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AI モデル 149
“Transcript AI”
emotion_text_classifier
DistilRoBERTa-base is a transformer model that performs sentiment analysis. I fine-tuned the model on transcripts from the Friends show with the goal of classifying emotions from text data, specifically dialogue from Netflix shows or movies. The model predicts 6 Ekman emotions and a neutral class. These emotions include anger, disgust, fear, joy, neutrality, sadness, and surprise. The model is a fine-tuned version of Emotion English DistilRoBERTa-base and DistilRoBERTa-base. This model was initially trained on the following table from Emotion English DistilRoBERTa-base: It was fine-tuned on: |Name|anger|disgust|fear|joy|neutral|sadness|surprise| |---|---|---|---|---|---|---|---| |Emotion Lines (Friends)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
distil-large-v3.5
Distil-Whisper is the knowledge-distilled version of OpenAI's Whisper-Large-v3, described in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. As the newest addition to the Distil-Whisper English family, Distil-Large-v3.5 maintains the high efficiency of its predecessors while delivering better performance. Compared to earlier models, Distil-Large-v3.5 has been trained on over 4× more diverse public data (98k hours) and uses a "patient" teacher with an extended training schedule and aggressive data augmentation (SpecAugment) during distillation. This results in enhanced robustness and accuracy compared to previous Distil-Whisper models, making it suitable as a drop-in replacement. 1. It offers a different balance between accuracy and efficiency, remains **~1.5x faster** than Whisper-Large-v3-Turbo while performing slightly better on short-form transcription and falling ~1% behind on long-form transcription. 2. It works perfectly as a draft model for **speculative decoding** with Whisper-Large-v3. By keeping the encoder frozen during training, we need to load just two extra decoder layers and forward the encoder only once. This achieves ~2x faster inference compared to Whisper-Large-v3 while maintaining identical outputs.
parakeet-tdt-0.6b-v3-coreml
On‑device multilingual ASR model converted to Core ML for Apple platforms. This model powers FluidAudio’s batch ASR and is the same model used in our backend. It supports 25 European languages and is optimized for low‑latency, private, offline transcription. The conversion script is here: https://github.com/FluidInference/mobius/tree/main/models/stt/parakeet-tdt-v3-0.6b/coreml And the benchmarks are here: https://github.com/FluidInference/FluidAudio/blob/main/Documentation/Benchmarks.md#transcription
LightOnOCR-3-1B-GGUF
> **LightOnOCR-3-1B**, developed by lightonai, is a highly performant, lightweight OCR and end-to-end document understanding model designed as a seamless, drop-in upgrade for existing LightOnOCR-2 deployments. Released under the Apache 2.0 license, it retains the proven LightOnOCR-2-1B architecture while introducing powerful new visual understanding capabilities, including a **grounding mode** (triggered by the `grounding` prompt) that outputs labeled bounding boxes for all document elements in normalized page coordinates, short descriptions for images, and structured HTML data extraction from charts. Alongside its default **transcription mode** that outputs full-page markdown text from an empty prompt, the model is highly versatile—effortlessly handling complex multi-column layouts, tables, receipts, forms, handwriting, and math notation—offering a ready-to-use, single-model alternative to traditional, multi-stage document processing pipelines when images are optimally preprocessed at 200 DPI.
canary-1b-gguf
Ported from upstream commit 1698acf, pinned 2026-05-08. Validated against the NeMo reference at transcribe.cpp commit db53eda on 2026-05-08. Offline multilingual speech-to-text and translation. A multitask AED with a 24-layer FastConformer encoder and a 24-layer Transformer decoder — the original canary release. Supports automatic speech recognition in English, German, Spanish, and French, and translation between supported pairs. Takes a 16 kHz mono WAV and produces a transcript. Not a streaming model. **License: CC-BY-NC-4.0 (non-commercial only)** — the only canary variant under a non-commercial license. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.
voxtral-mini-4b-realtime-GGUF
GGUF quantizations of mistralai/Voxtral-Mini-4B-Realtime-2602, a **4.4B-parameter realtime streaming speech-to-text model** with a causal audio encoder and configurable transcription delay. ⚠ This table used to name the F16 `voxtral-mini-4b-realtime.gguf`, and no such file was ever uploaded. The published name is `…-f16.gguf`, matching the quant suffix CrispASR's `-m auto:f16` resolver expects.
whisper-large-gguf
GGUF conversions of openai/whisper-large for use with transcribe.cpp. Ported from upstream commit 4ef9b41, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper large — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). Encoder-decoder transformer; 30-second windows with chunked long-form decoding.
finbert-pretrain
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. - Corporate Reports 10-K & 10-Q: 2.5B tokens - Earnings Call Transcripts: 1.3B tokens - Analyst Reports: 1.1B tokens Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).
Confucius4-R2T2
Confucius4-R2T2: A Low Latency and High Accuracy Real-Time Speech Recognition Model Confucius4-R2T2 is a low-latency and high-accuracy true streaming Automatic Speech Recognition (ASR) model that features fine-grained and configurable decoding chunks from 80 ms to 2 s. The model operates in append-only output mode: committing transcript text permanently without revising previous words, which is critical for applications where text must be processed or acted upon instantly. This results in a smoother user experience, avoiding disruptive text revisions and visual flickering in real-time applications, such as Real-Time Live Captioning & Subtitling, Downstream NLP Pipelines & LLM Agents, Simultaneous Speech Translation, etc. R2T2, short for Real Real-Time Transcription, is built upon the Qwen3-ASR model. And it is trained with a unique set of data construction techniques including stable-prefix data, forced time-alignment data, and token-level audio segmentation. Combined with a Longest Stable Prefix (LSP) learning paradigm (tech report will be released soon), R2T2 can dynamically determine when a stable prefix can be safely emitted and when additional audio context is needed. By exposing only stable prefixes, the model provides high-quality context that conditions subsequent predictions while guaranteeing that previously emitted text remains unchanged. Despite its streaming design, R2T2 maintains strong accuracy in offline recognition.
s2t-small-mustc-en-fr-st
`s2t-small-mustc-en-fr-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in this paper and released in this repository S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. This model can be used for end-to-end English speech to French text translation. See the model hub to look for other S2T checkpoints.