I want it to always listen to me but when I say hey water thats when it will print out saying hello, using the grok api jus tell me in to where i put in the api key, this a website so write it in html, css, and js but write it all in the html file. down below is doc of how to use it....Speech
Groq API is the fastest speech-to-text solution available, offering OpenAI-compatible endpoints that enable real-time transcriptions and translations. With Groq API, you can integrate high-quality audio processing into your applications at speeds that rival human interaction.

API Endpoints
We support two endpoints:

Transcriptions:

Usage: Convert audio to text.
API Endpoint: https://api.groq.com/openai/v1/audio/transcriptions
Translations:

Usage: Translate audio to English text.
API Endpoint: https://api.groq.com/openai/v1/audio/translations
Supported Models
Distil-Whisper English

Model ID: distil-whisper-large-v3-en
Description: Distil-Whisper English is a distilled, or compressed, version of OpenAI's Whisper model, designed to provide faster, lower cost English speech recognition while maintaining comparable accuracy.
Supported Language(s): English-only
Whisper large-v3

Model ID: whisper-large-v3
Description: Whisper large-v3 provides state-of-the-art performance with high accuracy for multilingual transcription and translation tasks.
Supported Language(s): Multilingual

Performance Comparisons
Compared to Whisper large-v3, Distil-Whisper English is 2 times faster and 49% smaller, with only 756 million parameters versus Whisper large-v3's 1.55 billion. Despite this reduction in size, Distil-Whisper English performs remarkably well, achieving a Word Error Rate (WER) within 1% of Whisper large-v3 on both short-form and long-form transcriptions. The distilled model excels in robustness to noise and shows reduced hallucination, with 1.3 times fewer instances of repeated 5-gram word duplicates and a 2.1% reduction in insertion error rate compared to Whisper large-v3.

Audio File Limitations
Max File Size: 25 MB
Minimum File Length: .01 seconds
Supported File Types: mp3, mp4, mpeg, mpga, m4a, wav, webm
Single Audio Track: Only the first track will be transcribed for files with multiple audio tracks. (e.g. dubbed video)
Supported Response Formats: json, verbose_json, text
Preprocessing Audio Files
Our speech-to-text models will downsample audio to 16,000 Hz mono before transcribing. This preprocessing can be performed client-side to reduce file size and allow longer files to be uploaded to Groq.


The following ffmpeg command can be used to reduce file size:


CopyCopy code
ffmpeg \
  -i <your file> \
  -ar 16000 \
  -ac 1 \
  -map 0:a: \
  <output file name>
Transcription Endpoint Usage
The transcription endpoint allows you to transcribe spoken words in audio or video files. You can provide optional request parameters to customize the transcription output.


Optional Request Parameters
prompt:
Description: Provide context or specify how to spell unfamiliar words (limited to 224 tokens).
Type: string
Default: None
response_format:
Description: Define the output response format.
Type: string
Default: json
Set to verbose_json to receive timestamps for audio segments.
Set to text to return a text response.
temperature:
Description: Specify a value between 0 and 1 to control the translation output.
Type: float
Default: None
language:
whisper-large-v3 only!
Description: Specify the language for transcription.
Type: string
Default: None (Models will auto-detect if not specified)
Use ISO 639-1 language codes (e.g. "en" for English, "fr" for French, etc.).
Specifying a language may improve transcription accuracy and speed.
Example Usage
Python
JavaScript
curl

The Groq SDK package can be installed using the following command:

Copy
npm install --save groq-sdk

The following code snippet demonstrates how to use Groq API to transcribe an audio file in JavaScript:

Copy
import fs from "fs";
import Groq from "groq-sdk";

// Initialize the Groq client
const groq = new Groq();

async function main() {
  // Create a transcription job
  const transcription = await groq.audio.transcriptions.create({
    file: fs.createReadStream("sample_audio.m4a"), // Required path to audio file - replace with your audio file!
    model: "distil-whisper-large-v3-en", // Required model to use for transcription
    prompt: "Specify context or spelling", // Optional
    response_format: "json", // Optional
    language: "en", // Optional
    temperature: 0.0, // Optional
  });
  // Log the transcribed text
  console.log(transcription.text);
}
main();
Translation Endpoint Usage
The translation endpoint allows yout to translate spoken words in audio or video files to English. You can provide optional request parameters to customize the translation output.


Optional Request Parameters:
prompt:
Description: Provide context or specify how to spell unfamiliar words (limited to 224 tokens).
Type: string
Default: None
response_format:
Description: Define the output response format.
Type: string
Default: json
Set to verbose_json to receive timestamps for audio segments.
Set to text to return a text response.
temperature:
Description: Specify a value between 0 and 1 to control the translation output.
Type: float
Default: None
Example Usage
Python
JavaScript
curl

The Groq SDK package can be installed using the following command:

Copy
npm install --save groq-sdk

The following code snippet demonstrates how to use Groq API to translate an audio file in JavaScript:

Copy
import fs from "fs";
import Groq from "groq-sdk";

// Initialize the Groq client
const groq = new Groq();
async function main() {
  // Create a translation job
  const translation = await groq.audio.translations.create({
    file: fs.createReadStream("sample_audio.m4a"), // Required path to audio file - replace with your audio file!
    model: "whisper-large-v3", // Required model to use for translation
    prompt: "Specify context or spelling", // Optional
    response_format: "json", // Optional
    temperature: 0.0, // Optional
  });
  // Log the transcribed text
  console.log(translation.text);
}
main();
Prompting Guidelines
The prompt parameter is an optional input of max 224 tokens that allows you to provide contextual information to the model, helping it maintain a consistent writing style.


How It Works


When you provide a prompt parameter, the speech-to-text model treats it as a prior transcript and follows its style, rather than adhering to the actual content of the audio segment. This means that the model will not:

Attempt to execute commands contained within the prompt
Follow instructions present in the prompt
In contrast to chat completion prompts, the prompt parameter is designed solely to provide stylistic guidance and contextual information to the model, rather than triggering specific actions or responses.


Best Practices

Provide contextual information about the audio segment, such as the type of conversation, topic, or speakers involved.
Use the same language as the language of the audio file.
Steer the model's output by denoting proper spellings or emulate a specific writing style or tone.
Keep the prompt concise and focused on stylistic guidance.
Use Cases
Groq API offers low latency and fast inference for speech recognition and transcription, enabling developers to build a wide range of highly accurate, real-time applications, such as:

Real-Time Audio Translation: Translate audio files or real-time audio streams to break language barriers and facilitate global communication.
Customer Service: Create real-time, AI-powered customer service solutions that use speech recognition to route calls, transcribe conversations, and respond to customer inquiries.
Automated Speech-to-Text Systems: Implement automated speech-to-text systems in industries like healthcare, finance, and education, where accurate transcription is critical for compliance, record-keeping, and decision-making.
Voice-Controlled Interfaces: Develop voice-controlled interfaces for smart homes, cars, and other devices, where fast and accurate speech recognition is essential for user experience and safety.
We can't wait to see what you build! 🚀