Thanks — I can help with that. Quick clarification so I give the right instructions: • Which environment or library are you using

word_tokenizer

in? (For example: Python NLTK, spaCy, a JavaScript library, a specific SDK, or a custom/internal function.) • What is your goal with tokenization? (e.g., splitting text into words for NLP preprocessing, keeping punctuation, lowercasing, handling emojis, language-specific tokenization.) • If you have any sample text or code you’ve tried, paste it here so I can tailor examples and troubleshoot errors. Once you tell me the environment and goal, I’ll provide a short, runnable example and common options (preserving punctuation, removing stopwords, handling contractions) plus debugging tips.

Thanks — this helps. I can definitely guide you, but I need a couple quick clarifications so I give the right code and examples. 1. Do you expect

word_tokenizer

to be a function provided by Deepgram (docs or example) or from a third‑party Python package (NLTK, Hugging Face tokenizers, spaCy, etc.)? If possible, paste the exact code/snippet where you saw

word_tokenizer

. 2. Which Deepgram SDK/version are you using (pip package name + version) or are you calling Deepgram via HTTP requests? 3. What is your goal with tokenization for TTS? ◦ Preparing/normalizing text before sending to Deepgram TTS? ◦ Creating word-level timing or aligning words to audio? ◦ Another purpose (e.g., splitting for SSML, handling punctuation/contractions)? 1. Share a small example of input text and the output you want (e.g., ["Hello", "world"] or ["Hello,", "world"]). Also paste any code you’ve tried and any error messages. If you just want a quick recommendation while you share details: using a standard Python tokenizer such as NLTK’s

word_tokenize

or spaCy’s tokenizer is common for preparing text. If you want, I can provide a short runnable example for: • NLTK

word_tokenize

• spaCy tokenizer • Hugging Face tokenizers Tell me which one you prefer and your answers to 1–4 and I’ll send a focused example for Deepgram TTS usage.

Thanks — the snippet helps. I can walk you through how to use the

word_tokenizer

parameter, but I need one quick clarification so I give a precise, runnable example. Are you planning to: • Provide a custom tokenizer instance (an object) that implements a

tokenize

method (or similar), or • Pass a function that accepts a string and returns a list of word tokens, or • Use a built-in tokenizer from a library (NLTK, spaCy, Hugging Face) and let the SDK consume it directly? While you answer, here are the three common ways to use

word_tokenizer

and examples you can adapt. Pick which approach you want and I’ll produce code tailored to your environment. 1) Pass a simple function • Provide a plain Python function that takes the input text and returns a list of tokens (e.g., ["Hello", "world"]). This is the simplest if the SDK accepts callables. 2) Provide an object with a

tokenize

method • If the SDK expects a tokenizer object, create a small class with a

tokenize(self, text)

method that returns tokens. This is useful if you need state or configuration. 3) Use a third-party tokenizer and wrap it • Use NLTK, spaCy, or Hugging Face tokenizer and either pass it directly (if compatible) or wrap it in a small adapter function/class so it matches what the SDK expects. Tell me which option you prefer and: • The Deepgram SDK version you’re using (or how you call TTS), • A short sample input string, and I’ll return a ready-to-run example (async if you’re using aiohttp/aio clients) showing how to supply

word_tokenizer

to your

TTS

class and how the output is used for TTS (e.g., splitting for SSML, word-level timing, etc.).