Create our-recommendations.md

ui/blogs/our-recommendations.md

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+---
+title: "Our model recommendation"
+date: "2024-03-03"
+author: "Kurain Benoy"
+# authorImg: "jjthomas.png"
+draft: true
+description: "Our list of best practises for each languages, this will help you getting a better idea for model recommendation."
+tags: [best-practises, model-selection, ASR]
+---
+
+## About Indic Subtitler
+
+An open source subtitling platform 💻 for transcribing videos/audios in Indic languages and translating subtitles as well using ML models.
+It is powered by models like SeamlessM4T, faster-whisper, WhisperX and Vegam-Whisper which support almost 12 Indic languages by default.
+
+Since Indic Subtitler comes with this much models, this article is an effort to recommend our platform users to share, which
+is the best models to use for your use case, in your own languages.
+
+## About the models available in Indic Subtitler
+
+1. SeamlessM4T(default)
+
+Seamless is a family of AI models that enable more natural and authentic communication across languages. SeamlessM4T is a massive multilingual multimodal machine translation model supporting around 100 languages. SeamlessM4T serves as the foundation for SeamlessExpressive, a model that preserves elements of prosody and voice style across languages and SeamlessStreaming, a model supporting simultaneous translation and streaming ASR for around 100 languages. SeamlessExpressive and SeamlessStreaming are combined into Seamless, a unified model featuring multilinguality, real-time, and expressive translations.
+
+2. Faster-whisper
+
+faster-whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, which is a fast inference engine for Transformer models. This implementation is up to 4 times faster than openai/whisper for the same accuracy while using less memory. The efficiency can be further improved with 8-bit quantization on both CPU and GPU.
+
+3. WhisperX
+
+WhisperX provides fast automatic speech recognition (70x realtime with large-v2) with word-level timestamps and speaker diarization. The features provided by WhisperX are: Batched inference for 70x realtime transcription using whisper large-v2, faster-whisper backend, requires <8GB GPU memory for large-v2 with beam_size=5, Accurate word-level timestamps using wav2vec2 alignment, Multispeaker ASR using speaker diarization from pyannote-audio (speaker ID labels) and VAD preprocessing, reduces hallucination & batching with no WER degradation.
+
+4. Vegam-whisper
+
+A faster-whisper fine-tuned version of Malayalam whisper, which performs decently in Malayalam. This is a conversion of `thennal/whisper-medium-ml` to the CTranslate2 model format.
+
+## Our Model recommendations
+
+### For English audio 
+
+WhisperX > faster-whisper > SeamlessM4T
+
+If you want better timestamp accuracy use SeamlessM4T
+
+### For Malayalam Audio
+
+Vegam-whisper = faster-whisper > SeamlessM4T
+
+### For hindi audio
+
+Faster-Whisper > WhisperX > SeamlessM4t
+