Quickstart: Azure Content Understanding gebruiken in Foundry Tools REST API

In deze quickstart ziet u hoe u de Rest API van Content Understanding gebruikt om gestructureerde gegevens op te halen uit multimodale inhoud in document-, afbeeldings-, audio- en videobestanden.

Vereisten

U hebt een actief Azure-abonnement nodig om aan de slag te gaan. Als u geen Azure-account hebt, kunt u er een gratis maken.
Zodra u uw Azure-abonnement hebt, maakt u een Microsoft Foundry-resource in Azure Portal. Zorg ervoor dat u deze maakt in een ondersteunde regio.
Stel standaardmodelimplementaties in voor uw Content Understanding-resource. Als u de standaardinstellingen instelt, wordt er een verbinding gemaakt met de Foundry-modellen die u gebruikt voor Aanvragen voor Content Understanding. Kies één van de volgende methoden:
- Portal
- REST API
1. Ga naar de pagina Inhoudsbegrip-instellingen
2. Selecteer de knop + Resource toevoegen in de linkerbovenhoek
3. Selecteer de Foundry-resource die u wilt gebruiken en klik op Volgende en klik vervolgens op Opslaan
  - Zorg ervoor dat u 'Automatisch implementeren voor vereiste modellen inschakelen als er geen standaardinstellingen beschikbaar zijn' ingeschakeld. Dit zorgt ervoor dat uw resource volledig is ingesteld met de vereiste modellen: GPT-4.1, GPT-4.1-mini en tekst-embedding-3-groot. Voor verschillende vooraf samengestelde analyses zijn verschillende modellen vereist.
Door deze stappen uit te voeren, stelt u een verbinding in tussen Content Understanding- en Foundry-modellen in uw Foundry-resource.
1. Maak Foundry Model-implementaties van GPT-4.1, GPT-4.1-mini en tekst-embedding-3-large in uw Foundry-resource. Zie Modelimplementaties maken in de Microsoft Foundry-portal voor meer informatie over het implementeren van deze modellen. Voor verschillende vooraf gedefinieerde analyses zijn verschillende modellen vereist, dus u moet alle drie de modellen implementeren.
2. Definieer standaardmodelimplementaties op resourceniveau.
  
  Voordat u de volgende cURL-opdracht uitvoert, moet u de volgende wijzigingen aanbrengen in de HTTP-aanvraag:
  - Vervang {endpoint} en {key} door de bijbehorende waarden uit uw Foundry-exemplaar in Azure Portal.
  - Vervang , {myGPT41Deployment}en {myGPT41MiniDeployment} door {myEmbeddingDeployment}uw werkelijke modelimplementatienamen uit uw Foundry-resource.
```
curl -i -X PATCH "{endpoint}/contentunderstanding/defaults?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "modelDeployments": {
          "gpt-4.1": "{myGPT41Deployment}",
          "gpt-4.1-mini": "{myGPT41MiniDeployment}",
          "text-embedding-3-large": "{myEmbeddingDeployment}"
        }
      }'
```
Door deze stappen uit te voeren, stelt u een verbinding in tussen Content Understanding- en Foundry-modellen in uw Foundry-resource.
In deze handleiding gebruiken we het opdrachtregelprogramma cURL. Als deze niet is geïnstalleerd, kunt u de juiste versie voor uw ontwikkelomgeving downloaden.

Aan de slag met een vooraf gedefinieerde analyse

Analyses definiëren hoe uw inhoud wordt verwerkt en welke inzichten worden geëxtraheerd. We bieden vooraf samengestelde analyses voor veelvoorkomende gebruiksvoorbeelden . U kunt vooraf gemaakte analyses aanpassen aan uw specifieke behoeften en gebruiksvoorbeelden. In deze quickstart worden vooraf samengestelde facturen, afbeeldingen, audio- en videoanalyses gebruikt om u te helpen aan de slag te gaan.

Een bestand verzenden voor analyse

Voordat u de volgende cURL-opdracht uitvoert, moet u de volgende wijzigingen aanbrengen in de HTTP-aanvraag:

Vervang {endpoint} en {key} door de bijbehorende waarden uit uw Foundry-exemplaar in Azure Portal.

