Groq API

Groq Whisper — Syntax Cheatsheet

Auth

All requests: Authorization: Bearer <GROQ_API_KEY>

Base URL: https://api.groq.com

Transcription Endpoint

POST /openai/v1/audio/transcriptions
Content-Type: multipart/form-data
Authorization: Bearer $GROQ_API_KEY

Form fields

*Either file or url must be provided.

Response (json format)

{
  "text": "Your transcribed text appears here...",
  "x_groq": {
    "id": "req_unique_id"
  }
}

Response (verbose_json format)

{
  "text": "Full transcript...",
  "segments": [
    {
      "start": 0.0,
      "end": 5.2,
      "text": "Segment text..."
    }
  ],
  "x_groq": { "id": "req_unique_id" }
}

Available Models

Recommendation: Use whisper-large-v3 for best accuracy with domain-specific jargon.

Prompt Engineering for Jargon

The prompt field biases the model toward specific vocabulary. Include terms the caller might say:

Tekmetric, JSON, API, ROsearch, R-O Search, RO Search, voicemail, integration,
webhook, Shopmonkey, Mitchell, CARFAX, parts ordering, repair order, invoice,
DMS, shop management, estimate

Rules:

• Prompt should be in the same language as the audio
• Keep under ~224 tokens
• Include proper capitalization/spelling of domain terms
• Include common abbreviations and their expansions

Translation Endpoint

POST /openai/v1/audio/translations
Content-Type: multipart/form-data
Authorization: Bearer $GROQ_API_KEY

Translates audio into English. Same parameters as transcription minus language.

curl Example

curl https://api.groq.com/openai/v1/audio/transcriptions \
  -H "Authorization: Bearer $GROQ_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@./recording.mp3" \
  -F model="whisper-large-v3" \
  -F language="en" \
  -F prompt="Tekmetric, JSON, API, ROsearch" \
  -F response_format="json"

Go Client Pattern

func (c *GroqClient) Transcribe(ctx context.Context, audioData io.Reader, filename string) (string, error) {
    var buf bytes.Buffer
    w := multipart.NewWriter(&buf)

    w.WriteField("model", "whisper-large-v3")
    w.WriteField("language", "en")
    w.WriteField("response_format", "json")
    w.WriteField("prompt", "Tekmetric, JSON, API, ROsearch, R-O Search")

    part, err := w.CreateFormFile("file", filename)
    if err != nil {
        return "", fmt.Errorf("groq create form file: %w", err)
    }
    if _, err := io.Copy(part, audioData); err != nil {
        return "", fmt.Errorf("groq copy audio: %w", err)
    }
    w.Close()

    req, _ := http.NewRequestWithContext(ctx, "POST",
        "https://api.groq.com/openai/v1/audio/transcriptions", &buf)
    req.Header.Set("Authorization", "Bearer "+c.apiKey)
    req.Header.Set("Content-Type", w.FormDataContentType())

    resp, err := c.httpClient.Do(req)
    if err != nil {
        return "", fmt.Errorf("groq transcribe: %w", err)
    }
    defer resp.Body.Close()

    if resp.StatusCode != http.StatusOK {
        body, _ := io.ReadAll(resp.Body)
        return "", fmt.Errorf("groq transcribe: status %d: %s", resp.StatusCode, body)
    }

    var result struct {
        Text string `json:"text"`
    }
    if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
        return "", fmt.Errorf("groq decode: %w", err)
    }
    return result.Text, nil
}

Gotchas

• Max audio file size not explicitly documented per model; keep under 25MB
• prompt field is for vocabulary guidance only — it will not be prepended to transcript
• temperature=0 is the default and recommended for transcription accuracy
• whisper-large-v3-turbo is faster but may miss subtle jargon — test both
• Rate limits apply per-org; check x-ratelimit-* response headers
• Context window is 448 tokens for Whisper models (refers to internal decoder, not input audio length)

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