Voice Activity Detection (VAD):

• To significantly reduce hallucinations, consider using a robust VAD solution like Silero VAD. You can find more options listed here: https://github.com/bigcash/awesome-vad.

Speech-to-Text (STT):

• For speech-to-text, Whisper V2 is a great option. Efficient implementations like Faster-Whisper or cloud services such as Groq are recommended.
• Importantly, you can enhance Whisper's accuracy by prompting it with relevant vocabulary.

Forced Alignment (FA):

• Forced alignment, which provides word-level timestamps, is crucial.
  • Faster-Whisper implements end-to-end FA directly within its tokenizer.
  • Some third-party APIs, like Fireworks.AI, also offer FA as a direct feature.
  • Further reading on this topic: https://arxiv.org/html/2406.19363v1

Sentence Segmentation (Disambiguation):

• For segmenting the transcription into sentences, advanced Large Language Models (LLMs) can be used effectively.
• Alternatively, you could utilize ACI Subtitle Group's private model.

Refinement/Proofreading:

• Fine-tuning the transcription will require advanced LLMs.
• Important Note: Be mindful that over-editing could introduce hallucinations. Determine if the initial STT output is of sufficient quality for translation before extensive correction.

Translation:

• Translation also necessitates advanced LLMs.
• Consider leveraging Agently to assist with development.

Subtitle Generation:

• For generating subtitles, it's advisable to directly adapt the scripting used in ACI Subtitle Group's example.