Speech-to-Text & Transcription Models

Whisper Large v3 wrapped with Hugging Face Transformers optimizations (batched inference, flash attention) for very high throughput. Transcribes hours of audio in minutes on a single GPU. Maintained by Vaibhav Srivastav. Good when you need bulk transcription fast.

OpenAI's Whisper running on Replicate. General-purpose speech recognition trained on 680k hours of multilingual audio. Transcribes and translates 99 languages, robust to accents and background noise, and outputs plain text, segments, or word-level timestamps.

OpenAI's Whisper model. State-of-the-art speech recognition supporting 99+ languages.

OpenAI's distilled Whisper Large v3. ~216x realtime, 99+ languages, MIT-licensed weights.

Deepgram's flagship STT. First to offer realtime multilingual transcription with self-serve customization.

Meta's SeamlessM4T multimodal translation model. Takes speech or text input and produces transcription or translation across about 100 languages, including speech-to-text and speech-to-speech. One model covers ASR plus cross-lingual translation without chaining separate systems.

Meta SeamlessM4T v2 Large speech mode. Speech-to-speech, speech-to-text, and text-to-speech translation across 100+ languages in a single unified model.

Whisper Large v3 Turbo combined with pyannote 4.0 for speaker diarization, returning who-said-what segments with timestamps. Built by Thomas Mol. Returns a clean JSON of speaker-labeled segments, handy for meeting notes, interviews, and podcasts.

WhisperX (Large v3) with forced alignment for accurate word-level timestamps plus optional speaker diarization. Uses VAD to cut long files into segments and a wav2vec2 aligner to pin each word to its exact time. Useful for subtitles and per-speaker transcripts.