STT Endpoint

Configure the Speech-to-Text (STT) endpoint for transcription.

Overview

Intervu uses an OpenAI-compatible STT endpoint to transcribe audio in real-time. We recommend Speaches for local, private transcription.

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Configuration

Open Settings (gear icon) and locate the STT section:

Setting	Description	Example
STT Endpoint	URL for the STT API	http://localhost:8000/v1/audio/transcriptions
STT Model	Model identifier	Systran/faster-distil-whisper-small.en
STT API Key	API key (if required)	Leave empty for Speaches

Default Values

STT Endpoint: http://localhost:8000/v1/audio/transcriptions
STT Model: Systran/faster-distil-whisper-small.en
STT API Key: (empty)

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Testing the Connection

After configuring the endpoint:

1. Click Test STT button in Settings
2. Intervu sends a silent audio sample to verify the connection
3. A success message confirms the endpoint is working

Test First

Always test the STT endpoint before starting an interview. This catches configuration errors early.

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Model Selection

Choose a model based on your needs:

Model	Speed	Accuracy	VRAM	Languages
Systran/faster-distil-whisper-small.en	⚡ Fast	Good	~1GB	English only
Systran/faster-distil-whisper-medium.en	Medium	Better	~2GB	English only
Systran/faster-whisper-small.en	Fast	Good	~1GB	Multilingual
openai/whisper-medium	Slow	Best	~5GB	Multilingual

Recommendations

• Real-time interviews: Use faster-distil-whisper-small.en for best speed
• Non-English: Use faster-whisper-small.en or multilingual models
• Maximum accuracy: Use whisper-medium or whisper-large

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Advanced STT Settings

In Settings → Advanced:

Silence RMS Threshold

Audio below this level is treated as silence and not sent to STT.

• Default: 0.005
• Higher: More aggressive filtering (may miss quiet speech)
• Lower: Less filtering (may send noise to STT)

Audio Chunk Duration

Length of audio chunks sent to STT.

• Default: 3 seconds
• Lower: Faster response, but more API calls
• Higher: Fewer API calls, but slower response

Hallucination Phrases

Comma-separated phrases that should be ignored.

• Default: you,thank you,thanks,thanks for watching,thank you for watching,thanks for listening,thank you for listening,bye,goodbye,the end,so,okay,hmm,uh,um,oh,ah,i
• Purpose: Whisper sometimes hallucinates these phrases from silence

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Using Other STT Services

Intervu supports any OpenAI-compatible STT endpoint.

OpenAI Whisper API

Endpoint: https://api.openai.com/v1/audio/transcriptions
Model: whisper-1
API Key: sk-...

Azure Speech Services

Endpoint: https://your-region.api.cognitive.microsoft.com/openai/deployments/whisper/audio/transcriptions?api-version=2024-02-15-preview
Model: whisper
API Key: your-azure-key

Custom Endpoint Requirements

Your endpoint must accept:

• POST request to /v1/audio/transcriptions
• Multipart form with audio file
• model parameter
• Return JSON: { text: "transcribed text" }

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Troubleshooting

Connection Failed

• Verify Speaches is running: docker ps
• Check the endpoint URL is correct
• Try curl http://localhost:8000/v1/models

Slow Transcription

• Use a smaller model
• Ensure GPU acceleration is working
• Check Speaches logs: docker logs speaches

Poor Accuracy

• Speak clearly and close to microphone
• Try a larger model
• Adjust silence threshold in Advanced settings

Hallucinated Text

• Check hallucination phrases in Advanced settings
• Add common false positives to the list

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Next Steps

• LLM Endpoint — Configure answer generation
• Advanced Settings — Fine-tune behavior