Navana Streaming ASR Instructions

How to use

Connection Instructions for streaming

• Endpoint: Websocket streaming speech API endpoint

• wss://bodhi.navana.ai

• Sample Script:

• streaming.py (for static audio files)

• streaming-microphone.py (for real-time audio capture from the microphone)

Connection Instructions for non streaming

• Endpoint: Websocket streaming speech API endpoint

• https://bodhi.navana.ai/api/transcribe

• Sample Script:

• non-streaming-api.py (for local audio files)

Access Token

Store the authentication headers in env to access the streaming speech API endpoints:

$  export  API_KEY=YOUR_API_KEY

$  export  CUSTOMER_ID=YOUR_CUSTOMER_ID

Brief Description of Response Format

The received response format will be a JSON object.

{
  "call_id": "CALL_ID",

  "text": "TRANSCRIPT",

  "segment_id": "SEGMENT_ID",

  "eos": false,

  "type": "partial"
}

Note: This JSON structure outlines the fields returned in responses. However, segment_id, eos, and type are exclusive to streaming responses.

Keys Description

• Call_id:

• Unique identifier associated with every streaming connection

• Segment_id:

• Unique identifier associated with every speech segment during the entire active socket connection

• Text:

• If type = "partial"

• Partial transcript corresponding to every streaming audio chunk

• Partial transcripts for every audio chunk (will be for a 100ms audio chunk if streaming audio packet size is 100ms)

• If type = "complete"

• Complete/final transcript generated for each speech segment

• Generated once per segment_id i.e., when the speech segment end is reached

• eos:

• If 'eos' is true, marks the end of the streaming connection

Install packages

$  pip  install  -r  requirements.txt

Usage

$  python  streaming.py  -f  loan.wav

OR

$  python  streaming-microphone.py

OR

$  python3  non-streaming-api.py  -f  loan.wav

Options:

-f: File name of the audio file to be streamed.

Configuring the websocket

After connecting to the websocket, you are required to send a configuration object specifying the model you would like to interact with amongst other options. You can do so in the following fashion:

await ws.send(
                json.dumps(
                    {
                        "config": {
                            "sample_rate": sample_rate, // Required - specify the sample rate of the audio being streamed to the server. 
                            "transaction_id": str(uuid.uuid4()), // Required - generate a unique UUID to tag the session
                            "model": "hi-general-v2-8khz", // Required - specify the model you would like to use 
                            "parse_number" : True, // Optional - convert text representing numbers into numericals
                            "exclude_partial": True,  // Optional - only provide complete responses
                        }
                    }
                )
            )

Audio Stream Requirements

To ensure optimal compatibility and performance with our audio processing system, please adhere to the following audio stream requirements:

• Encoding/Bit Depth: 16Bit PCM with a 2 Byte depth, providing high-quality audio representation.

• Minimum Sample Rate: The audio must have a sample rate of at least 8000Hz.

• Fixed Streaming Rate: Audio packets should be streamed at (chunk_duration_ms) a fixed size (50 - 500 ms), ensuring consistent data flow. We recommend using 100 ms as shown in the example script.

• Channels: Audio must be single-channel (Mono) to ensure compatibility with our processing pipeline.

• Speakers: Initially, support is provided for a single speaker per channel. However, support for multiple speakers on a single channel is under development and will be announced soon.

Available ASR Models for Testing

• Hindi: hi-general-v2-8khz

• Hindi-Banking: hi-banking-v2-8khz

• Kannada: kn-general-v2-8khz

• Kannada-Banking: kn-banking-v2-8khz

• Marathi: mr-general-v2-8khz

• Marathi Banking: mr-banking-v2-8khz

• Tamil: ta-general-v2-8khz

• Tamil Banking: ta-banking-v2-8khz

• Bengali bn-general-v2-8khz

• Bengali Banking bn-banking-v2-8khz

• English en-general-v2-8khz

• English Banking en-banking-v2-8khz

• Gujarati gu-general-v2-8khz

• Gujarati Banking gu-banking-v2-8khz

• Telugu te-general-v2-8khz

• Telugu Banking te-banking-v2-8khz

• Malayalam ml-general-v2-8khz

• Malayalam Banking ml-banking-v2-8khz

For testing the code, modify the .py file with the model name you want to use.