POST-aanvraag

In dit voorbeeld wordt de prebuilt-invoice analyse gebruikt om gestructureerde gegevens uit een factuurdocument te extraheren.

curl -i -X POST "{endpoint}/contentunderstanding/analyzers/prebuilt-invoice:analyze?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "inputs":[{"url": "https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/invoice.pdf"}]
      }'

In dit voorbeeld wordt de prebuilt-imageSearch analyse gebruikt om een beschrijving van de afbeelding te genereren.

curl -i -X POST "{endpoint}/contentunderstanding/analyzers/prebuilt-imageSearch:analyze?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "inputs":[
          {
            "url": "https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/pieChart.jpg"
          }          
        ]
      }'

In dit voorbeeld wordt de prebuilt-audioSearch analyzer gebruikt om de audiotranscriptie te extraheren, een samenvatting te genereren en sprekerherkenning uit te voeren.

curl -i -X POST "{endpoint}/contentunderstanding/analyzers/prebuilt-audioSearch:analyze?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "inputs":[
          {
            "url": "https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/audio.wav"
          }          
        ]
      }'

In dit voorbeeld wordt de prebuilt-videoSearch analyse gebruikt om sleutelframes, transcripties en hoofdstuksegmenten uit video te extraheren.

curl -i -X POST "{endpoint}/contentunderstanding/analyzers/prebuilt-videoSearch:analyze?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}" \
  -H "Content-Type: application/json" \
  -d '{
        "inputs":[
          {
            "url": "https://github.com/Azure-Samples/azure-ai-content-understanding-python/raw/refs/heads/main/data/FlightSimulator.mp4"
          }          
        ]
      }'

POST-antwoord

De antwoordheader bevat een Operation-Location veld dat u gebruikt om de resultaten van de asynchrone analysebewerking op te halen.

HTTP/1.1 202 Accepted
Transfer-Encoding: chunked
Content-Type: application/json
request-id: aaa-bbb-ccc-ddd
x-ms-request-id: aaa-bbb-ccc-ddd
Operation-Location: {endpoint}/contentunderstanding/analyzerResults/{request-id}?api-version=2025-11-01
api-supported-versions: 2024-12-01-preview,2025-05-01-preview,2025-11-01
x-envoy-upstream-service-time: 800
apim-request-id: {request-id}
Strict-Transport-Security: max-age=31536000; includeSubDomains; preload
x-content-type-options: nosniff
x-ms-region: West US
Date: Fri, 31 Oct 2025 05:30:17 GMT
Connection: close

Belangrijk

Kopieer de URL van de bewerkingslocatie uit de antwoordheader. In de volgende stap gebruikt u deze URL om de analyseresultaten op te halen.

Analyseresultaat ophalen

Gebruik het Operation-Location van het POSTantwoord en haal het resultaat van de analyse op.

Opmerking

Wanneer u de videoanalyse gebruikt, worden sleutelframes geretourneerd als URL's in het JSON-antwoord (bijvoorbeeld onder result.contents.frames[]).
U kunt deze sleutelframes downloaden met behulp van een standaard HTTP-aanvraag voor elke keyframe-URL GET .
Voorbeeld (Bash):

curl -O "<keyframeUrl>"

Herhaal deze opdracht voor elke keyframe-URL die u wilt opslaan.

GET-aanvraag

curl -i -X GET "{endpoint}/contentunderstanding/analyzerResults/{request-id}?api-version=2025-11-01" \
  -H "Ocp-Apim-Subscription-Key: {key}"

GET-antwoord

Het JSON-antwoord 200 (OK) bevat een status veld dat de status van de bewerking aangeeft. Als de bewerking niet is voltooid, is status of RunningNotStarted. In dergelijke gevallen moet u de GET aanvraag opnieuw verzenden, handmatig of via een script. Wacht een interval van één seconde of meer tussen aanroepen.

