openai/whisper-medium

automatic-speech-recognition model by openai

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Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al from OpenAI. The original code repository can be found here. Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained…

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Model overview

openai/whisper-medium is a automatic-speech-recognition model repository published by openai on Hugging Face. The source reports the transformers library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: automatic-speech-recognition
Library: transformers
Recent downloads (30 days): 302,994
Cumulative likes: 302
Hugging Face trending score: 1
Reported safetensors parameters: 763,857,920
Architecture: WhisperForConditionalGeneration
License: apache-2.0
Access: Not gated by Hugging Face
Created: 2022-09-26T06:52:52.000Z
Last modified: 2024-02-29T10:57:42.000Z

Compatibility and lineage

Reported languages: en, zh, de, es, ru, ko, fr, ja, pt, tr, pl, ca, nl, ar, sv, it, id, hi, fi, vi, he, uk, el, ms, cs, ro, da, hu, ta, no, th, ur, hr, bg, lt, la, mi, ml, cy, sk

Base-model lineage was not included.

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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