What should I use for automatic speech recognition if I need timestamps and speaker separation?

If you need automatic speech recognition (ASR) with timestamps and speaker separation, you usually want speaker diarization + ASR as a combined pipeline.

Best options

1. Whisper + diarization

• ASR: OpenAI Whisper or Whisper-based models
• Speaker separation: a diarization model like pyannote.audio
• Why use it: Good accuracy, flexible, widely used
• Output: word/segment timestamps plus speaker labels if you combine the two

This is a common open-source choice:

• WhisperX is especially popular because it combines:
  • Whisper transcription
  • word-level timestamps
  • speaker diarization support

2. Cloud speech APIs

If you want something managed and easier to deploy:

• Google Cloud Speech-to-Text
• Azure Speech
• AWS Transcribe
• AssemblyAI
• Deepgram

These often provide:

• timestamps
• speaker diarization
• punctuation
• sometimes word-level timing

Recommended picks

• Best open-source: WhisperX
• Best managed/simple API: Deepgram or AssemblyAI
• Best enterprise cloud integration: Google / Azure / AWS

Important note

“Speaker separation” can mean:

• diarization = who spoke when
• separation/source separation = splitting overlapping voices into separate audio streams

For most ASR use cases, you want diarization, not true audio source separation.

If you want, I can recommend the best option based on:

1. open-source vs cloud
2. real-time vs batch
3. budget / accuracy / latency needs