How to enable speaker diarization

Speaker diarization identifies and labels different speakers in your audio, making it easy to know “who said what.” This is ideal for meetings, interviews, podcasts, and call center recordings.

Key Features

• Automatic speaker detection
• Support for up to 20 speakers
• Speaker-wise transcription with timestamps

Parameters

Example Code

from sarvamai import SarvamAI
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
# Create batch job with diarization
job = client.speech_to_text_job.create_job(
    model="saaras:v4",
    language_code="hi-IN",
    mode="transcribe",
    with_diarization=True
)
# Upload audio files
job.upload_files(file_paths=["meeting_recording.mp3"])
# Start processing
job.start()
# Wait for completion
job.wait_until_complete()
# Download results
job.download_outputs(output_dir="./output")

Output Format

When with_diarization=True is passed, the response includes a diarized_transcript field with speaker information:

{
  "request_id": "20260130_d8d2c0e6-1eb6-4982-8045-b267d5165c44",
  "transcript": "Full transcript text...",
  "timestamps": {
    "words": ["Hello, how can I help you today?", "I have a question about my order."],
    "start_time_seconds": [0.01, 2.8],
    "end_time_seconds": [2.5, 5.2]
  },
  "diarized_transcript": {
    "entries": [
      {
        "transcript": "Hello, how can I help you today?",
        "start_time_seconds": 0.01,
        "end_time_seconds": 2.5,
        "speaker_id": "0"
      },
      {
        "transcript": "I have a question about my order.",
        "start_time_seconds": 2.8,
        "end_time_seconds": 5.2,
        "speaker_id": "1"
      }
    ]
  },
  "language_code": "en-IN"
}

Each entry contains:

• transcript: The text spoken by the speaker
• start_time_seconds: When the speaker started speaking (float)
• end_time_seconds: When the speaker stopped speaking (float)
• speaker_id: Unique identifier for the speaker (e.g., “0”, “1”)

Use Cases

→ Full Batch API Documentation