.0.0.0",
      "duration": "0:01:50.486",
      "sourceLanguage": "en-US",
      "sourceLanguages": [
        "es-ES",
        "en-US"
      ],
      "language": "en-US",
      "languages": [
        "en-US"
      ],
      "transcript": [
        {
          "id": 1,
          "text": "Hi, I'm Doug from office. We're talking about new features that office insiders will see first and I have a program manager,",
          "confidence": 0.8879,
          "speakerId": 1,
          "language": "en-US",
          "instances": [
            {
              "adjustedStart": "0:00:00",
              "adjustedEnd": "0:00:05.75",
              "start": "0:00:00",
              "end": "0:00:05.75"
            }
          ]
        },
        {
          "id": 2,
          "text": "Emily Tran, with office graphics.",
          "confidence": 0.8879,
          "speakerId": 1,
          "language": "en-US",
          "instances": [
            {
              "adjustedStart": "0:00:05.75",
              "adjustedEnd": "0:00:07.01",
              "start": "0:00:05.75",
              "end": "0:00:07.01"
            }
          ]
        },

Important

It is important to read the transparency note overview for all VI features. Each insight also has transparency notes of its own:

Transcription, translation, and language identification notes

When used responsibly and carefully, Azure AI Video Indexer is a valuable tool for many industries. You must always respect the privacy and safety of others, and to comply with local and global regulations. We recommend:

  • Carefully consider the accuracy of the results, to promote more accurate data, check the quality of the audio, low quality audio might affect the detected insights.
  • Video Indexer doesn't perform speaker recognition so speakers aren't assigned an identifier across multiple files. You're unable to search for an individual speaker in multiple files or transcripts.
  • Speaker identifiers are assigned randomly and can only be used to distinguish different speakers in a single file.
  • Cross-talk and overlapping speech: When multiple speakers talk simultaneously or interrupt each other, it becomes challenging for the model to accurately distinguish and assign the correct text to the corresponding speakers.
  • Speaker overlaps: Sometimes, speakers might have similar speech patterns, accents, or use similar vocabulary, making it difficult for the model to differentiate between them.
  • Noisy audio: Poor audio quality, background noise, or low-quality recordings can hinder the model's ability to correctly identify and transcribe speakers.
  • Emotional Speech: Emotional variations in speech, such as shouting, crying, or extreme excitement, can affect the model's ability to accurately diarize speakers.
  • Speaker disguise or impersonation: If a speaker intentionally tries to imitate or disguise their voice, the model might misidentify the speaker.
  • Ambiguous speaker identification: Some segments of speech might not have enough unique characteristics for the model to confidently attribute to a specific speaker.
  • Audio that contains languages other than the ones you selected produces unexpected results.
  • The minimal segment length for detecting each language is 15 seconds.
  • The language detection offset is 3 seconds on average.
  • Speech is expected to be continuous. Frequent alternations between languages might affect the model's performance.
  • The speech of non-native speakers might affect the model's performance (for example, when speakers use their first language and they switch to another language).
  • The model is designed to recognize spontaneous conversational speech with reasonable audio acoustics (not voice commands, singing, etc.).
  • Project creation and editing aren't available for multi-language videos.
  • Custom language models aren't available when using multi-language detection.
  • Adding keywords isn't supported.
  • The language indication isn't included in the exported closed caption file.
  • The update transcript in the API doesn't support multiple languages files.
  • The model is designed to recognize a spontaneous conversational speech (not voice commands, singing, and so on).
  • If Azure AI Video Indexer can't identify the language with a high enough confidence (greater than 0.6), the fallback language is English.

Here's a list of supported languages.

Transcription, translation, and language identification components

During the transcription, translation and language identification procedure, speech in a media file is processed, as follows:

Component Definition
Source language The user uploads the source file for indexing, and either:
- Specifies the video source language.
- Selects auto detect single language (LID) to identify the language of the file. The output is saved separately.
- Selects auto detect multi language (MLID) to identify multiple languages in the file. The output of each language is saved separately.
Transcription API The audio file is sent to Azure AI services to get the transcribed and translated output. If a language is specified, it's processed accordingly. If no language is specified, a LID or MLID process is run to identify the language after which the file is processed.
Output unification The transcribed and translated files are unified into the same file. The outputted data includes the speaker ID of each extracted sentence together with its confidence level.
Confidence value The estimated confidence level of each sentence is calculated as a range of 0 to 1. The confidence score represents the certainty in the accuracy of the result. For example, an 82% certainty is represented as an 0.82 score.

Sample code

See all samples for VI