> For the complete documentation index, see [llms.txt](https://navana.gitbook.io/bodhi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://navana.gitbook.io/bodhi/quickstart/non-streaming-api/advanced-features.md). # Advanced Features ### :mag: Context Biasing (Hotwords) You can boost recognition of important or uncommon phrases by specifying hotwords during the request. #### Using Hotwords Define your hotwords as a JSON array. You can specifiy a higher "boosting score" if you would like to provide extra emphasis to longer phrases (recommended!). Currently, the default score applied is `1.5` which should be sufficient for single words. ```bash curl --location 'https://bodhi.navana.ai/api/transcribe' \ --header 'x-customer-id: ' \ --header 'x-api-key: ' \ --form 'transaction_id=' \ --form 'audio_file=@""' \ --form 'model="hi-banking-v2-8khz"' \ --form 'hotwords="[{\"phrase\":\"बोधी\"},{\"phrase\":\"स्पीच रिकग्निशन\",\"score\":4.5}]"' ``` #### Best Practices | Best Practice | Description | | -------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | | ✅ Use uncommon words | Target domain-specific or rare phrases like `"`बोधी स्पीच रिकग्निशन`"` | | ✅ Use local script | Always write in Devanagari (e.g. बोधी, not `bodhi`) | | ✅ Avoid punctuation | Remove quotes, commas, periods | | ✅ Use higher scores for longer phrases |

e.g. "बोधी स्पीच रिकग्निशन " -> 2.5 vs

"बोधी" -> 1.5

| > Avoid copying hotwords from other providers without validation. Bodhi may already support commonly spoken Hindi words natively. #### Warnings * Avoid very short particles like `"का"`, `"की"`, `"ए"`, etc. * Don’t boost every word in a sentence — only uncommon or error-prone segments. * Phrases work better for commonly missed phrases, individual tokens are better for rare words. * Avoid boosting words that already work as is. *** ### 🔢 Parse Numbers into Numerals Bodhi supports converting spoken number words into actual digits using the `parse_number` flag in the form values. This is useful when transcribing sentences that include monetary values, phone numbers, addresses, or quantities — especially for use cases like banking, insurance, and logistics. ```bash curl --location 'https://bodhi.navana.ai/api/transcribe' \ --header 'x-customer-id: ' \ --header 'x-api-key: ' \ --form 'transaction_id=""' \ --form 'audio_file=@""' \ --form 'model="hi-banking-v2-8khz"' \ --form 'parse_number="True"' ``` #### 🧾 Example | Mode | Output | | ----------------------------- | ------------------------------------------------ | | **Without `parse_number`** | `"घर बनाने के लिए मुझे पच्चीस लाख का लोन चाहिए"` | | **With `parse_number: True`** | `"घर बनाने के लिए मुझे 2500000 का लोन चाहिए"` | *** #### 🌐 Language Support This feature is currently available for: * Hindi (`hi`) * Malayalam (`ml`) * Kannada (`kn`) * Gujarati (`gu`) * Marathi (`mr`) > Want support for another language? Reach out to *** ### 📦 Aux Metadata Set `aux: True` in your form values to receive **server-side diagnostic metadata** along with your transcript response. This is useful for logging, benchmarking, or correlating timestamps across systems. #### ```bash curl --location 'https://bodhi.navana.ai/api/transcribe' \ --header 'x-customer-id: ' \ --header 'x-api-key: ' \ --form 'transaction_id=""' \ --form 'audio_file=@""' \ --form 'model="hi-banking-v2-8khz"' \ --form 'aux="true"' ``` *** #### 📘 What You Get When enabled, each final transcript message will include an `aux_info` block: ```json "aux_info": { "request_time": 0.273680048, "received_request_time": "2025-05-19T09:44:50.975311686Z", "segments_meta": [ { "tokens": [ " घ", "र", " बना", "ने", " के", " लिए", " मुझे", " प", "च", "्", "च", "ी", "स", " लाख", " का", " ल", "ो", "न", " चाहिए" ], "timestamps": [ 1, 1.16, 1.4399999, 1.7199999, 1.8399999, 2, 2.24, 2.44, 2.48, 2.6399999, 2.6799998, 2.72, 2.76, 2.9199998, 3.12, 3.28, 3.32, 3.4399998, 3.72 ], "start_time": 0, "end_time": 3.72, "text": " घर बनाने के लिए मुझे पच्चीस लाख का लोन चाहिए", "confidence": 0.8847437 } ], "confidence": 0.8847437 } ``` | Field | Description | | ----------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `request_time (float)` | Total time in seconds that the server spent handling this request (excluding network transfer delays). | | `received_request_time (timestamp)` | The timestamp (UTC) when the server received the initial WebSocket connection or request. | | `segments_meta (array of objects)` |

Detailed view of all segment objects (transcripts separated by silences) recognized for the audio file provided. Each segment object has the following information:

  • tokens: Array of strings representing individual text pieces (or "tokens") recognized from the segment. Tokens may include words or parts of words.

  • timestamps: Array of numerical values indicating when each token was detected in the segment (in seconds). Each timestamp aligns with the tokens array, so the i-th timestamp represents the time at which the i-th token was spoken. Useful for measuring latency.

  • start\_time: Starting point (in seconds) of the current segment in the overall audio timeline.
  • end\_time: Ending point (in seconds) of the current segment in the overall audio timeline.
  • text: Transription belonging to the current segment

  • confidence: Confidence score (float between 0 and 1) for the model’s prediction for this segment.

| | `confidence` |

Confidence score (float between 0 and 1) for the model’s prediction for the entire audio.

Note: This is an average of all segment confidences.
Note: This field will not be present if the model does not predict any text for the audio.

| This can help you: * Profile server-side performance * Track session start times * Debug slow or idle sessions * Assessing how confident the model is about its prediction