{
  "id": "ce05fb5a-579e-4f0b-afb5-3532bcddeaee",
  "status": "Succeeded",
  "result": {
    "analyzerId": "prebuilt-invoice",
    "apiVersion": "2025-11-01",
    "createdAt": "2025-11-13T20:04:55Z",
    "warnings": [],
    "contents": [
      {
        "path": "input1",
        "markdown": "CONTOSO LTD.\n\n\n# INVOICE\n\nContoso Headquarters\n123 456th St\nNew York, NY, 10001\n\nINVOICE: INV-100\n\nINVOICE DATE: 11/15/2019\n\nDUE DATE: 12/15/2019\n\nCUSTOMER NAME: MICROSOFT CORPORATION...",
        "fields": {
          "AmountDue": {
            "type": "object",
            "valueObject": {
              "Amount": {
                "type": "number",
                "valueNumber": 610,
                "spans": [
                  {
                    "offset": 1522,
                    "length": 7
                  }
                ],
                "confidence": 0.773,
                "source": "D(1,7.3628,8.0459,7.9272,8.0459,7.9272,8.2070,7.3628,8.2070)"
              },
              "CurrencyCode": {
                "type": "string",
                "valueString": "USD"
              }
            }
          },
          "BalanceForward": {
            "type": "object",
            "valueObject": {
              "Amount": {
                "type": "number",
                "valueNumber": 500,
                "spans": [
                  {
                    "offset": 1474,
                    "length": 7
                  }
                ],
                "confidence": 0.901,
                "source": "D(1,7.3628,7.7445,7.9278,7.7467,7.9272,7.9092,7.3622,7.9070)"
              },
              "CurrencyCode": {
                "type": "string",
                "valueString": "USD"
              }
            }
          },
          "BillingAddress": {
            "type": "string",
            "valueString": "123 Bill St, Redmond WA, 98052",
            "spans": [
              {
                "offset": 325,
                "length": 12
              },
              "..."
            ],
            "confidence": 0.712,
            "source": "D(1,0.5805,3.9471,1.2858,3.9478,1.2856,4.1115,0.5803,4.1108);..."
          },
          "BillingAddressRecipient": {
            "type": "string",
            "valueString": "Microsoft Finance",
            "spans": [
              {
                "offset": 307,
                "length": 17
              }
            ],
            "confidence": 0.815,
            "source": "D(1,0.5734,3.7392,1.8060,3.7521,1.8043,3.9201,0.5717,3.9072)"
          },
          "CountryRegion": {
            "type": "string",
            "valueString": "USA"
          },
          "CustomerAddress": {
            "type": "string",
            "valueString": "123 Other St, Redmond WA, 98052",
            "spans": [
              "..."
            ],
            "confidence": 0.744,
            "source": "..."
          },
          "CustomerAddressRecipient": {
            "type": "string",
            "valueString": "Microsoft Corp",
            "spans": [
              "..."
            ],
            "confidence": 0.437,
            "source": "..."
          },
          "CustomerId": {
            "type": "string",
            "valueString": "CID-12345",
            "spans": [
              "..."
            ],
            "confidence": 0.936,
            "source": "..."
          },
          "CustomerName": {
            "type": "string",
            "valueString": "MICROSOFT CORPORATION",
            "spans": [
              "..."
            ],
            "confidence": 0.46,
            "source": "..."
          },
          "CustomerTaxId": {
            "type": "string",
            "confidence": 0.912
          },
          "DueDate": {
            "type": "date",
            "valueDate": "2019-12-15",
            "spans": [
              "..."
            ],
            "confidence": 0.97,
            "source": "..."
          },
          "InvoiceDate": {
            "type": "date",
            "valueDate": "2019-11-15",
            "spans": [
              "..."
            ],
            "confidence": 0.939,
            "source": "..."
          },
          "InvoiceId": {
            "type": "string",
            "valueString": "INV-100",
            "spans": [
              "..."
            ],
            "confidence": 0.733,
            "source": "..."
          },
          "LineItems": {
            "type": "array",
            "valueArray": [
              {
                "type": "object",
                "valueObject": {
                  "Date": {
                    "type": "date",
                    "valueDate": "2021-03-04",
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.894,
                    "source": "..."
                  },
                  "Description": {
                    "type": "string",
                    "valueString": "Consulting Services",
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.589,
                    "source": "..."
                  },
                  "ProductCode": {
                    "type": "string",
                    "valueString": "A123",
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.879,
                    "source": "..."
                  },
                  "Quantity": {
                    "type": "number",
                    "valueNumber": 2,
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.939,
                    "source": "..."
                  },
                  "QuantityUnit": {
                    "type": "string",
                    "valueString": "hours",
                    "spans": [
                      "..."
                    ],
                    "confidence": 0.85,
                    "source": "..."
                  },
                  "TaxAmount": {
                    "type": "object",
                    "valueObject": {
                      "Amount": {
                        "type": "number",
                        "valueNumber": 6,
                        "spans": [
                          "..."
                        ],
                        "confidence": 0.522,
                        "source": "..."
                      },
                      "CurrencyCode": {
                        "type": "string",
                        "valueString": "USD"
                      }
                    }
                  },
                  "TaxRate": {
                    "type": "number",
                    "confidence": 0.915
                  },
                  "TotalAmount": {
                    "type": "object",
                    "valueObject": {
                      "Amount": {
                        "type": "number",
                        "valueNumber": 60,
                        "spans": [
                          "..."
                        ],
                        "confidence": 0.972,
                        "source": "..."
                      },
                      "CurrencyCode": {
                        "type": "string",
                        "valueString": "USD"
                      }
                    }
                  },
                  "UnitPrice": {
                    "type": "object",
                    "valueObject": {
                      "Amount": {
                        "type": "number",
                        "valueNumber": 30,
                        "spans": [
                          "..."
                        ],
                        "confidence": 0.97,
                        "source": "..."
                      },
                      "CurrencyCode": {
                        "type": "string",
                        "valueString": "USD"
                      }
                    }
                  }
                }
              },
              "... (2 additional line items)"
            ]
          }
          /*additional fields omitted*/
        },
        "kind": "document",
        "startPageNumber": 1,
        "endPageNumber": 1,
        "unit": "inch",
        "pages": [
          {
            "pageNumber": 1,
            "angle": 0,
            "width": 8.5,
            "height": 11,
            "words": [
              "... (words omitted for brevity)"
            ],
            "selectionMarks": [],
            "lines": [
              "... (lines omitted for brevity)"
            ],
            "barcodes": [],
            "formulas": []
          }
        ],
        "tables": [
          "... (tables omitted for brevity)"
        ],
        "keyValuePairs": [
          "... (key-value pairs omitted for brevity)"
        ],
        "analyzerId": "prebuilt-invoice",
        "mimeType": "application/pdf"
      }
    ]
  },
  "usage": {
    "documentStandardPages": 1,
    "contextualizationTokens": 2345,
    "tokens": {
      "gpt-4.1-mini-input": 1234,
      "gpt-4.1-mini-output": 567
    }
  }
}

