VOSK Versus Coqui STT - A practical comparison.

### [VOSK](https://github.com/alphacep/vosk-api) Vs [Coqui STT](https://github.com/coqui-ai/STT)

**Description:**

A hard to transcribe (bad words, slangs, low voice, background noise, typical US accent ...etc.) audio from the famous US film: [Reservoir Dogs 1992 ](https://www.imdb.com/title/tt0105236/)was used to get the outputs from both the models and then compared to it's original SRT file.

The original SRT file of the film was converted to text with US spell & grammar corrections applied by another AI project. Also the audio was spitted using [Spleeter](https://github.com/deezer/spleeter) to check (background noise removed) it's effect on the transcriptions.

Finally, post-processing of the transcribed files of the 2 models were carried out using the same project that was used to correct the original SRT file.

_Please Note:_ Best models available for the Vosk & STT (does not have a dedicated US model) were used.

WER & WRR scores were calculated using [asr-evaluation](https://github.com/belambert/asr-evaluation) and compared to the original & post-processed transcribed files (with & without spleeter):

**-------------Original Transcriptions-----------------------**
STT

WER: 79.357% ( 10795 / 13603)
WRR: 21.135% ( 2875 / 13603)

STT-Spleeter

WER: 79.821% ( 10858 / 13603)
WRR: 20.635% ( 2807 / 13603)

VOSK

WER: 54.554% ( 7421 / 13603)
WRR: 47.151% ( 6414 / 13603)

VOSK-Spleeter

WER: 56.333% ( 7663 / 13603)
WRR: 45.181% ( 6146 / 13603)

**---------------Post Processing (with spelling corrections)------------**
STT

WER: 78.130% ( 10628 / 13603)
WRR: 22.473% ( 3057 / 13603)

STT-Spleeter

WER: 78.571% ( 10688 / 13603)
WRR: 21.951% ( 2986 / 13603)

VOSK

WER: 52.047% ( 7080 / 13603)
WRR: 49.835% ( 6779 / 13603)

VOSK-Spleeter

WER: 53.922% ( 7335 / 13603)
WRR: 47.835% ( 6507 / 13603)

**Conclusion:**
1) Spleeter does not improve transcription in any way.
2) Post-processing of the transcribed file is recommended (~2.49% improvement in the best model i.e. VOSK).
3) Dedicated models for language dialects is better approach...till some exceptional breakthrough to detect dialects in voice is practically implemented.
4) Winner: VOSK,...not only performed better in WER scores but also in word segmentation...STT has word segmentation problem.

**Future:**
Vosk needs better training ways with proper documentation to train models with film audio, to do exceptionally well in the real world of practical SRT file generation and keep itself ahead in the competition.