![]() ![]() Using batched whisper with faster-whisper backend! v3 released, 70x speed-up open-sourced.v3 transcript segment-per-sentence: using nltk sent_tokenize for better subtitlting & better diarization.1st place at Ego4d transcription challenge □.Speaker Diarization is the process of partitioning an audio stream containing human speech into homogeneous segments according to the identity of each speaker. Voice Activity Detection (VAD) is the detection of the presence or absence of human speech. A popular example model is wav2vec2.0.įorced Alignment refers to the process by which orthographic transcriptions are aligned to audio recordings to automatically generate phone level segmentation. ![]() Phoneme-Based ASR A suite of models finetuned to recognise the smallest unit of speech distinguishing one word from another, e.g. ![]() OpenAI's whisper does not natively support batching. Whilst it does produces highly accurate transcriptions, the corresponding timestamps are at the utterance-level, not per word, and can be inaccurate by several seconds. Whisper is an ASR model developed by OpenAI, trained on a large dataset of diverse audio. □️ VAD preprocessing, reduces hallucination & batching with no WER degradation.□♂️ Multispeaker ASR using speaker diarization from pyannote-audio (speaker ID labels).□ Accurate word-level timestamps using wav2vec2 alignment. ![]()
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