--- license: apache-2.0 language: de thumbnail: null library_name: ctranslate2 tags: - automatic-speech-recognition - whisper-event --- ![Model architecture](https://img.shields.io/badge/Model_Architecture-seq2seq-lightgrey) ![Model size](https://img.shields.io/badge/Params-1550M-lightgrey) ![Language](https://img.shields.io/badge/Language-German-lightgrey) # Fine-tuned German whisper-large-v2 model for CTranslate2 This repository contains the [bofenghuang/whisper-large-v2-cv11-german](https://huggingface.co/bofenghuang/whisper-large-v2-cv11-german) model converted to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) format. ## Usage ```python from faster_whisper import WhisperModel from huggingface_hub import snapshot_download downloaded_model_path = snapshot_download(repo_id="bofenghuang/whisper-large-v2-cv11-german-ct2") # Run on GPU with FP16 model = WhisperModel(downloaded_model_path, device="cuda", compute_type="float16") # or run on GPU with INT8 # model = WhisperModel(downloaded_model_path, device="cuda", compute_type="int8_float16") # or run on CPU with INT8 # model = WhisperModel(downloaded_model_path, device="cpu", compute_type="int8") segments, info = model.transcribe("./sample.wav", beam_size=1) print("Detected language '%s' with probability %f" % (info.language, info.language_probability)) for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` You can also use the following Google Colab Notebook to infer the converted Whisper models. Open In Colab ## Conversion The original model was converted with the following command: ```bash ct2-transformers-converter --model bofenghuang/bofenghuang/whisper-large-v2-cv11-german --output_dir bofenghuang/whisper-large-v2-cv11-german-ct2 --quantization float16 ```