{
  "id": "fe6bb69d-1d6b-4698-a50c-ce798bacdd95",
  "status": "Succeeded",
  "result": {
    "analyzerId": "prebuilt-imageSearch",
    "apiVersion": "2025-11-01",
    "createdAt": "2025-11-13T20:11:50Z",
    "warnings": [],
    "contents": [
      {
        "path": "input1",
        "markdown": "![image](pages/1)\n",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "The image is a 3D pie chart illustrating the distribution of hours spent in four different ranges. The largest segment, colored purple, represents 60+ hours at 37.8%. The second largest segment, in teal, represents 50-60 hours at 36.6%. The red segment shows 40-50 hours at 18.9%, and the smallest orange segment represents 1-39 hours at 6.7%. Each segment is labeled with its corresponding range and percentage."
          }
        },
        "kind": "document",
        "startPageNumber": 1,
        "endPageNumber": 1,
        "unit": "pixel",
        "pages": [
          {
            "pageNumber": 1,
            "spans": []
          }
        ],
        "analyzerId": "prebuilt-imageSearch",
        "mimeType": "image/jpeg"
      }
    ]
  },
  "usage": {
    "contextualizationTokens": 1000,
    "tokens": {
      "gpt-4.1-mini-input": 199,
      "gpt-4.1-mini-output": 106
    }
  }
}

{
  "id": "<request-id>",
  "status": "Succeeded",
  "result": {
    "analyzerId": "prebuilt-audioSearch",
    "apiVersion": "2025-11-01",
    "createdAt": "YYYY-MM-DDTHH:MM:SSZ",
    "stringEncoding": "utf8",
    "warnings": [],
    "contents": [
      {
        "path": "input1",
        "markdown": "# Audio: 00:00.000 => 01:54.670\n\nTranscript\n```\nWEBVTT\n\n00:00.080 --> 00:02.160\n<v Speaker 1>Thank you for calling Woodgrove Travel...",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "John Smith contacted Woodgrove Travel to report a negative experience with his flight from New York City to Los Angeles..."
          }
        },
        "kind": "audioVisual",
        "startTimeMs": 0,
        "endTimeMs": 114670,
        "analyzerId": "prebuilt-audioSearch",
        "mimeType": "audio/wav"
      }
    ]
  },
  "usage": {
		"audioHours": 0.032,
    "contextualization": 3194.445
    "tokens": {
      "gpt-4.1-input": 1234, 
      "gpt-4.1-output": 2345,
      "text-embedding-3-large": 3456 
    }
 
