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.