Batch Speech-to-Text Translate (STTT) API Tutorial Using Saaras Model

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!pip install -U sarvamai

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API_KEY = "YOUR_API_KEY_HERE"

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from pathlib import Path
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from sarvamai import SarvamAI
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API_KEY = "YOUR_API_KEY_HERE"
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audio_files = ["/path/to/your/audio1.mp3", "/path/to/your/audio2.mp3"]  # Update with your file paths
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output_dir = Path("/output")
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output_dir.mkdir(exist_ok=True)
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def run_sttt_sync():
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    client = SarvamAI(api_subscription_key=API_KEY)
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    # Create and configure batch STTT job
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    job = client.speech_to_text_translate_job.create_job(
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        model="saaras:v2.5",
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        with_diarization=True,
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        num_speakers=2,
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        prompt="Official meeting"
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    )
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    print(f"Job created: {job._job_id}")
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    # Upload and process files
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    job.upload_files(file_paths=audio_files, timeout=120.0)
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    job.start()
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    print("Translation started...")
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    # Wait for completion
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    job.wait_until_complete(poll_interval=5, timeout=600)
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    # Check file-level results
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    file_results = job.get_file_results()
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    print(f"\nSuccessful: {len(file_results['successful'])}")
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    for f in file_results['successful']:
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        print(f"  ✓ {f['file_name']}")
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    print(f"\nFailed: {len(file_results['failed'])}")
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    for f in file_results['failed']:
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        print(f"  ✗ {f['file_name']}: {f['error_message']}")
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    # Handle all files failed
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    if len(file_results['successful']) == 0:
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        print("\nAll files failed.")
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        return
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    # Download outputs for successful files
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    job.download_outputs(output_dir=str(output_dir))
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    print(f"\nDownloaded {len(file_results['successful'])} file(s) to: {output_dir}")
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run_sttt_sync()

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import asyncio
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from pathlib import Path
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from sarvamai import AsyncSarvamAI
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API_KEY = "YOUR_API_KEY_HERE"
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audio_files = ["/path/to/your/audio1.mp3", "/path/to/your/audio2.mp3"]  # Update with your file paths
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output_dir = Path("/output")
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output_dir.mkdir(exist_ok=True)
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async def run_sttt_async_job():
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    client = AsyncSarvamAI(api_subscription_key=API_KEY)
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    # Create and configure batch STTT job
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    job = await client.speech_to_text_translate_job.create_job(
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        model="saaras:v2.5",
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        with_diarization=True,
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        num_speakers=2,
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        prompt="Official meeting"
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    )
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    print(f"Job created: {job._job_id}")
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    # Upload and process files
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    await job.upload_files(file_paths=audio_files, timeout=120.0)
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    await job.start()
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    print("Translation started...")
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    # Wait for completion
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    await job.wait_until_complete(poll_interval=5, timeout=600)
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    # Check file-level results
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    file_results = await job.get_file_results()
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    print(f"\nSuccessful: {len(file_results['successful'])}")
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    for f in file_results['successful']:
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        print(f"  ✓ {f['file_name']}")
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    print(f"\nFailed: {len(file_results['failed'])}")
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    for f in file_results['failed']:
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        print(f"  ✗ {f['file_name']}: {f['error_message']}")
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    # Handle all files failed
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    if len(file_results['successful']) == 0:
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        print("\nAll files failed.")
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        return
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    # Download outputs for successful files
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    await job.download_outputs(output_dir=str(output_dir))
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    print(f"\nDownloaded {len(file_results['successful'])} file(s) to: {output_dir}")
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# For Jupyter environments:
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import nest_asyncio
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nest_asyncio.apply()
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await run_sttt_async_job()