	}
}

{
  "id": "2689a699-fa3a-4ddf-9a27-c34ceaa6c597",
  "status": "Succeeded",
  "result": {
    "analyzerId": "prebuilt-videoSearch",
    "apiVersion": "2025-11-01",
    "createdAt": "2025-11-13T16:11:17Z",
    "warnings": [],
    "contents": [
      {
        "markdown": "# Video: 00:00.733 => 00:15.467\nWidth: 1080\nHeight: 608\n\nTranscript\n```\nWEBVTT\n\n00:01.360 --> 00:06.640\n<Speaker 1>When it comes to the neural TTS, in order to get a good voice, it's better to have good data.\n\n00:07.120 --> 00:13.320\n<Speaker 2>To achieve that, we build a universal TTS model based on 3,000 hours of data.\n\n00:13.440 --> 00:23.680\n<Speaker 1>We actually accumulated tons of the data so that this universal model is able to capture the nuance of the audio and generate a more natural voice for the algorithm.\n```\n\nKey Frames\n- 00:00.733 ![](keyFrame.733.jpg)\n- 00:02.067 ![](keyFrame.2067.jpg)\n...\n- 00:14.833 ![](keyFrame.14833.jpg)\n- 00:15.467 ![](keyFrame.15467.jpg)",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "The video opens with a Flight Simulator logo alongside Microsoft Azure AI branding, followed by an interview with a man discussing the importance of good data for neural text-to-speech (TTS) technology. He explains the creation of a universal TTS model trained on 3,000 hours of data to capture audio nuances and produce natural-sounding voices. Visuals include audio waveform displays and shots of data centers and server rooms, emphasizing the technological infrastructure behind the TTS model."
          }
        },
        "kind": "audioVisual",
        "startTimeMs": 733,
        "endTimeMs": 15467,
        "width": 1080,
        "height": 608,
        "KeyFrameTimesMs": [
          733,
          2067
          /*... (14 additional keyframes)*/
        ],
        "transcriptPhrases": [
          {
            "speaker": "Speaker 1",
            "startTimeMs": 1360,
            "endTimeMs": 6640,
            "text": "When it comes to the neural TTS, in order to get a good voice, it's better to have good data.",
            "confidence": 0.937,
            "words": [
              {
                "startTimeMs": 1360,
                "endTimeMs": 1600,
                "text": "When"
              },
              {
                "startTimeMs": 1600,
                "endTimeMs": 1760,
                "text": "it"
              }
              /*... (18 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 2",
            "startTimeMs": 7120,
            "endTimeMs": 13320,
            "text": "To achieve that, we build a universal TTS model based on 3,000 hours of data.",
            "confidence": 0.937,
            "words": [
              {
                "startTimeMs": 7120,
                "endTimeMs": 7360,
                "text": "To"
              },
              {
                "startTimeMs": 7560,
                "endTimeMs": 7880,
                "text": "achieve"
              }
              /*... (13 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 1",
            "startTimeMs": 13440,
            "endTimeMs": 23680,
            "text": "We actually accumulated tons of the data so that this universal model is able to capture the nuance of the audio and generate a more natural voice for the algorithm.",
            "confidence": 0.937,
            "words": [
              {
                "startTimeMs": 13440,
                "endTimeMs": 13600,
                "text": "We"
              },
              {
                "startTimeMs": 13600,
                "endTimeMs": 14000,
                "text": "actually"
              }
              /*... (28 additional words)*/
            ],
            "locale": "en-US"
          }
        ],
        "cameraShotTimesMs": [
          1467,
          3233
          /*... (13 additional camera shots)*/
        ],
        "mimeType": "video/x-m4v"
      },
      {
        "markdown": "# Video: 00:15.467 => 00:23.100\nWidth: 1080\nHeight: 608\n\n\n\nKey Frames\n- 00:15.467 ![](keyFrame.15467.jpg)\n- 00:16.933 ![](keyFrame.16933.jpg)\n...\n- 00:22.367 ![](keyFrame.22367.jpg)\n- 00:23.100 ![](keyFrame.23100.jpg)",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "The video transitions to scenic aerial views from the Flight Simulator, showcasing detailed landscapes including coastlines, mountains, and castles. This segment highlights the realistic graphics and immersive experience of the Flight Simulator, demonstrating the integration of advanced AI technologies to enhance the simulation."
          }
        },
        "kind": "audioVisual",
