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O Speech-to-Text oferece dois modelos médicos, além dos outros
modelos de reconhecimento de fala padrão e aprimorado.
Os modelos médicos são adaptados especificamente para o reconhecimento de palavras
comuns em cenários médicos, como diagnósticos, medicamentos, sintomas,
tratamentos e condições. Se você quiser reconhecer esse tipo de dados de áudio,
é possível melhorar os resultados da transcrição usando esses modelos.
Existem dois modelos médicos, cada um personalizado para casos de uso específicos:
medical_conversation: para conversas entre um prestador de serviços médicos,
por exemplo, uma médica/enfermeira e um paciente. Use esse modelo
quando ambos os profissionais e um paciente estiverem falando. As palavras
faladas por cada pessoa são detectadas e rotuladas automaticamente na
transcrição retornada.
medical_dictation: para anotações ditadas por um único servidor de serviços médicos, por exemplo, um médico fazendo observações sobre os resultados do exame de sangue
de um paciente.
Use modelos médicos apenas com os recursos do Speech-to-Text a seguir. Os recursos
omitidos nesta lista não podem ser usados com nenhum modelo médico. O recurso
de pontuação automática é ativado por padrão.
Você receberá uma resposta JSON semelhante a esta:
"results": [
{
"alternatives": [
{
"transcript": "Um-hum . Yeah. Hello , good morning . Good
morning . So , tell me what's going on . Uh , sure , so , um , I
woke up probably three or four days ago , which , uh , wheezing and short of breath .
Okay , any cough or chest pain ? I cough infrequently , but no ,
uh , chest pain . Have you been exposed to anyone with covid ?
Uh , no , and I also took a test , which was negative . Uh , is it getting
worse , or better ? Uh , it has been getting a lot worse"
}
]
},
{
"alternatives": [
{
"transcript": "Okay . Was there something that triggered this exposure to cold , for
example ? Um , I had a gone hiking , and I got caught in the rain the day
before this all started ."
}
]
}
]
}
Pontuação falada
O modelo de ditado médico oferece suporte à pontuação falada para observações médicas. Esse
recurso é ativado por padrão e não pode ser desativado. A pontuação falada é
delineada por colchetes na transcrição de fala. Por exemplo, a transcrição
retornada pode ser semelhante a esta:
Patient could be showing signs of trauma [question mark] They said they were [quote] having elevated heart rate [unquote].
O Speech-to-Text oferece suporte à seguinte pontuação falada:
ponto final
vírgula
dois-pontos
Caps Lock
barra
traço
hífen
ponto de interrogação
ponto e vírgula
aspas
aspas de fechamento
fechar aspas
parêntese de abertura
parêntese de fechamento
parêntese de fechamento
Comandos de formatação
O modelo de ditado médico oferece suporte a comandos falados para observações de formatação. Esse
recurso é ativado por padrão e não pode ser desativado. Os comandos falados serão
delineados por colchetes na transcrição da fala. Por exemplo, a transcrição
retornada pode ser semelhante a esta:
[next line] Patient says they are experiencing fever [next point].
O Speech-to-Text oferece suporte aos seguintes comandos falados:
próximo ponto
próximo número
próximo parágrafo
Caps Lock
letras maiúsculas
nova linha
próximo item
próximo problema
próximo número de problema
próxima linha
próxima seção
próximo número
corrigir
corrigir isso
fim do ditado
Títulos falados
O modelo de ditado médico oferece suporte a títulos falados de observações ditadas. Esse
recurso é ativado por padrão e não pode ser desativado. Os títulos vão ser
delineados por colchetes e ficar em letras maiúsculas na transcrição. Por
exemplo, a transcrição retornada pode ser semelhante a esta:
[CURRENT MEDICATIONS] Patient is currently taking no medications.
O Speech-to-Text oferece suporte aos seguintes títulos falados:
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-09-02 UTC."],[],[],null,["# Recognize speech by using medical models\n\nSpeech-to-Text offers two *medical* models in addition the other\n[standard and enhanced speech recognition models](https://cloud.google.com/speech-to-text/docs/transcription-model).\nThe medical models are specifically tailored for recognition of words that\nare common in medical settings, such as diagnoses, medications, symptoms,\ntreatments, and conditions. If you want to recognize this type of audio data,\nyou can improve your transcription results by using these models.\n\nThere are two medical models, each tailored to specific use cases:\n\n- `medical_conversation`: for conversations between a medical provider---for example, a doctor or nurse---and a patient. Use this model when both a provider and a patient are speaking. Words uttered by each speaker are automatically detected and labeled in the returned transcript.\n- `medical_dictation`: for dictated notes spoken by a single medical provider---for example, a doctor dictating notes about a patient's blood test results.\n\nUse medical models only with the following Speech-to-Text features. Features\nomitted from this list can't be used with either medical model. The automatic\npunctuation feature is enabled by default.