Speaker Diarization not working for merged raw PCM audio (all detected as Speaker 0)

[sly isle]

I need help with a diarization issue.

I’m using Deepgram STT with diarization enabled. For normal recorded audio files (e.g., WAV, MP3) the speaker separation works perfectly — Deepgram correctly identifies speaker 0, speaker 1, etc.

But when I generate audio by merging raw 16-bit PCM chunks (recorded via AudioWorklet in the browser) and then convert it into a single audio file, Deepgram fails to separate speakers. It returns only speaker: 0 for all utterances — both the user and the agent.

Details:

Raw PCM, 16-bit, mono, 16kHz

PCM chunks concatenated programmatically

Final file plays normally

Diarization = true

All segments are labeled speaker 0

Questions:

1. Does Deepgram require additional metadata (channel info, timestamps, etc.) for diarization to work properly on raw PCM merged audio?

2. Is there a recommended way to merge PCM chunks so diarization is preserved?

3. Should I convert PCM → WAV first and embed channel info?

4. Are there known limitations with diarization on mono PCM?

[dry rune]

Hey there! This channel allows you to ask Kapa.ai for assistance. But it looks like you haven't connected your Discord profile to your Deepgram account yet. You can do so at https://community.deepgram.com - by doing so, you'll be able to ask questions in #1115960287183511643 if you need human assistance!

[hollow orbit]

Hi @sly isle. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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Certainly! Here are answers to your questions about Deepgram diarization and raw PCM audio, based strictly on the provided knowledge sources:

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1. Does Deepgram require additional metadata (channel info, timestamps, etc.) for diarization to work properly on raw PCM merged audio?

Deepgram does not require additional metadata like timestamps for diarization, but it does need to know the audio format details (encoding and sample rate) when processing raw PCM audio, especially for streaming. For pre-recorded (batch) transcription, Deepgram expects containerized audio (like WAV or MP3) where format information is embedded in the file header. If you send raw PCM, you must specify the encoding and sample rate in your request for streaming, but for batch transcription, Deepgram may not process raw PCM correctly without a container header, which can affect diarization and transcription results. There is no mention in the sources that additional metadata (beyond format/encoding) is required for diarization to function, but the lack of a proper container can cause issues with processing the audio at all, including diarization failures Determining Your Audio Format for Live Streaming Audio.

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2. Is there a recommended way to merge PCM chunks so diarization is preserved?

The knowledge sources do not provide a specific method for merging PCM chunks for diarization. However, they do emphasize that for batch transcription, Deepgram expects containerized audio formats (like WAV), not raw PCM. If you concatenate PCM chunks, you should convert the result to a standard audio container (e.g., WAV) before sending it to Deepgram. This ensures the audio is properly recognized and processed, which is necessary for diarization to work I cannot transcribe a local file of PCM data.

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3. Should I convert PCM → WAV first and embed channel info?

Yes, you should convert your merged PCM data to a WAV file before sending it to Deepgram for batch transcription. The WAV header will provide Deepgram with the necessary audio format information. If you have multichannel audio (e.g., stereo with each speaker on a separate channel), you can embed that info in the WAV file, and then use Deepgram's multichannel or diarization features as appropriate. For mono audio, diarization is still supported, but the WAV container ensures the audio is interpreted correctly Which request parameters are correct for my audio?.

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4. Are there known limitations with diarization on mono PCM?

Yes, there are some limitations:

• Diarization works best when there is enough audio quality and speaker differentiation. Low-quality or low-bitrate audio can cause diarization to fail, resulting in all words being labeled as speaker 0 Speaker diarization not working.
• For mono audio, diarization is appropriate when all speakers are on the same channel. However, diarization accuracy can be reduced if the audio is short, of poor quality, or if speakers overlap significantly Improving Diarization by Prepending Audio from the Primary Speaker.
• If you need perfect speaker separation and each speaker is on a separate channel, use multichannel audio and specify this in your request. Diarization is for distinguishing speakers on a single channel, while multichannel is for audio where each speaker is isolated to their own channel When To Use Multichannel and Diarization.

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Summary:

• For best results, merge your PCM chunks and convert the result to a WAV file before sending to Deepgram.
• Diarization does not require extra metadata, but proper audio format/container is essential.
• Mono audio is supported, but diarization accuracy depends on audio quality and speaker separation.
• If you have multichannel audio, use a container that preserves channel info and use Deepgram's multichannel feature.

If you continue to see all words labeled as speaker 0 after converting to WAV and ensuring good audio quality, it may be due to the nature of the audio (e.g., not enough speaker differentiation, short duration, or overlapping speech) Diarization not working.

If you need further help with the conversion process (e.g., using ffmpeg to convert PCM to WAV), let me know!

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

@sly isle Kapa.ai is still learning and improving, please let me know how I did by reacting below.

[sly isle]

I have a question about Deepgram’s speaker diarization behavior.

I’m testing diarization on multiple audio files. I noticed something strange:

✅ For 4+ minute recordings → diarization works (speaker 0, speaker 1 separated correctly)

❌ For 1–2 minute recordings → diarization fails and everything is labeled as speaker: 0

The audio format, pipeline, and transcription settings are exactly the same — the only difference is duration.

Questions:

1. Is there a minimum audio duration required for diarization to work?

2. Does diarization need a certain amount of audio per speaker to classify them reliably?

3. Are short files (1–2 minutes) known to be problematic?

4. Any recommended configuration or parameters to improve diarization on shorter audio files?

Diarization is enabled like this:

{"diarization": true, "model": "nova-3-general"}

I’ve confirmed the audio contains multiple speaking turns.