        "startTimeMs": 15467,
        "endTimeMs": 23100,
        "width": 1080,
        "height": 608,
        "KeyFrameTimesMs": [
          15467,
          16933
          /*... (7 additional keyframes)*/
        ],
        "transcriptPhrases": [],
        "cameraShotTimesMs": [
          1467,
          3233
          /*... (13 additional camera shots)*/
        ]
      },
      {
        "markdown": "# Video: 00:23.100 => 00:43.233\nWidth: 1080\nHeight: 608\n\nTranscript\n```\nWEBVTT\n\n00:24.040 --> 00:29.120\n<Speaker 3>What we liked about cognitive services offerings were that they had a much higher fidelity.\n\n00:29.600 --> 00:32.880\n<Speaker 3>And they sounded a lot more like an actual human voice.\n\n00:33.680 --> 00:37.200\n<Speaker 4>Orlando ground 9555 requesting the end of pushback.\n\n00:38.680 --> 00:41.280\n<Speaker 4>9555 request to end pushback received.\n```\n\nKey Frames\n- 00:23.100 ![](keyFrame.23100.jpg)\n- 00:24.833 ![](keyFrame.24833.jpg)\n...\n- 00:42.633 ![](keyFrame.42633.jpg)\n- 00:43.233 ![](keyFrame.43233.jpg)",
        "fields": {
          "Summary": {
            "type": "string",
            "valueString": "The focus shifts back to an interview with another man discussing the high fidelity and human-like quality of voices produced by cognitive services offerings. The segment concludes with visuals of an airplane on the tarmac, ground crew directing the plane, and the plane preparing for pushback, accompanied by realistic audio communications between ground control and the aircraft."
          }
        },
        "kind": "audioVisual",
        "startTimeMs": 23100,
        "endTimeMs": 43233,
        "width": 1080,
        "height": 608,
        "KeyFrameTimesMs": [
          23100,
          24833
          /*... (19 additional keyframes)*/
        ],
        "transcriptPhrases": [
          {
            "speaker": "Speaker 3",
            "startTimeMs": 24040,
            "endTimeMs": 29120,
            "text": "What we liked about cognitive services offerings were that they had a much higher fidelity.",
            "confidence": 0.937,
            "words": [
              {
                "startTimeMs": 24040,
                "endTimeMs": 24240,
                "text": "What"
              },
              {
                "startTimeMs": 24240,
                "endTimeMs": 24320,
                "text": "we"
              }
              /*... (13 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 3",
            "startTimeMs": 29600,
            "endTimeMs": 32880,
            "text": "And they sounded a lot more like an actual human voice.",
            "confidence": 0.823,
            "words": [
              {
                "startTimeMs": 29600,
                "endTimeMs": 30080,
                "text": "And"
              },
              {
                "startTimeMs": 30080,
                "endTimeMs": 30160,
                "text": "they"
              }
              /*... (9 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 4",
            "startTimeMs": 33680,
            "endTimeMs": 37200,
            "text": "Orlando ground 9555 requesting the end of pushback.",
            "confidence": 0.823,
            "words": [
              {
                "startTimeMs": 33680,
                "endTimeMs": 34160,
                "text": "Orlando"
              },
              {
                "startTimeMs": 34160,
                "endTimeMs": 34600,
                "text": "ground"
              }
              /*... (6 additional words)*/
            ],
            "locale": "en-US"
          },
          {
            "speaker": "Speaker 4",
            "startTimeMs": 38680,
            "endTimeMs": 41280,
            "text": "9555 request to end pushback received.",
            "confidence": 0.823,
            "words": [
              {
                "startTimeMs": 38680,
                "endTimeMs": 39600,
                "text": "9555"
              },
              {
                "startTimeMs": 39600,
                "endTimeMs": 40080,
                "text": "request"
              }
              /*... (4 additional words)*/
            ],
            "locale": "en-US"
          }
        ],
        "cameraShotTimesMs": [
          1467,
          3233
          /*... (13 additional camera shots)*/
        ]
      }
    ]
  },
  "usage": {
    "videoHours": 0.013,
    "contextualizationTokens": 12222,
    "tokens": {
      "gpt-4.1-mini-input": 8976,
      "gpt-4.1-mini-output": 439
    }
  }
}

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Last updated on 2026-01-13