\n\n- [Automatic punctuation](/speech-to-text/docs/automatic-punctuation)\n- [Alternate transcriptions](/speech-to-text/docs/speech-to-text-requests#selecting_alternatives)\n- [Word timestamps](/speech-to-text/docs/async-time-offsets)\n- [Word-level confidence](/speech-to-text/docs/word-confidence)\n\nThe medical conversation model supports the following features:\n\n- [Speaker diarization](/speech-to-text/docs/multiple-voices)\n\nThe medical dictation model supports the following features:\n\n- [Spoken Punctuation](#spoken_punctuation)\n- [Formatting Commands](#formatting_commands)\n- [Spoken Headings](#spoken_headings)\n\nSend a transcription request\n----------------------------\n\n### REST\n\nThe following code sample uses the `medical_conversation` model to transcribe\nan audio file in a public Cloud Storage bucket.\n\n\nBefore using any of the request data,\nmake the following replacements:\n\n- \u003cvar translate=\"no\"\u003eLANGUAGE_CODE\u003c/var\u003e: the BCP-47 code of the language spoken in your audio clip. Medical models are only available for *en-US*.\n- \u003cvar translate=\"no\"\u003eENCODING\u003c/var\u003e: the encoding of the audio you want to transcribe. If you are using the public audio sample, the encoding is `LINEAR16`.\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: the alphanumeric ID of your Google Cloud project.\n\n\nHTTP method and URL:\n\n```\nPOST https://speech.googleapis.com/v1/speech:recognize\n```\n\n\nRequest JSON body:\n\n```\n{\n \"config\": {\n \"languageCode\": \"LANGUAGE_CODE\",\n \"encoding\": \"ENCODING\",\n \"model\": \"medical_conversation\"\n },\n \"audio\": {\n \"uri\": \"gs://cloud-samples-data/speech/medical_conversation_2.wav\"\n }\n}\n```\n\nTo send your request, expand one of these options:\n\n#### curl (Linux, macOS, or Cloud Shell)\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) , or by using [Cloud Shell](/shell/docs), which automatically logs you into the `gcloud` CLI . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nSave the request body in a file named `request.json`,\nand execute the following command:\n\n```\ncurl -X POST \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n -H \"x-goog-user-project: PROJECT_ID\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d @request.json \\\n \"https://speech.googleapis.com/v1/speech:recognize\"\n```\n\n#### PowerShell (Windows)\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nSave the request body in a file named `request.json`,\nand execute the following command:\n\n```\n$cred = gcloud auth print-access-token\n$headers = @{ \"Authorization\" = \"Bearer $cred\"; \"x-goog-user-project\" = \"PROJECT_ID\" }\n\nInvoke-WebRequest `\n -Method POST `\n -Headers $headers `\n -ContentType: \"application/json; charset=utf-8\" `\n -InFile request.json `\n -Uri \"https://speech.googleapis.com/v1/speech:recognize\" | Select-Object -Expand Content\n```\n\nYou should receive a JSON response similar to the following:\n\n```\n \"results\": [\n {\n \"alternatives\": [\n {\n \"transcript\": \"Um-hum . Yeah. Hello , good morning . Good\n morning . So , tell me what's going on . Uh , sure , so , um , I\n woke up probably three or four days ago , which , uh , wheezing and short of breath .\n Okay , any cough or chest pain ? I cough infrequently , but no ,\n uh , chest pain . Have you been exposed to anyone with covid ?\n Uh , no , and I also took a test , which was negative . Uh , is it getting\n worse , or better ? Uh , it has been getting a lot worse\"\n }\n ]\n },\n {\n \"alternatives\": [\n {\n \"transcript\": \"Okay . Was there something that triggered this exposure to cold , for\n example ? Um , I had a gone hiking , and I got caught in the rain the day\n before this all started .\"\n }\n ]\n }\n ]\n}\n```\n\n\u003cbr /\u003e\n\nSpoken punctuation\n------------------\n\nThe medical dictation model supports spoken punctuation for medical notes. This\nfeature is enabled by default, and cannot be disabled. Spoken punctuation is\ndelineated by brackets in the speech transcription. For example, your returned\ntranscription might look similar to the following:\n\n`Patient could be showing signs of trauma [question mark] They said they were [quote] having elevated heart rate [unquote]`.\n\nSpeech-to-Text supports the following spoken punctuation:\n\n- period\n- comma\n- colon\n- caps\n- slash\n- dash\n- hyphen\n- question mark\n- semicolon\n- quote\n- unquote\n- end quote\n- open parenthesis\n- close parenthesis\n- end parenthesis\n\nFormatting commands\n-------------------\n\nThe medical dictation model supports spoken commands for formatting notes. This\nfeature is enabled by default, and cannot be disabled. The spoken commands will\nbe delineated by brackets in the speech transcription. For example, your\nreturned transcription might look similar to the following:\n\n`[next line] Patient says they are experiencing fever [next point]`.\n\nSpeech-to-Text supports the following spoken commands:\n\n- next point\n- next number\n- next paragraph\n- caps\n- capitalization\n- new line\n- next item\n- next problem\n- next problem number\n- next row\n- next section\n- number next\n- scratch\n- scratch that\n- end dictation\n\nSpoken headings\n---------------\n\nThe medical dictation model supports spoken headings for dictated notes. This\nfeature is enabled by default, and cannot be disabled. The headings will be\ndelineated by brackets in the transcription and will be capitalized. For\nexample, your returned transcription might look similar to the following:\n\n`[CURRENT MEDICATIONS] Patient is currently taking no medications`.\n\nSpeech-to-Text supports the following spoken headings:\n\n- CHIEF COMPLAINT\n- CURRENT MEDICATIONS\n- DISCHARGE MEDICATIONS\n- DISCHARGE PLAN\n- FAMILY HISTORY\n- FINDINGS\n- REVIEW OF SYSTEMS\n- HISTORY OF PRESENT ILLNESS\n- INDICATIONS\n- LABS\n- PAST SURGICAL HISTORY\n- PHYSICAL EXAM\n- REVIEW OF SYSTEMS\n- RADIOLOGY"]]